jfa journal

AND option

OR option

FREQUENCY OF CONDITIONS ASSOCIATED WITH DECLINES IN INTRINSIC CAPACITY ACCORDING TO A SCREENING TOOL IN THE CONTEXT OF INTEGRATED CARE FOR OLDER PEOPLE

 

E. González-Bautista1, P. de Souto Barreto1,2, K. Virecoulon Giudici1, S. Andrieu1,2, Y. Rolland1,2, B. Vellas1,2, for the MAPT/DSA group*

 

1. Gerontopole of Toulouse, Institute of Aging, Toulouse University Hospital (CHU Toulouse), Toulouse, France; 2. UPS/Inserm UMR1027, University of Toulouse III, Toulouse, France; *The members are listed at the end of the manuscript.
Corresponding author: Emmanuel González-Bautista. Gerontopole of Toulouse, Institute of Ageing, Toulouse University Hospital (CHU Toulouse), 37 Allée Jules Guesde, 31000 Toulouse, France. Mobile 06 22 10 14 96 emmanuel.scout@gmail.com

J Frailty Aging 2020;in press
Published online August 7, 2020, http://dx.doi.org/10.14283/jfa.2020.42

 


Abstract

Background: The screening tool of the Integrated Care for Older People (ICOPE Step 1), designed to detect declines in the domains of intrinsic capacity, has been incipiently investigated in older adult populations. Objectives: To retrospectively estimate the frequency of priority conditions associated with declines in intrinsic capacity according to an adaptation of the screening tool ICOPE Step 1 among participants of the Multidomain Alzheimer Preventive Trial (MAPT). Design: A cross-sectional retrospective analysis from the baseline assessment of the MAPT. Setting: The data was gathered during a preventive consultation for cardiovascular risk factors in memory clinics in France. Participants : Seven hundred fifty-nine older adults aged 70-89 years with memory complaints, allocated to the multidomain groups of the MAPT study. Measurements: Five domains of intrinsic capacity (cognition, locomotion, nutrition, sensorial, and psychological) were assessed using a screening tool similar to the ICOPE Step 1 (MAPT Step 1). The frequency of six conditions associated with declines in intrinsic capacity (cognitive decline, limited mobility, malnutrition, visual impairment, hearing loss, and depressive symptoms) was obtained for older adults with memory complaints participating in the MAPT study. Results: Overall, 89.3% of the participants had one or more conditions associated with declines in intrinsic capacity. The overall frequency of each condition was: 52.2% for cognitive decline, 20.2% for limited mobility, 6.6% for malnutrition, 18.1% for visual impairment, 56.2% for hearing loss, and 39% for depressive symptoms. Conclusion: After being screened with an adaptation of the ICOPE step 1 (MAPT step 1) tool, 9/10 older adults had one or more conditions associated with declines in intrinsic capacity. The relative frequency differs across conditions and could probably be lower in a population without memory complaints. The frequency of screened conditions associated with declines in IC highlights how relevant it is to develop function-centered care modalities to promote healthy aging.

Key words: Intrinsic capacity, screening, physical functions, integrated care, older adults.

Abbreviations: CCHA: Clinical Consortium for Healthy Ageing; COPD: Chronic obstructive pulmonary disease; FA: Functional ability; IC: Intrinsic capacity; ICOPE: Integrated Care for Older People; MAPT: Multidomain Alzheimer Preventive Trial; NCD: Non-communicable disease; OA: Older adults; WHO: World Health Organization.


 

Background

Screening for early declines of intrinsic capacity (IC) is crucial for the implementation of the Integrated Care for Older People (ICOPE)(1). The World Health Organization (WHO) Clinical Consortium for Healthy Ageing (CCHA) and other public health and aging experts developed the ICOPE guidelines. The objective of the ICOPE guidelines is to help key stakeholders in the health and social care arenas to design and implement integrated person-centered models of care (1–3).
The ICOPE approach might prevent care-dependency by timely detecting and managing conditions associated with declines in intrinsic capacity (IC). IC is the composite of all physical and mental capacities of an individual, organized in five domains: cognition, mobility, nutrition, sensorial, and psychological. The interaction between the IC and the environment determines functional ability and healthy aging (4,5). The WHO ICOPE approach has, thus, the goal of helping health systems support healthy aging (4) through the maintenance of optimal functional ability levels during aging.
The clinical care pathways proposed by the ICOPE (1) start with a screening process (ICOPE Step 1). The purpose of the screening is to detect conditions associated with declines in IC at the community level, namely cognitive decline, limited mobility, malnutrition, visual impairment, hearing loss, and depressive symptoms. People identified as having normal IC levels will receive general health advice (i.e., physical activity, nutrition). In contrast, those with IC declines will receive an in-depth assessment (ICOPE Step 2) to confirm or rule such declines. Afterward, they will follow the next steps in the care pathway (i.e., searching the causes of low IC levels, designing a person-centered care plan).
For health service providers, ICOPE Step 1 has a strategic role because it opens the door for the subsequent steps in the healthcare pathway. By managing the adequate «filter» to identify the individuals who can benefit the most from health and social interventions, the healthcare system could enhance the effective use of the available resources.
We have recently reviewed the literature on the topic (in press) and observed only ten original studies on the topic. None of those studies investigated the frequency of low levels of IC according to the screening tool. To our knowledge, measurements of conditions associated with declines in intrinsic capacity, according to ICOPE Step 1 have not been reported so far. Therefore, this study aimed to retrospectively estimate the frequency of conditions associated with declines in intrinsic capacity according to an adaptation of the screening tool ICOPE Step 1 among participants of the Multidomain Alzheimer Preventive Trial (MAPT).

 

Methods

This study uses cross-sectional data to describe the baseline frequency of conditions associated with IC declines among the participants of the Multidomain Alzheimer Preventive Trial (MAPT). The MAPT was not designed to assess the ICOPE screening; thus, we used a retrospective approach to define the variables of interest according to the availability of data. The detailed methodology of MAPT has been described elsewhere(6, 7). Briefly, MAPT was a 3-year randomized controlled trial on the effect of a multidomain intervention (nutritional and physical activity counseling, cognitive training, and annual preventive consultations for the management of cardiovascular risk factors and the detection of functional impairments) with and without supplementation of omega-3 polyunsaturated fatty acids (PUFA) on the prevention of cognitive decline among community-dwelling adults aged 70 years and older. The trial protocol (ClinicalTrials.gov identifier: NCT00672685) was approved by the French Ethical Committee located in Toulouse (CPP SOOM II) and was authorized by the French Health Authority. All participants signed their consent before any study assessment.

Participants

Inclusion criteria for the MAPT study were meeting at least one of three conditions: a) spontaneous memory complaint expressed to their physician, b) limitation in one instrumental activity of daily living (IADL), or c) slow gait speed (≤0.8 m/s). Exclusion criteria comprised participants with a Mini-Mental State Examination (MMSE) score < 24, diagnosis of dementia, the limitation for any of the basic activities of daily living, and those taking PUFA supplements at baseline.
The 759 subjects allocated to the multidomain intervention groups of MAPT constitute our study sample. Data on the five domains of IC was available only for them because they underwent a preventive consultation with a physician who assessed for the hearing and vision capacities. The rest of the 1,679 participants initially enrolled in MAPT were lacking data on the sensorial domain.

IC domains assessment – Step 1 (screening)

We followed the recommendations from the WHO ICOPE Handbook to operationalize the ICOPE Step 1 tool(1). To be consistent with the terms used in the Handbook, we used «cognitive decline,» «limited mobility,» «malnutrition,» «visual impairment,» «hearing loss» and «depressive symptoms» to refer to the conditions associated with declines in IC. These terms are not equivalent to clinical diagnoses.
The same items recommended by the WHO were used to evaluate three domains: cognition, locomotion, and vitality/nutrition. Nevertheless, due to data availability, we adapted the operationalization of the following conditions associated with declines in IC: visual impairment: was assessed by self-reported visual acuity items; hearing loss: was measured with item number 3 of the screening version of the hearing handicap inventory for the elderly (HHSE-S(8,9)); depressive symptoms: were defined according to items 2 and 7 of the Geriatric Depression Scale (GDS-15) (10), which were judged by three experts (one geriatrician, one general practitioner, and one researcher in clinical gerontology) as being the most similar items compared to those recommended by WHO. This resulting adapted screening tool was then called «MAPT Step 1» (Table 1).
Specifically, for hearing loss, we used the validated HHIE-S(8) because studies have validated this instrument against pure tone audiometry. Sindhusake et al. (11) concluded that HHIE-S has adequate sensitivity and specificity for detecting moderate hearing loss (audiometry hearing threshold of >40 dB). The HHIE-S cut-off >8 points is established in the guidelines of the American Speech-Language-Hearing Association(12) as a criterion for a referral to further audiological testing.
More sophisticated measurements of IC have been published but are not feasible in a clinical routine setting(13,14). A screening procedure like ICOPE Step 1 may help to target an at-risk population that would, then, receive in-depth assessments and a closer follow-up.

Table 1
Comparison of the operationalization of the conditions associated with declines in IC between the ICOPE handbook and the definitions applied in MAPT study

*Participants were explicitly asked for each of the items in the time and spatial orientation, and not only an open-ended question. The participant was recorded as with cognitive decline if he/she was wrong to tell the date (number and name of the day, month, year), or wrong to tell the name of the hospital, the level of the building, department and region.
†We used item number 3 of the HHIE-S because of its similarity with the whisper test.

 

IC domains in-depth assessment

The following tests were performed for an in-depth assessment of the IC domains:
• Cognition: Mini-Mental State Examination (MMSE) (15).
• Locomotion: Short Performance Physical Battery (SPPB) (16)
• Vitality/nutrition: Mini Nutritional Assessment (full version MNA)(17).
• Vision: Monoyer vision chart (18).
• Hearing: Hearing Handicap Inventory for the Elderly – Screening version (HHIE-S) (8).
• Psychological: 15-item Geriatric Depression Scale (GDS-15) (10).

Statistical Analysis

We used percentages to report the frequency of the declines in IC in our study population and by age and sex subgroups. Scores of the in-depth assessments were described using means and standard deviation (SD). Data were analyzed using STATA 14®.

 

Results

The mean age of our study population was 75.2 years (SD=4.3), 63.6% of them were women, and 28.9% reported 12 years or more of formal education (Table 2). Overall, 89.3% of the population presented one or more conditions associated with declines in IC (87.4% among females, 92.8% among males). Table 3 shows the frequency of conditions associated with declines in IC by domain. Relative frequency of the conditions of interest and mean scores of the tests used for in-depth assessment for sex and age-groups are reported in Supplementary Table S1.

Table 2
Sex, frailty status, number of instrumental activities impaired and mean values of functional performance tests by age group among participants of the MAPT Study

MMSE= mini-mental state examination. SPPB= short performance physical battery. MNA= mini nutritional assessment full version. HHIE-S= Hearing Handicap Inventory for the Elderly – Screening version. GDS= Geriatric Depression Scale; * provided in decimal acuity. For reference, 0.8 decimal = 20/25 imperial= 6/7.5 metric = 0.1 LogMAR.

Table 3
Frequency of conditions associated with declines in IC according to the ICOPE step 1 screening tool by age group among participants of the MAPT Study

*Maximum of possible conditions is six because the sensorial domain includes visual impairment and hearing loss

 

Cognitive domain

Half of the studied population presented signs of cognitive decline. Table 4 shows the details of the cognitive items of MAPT Step 1. Failing in the word recall section of the MMSE was three times more frequent than failing in the orientation section. In all age groups, the most frequently mistaken word recall was for the last word in the list given to the participants (i.e., cigarette, flower, door – participants less often recalled the word «door»).

Table 4
Prevalence of cognitive sub-domains and mistaken item from the Mini-Mental State Examination (MMSE) including alternative definitions for cognitive decline by age group among participants of the MAPT Study

* Areas of political division. †MAPT participants used the French version during the study.

 

Mobility domain

Overall, the mean of the time to perform five chair rises was 11.9 seconds (SD=4.5), with age-specific averages ranging from 11.0 (70-74 years) to 16.0 seconds (85-89 years). For the group aged 85-89 years (n=22), the cut-off of 14 seconds proposed by ICOPE Step 1 was at the percentile 52. Further details about the distribution of the sex- and age-specific chair rise times are provided in Supplementary Table S1.

Vitality/Nutrition domain

The mean score in the MNA was 27.6 points, ranging from 27.7 in the youngest group to 26.8 in the oldest group. The frequency of self-reported weight loss or appetite loss was lower than 5% for all the age groups, except for the appetite loss in those aged 80-84 years (7.3%).

Sensorial domain

Vision. In our study population, 92.1% of participants used glasses, contact glasses, implants, or magnifiers at the time of the interview. Even with their supportive devices, up to 14.5% of the participants found it hard to read a newspaper or watch television.
Hearing. Among the participants, 18.2% were using a hearing assistive device at the time of the interview. The screening question for hearing loss identified 55% of those aged 70-74 years and 68% of those aged 85-89 years as positive for hearing loss.

 

Discussion

Our study is the first to describe the frequency of the IC declines according to an adaptation of the ICOPE Step 1 screening tool (MAPT step 1) in a selected cohort of memory clinic attendees. Overall, 89.3% of the participants had one or more conditions associated with declines in IC, according to MAPT Step 1 (87.4% among females, 92.8% among males). Nine in every ten screened older adults would be referred to a specialized, in-depth evaluation. It should be noted, however, that MAPT participants expressed memory complaints at recruitment.
We found a high demand for an in-depth assessment. Consider that the MAPT population is more fit than others reported in French studies (except perhaps for cognitive function due to the inclusion criteria). Compared to a random sample used in the French Three-City Study published by Avila-Funes et al. (19), our population was slightly older (mean age 74.1, SD=5.2 vs. 75.2 years, SD=4.3), reported higher levels of educational attainment (>12 years: 17.0% vs. 28.9%) and a lower frequency of frailty (7.0% vs. 3.2%). Therefore, our findings might be underestimating the frequency of IC declines detected by MAPT Step 1 in a real-world population of users of the healthcare system (except for cognitive decline). Our results highlight the need for adapting our health care system to improve the assessment of functions to prevent functional decline. For example, people detected with signs of cognitive decline using the screening tool ICOPE Step 1 could benefit from multidomain interventions (20–22).
Half of the study population showed signs of cognitive decline. Interestingly, participants more frequently failed in selected items (i.e., recalling the name of the day or the last word in a list of three). Therefore, it will be interesting to explore the domain and item’s capacity to predict health events such as frailty incidence in future studies.
Regarding locomotion, the cut-off time to perform five chair rises deserves further investigation (the ICOPE handbook suggests 14 seconds). A cut-off of 15 sec was used in a study measuring the time to complete ten rather than five chair-rises (23). A meta-analysis by Bohannon concluded that 11.4 and 12.7 seconds are suitable cut-off values among subjects aged 60-69 and 80-89, respectively (24). In the SPPB validation study, Guralnik et al. (16) found that 13.7 sec corresponded to the 50th percentile of performance in the chair rise test in a population of more than 5,000 American people aged 71 years or older in 1981. However, current generations might have a better physical performance than those assessed 30 years ago. Establishing age- tailored cut-offs for the chair rise test would allow for a better classification of the performance levels. They would not add difficulty to the implementation of the ICOPE care pathway.
Malnutrition was the least frequent condition. The average score for the full MNA was 27.6, which is higher than the cut-off often used to define the risk of malnutrition (23.5) (17). We think that clinicians should address anorexia and weight loss even if the MNA test if above the cut-off designed for malnutrition. According to the Global Leadership Initiative on Malnutrition (GLIM) criteria for malnutrition in adults (25), reduced food intake/assimilation and weight loss are sufficient to integrate the diagnosis of malnutrition. Also, the Beck depression inventory (26) and by the Center for Epidemiologic Studies Depression (CES-D) scale (27) consider the loss of appetite as a depressive symptom. Moreover, those who leave alone are at higher nutritional risk (28).
For the psychological domain, we did not use the exact questions suggested in the ICOPE screening tool Step 1 because they were not available in MAPT Study. Therefore, we selected from the GDS-15 the two items that more closely matched the ICOPE definition (Table 1). The frequency of depressive symptoms in our study was high (39%), compared to a previous investigation among French older adults (13.8% evaluated with the CES-D in people aged 75 years; SD=6.8) (29). Shared risk factors for cognitive decline and depressive symptoms can explain this difference. Due to MAPT inclusion criteria, participants were at increased risk for cognitive decline, and more than 40% presented mild cognitive impairment (MCI). For instance, using the MAPT Step 1 tool resulted in an overlap of 72% of the participants according to their cognitive decline and depressive symptoms status (7 out of 10 were simultaneously free from or afflicted by both conditions). The connexions across the IC domains are a hallmark of the ICOPE approach (30, 31). Timely interventions targeting these interactions can prevent further losses of ADL ADLs (32).
In the visual domain, we used three questions related to self-reported problems for vision, even when using correction devices (Table 1). Of note, the frequency of visual impairment in our study may have been underestimated because the questions used in MAPT are more specific than the general question proposed in ICOPE Step 1. Furthermore, we did not consider if the person had hypertension or diabetes, as suggested in the ICOPE tool. Evidence suggests that more than half of the cases of visual impairment in older ages are due to cataract and refractive errors, with less than 5% due to diabetic retinopathy (33–35). Perceived difficulty in reading a journal or watching television was reported in 15% of the population wearing vision aids. We consider this ratio as an indicator of unsatisfied demand for visual correction adjustment.
Our hearing loss estimates show that the age-specific frequency of hearing handicap was higher than the ones reported in the study of Wiley et al. performed with 3,471 non-Hispanic whites in Wisconsin, USA. Our figures were similar to those reported by Tomioka et al. (36), with 1,731 community-dwelling older adults from Nara and Osaka, Japan. The different age distribution could explain the dissimilarities.
Our study has strengths, such as being among the first to report the frequency of conditions associated with declines in the five IC domains in a selected cohort of memory clinic attendees. Moreover, the MAPT Step 1 and the ICOPE Step 1 use the same items for most of the IC domains. Therefore, the frequency of declines on visual, hearing, and psychological domains might have been different from our findings if the WHO ICOPE screening tool had been used. On the other hand, some limitations should be mentioned. There was a potential selection bias towards cognitive decline, given that having a spontaneous memory complaint was one of the inclusion criteria in MAPT. However, in a sensitivity analysis removing participants with a cognitive decline in the MAPT Step 1, 78% of the remaining population still had one or more conditions associated with declines in IC (Supplementary Table S2). Our data should not be generalized to other populations. Compared to the age and sex distribution of French older adults, our population overrepresented women (57.1% vs. 63.6%) and adults aged 70-79 years (39.1% vs. 49.8%) (37).
In summary, almost 90% of adults aged 70 years and older in a selected cohort of memory clinic attendees had at least one condition associated with declines in IC. Frequencies varied from 52.2% in the cognitive domain to 6.2% in the nutrition domain. These findings suggest that implementing the ICOPE Step 1 at the community level will help screen for conditions associated with declines in intrinsic capacity. The frequency of declines in IC can provide health systems managers with an estimation of the amount and the type of resources needed to implement the ICOPE clinical pathways. For example, satisfying the demand for visual aids adjustment, or recruitment of health workforce with psychological training. Interesting questions emerged from this descriptive study. For example, should some items be changed to increase the chances of detecting most of the people at risk? Also, are age-specific cut-offs the most suitable approach for some IC domains in the ICOPE screening tool (notably, locomotion)?

 

MAPT/DSA group: Principal investigator: Bruno Vellas (Toulouse); Coordination: Sophie Guyonnet; Project leader: Isabelle Carrié; CRA: Lauréane Brigitte; Investigators: Catherine Faisant, Françoise Lala, Julien Delrieu, Hélène Villars; Psychologists: Emeline Combrouze, Carole Badufle, Audrey Zueras; Methodology, statistical analysis and data management: Sandrine Andrieu, Christelle Cantet, Christophe Morin; Multidomain group: Gabor Abellan Van Kan, Charlotte Dupuy, Yves Rolland (physical and nutritional components), Céline Caillaud, Pierre-Jean Ousset (cognitive component), Françoise Lala (preventive consultation) (Toulouse). The cognitive component was designed in collaboration with Sherry Willis from the University of Seattle, and Sylvie Belleville, Brigitte Gilbert and Francine Fontaine from the University of Montreal. Co-Investigators in associated centres: Jean-François Dartigues, Isabelle Marcet, Fleur Delva, Alexandra Foubert, Sandrine Cerda (Bordeaux); Marie-Noëlle-Cuffi, Corinne Costes (Castres); Olivier Rouaud, Patrick Manckoundia, Valérie Quipourt, Sophie Marilier, Evelyne Franon (Dijon); Lawrence Bories, Marie-Laure Pader, Marie-France Basset, Bruno Lapoujade, Valérie Faure, Michael Li Yung Tong, Christine Malick-Loiseau, Evelyne Cazaban-Campistron (Foix); Françoise Desclaux, Colette Blatge (Lavaur); Thierry Dantoine, Cécile Laubarie-Mouret, Isabelle Saulnier, Jean-Pierre Clément, Marie-Agnès Picat, Laurence Bernard-Bourzeix, Stéphanie Willebois, Iléana Désormais, Noëlle Cardinaud (Limoges); Marc Bonnefoy, Pierre Livet, Pascale Rebaudet, Claire Gédéon, Catherine Burdet, Flavien Terracol (Lyon), Alain Pesce, Stéphanie Roth, Sylvie Chaillou, Sandrine Louchart (Monaco); Kristelle Sudres, Nicolas Lebrun, Nadège Barro-Belaygues (Montauban); Jacques Touchon, Karim Bennys, Audrey Gabelle, Aurélia Romano, Lynda Touati, Cécilia Marelli, Cécile Pays (Montpellier); Philippe Robert, Franck Le Duff, Claire Gervais, Sébastien Gonfrier (Nice); Yannick Gasnier and Serge Bordes, Danièle Begorre, Christian Carpuat, Khaled Khales, Jean-François Lefebvre, Samira Misbah El Idrissi, Pierre Skolil, Jean-Pierre Salles (Tarbes). MRI group: Carole Dufouil (Bordeaux), Stéphane Lehéricy, Marie Chupin, Jean-François Mangin, Ali Bouhayia (Paris); Michèle Allard (Bordeaux); Frédéric Ricolfi (Dijon); Dominique Dubois (Foix); Marie Paule Bonceour Martel (Limoges); François Cotton (Lyon); Alain Bonafé (Montpellier); Stéphane Chanalet (Nice); Françoise Hugon (Tarbes); Fabrice Bonneville, Christophe Cognard, François Chollet (Toulouse). PET scans group: Pierre Payoux, Thierry Voisin, Julien Delrieu, Sophie Peiffer, Anne Hitzel, (Toulouse); Michèle Allard (Bordeaux); Michel Zanca (Montpellier); Jacques Monteil (Limoges); Jacques Darcourt (Nice). Medico-economics group: Laurent Molinier, Hélène Derumeaux, Nadège Costa (Toulouse). Biological sample collection: Bertrand Perret, Claire Vinel, Sylvie Caspar-Bauguil (Toulouse). Safety management: Pascale Olivier-Abbal. DSA Group: Sandrine Andrieu, Christelle Cantet, Nicola Coley.
Funding: The present work was performed in the context of the Inspire Program, a research platform supported by grants from the Region Occitanie/Pyrénées-Méditerranée (Reference number: 1901175) and the European Regional Development Fund (ERDF) (Project number: MP0022856). The sponsors had no role in the design and conduct of the study; in the collection, analysis, and interpretation of data; in the preparation of the manuscript; or in the review or approval of the manuscript.
Acknowledgements: NA
Authors contributions: PB, BV and EG conceived the study. EG, statistics and manuscript writing. KV, PB, LM, BV and SA provided inputs and reviewed the manuscript. BV and SA are PIs in MAPT.
Funding section: The present work was performed in the context of the Inspire Program, a research platform supported by grants from the Region Occitanie/Pyrénées-Méditerranée (Reference number: 1901175) and the European Regional Development Fund (ERDF) (Project number: MP0022856). The sponsors had no role in the design and conduct of the study; in the collection, analysis, and interpretation of data; in the preparation of the manuscript; or in the review or approval of the manuscript. “This study received funds from Alzheimer Prevention in Occitania and Catalonia (APOC Chair of Excellence – Inspire Program). The MAPT study was supported by grants from the Gérontopôle of Toulouse, the French Ministry of Health (PHRC 2008, 2009), Pierre Fabre Research Institute (manufacturer of the omega-3 supplement), ExonHit Therapeutics SA, and Avid Radiopharmaceuticals Inc. The promotion of this study was supported by the University Hospital Center of Toulouse. The data sharing activity was supported by the Association Monegasque pour la Recherche sur la maladie d’Alzheimer (AMPA) and the INSERM-University of Toulouse III UMR 1027 Unit».
Conflict of interest: The authors declare no competing interest relevant to this article.
Ethical standard: This study did not include any experiments involving humans or other animals.

 

SUPPLEMENTARY MATERIAL

 

References

1. World Health Organization. Integrated care for older people (ICOPE): Guidance for person-centred assessment and pathways in primary care. [Internet]. Geneva: WHO; 2019 [cited 2019 Nov 14]. 87 p. Available from: https://apps.who.int/iris/bitstream/handle/10665/326843/WHO-FWC-ALC-19.1-eng.pdf?sequence=17
2. Thiyagarajan JA, Araujo de Carvalho I, Peña-Rosas JP, Chadha S, Mariotti SP, Dua T, et al. Redesigning care for older people to preserve physical and mental capacity: WHO guidelines on community-level interventions in integrated care. PLOS Med [Internet]. 2019 Oct 18 [cited 2020 Jan 8];16(10):e1002948. Available from: http://dx.plos.org/10.1371/journal.pmed.1002948
3. World Health Organisation. WHO Clinical Consortium on Healthy Ageing. Report of consortium meeting 1-2 December 2016 in Geneva, Switzerland. 2017;(December).
4. World Health Organization. World Report on Ageing and Health. Geneva: WHO Press; 2015.
5. Cesari M, De Carvalho IA, Thiyagarajan JA, Cooper C, Martin FC, Reginster JY, et al. Evidence for the domains supporting the construct of intrinsic capacity. Vol. 73, Journals of Gerontology – Series A Biological Sciences and Medical Sciences. Oxford University Press; 2018. p. 1653–60.
6. Vellas B, Carrie I, Gillette-Guyonnet S, Touchon J, Dantoine T, Dartigues JF, et al. MAPT study: A multidomain approach for preventing Alzheimer’s disease: design and baseline data. J Prev Alzheimer’s Dis [Internet]. 2014 Jun [cited 2020 Feb 5];1(1):13–22. Available from: http://www.ncbi.nlm.nih.gov/pubmed/26594639
7. Andrieu S, Guyonnet S, Coley N, Cantet C, Bonnefoy M, Bordes S, et al. Effect of long-term omega 3 polyunsaturated fatty acid supplementation with or without multidomain intervention on cognitive function in elderly adults with memory complaints (MAPT): a randomised, placebo-controlled trial. Lancet Neurol. 2017 May 1;16(5):377–89.
8. Ventry IM, Weinstein BE. The hearing handicap inventory for the elderly: A new tool. Ear Hear. 1982;3(3):128–34.
9. Bagai A. Does This Patient Have Hearing Impairment ? JAMA. 2008;295(4):416–28.
10. Yesavage JA, Sheikh JI. Geriatric Depression Scale (GDS). Clin Gerontol [Internet]. 1986 Nov 18;5(1–2):165–73. Available from: https://doi.org/10.1300/J018v05n01_09
11. Sindhusake D, Mitchell P, Smith W, Golding M, Newall P, Hartley D, et al. Validation of self-reported hearing loss. The blue mountainshearing study. Int J Epidemiol. 2001;30(6):1371–8.
12. Association AS-L-H. Guidelines for Audiologic Screening. www.asha.org/policy [Internet]. 1997 [cited 2020 Feb 7];1–64. Available from: http://www.asha.org/policy/GL1997-00199/
13. Beard JR, Jotheeswaran AT, Cesari M, Araujo de Carvalho I. The structure and predictive value of intrinsic capacity in a longitudinal study of ageing. BMJ Open [Internet]. 2019 Nov 2 [cited 2019 Nov 7];9(11):e026119. Available from: http://www.ncbi.nlm.nih.gov/pubmed/31678933
14. Gutiérrez-Robledo LM, García-Chanes RE, Pérez-Zepeda MU. Allostatic Load as a Biological Substrate to Intrinsic Capacity: A Secondary Analysis of CRELES. J Nutr Health Aging [Internet]. 2019 [cited 2019 Nov 5];23(9):788–95. Available from: http://www.ncbi.nlm.nih.gov/pubmed/31641727
15. Folstein MF, Folstein SE, McHugh PR. “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975;12(3):189–98.
16. Guralnik JM, Simonsick EM, Ferrucci L, Glynn RJ, Berkman LF, Blazer DG, et al. A short physical performance battery assessing lower extremity function: Association with self-reported disability and prediction of mortality and nursing home admission. Journals Gerontol. 1994;49(2).
17. Vellas B, Guigoz Y, Garry PJ, Nourhashemi F, Bennahum D, Lauque S, et al. The mini nutritional assessment (MNA) and its use in grading the nutritional state of elderly patients. Nutrition. 1999 Feb;15(2):116–22.
18. World Health Organization. World report on vision. Vol. 214, World report on vision. Geneva: WHO Press; 2019. 180–235 p.
19. Ávila-Funes JA, Helmer C, Amieva H, Barberger-Gateau P, Le Goff M, Ritchie K, et al. Frailty among community-dwelling elderly people in france: The three-city study. Journals Gerontol – Ser A Biol Sci Med Sci. 2008;63(10):1089–96.
20. Rosenberg A, Mangialasche F, Ngandu T, Solomon A, Kivipelto M. Multidomain Interventions to Prevent Cognitive Impairment, Alzheimer’s Disease, and Dementia: From FINGER to World-Wide FINGERS. J Prev Alzheimer’s Dis [Internet]. 2020 [cited 2020 Mar 23];7(1):29–36. Available from: http://www.ncbi.nlm.nih.gov/pubmed/32010923
21. Cherian L, Wang Y, Fakuda K, Leurgans S, Aggarwal N, Morris M. Mediterranean-Dash Intervention for Neurodegenerative Delay (MIND) Diet Slows Cognitive Decline After Stroke. J Prev Alzheimer’s Dis [Internet]. 2019 [cited 2020 Mar 23];6(4):267–73. Available from: http://www.ncbi.nlm.nih.gov/pubmed/31686099
22. Udeh-Momoh C, Price G, Ropacki MT, Ketter N, Andrews T, Arrighi HM, et al. Prospective Evaluation of Cognitive Health and Related Factors in Elderly at Risk for Developing Alzheimer’s Dementia: A Longitudinal Cohort Study. J Prev Alzheimer’s Dis [Internet]. 2019 [cited 2020 Mar 23];6(4):256–66. Available from: http://www.ncbi.nlm.nih.gov/pubmed/31686098
23. Hardy R, Cooper R, Shah I, Harridge S, Guralnik J, Kuh D. Is chair rise performance a useful measure of leg power? Aging Clin Exp Res. 2010;22(5–6):412–8.
24. BOHANNON RW. Reference Values for the Five-Repetition Sit-To-Stand Test: a Descriptive Meta-Analysis of Data From Elders. Percept Mot Skills. 2006;103(5):215.
25. Cederholm T, Jensen GL, Correia MITD, Gonzalez MC, Fukushima R, Higashiguchi T, et al. GLIM criteria for the diagnosis of malnutrition – A consensus report from the global clinical nutrition community. J Cachexia Sarcopenia Muscle [Internet]. 2019 Feb 1 [cited 2020 Feb 27];10(1):207–17. Available from: https://onlinelibrary.wiley.com/doi/abs/10.1002/jcsm.12383
26. Beck AT, Ward CH, Mendelson M, Mock J, Erbaugh J. An Inventory for Measuring Depression. Arch Gen Psychiatry. 1961;4(6):561–71.
27. Radloff LS. The CES-D scale: A self-report depression scale for research in the general population. Appl Psychol Meas. 1977;1(3):385–401.
28. Suthutvoravut U, Tanaka T, Takahashi K, Akishita M, Iijima K. Living with Family yet Eating Alone is Associated with Frailty in Community-Dwelling Older Adults: The Kashiwa Study. J frailty aging [Internet]. 2019 [cited 2020 Mar 23];8(4):198–204. Available from: http://www.ncbi.nlm.nih.gov/pubmed/31637406
29. Montagnier D, Barberger-Gateau P, Jacqmin-Gadda H, Dartigues JF, Rainfray M, Pérès K, et al. Evolution of prevalence of depressive symptoms and antidepressant use between 1988 and 1999 in a large sample of older French people: Results from the personnes agées quid study. J Am Geriatr Soc [Internet]. 2006 Dec [cited 2020 Feb 13];54(12):1839–45. Available from: http://www.ncbi.nlm.nih.gov/pubmed/17198488
30. Kiiti Borges M, Oiring de Castro Cezar N, Silva Santos Siqueira A, Yassuda M, Cesari M, Aprahamian I. The Relationship between Physical Frailty and Mild Cognitive Impairment in the Elderly: A Systematic Review. J frailty aging [Internet]. 2019 [cited 2020 Mar 23];8(4):192–7. Available from: http://www.ncbi.nlm.nih.gov/pubmed/31637405
31. Kwan RYC, Leung AYM, Yee A, Lau LT, Xu XY, Dai DLK. Cognitive Frailty and Its Association with Nutrition and Depression in Community-Dwelling Older People. J Nutr Health Aging [Internet]. 2019 Dec 1 [cited 2020 Mar 23];23(10):943–8. Available from: http://www.ncbi.nlm.nih.gov/pubmed/31781723
32. McGrath R, Erlandson KM, Vincent BM, Hackney KJ, Herrmann SD, Clark BC. Decreased Handgrip Strength is Associated With Impairments in Each Autonomous Living Task for Aging Adults in the United States. J frailty aging. 2019;8(3):141–5.
33. Zhu RR, Shi J, Yang M, Guan HJ. Prevalences and causes of vision impairment in elderly chinese: A socioeconomic perspective of a comparative report nested in Jiangsu Eye Study. Int J Ophthalmol. 2016 Jul 18;9(7):1051–6.
34. Murthy GVS, Vashist P, John N, Pokharel G, Ellwein LB. Prevalence and causes of visual impairment and blindness in older adults in an area of India with a high cataract surgical rate. Ophthalmic Epidemiol. 2010 Aug;17(4):185–95.
35. Bourne RRA, Jonas JB, Bron AM, Cicinelli MV, Das A, Flaxman SR, et al. Prevalence and causes of vision loss in high-income countries and in Eastern and Central Europe in 2015: Magnitude, temporal trends and projections. Br J Ophthalmol. 2018 May 1;102(5):575–85.
36. Tomioka K, Ikeda H, Hanaie K, Morikawa M, Iwamoto J, Okamoto N, et al. The Hearing Handicap Inventory for Elderly-Screening (HHIE-S) versus a single question: reliability, validity, and relations with quality of life measures in the elderly community, Japan. Qual Life Res [Internet]. 2013 Jun [cited 2020 Feb 12];22(5):1151–9. Available from: http://www.ncbi.nlm.nih.gov/pubmed/22833152
37. United Nations Department of Economic and Social Affairs. Population Division. Ageing Profiles [Internet]. Ageing Profiles. 2019 [cited 2020 Feb 13]. Available from: https://population.un.org/ProfilesOfAgeing2019/index.html

CAN THE COMBINED USE OF TWO SCREENING INSTRUMENTS IMPROVE THE PREDICTIVE POWER OF DEPENDENCY IN (INSTRUMENTAL) ACTIVITIES OF DAILY LIVING, MORTALITY AND HOSPITALIZATION IN OLD AGE?

 

L.P.M. Op het Veld1,2, E. van Rossum1,2, G.I.J.M. Kempen2, A.J.H.M. Beurskens1,3, K.J. Hajema4, H.C.W. de Vet5

 

1. Centre of Research Autonomy and Participation for Persons with a Chronic Illness, Faculty of Health, Zuyd University of Applied Sciences, P.O. Box 550, 6400 AN Heerlen, the Netherlands; 2. CAPHRI, Care and Public Health Research Institute, Department of Health Services Research, Maastricht University, P.O. Box 616, 6200 MD Maastricht, the Netherlands; 3. CAPHRI, Care and Public Health Research Institute, Department of Family Practice, Maastricht University, P.O. Box 616, 6200 MD Maastricht, the Netherlands;
4. Community Health Service South Limburg, Academic Collaborative Centres Public Health (ACC), P.O. Box 33, 6400 AA Heerlen, the Netherlands; 5. Department of Epidemiology and Biostatistics, Amsterdam Public Health research institute, Amsterdam University Medical Centers, location VU University, De Boelelaan 1089A, 1081 HV Amsterdam, the Netherlands. Corresponding author: Linda P.M. Op het Veld, MSc, Centre of Research Autonomy and Participation for Persons with a Chronic Illness, Faculty of Health, Zuyd University of Applied Sciences, P.O. Box 550, 6400 AN Heerlen, the Netherlands, Phone: +31 (0)45 400 6538, E-mail: linda.ophetveld@zuyd.nl

J Frailty Aging 2019;in press
Published online June 12, 2019, http://dx.doi.org/10.14283/jfa.2019.17

 


Abstract

Background: Due to differences in the definition of frailty, many different screening instruments have been developed. However, the predictive validity of these instruments among community-dwelling older people remains uncertain. Objective: To investigate whether combined (i.e. sequential or parallel) use of available frailty instruments improves the predictive power of dependency in (instrumental) activities of daily living ((I)ADL), mortality and hospitalization. Design, setting and participants: A prospective cohort study with two-year follow-up was conducted among pre-frail and frail community-dwelling older people in the Netherlands. Measurements: Four combinations of two highly specific frailty instruments (Frailty Phenotype, Frailty Index) and two highly sensitive instruments (Tilburg Frailty Indicator, Groningen Frailty Indicator) were investigated. We calculated sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for all single instruments as well as for the four combinations, sequential and parallel. Results: 2,420 individuals participated (mean age 76.3 ± 6.6 years, 60.5% female) in our study. Sequential use increased the levels of specificity, as expected, whereas the PPV hardly increased. Parallel use increased the levels of sensitivity, although the NPV hardly increased. Conclusions: Applying two frailty instruments sequential or parallel might not be a solution for achieving better predictions of frailty in community-dwelling older people. Our results show that the combination of different screening instruments does not improve predictive validity. However, as this is one of the first studies to investigate the combined use of screening instruments, we recommend further exploration of other combinations of instruments among other study populations.

Key words: Frail older people, frailty (instruments), screening, sensitivity and specificity, combined use.


 

 

Introduction

Life expectancy is increasing in most Western countries, resulting in larger populations of older and frail older people (1). Although the debate concerning the conceptualization of frailty is ongoing, there is consensus that being frail increases the risk of adverse outcomes, such as mortality, hospitalization and functional decline (2). The variety in definitions has led to the development and use of many different instruments to identify frail community-dwelling older people; however, the predictive validity of these instruments is generally limited (3).
In a recent study, Op het Veld and colleagues investigated the ability of various indices to predict mortality, hospitalization and dependency in (instrumental) activities of daily living ((I)ADL), namely: the Frailty Phenotype (FP), the Groningen Frailty Indicator (GFI), the Tilburg Frailty Indicator (TFI) and the Frailty Index (FI) (4). All frailty instruments performed poorly in predicting mortality, hospitalization and (I)ADL dependency (area under the receiver operating characteristic curve [AUC] 0.62–0.65, 0.59–0.63 and 0.60–0.64, respectively). Several other studies have demonstrated somewhat more positive outcomes. A study of Gobbens and colleagues showed one of the highest AUCs: 0.80-0.83 for the TFI in predicting (I)ADL disability over a one- and two-year period (5). Nevertheless the AUCs of frailty instruments are generally not very convincing (6).
It has been suggested that the combined use of two frailty screening measures could provide complementary information and might increase the predictive power (7, 8). Instruments can be applied sequentially or in parallel. Sequential use means that the second instrument is only applied when the first instrument gives a positive result. When used in parallel, both instruments are applied at the same time. Sequential use maximizes specificity and the positive predictive value, i.e. the probability that a person with positive test results is indeed frail (9). Starting with the test with the highest specificity is most efficient, as it requires fewer persons to undergo both screening measures. In contrast, parallel use maximizes sensitivity and the negative predictive value. By applying the two instruments at the same time, frailty will be less likely to be missed and the results are more rapidly available.
The aim of our study was to investigate whether the combined use of available frailty screening instruments, sequential and parallel, would result in a better prediction of frailty in terms of (I)ADL dependency, mortality and hospitalization compared to the use of a single frailty instrument.

 

Methods

We conducted a prospective cohort study with a two-year follow-up. The study was approved by the medical ethical committee of Zuyderland and Zuyd University of Applied Sciences in the Netherlands (METC Z, 12-N-129).

Participants

A detailed description of the selection of participants is provided elsewhere (10). Briefly, 56,000 people aged 55 years and over, living in the province of Limburg, a southern region of the Netherlands, received first an extensive general health questionnaire sent out by the Dutch Community Health Services. The respondents, who were at least 65 years old and pre-frail or frail, according to Fried’s frailty criteria, were then asked to participate in our study. In total, 2,420 persons gave informed consent and participated in the baseline of the present study. Gender, age, living situation and educational level were assessed at baseline.

Frailty instruments

For the combined use of the two frailty instruments, combinations of four different frailty screening instruments were tested. Instruments with high specificity values (Frailty Phenotype [FP], Frailty Index [FI]), as presented in previous research (4), were combined with instruments with high levels of sensitivity (Tilburg Frailty Indicator [TFI], Groningen Frailty Indicator [GFI]), resulting in four combinations that were investigated: FP-TFI, FP-GFI, FI-TFI and FI-GFI.
The  FP, as described by Fried and colleagues, includes five criteria (weight loss, exhaustion, physical activity, walk time and handgrip strength) for the identification of physical frailty among older people (11). Questions about weight loss and exhaustion were asked as proposed by Fried and colleagues. The Short Questionnaire to Assess Health-enhancing physical activity (SQUASH) was used to determine the physical activity criterion (12). Walk time and handgrip strength were measured with the self-report questions ‘Can you reach the other side of the road when the light turns green at a zebra crossing?’ and ‘Do you experience difficulties in daily life because of low grip strength?’ respectively, rather than using a performance based measure. A detailed description of the self-report measures for these criteria can be found elsewhere (13). Theoretical scores range from 0 to 5 and classify individuals into non-frail (score 0), pre-frail (score 1–2) or frail (score 3–5). As mentioned previously, only pre-frail and frail persons were included in the baseline assessment of the present study.
The FI, developed by Rockwood and Mitnitski, is characterized by a non-fixed set of so-called ‘deficits’ (14). We created an FI using the guidelines provided by Searle and colleagues (15). First, we chose all available items from the questionnaire sent by the Dutch Community Health Services, that were presumably related to frailty. We selected 61 potential items that covered several topics, such as (chronic) diseases, loneliness, physical limitations and psychological distress. All items were then dichotomized into the presence ‘1’ or absence ‘0’ of the item. Next, items with a prevalence of less than five percent were excluded, as proposed by Drubbel and colleagues (16). Finally, we ended up with an FI that consisted of 53 items. The final score of the FI can be calculated by dividing the number of deficits present by the total number of deficits that are measured. Theoretical scores range from 0 to 1, with higher scores indicating a higher level of frailty. A cut-off value of 0.25 was used to distinguish between frail and non-frail individuals (17).
The TFI was developed by Gobbens and colleagues (18). This 15-item questionnaire comprises items in the physical (8 items), psychological (4 items) and social (3 items) domains. Theoretical scores range from 0 to 15, with higher scores indicating a higher level of frailty. A person is considered frail with a score of ≥ 5 (18).
The GFI was developed by Steverink and colleagues (19). This 15-item questionnaire comprises items in the physical (9 items), cognitive (1 item), social (3 items) and psychological (2 items) domains. Theoretical scores range from 0 to 15, with higher scores indicating a higher level of frailty. Persons with a score ≥ 4 are considered frail (20).

Outcome measures

The outcome measure (I)ADL dependency was defined as an increase in having to depend on someone else when performing (instrumental) activities of daily living, which was determined by the Groningen Activity Restriction Scale (GARS) (21) at baseline and after two years. The GARS is composed of 18 questions about the degree to which someone is able to perform ADL and IADL activities independently. The four response options for each activity are: 1. ‘Yes, I can do it fully independently without any difficulty’, 2. ‘Yes, I can do it fully independently but with some difficulty’, 3. ‘Yes, I can do it fully independently but with great difficulty’, 4. ‘No, I cannot do it fully independently, I can only do it with someone’s help’. For each question, the results were dichotomized into being independent (options 1–3) or dependent (option 4), as described in the GARS manual (22). Changes over time per item were then analysed. An increase in dependency was defined as more changes from independent to dependent than vice versa over the two-year observation period.
Data on mortality (deceased yes/no) at two-year follow-up were provided by Statistics Netherlands. The outcome hospitalization was dichotomized into ‘Yes’ when someone was admitted at least once to a hospital during the study period, or ‘No’ when no hospital admission had taken place.

Statistical analysis

Missing values were handled as proposed in prior research. Case mean substitution was applied when missing items were less than 25% for the TFI and GFI (23) and 50% for the GARS (21). On the FP, one missing value was allowed when a person had a valid score of 0–2 and two missing values were allowed if the total score was ≥3 (13). For the FI, the non-missing population mean of an item was imputed for each missing item (24).
Descriptive statistics were computed to provide information on the characteristics of the study population. Cut-off values for frailty were used as proposed by the developers of the instruments. Analyses regarding the sequential use of instruments were conducted as follows: first, participants were selected who were frail according to a specific frailty instrument; second, of these frail participants, only those who were also frail based on a sensitive frailty instrument were finally classified as frail. All others were considered non-frail. For analyses regarding the parallel use of instruments, participants were considered frail when at least one of the two instruments classified them as frail. Participants were only considered non-frail when they were non-frail according to both frailty instruments. Sensitivity, specificity, positive and negative predictive values were then calculated for each single instrument and for the combined instruments (both sequential and parallel), for all three outcome measures.
All analyses were performed using SPSS 22 (IBM SPSS Statistics for Windows, IBM Corp., Armonk, NY).

 

Results

In total, 2,420 persons participated in the study. Their mean age was 76.3 ± 6.6 years and 60.5% were females. Additional baseline characteristics are presented in Table 1. At the two-year follow-up, data on changes in (I)ADL dependency were available for 1,872 individuals of whom 35.7% experienced an increase in dependency. Hospitalization was reported by 836 participants (46.4% of 1,803 valid cases) and 182 participants (7.5% of 2,420 valid cases) died during the study period. Missing data for the outcomes (I)ADL dependency and hospitalization were partly due to mortality (n = 182) and admission to a long-term care facility (n = 53). The remaining participants were lost to follow-up for other (unknown) reasons (n = 313 for (I)ADL dependency and n = 382 for hospitalization).
The sequential use of two frailty instruments is presented in Figure 1. Graph A displays the distribution of all participants (n = 1,872) who did and did not experience an increase in (I)ADL dependency on the FI, the specific instrument. Only those classified as frail (n = 480) are included in graph B, which shows the distribution of persons who did and did not experience an increase in (I)ADL dependency on the TFI, the sensitive instrument. Similar results were found for the other sequential combinations of frailty instruments.

Table 1 Baseline characteristics of the study population

FP: Frailty Phenotype; FI: Frailty Index; TFI: Tilburg Frailty Indicator; GFI: Groningen Frailty Indicator; GARS: Groningen Activity Restriction Scale; SD: Standard deviation; * Low educational level = no education, completion of primary school or pre-vocational secondary education; high educational level = higher than primary school or pre-vocational secondary education

 

Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for the single and the combined instruments for (I)ADL dependency are presented in Table 2. For the single instruments, the FP and FI showed higher values of specificity, whereas the TFI and GFI had higher values of sensitivity. As expected, the sequential use of two frailty instruments resulted in lower levels of sensitivity and NPV, together with  higher levels of specificity and PPV. However, the degree of change for the PPV and NPV was slight. The parallel use of the two frailty instruments, in general, resulted in high levels of sensitivity and NPV, together with lower levels of specificity and PPV. The PPV and NPV again changed only slightly, as in the other combination. Comparable results were found for the outcomes hospitalization and mortality (see online Supplement 1).

Table 2 The number of frail persons at baseline and sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of the four single frailty instruments and the combined frailty instruments (sequential and parallel) for the outcome (I)ADL dependency at two-year follow-up

Table 2
The number of frail persons at baseline and sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of the four single frailty instruments and the combined frailty instruments (sequential and parallel) for the outcome (I)ADL dependency at two-year follow-up

FP: Frailty Phenotype; FI: Frailty Index; TFI: Tilburg Frailty Indicator; GFI: Groningen Frailty Indicator

Figure 1 Sequential use of the Frailty Index (FI) and the Tilburg Frailty Indicator (TFI) for the outcome increase in dependency in (instrumental) activities of daily living ((I)ADL). A) Distribution of all participants who did and did not experience an increase in (I)ADL dependency on the FI. B) Distribution of individuals, who were frail on the FI, who did or did not experience an increase in (I)ADL dependency on the TFI. Cut-off values are presented as dotted lines

Figure 1
Sequential use of the Frailty Index (FI) and the Tilburg Frailty Indicator (TFI) for the outcome increase in dependency in (instrumental) activities of daily living ((I)ADL). A) Distribution of all participants who did and did not experience an increase in (I)ADL dependency on the FI. B) Distribution of individuals, who were frail on the FI, who did or did not experience an increase in (I)ADL dependency on the TFI. Cut-off values are presented as dotted lines

 

Discussion

The aim of our study was to investigate whether the combined use of frailty instruments, either sequential or parallel, would result in a better prediction of (I)ADL dependency, mortality and hospitalization, compared to the use of a single frailty instrument. In our study, we were unable to demonstrate a clear beneficial effect of using either combination of frailty instruments. As expected, specificity levels increased when applying the instruments sequentially; however, the PPV hardly increased. The parallel use of two instruments increased sensitivity; however, the NPV hardly increased.
To the best of our knowledge, this is the first study to investigate the possible value of the combined application of two frequently used frailty screening instruments. In some other studies, a frailty instrument has been combined with another measurement. For instance, Kenig and colleagues examined frailty (defined by deficits in two or more domains of the comprehensive geriatric assessment) and the Surgical Apgar Score (25). Compared to the individual instruments, the combination did not increase the PPV for 30-day morbidity and only slightly increased the NPV for 30-day mortality among older patients undergoing abdominal cancer surgery. Also, frailty screening can be followed by a more thorough assessment. For example, the ‘Prevention of Care’ programme comprises screening with the GFI (26). When someone scores 5 or higher, a multidimensional assessment is conducted by a practice nurse at the patient’s home to gain insight into problems in performing daily activities and risk factors for disability. However, the screening instruments used in such approaches often include many false-positive cases, which render them inefficient, and the second steps are often very time consuming. In these cases, the sequential use of two screening instruments might be relevant.
A major strength of this study is the simultaneous assessment of four available frailty instruments in a large cohort of community-dwelling older people, which is the best strategy for comparing the performance of instruments. In particular, PPV and NPV, which are affected by the prevalence of the outcomes, are difficult to compare when the results are obtained from different studies. By applying instruments sequentially, a higher PPV can be achieved (9). At the same time, it also causes more false-negative cases, indicating that frail persons are missed in screening. One might utilize this strategy, for example, when costly or time-consuming clinical management follows in terms of advanced diagnostics or expensive treatment. On the other hand, while parallel testing increases the NPV, it causes more false-positive cases. This method would be best applied if one desired to include as many frail persons as possible, for research purposes or in daily practice. However, follow-up and interventions would then often be applied to those not needing extensive monitoring.
Our study population consisted of pre-frail and frail patients and did not include non-frail persons. In daily practice, frailty instruments are most often applied by healthcare professionals in persons who are at risk of becoming frail. The inclusion of pre-frail and frail persons makes our population more reflective of the persons for whom frailty measures are useful rather than persons sampled from the general population. Nevertheless, for the selection of the cohort the FP was used, which focusses on the physical aspects of frailty. Persons that were frail in other domains (e.g. psychological or social) might therefore have been excluded, which may have influenced the results.
All frailty instruments were assessed as proposed by the developers, except for the FP, for which we used self-report questions instead of performance-based measures, potentially having a slight influence on the results (27). In our study, the FP and FI were handled as specific instruments and the TFI and GFI as sensitive instruments (4). Some studies, however, show other values of sensitivity and/or specificity (5, 28). The combined use of instruments should therefore be studied further with different instruments (with high levels of sensitivity and/or specificity), in other study populations and/or with different (handling of) outcome measures. One of the instruments that might be interesting to investigate is the Vulnerable Elders Survey (VES)-13 (29). In a recent study of Bongue and colleagues this instrument demonstrated very high levels of sensitivity for various outcome measures (30). Moreover, this instrument has often been cited over the past years and is thus of interest to many researchers (31). Regarding the investigation of another study population, the oldest old (80+ years) could be considered. Frailty is more present among people in this age group and older people are more at risk for adverse health outcomes compared to younger ones. An example of a different handling of an outcome measure is the number of hospital admissions. From the participants who reported to be admitted to a hospital in our study, 355 (42%) were admitted once, 196 (23%) twice, and 227 (27%) three times or more (missing values: n = 58 (7%)). Clearly there is a large variation in the number of admissions. Hospital admissions can be caused by factors unrelated to frailty. It is unknown if multiple admissions are more often related to frailty compared to one admission and if combined use of frailty instruments can predict multiple admissions.
Based on our results, we conclude that the combined application of two frailty instruments might not be a solution to achieve a better identification of frailty in community-dwelling older people. However, as this is one of the first studies to investigate the combined use of screening instruments, we recommend further exploration of other combinations of instruments in various study populations.

 

Acknowledgements: We thank all participants for completing the questionnaires. The Community Health Services in Limburg are acknowledged for providing baseline data and the possibility of creating the cohort. We thank the center for data and information management of Maastricht University, MEMIC for data management support.
Funding: This work was supported by the Nationaal Regieorgaan Praktijkgericht Onderzoek SIA (project number PRO -1-007) and Zuyd University of Applied Sciences. Both organizations had no role in the design and conduct of the study; in the collection, analysis, and interpretation of data; in the preparation of the manuscript; or in the review or approval of the manuscript.
Conflict of interest: Drs. Op het Veld has nothing to disclose. Dr. van Rossum has nothing to disclose. Dr. Kempen has nothing to disclose. Dr. Beurskens has nothing to disclose. Dr. Hajema has nothing to disclose. Dr. de Vet has nothing to disclose.
Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

MATERIAL ONLINE

 

References

1.    Christensen K, Doblhammer G, Rau R, Vaupel JW. Ageing populations: the challenges ahead. Lancet. 2009;374(9696):1196-208.
2.    Rodriguez-Manas L, Feart C, Mann G, et al. Searching for an operational definition of frailty: a Delphi method based consensus statement: the frailty operative definition-consensus conference project. J Gerontol A Biol Sci Med Sci. 2013;68(1):62-7.
3.    Apostolo J, Cooke R, Bobrowicz-Campos E, et al. Predicting risk and outcomes for frail older adults: an umbrella review of frailty screening tools. JBI Database System Rev Implement Rep. 2017;15(4):1154-1208.
4.    Op Het Veld LPM, Beurskens AJHM, de Vet HCW, et al. The ability of four frailty screening instruments to predict mortality, hospitalization and dependency in (instrumental) activities of daily living. Eur J Ageing. 2019; doi.org/10.1007/s10433-019-00502-4
5.    Gobbens RJ, van Assen MA, Luijkx KG, Schols JM. The predictive validity of the Tilburg Frailty Indicator: disability, health care utilization, and quality of life in a population at risk. Gerontologist. 2012;52(5):619-31.
6.    Theou O, Brothers TD, Mitnitski A, Rockwood K. Operationalization of frailty using eight commonly used scales and comparison of their ability to predict all-cause mortality. J Am Geriatr Soc. 2013;61(9):1537-51.
7.    Cesari M, Gambassi G, van Kan GA, Vellas B. The frailty phenotype and the frailty index: different instruments for different purposes. Age Ageing. 2014;43(1):10-2.
8.    Dent E, Kowal P, Hoogendijk EO. Frailty measurement in research and clinical practice: A review. Eur J Intern Med. 2016;31:3-10.
9.    Fletcher R, Fletcher S, Fletcher G. Clinical Epidemiology: The Essentials. 2014. Lippincott Williams & Wilkins, Philadelphia.
10.    Op Het Veld LPM, Ament BHL, van Rossum E, et al. Can resources moderate the impact of levels of frailty on adverse outcomes among (pre-) frail older people? A longitudinal study. BMC Geriatr. 2017;17(1):185.
11.    Fried LP, Tangen CM, Walston J, et al. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci. 2001;56(3):M146-56.
12.    Wendel-Vos GC, Schuit AJ, Saris WH, Kromhout D. Reproducibility and relative validity of the short questionnaire to assess health-enhancing physical activity. J Clin Epidemiol. 2003;56(12):1163-9.
13.    Op het Veld LP, van Rossum E, Kempen GI, et al. Fried phenotype of frailty: cross-sectional comparison of three frailty stages on various health domains. BMC Geriatr. 2015;15:77.
14.    Rockwood K and Mitnitski A. Frailty in relation to the accumulation of deficits. J Gerontol A Biol Sci Med Sci. 2007;62(7):722-7.
15.    Searle SD, Mitnitski A, Gahbauer EA, Gill TM, Rockwood K. A standard procedure for creating a frailty index. BMC Geriatr. 2008;8:24.
16.    Drubbel I, de Wit NJ, Bleijenberg N, et al. Prediction of adverse health outcomes in older people using a frailty index based on routine primary care data. J Gerontol A Biol Sci Med Sci. 2013;68(3):301-8.
17.    Rockwood K, Andrew M, Mitnitski A. A comparison of two approaches to measuring frailty in elderly people. J Gerontol A Biol Sci Med Sci. 2007;62(7):738-43.
18.    Gobbens RJ, van Assen MA, Luijkx KG, Wijnen-Sponselee MT, Schols JM. The Tilburg Frailty Indicator: psychometric properties. J Am Med Dir Assoc. 2010;11(5):344-55.
19.    Steverink N, Slaets JP, Schuurmans H, van Lis M. Measuring frailty: development and testing of the Groningen Frailty Indicator (GFI). The Gerontologist. 2001;41(special issue 1):236-237.
20.    Schuurmans H, Steverink N, Lindenberg S, Frieswijk N, Slaets JP. Old or frail: what tells us more? J Gerontol A Biol Sci Med Sci. 2004;59(9):M962-5.
21.    Kempen GI, Miedema I, Ormel J, Molenaar W. The assessment of disability with the Groningen Activity Restriction Scale. Conceptual framework and psychometric properties. Soc Sci Med. 1996;43(11):1601-10.
22.    Kempen GI, Doeglas DM, Suurmeijer TPBM. Groningen Activity Restriction Scale (GARS): een handleiding. Tweede herziene druk. 2012. UMCG / Rijksuniversiteit Groningen, Research Institute SHARE.
23.    Metzelthin SF, Daniels R, van Rossum E, et al. The psychometric properties of three self-report screening instruments for identifying frail older people in the community. BMC Public Health. 2010;10:176.
24.    Song X, Mitnitski A, Rockwood K. Prevalence and 10-year outcomes of frailty in older adults in relation to deficit accumulation. J Am Geriatr Soc. 2010;58(4):681-7.
25.    Kenig J, Mastalerz K, Mitus J, Kapelanczyk A. The Surgical Apgar score combined with Comprehensive Geriatric Assessment improves short- but not long-term outcome prediction in older patients undergoing abdominal cancer surgery. J Geriatr Oncol. 2018.
26.    Daniels R, van Rossum E, Metzelthin S, et al. A disability prevention programme for community-dwelling frail older persons. Clin Rehabil. 2011;25(11):963-74.
27.    Op Het Veld LPM, de Vet HCW, van Rossum E, et al. Substitution of Fried’s performance-based physical frailty criteria with self-report questions. Arch Gerontol Geriatr. 2018;75:91-95.
28.    Daniels R, van Rossum E, Beurskens A, van den Heuvel W, de Witte L. The predictive validity of three self-report screening instruments for identifying frail older people in the community. BMC Public Health. 2012;12:69.
29.    Saliba D, Elliott M, Rubenstein LZ, et al. The Vulnerable Elders Survey: a tool for identifying vulnerable older people in the community. J Am Geriatr Soc. 2001;49(12):1691-9.
30.    Bongue B, Buisson A, Dupre C, et al. Predictive performance of four frailty screening tools in community-dwelling elderly. BMC Geriatr. 2017;17(1):262.
31.    Buta BJ, Walston JD, Godino JG, et al. Frailty assessment instruments: Systematic characterization of the uses and contexts of highly-cited instruments. Ageing Res Rev. 2016;26:53-61.

POLICY AND ECONOMIC CONSIDERATIONS FOR FRAILTY SCREENING IN THE CANADIAN HEALTHCARE SYSTEM

 

K. Grimes1, J. Kitts2, B. Tholl3, C. Samuelson-Kiraly4, J.I. Mitchell5 On behalf of Canadian Frailty Network

 

1. Consultant and Executive Director, CHLNet; Canada; 2. Former Director, Policy and Strategy, HealthCareCAN, Senior Director, Health Policy, Canadian Medical Association, Canada; 3. Former President and CEO, HealthCareCAN, Senior Health Consultant, Tholl Health and Leadership Consulting, Canada; 4. Research and Policy Analyst, HealthCareCAN, Canada;
5. Vice-President, Research and Policy, HealthCareCAN, Canada.
Corresponding author: Jonathan Mitchell, Vice-President, Research and Policy, HealthCareCAN, 17 York Street, Suite 100, Ottawa, ON K1N-5S7, (613) 241-8005, ext. 214,
fax: 613-241-5055, jmitchell@healthcarecan.ca

J Frailty Aging 2018;7(4):233-239
Published online October 8, 2018, http://dx.doi.org/10.14283/jfa.2018.32

 


Abstract

Canada faces significant policy and economic challenges related to healthcare for frail older adults. Annual per capita healthcare costs for people over age 65 are five times those for people under 65. Flat economic growth and an aging workforce decrease tax revenue, which funds 70% of health spending. Governments are shifting policy to enhance person-centered care and shifting spending from hospitals to primary and community care. Recognizing that frailty and evidence-based frailty screening can contribute directly to reform initiatives, what are the policy and economic considerations, both nationally and internationally, around frailty screening that will benefit patients, families and/or the wider health system? Based on key informant interviews, we present recommendations for approaching policy and economic challenges in frailty through the following healthcare policy instruments: financing, funding, legislation, regulation, technology, interdisciplinary care, person-centered service and health promotion.

Key words: Frailty, screening, older adults, policy, economic.


 

Frail older adults in the Canadian healthcare system

Canada has significant policy and economic challenges that impact our healthcare system’s focus on frail older adults. Baby boomers, the cohort born between 1946 and 1964 and the largest segment of our population, are placing – and will continue to place – increased demands on the healthcare system for both themselves and their families. Facts to support this are numerous:
•    The prevalence of chronic disease and functional impairment increases with age (1). Nearly one in eight, or 11.7%, of Canadians aged 65 and older have at least two of the four major chronic diseases, compared to one in 30, or 3.6%, of Canadian adults aged 20 years and older (2).
•    Individuals designated for Alternate Level of Care – those who no longer require acute care services but wait in acute care beds for placement in a more appropriate setting (3) – occupy a large percentage (14%) of inpatient beds (3), and 85% of these designated individuals are aged 65 or older.
•    One in three emergency department visits is potentially avoidable for seniors who live in long-term care facilities (4).
•    Older adults often have more than one chronic disease and multiple morbidities such as cognitive impairment (5, 6). More than half (58%) of all annual healthcare spending in Canada is for chronic disease, at a cost of $68 billion per year (7).

Frailty, a state of increased vulnerability resulting in reduced physical reserve and loss of function across multiple body systems (8), affects not only seniors, but all ages, and can be a significant part of any chronic disease. This paper focuses on frailty at the later stages of life.
Economically, Canada invests significantly in health. In 2015, total health expenditure reached $219 billion or 10.9% of GDP. The annual per capita cost of healthcare for Canadians under the age of 65 is one fifth the cost for those over 65 and one tenth the cost for those over 80 (9). Economists forecast that spending on continuing care for seniors will increase from $29.3 billion in 2011 to $184.2 billion in 2046 (10). Despite significant investment, jurisdictional challenges resulting from a fragmented health system have caused Canada’s health performance to suffer in recent years.
In tandem with population aging, lengthened life expectancy and the increased prevalence of chronic illnesses, the demand for family caregivers will also continue to grow. In 2012, 8 million Canadians, or 28% of the population aged 15 and over, provided care to family members or friends with a long-term health condition, a disability or problems associated with aging (11). In 2007, informal caregivers provided over 1.5 billion hours of home care, and in 2009 they incurred over $80 million in out-of-pocket expenses related to caregiving. During the same year, the economic contribution of informal caregivers was estimated to be $25 billion (12).
Enabling aging Canadians to remain at home not only benefits the care-receiver, it also translates to savings for the Canadian healthcare system (13). A 2012 study in Ontario estimated that between 14% and 37% of those waiting for long-term care could be supported safely and cost-effectively at home (14). Public policy around the support needed by informal caregivers for their unpaid work is becoming a necessity.
The combination of flat-lined economic growth and an aging work force will decrease tax revenue. For example, in 1971 there were 15 seniors for every 100 working-age people; in 2006, there were 21. In another 50 years, projections forecast 50 seniors for every 100 workers (15). The specific impact of an aging population on overall tax revenue will be significant, especially as this large cohort of aging baby boomers retires, many lacking sufficient savings outside of defined pension plans (16). Canada needs to devise strategies to keep older Canadians productive for longer (17), and the federal government’s decision to move the age of eligibility for the Old Age Security pension from 67 years back to 65 years will impact those strategies.
Technological changes are another significant cost driver in healthcare, though the impact has been difficult to quantify. A survey of technology and its impact on healthcare spending concluded that technology-related changes have contributed to the growth of per capita healthcare spending in the United States by 38% to 65%. While technology can increase costs in the short term, technological advancements can also significantly reduce medium- and long-term costs (18).
Overall price effects, such as rising costs of drugs and increased compensation for health providers, have been the most significant driver of overall health spending (9). Seniors still account for a high proportion of public drug program spending. For example, in 2011, seniors accounted for 60% of government spending on prescribed drugs (19). Polypharmacy is a growing concern and contribution to frailty, with many more seniors on multiple drugs from multiple providers. Polypharmacy increases the probability that a senior will require emergency medical attention as a result of adverse drug reactions and incidents such as falls (20). Pharmacists are playing a more active role in strategies to de-prescribe drugs, notwithstanding financial incentives to the contrary (21).
These changes in context are requiring governments to embrace new policies and strategies around person-centered care and to move spending from acute settings to primary and community care. Frailty feeds directly into many reform initiatives. However, frailty remains under-recognized and not well understood. In particular, few understand that not just older adults are frail and that frailty is often the consequence of unmanaged chronic disease. The progression of frailty can be delayed by combining evidenced-based health assessments with prevention strategies such as physical activity and exercise, coaching, community engagement and wellness plans (22). A well-thought-out integrated strategy for frailty can be powerful. For example, the pilot study of CARES (Community Actions and Resources Empowering Seniors; 23) in British Columbia and Nova Scotia shows that primary care providers in the community are critical to stemming the tide of frailty.
Feeding into the frailty discussion are validated screening tools for frailty, some electronic and some paper-based. These tools, such as the Rockwood, interRAI, Edmonton Frail Scale and Fried Phenotype, are administered in different settings and by different providers: nurses, physicians or individuals themselves (24). Different settings across the care and service continuum, from primary to acute care and across provider groups, require different strategies and tools (24), but such an approach creates much confusion. Given the economic challenges that face our health system, it is becoming apparent that scaling up frailty screening practices requires policy and economic changes or instruments.
HealthCareCAN (www.healthcarecan.ca) is the national voice of healthcare organizations and hospitals across Canada, with the goal to improve the health of Canadians through an evidence-based and innovative healthcare system. In January and into early March 2016, HealthCareCAN, on behalf of the Canadian Frailty Network (formerly the Technology Evaluation in the Elderly Network), undertook a series of key informant interviews to accompany a brief literature review on the following research question:
“What are the policy and economic considerations, both for Canada and internationally, to be taken into account around frailty screening that will benefit patients, families and/or the wider health system?”
The methodology and results of this process are presented here.

 

Methods

An advisory committee, consisting of experts in frailty and policy in Canada from organizations including the Canadian Nurses Association, the Canadian Medical Association, the Canadian Pharmacists Association and the Canadian Hospice Palliative Care Association, guided this work. Our multi-pronged approach provided wider insight, given the scope of the research question.

Key Informant Interviews

Sixteen individuals were recruited through HealthCareCAN networks and participated in interviews lasting approximately 45 minutes. These key informant interviews proved foundational for the insights and recommendations in this paper. Key informants were selected to balance multiple criteria:
•    Extensive knowledge of Canadian health policy, health economics or frailty issues.
•    Stakeholder group type: policy maker, researcher, provider or individual.
•    Geographic diversity.
•    Level: macro (government), meso (institutions) or micro (provider, individual and family) viewpoints.

Telephone interviews were conducted using a standardized interview guide that was validated and pilot-tested with the Advisory Committee (Appendix 1). To help inform the key informant interviews, a literature review was undertaken in which national and international peer-reviewed and grey literature was searched. While the information collected proved invaluable, the aim of the literature review was to contextualize the key informant interviews. A second round of interviews was not deemed necessary given that data saturation was achieved with the sixteen interviews completed and common themes and comments were achieved.

Webinar

On April 12, 2016, preliminary key findings from our literature review and key informant interviews were shared at a webinar hosted by HealthCareCAN and the Canadian Frailty Network. Presenters were Dr. John Muscedere, Mr. Bill Tholl, Ms. Jennifer Kitts and Ms. Kelly Grimes. Over 180 people registered and almost 90 participated in the webinar. The questions and feedback from the webinar helped to frame the top recommendations in this paper.

 

Policy and Economic Opportunities in Canada

Numerous policy instruments are in place to advance public policy for the health system, including: financing and funding, legislation and regulation, partnerships and networks, technology, and evidence-based public awareness campaigns and other communication strategies. Typically, a combination of instruments is the most effective in achieving desired results.
Our key informant interviews brought to light the many policy and economic challenges and opportunities in Canada regarding frailty and frailty screening. Key informants note that, with continuing growth of our aging population, frailty has long been on policy agendas yet has not been addressed as a whole in policy and economic mechanisms. Nearly two-thirds of Canadians are worried that our healthcare system is falling behind our needs (25).
Outlined here are the most prevalent policy and economic themes cited by our interviewees as shaping the focus on frail older adults, both now and in the future. Other forces in play, such as health provider supply, education and training, are not explored in depth.

Canada’s Federated Health System

Canada has 14 different health systems through provincial, territorial and federal funding for health programs, each system with its own structures and processes. Marchildon (26) notes that this is one of the most decentralized health systems in the world. Internationally, Canada’s health performance continues to decrease. The 2014 Commonwealth Fund International Health Policy Survey of Older Adults (27) ranked Canada 10th out of 11 countries. Many programs for seniors fall outside of the Canada Health Act, resulting in large and growing interprovincial discrepancies around prescription medication coverage, home care and long-term care. Frail Canadians can wait many months to access long-term care facilities. An Economist report (28) on palliative care rated Canada 11th, down from 9th five years prior.
The mandate letter for the federal Minister of Health continues to highlight the need for better home care services, including access to high-quality in-home caregivers and financial supports for family care. Pan-Canadian collaborations, including those on health innovation, will make addressing the deficiencies in our health system a priority. Dr. David Naylor’s Advisory Panel on Healthcare Innovation (29) states that Canada must unleash innovation in order to provide excellent healthcare for all. To achieve this, the Panel argues that the federal government must make changes to its current means for pan-Canadian collaboration, in addition to major investments to support provinces and territories in implementing fundamental changes to their systems. However, the Conference Board of Canada’s report on How Canada Performs (30) gives Canada a “C” overall on innovation. Ontario is a top performer, after Sweden, Denmark, Finland and the United States. These policy directions of innovation, home care and palliative care will become increasingly important.
The following priorities emerged from the key informant interviews:
•    Complete the dialogue and achieve consensus on a common language for frailty among healthcare providers, researchers, policy makers, administrators and, especially, individuals and their families (31, 32). Frailty is a multifaceted and multidimensional concept.
•    Link health and social services. To improve accountability and lessen the impact of frailty, we require a more systematic approach across public policies and at all levels of the health system (macro, meso and micro). Health and social services are often administered by separate government ministries within provinces.
•    Scale up and spread leading practices in frailty screening through an ongoing pan-Canadian advocacy strategy based on government mandates. Promote frailty as a level one policy that crosses settings, similar to advanced care planning in Alberta.
•    Act to shift reform from population aging to chronic disease management with a health promotion and disease prevention focus and a team-based approach. Link productivity and performance to frailty.

Financing and Funding Models

Public sources, primarily taxation, finance 70% of Canadian health expenditures (9). Tax and loan policies could be considered to support individuals and their families living with frailty (although perhaps not specifically screening), including caregiver tax credits, physical activity tax credits, reverse mortgages or home capital upgrades. Caregiver tax credits are seen as one method to support informal caregivers financially and could be built on the existing Canada Caregiver Tax Credit and the Canada Family Caregiver Tax Credit. Internationally, paid or unpaid leave from work and pension tax credits are more common (33).
Some countries are looking at prefunding services for seniors through mechanisms such as long-term care insurance that is publicly administered (possible in Canada through the Canada Pension Plan) or personal health savings accounts. A 2010 Canadian Medical Association survey (34) asked citizens about their interest in a tax shelter such as a Registered Long-Term Care Savings Account. Sixty-five percent of respondents with a Tax-Free Savings Account indicated that they would be very or somewhat likely to use an analogous long-term care savings account, but only 47% of respondents without a Tax-Free Savings Account indicated interest (34).
Provincial health funding models vary widely. With the exception of physicians, health providers are typically remunerated by salary. Historically, physician services have been primarily funded through fee-for-service systems, but the percentage of total clinical payments through fee-for-service has dropped over time and flat-lined in the last five years at about 72% (35). Over time, alternative payment plans such as capitation have grown, with physicians paid a specific amount (possibly risk-adjusted) for each individual enrolled under their care. Team-based remuneration, particularly in primary care, is also on the rise. Ontario uses Family Health Teams, although physicians are still compensated through either Family Health Organizations or Family Health Networks; Alberta uses Family Care Clinics and Primary Care Networks, although most physicians are still compensated through fee-for-service with supplemental per capita funding. In 2015, delegates to the Canada 2020 Summit asked the federal government to encourage structural reforms such as shifting payment for health services “from fee-for-service and volume-based funding to payment based on patient and value outcomes.” (36).
In Ontario, Community Health Links provide individualized, coordinated care to patients with complex needs. Under the Health Links model, hospitals, family physicians, long-term care homes, community organizations and others work as a team to provide patients, often suffering from multiple complex conditions, with improved coordinated care. Providers design individualized care plans for each patient and work with patients and their families to ensure they receive the care they need. Health Links managed by health service providers shifted to a Local Health Integration Network (LHIN)-managed funding approach where LHINs were granted discretion to plan and fund Health Links according to their regional priorities. Health Links led by primary care teams continue to be funded directly by the ministry and work collaboratively with LHINs to ensure consistency with regional Health Links priorities (37).
British Columbia has embraced the New Zealand-based Divisions of Family Practice Model, which has built-in flexibility to reimburse for health interventions that benefit the wider population (such as interventions for diabetes, mental health or chronic disease). Thirty-five divisions in British Columbia tailor projects on the basis of community need. This is a noteworthy endeavor to monitor and evaluate.
The following priorities emanated from the key informant interviews:
•    Advocate that age be a determining factor in health transfer payments to the provinces and territories.
•    Further explore granting tax credits for supporting individuals with frailty and prefunding health services for seniors through mechanisms such as a tax-free long-term care savings account.
•    Given that most physicians are still remunerated through fee-for-service, advocate to governments to adjust current fee code structures to include assessment of pre-frail seniors annually as a preventive screening practice.
•    Encourage team-based funding models that embrace frailty screening as a preventive screening tool.

Regulation and Legislation

Self-regulation of health professionals varies considerably across the country; it ensures the accountability of many, but not all, professionals. For example, physician assistants are not regulated in Ontario but are in Manitoba. Legislation outlines scope of practice for each type of professional, although the respective College is responsible for overseeing licensing and registration for practice. The trend is to widen scopes of practice, such as pharmacists now administering influenza immunizations, but new education models must accompany such changes.
Legislation to govern health delivery organizations also varies. For example, in British Columbia some long-term care homes (contracted care homes) fall under the Community Care and Assisted Living Act, whereas others fall under the Health Authorities Act. As a result, different standards of care are applied; the Hospital Act is less stringent in requirements for regular medication reviews in residential homes. One key informant noted that assisted living is more like “unassisted living” because the incentive in private care is to keep individuals in care longer.
The following priorities emanated from the key informant interviews:
•    Encourage frailty screening within the widening scopes of practice of other healthcare provider groups, especially physician assistants and nurse practitioners.
•    Promote frailty screening as a standard of care both in legislation and in standards (including through Accreditation Canada).

Technology

Electronic health record data is now available for 93.8% of Canadians (38). However, Canada still is behind other jurisdictions in many ways, including the use of digital resources, securing access to individuals’ records, individuals’ access to their own records, and the development of virtual care applications (29). Some deficiencies are being addressed; Canada Health Infoway is coordinating a multi-jurisdiction e-prescribing solution for safer drug use, especially among seniors. Other deficiencies could be addressed with the 2016 federal budget allocation of $50 million over two years to fund Canada Health Infoway’s e-health initiatives.
Almost all key informants mentioned the need to embed frailty screening tools into electronic medical records. One leader in this field is the InterRAI collaborative, which has developed tools that cross health settings and promote interdisciplinary collaboration (39). InterRAI is a non-profit research network of 60 health researchers from 30 countries. Many Canadian jurisdictions have mandated one or more InterRAI tools, and 15 commercial vendors sell software for InterRAI (40). The Canadian Institute for Health Information uses InterRAI’s instruments. In British Columbia, Deloitte is developing a frailty tool for primary care. The output of frailty screening with the aggregation of this data, if robust enough, can provide a picture of needs across the population. Such data could inform policy, decision making and case-mix classification (41).
The following priorities emanated from the key informant interviews:
•    Encourage the computerization of frailty screening tools.
•    Promote integration of frailty screening tools with electronic medical records and support data aggregation across systems and settings.

Interdisciplinary and Collaborative Care

Growth of demand for labor in the continuing care sector is predicted to far exceed general labor force growth (33). To meet this demand and combat escalating costs, staff mixes are being adjusted in long-term care centers. For example, in New Brunswick, professional staff ratios are shifting from 20% to 15% registered nurses, from 40% to 25% licensed practical nurses, and from 40% to 60% personal care workers.
Interdisciplinary team-based care is increasingly prevalent, in particular in the primary care sector. Primary healthcare is often burdened with being viewed as the panacea for all healthcare problems and, as stated by one key informant, “the solution to world peace.” However, resources have shifted only minimally from acute care. Community-based upstream interventions such as family health teams are embedding health promotion and prevention into their team-based care. British Columbia’s pilot study for CARES is a powerful example of promoting teams and linking to community services (23).
Many key informants spoke to linking frailty screening to standards and care pathways, especially in transitions between care settings. Settings requiring frailty screening tools include the community, supportive settings, assisted living, long-term care, emergency medical services, emergency departments, acute care, restorative care and palliative care (41–47). Screening results must also be linked to interventions such as exercise, diet or wellness plans. This linking is being addressed through multiple provincial strategies:
•    Alberta has implemented a Seniors Health Strategic Clinical Network and has identified frailty as a “Robust” Clinical Knowledge Topic for its Clinical Knowledge and Content Management team.
•    In 2010, the Council of Academic Hospitals of Ontario launched the Adopting Research to Improve Care (ARTIC) program to build care pathways. One is MOVE ON (Mobilization of Vulnerable Elders in Ontario), the aim of which is to implement and evaluate the impact of an evidence-based strategy to promote early mobilization and prevent functional decline in older patients admitted to hospitals in Ontario (42).
•     In Toronto, Mount Sinai Hospital has in place the Acute Care for Elders (ACE) Strategy that spans the care continuum. This strategy aims to address the needs of the elderly beyond acute care and presenting illness to prevent the decline of cognitive and physical functioning. In this way, patients can return to an equivalent care setting after they have been discharged from hospital. The program is being expanded across the country.
•    Nova Scotia’s PATH (Palliative and Therapeutic Harmonization) is another exemplary practice for older adults. The PATH model is designed to improve appropriateness of care and use of resources across the healthcare continuum. PATH places frailty at the forefront of evidence-informed decision making. This approach has been translated into clinical programs that have been implemented in the community, tertiary care, home care and long-term care. The end result is that patients and families feel empowered to make decisions about surgery, medical interventions, dialysis, nursing-home placement and end-of-life care that are appropriate for their frailty burden.

The following priorities emanated from the key informant interviews:
•    Adopt a right people, right tool, right setting and right time approach based on evidence. A one-size-fits-all approach is difficult to achieve for frailty screening, and choosing the best tools to be used in each setting can be confusing. Consider developing one simpler, overall and more generic tool to be applied by lay people and families across settings. Other tools are also needed that are more specific to setting and team or provider.
•    Make the business case because, in the short term, applying assessment tools and developing interventions may increase costs. The ever-present question is who will bear the burden in paying for these tools.
•    Find consensus on who should be screened for frailty. Is it a combination of age, health condition, psychosocial disorder, heavy use of the health system, change in living situation and many more criteria (48, 49)? No consensus approach arose from our interviews.
•    Advocate to colleges and universities for more sharing of frailty knowledge at both the undergraduate and postgraduate levels. Training and education should be more collaborative among provider groups but also among topic areas such as gerontology and palliative care.

Person-Centered Care

Key informants noted that healthcare has focused on the business model of delivery, especially around improving process efficiencies (e.g., Lean) and utilization outcomes (e.g., length of stay, alternative level of care days, patient flow). A move to more person-centered care is needed, and most provincial/territorial governments are demanding this transformation. For example, Ontario has Patients First, a person-centered strategy that strengthens integration and equity, offering more consistent and accessible home and community care. Ontario also has Community Health Links to provide coordinated, efficient and effective care to people with complex needs.
Each individual’s desires and goals for care (often to be home longer) must be heard. The Way Forward is an integrated palliative care approach to care that ensures that the caregiver voice is heard; the Canadian Home Care Association (49) consulted extensively on supports required to carry out this strategy. Another initiative, Choosing Wisely, is trying to engage individuals and their care providers in conversations about unnecessary tests and treatments, to help them make smart and effective choices for high-quality care.
The following priorities emanated from the key informant interviews:
•    Place the individual and family at the center of any policy action required around frailty.
•    Integrate the collective voice of the individual in frailty screening work. This may be achieved via numerous partnerships, such as IMPACT BC’s Patient Voices Network and Patients Canada.

 

Policy Options

The economics of frailty screening are not clear, although the policy implications are more so. Evaluation is needed for the impact of frailty screening and the potential return on investment to the individual, provider and the health system (50). Evaluation data to feed into a business case or impact analysis is warranted, in particular for further public policy and engagement strategies. Evidence-based public policy options are needed to scale up frailty screening in Canada.
For any approach chosen to develop policy options, key informants offered four pieces of sage advice:
•    Integrate work into jurisdictional strategies on chronic disease management, continuing care and seniors’ healthy aging.
•    Pilot test options or undertake small samplings first through an organization at arms length from government.
•    Keep pilot tests small and nimble, with shorter timelines such as six months.
•    Apply implementation science and innovation principles to build the evidence base.

 

Recommendations

Frailty screening allows healthcare providers to better assess and treat the underlying causes of frailty. The ability to identify these health problems early and to recognize the appropriate treatment measures will improve not only life expectancy, but quality of life. Using information and feedback from our key informants, advisory committee members and webinar participants, we evolved five top recommendations for action on frailty and frailty screening:
1) Complete consensus dialogues on:
a.    a common language for frailty
b.    the best tools to be used in each setting, applying a right people, right tool, right setting and right time approach
c.    who should be screened for frailty.
2) Build a business case on the potential impact of frailty screening in various settings. Include analysis of both direct and indirect costs and impact on comorbidities.
3) Develop a pan-Canadian advocacy strategy to scale up and spread leading frailty practices. Key pieces to advocacy are to:
a.    ensure the individual and family are at the center of an action
b.    promote frailty, based on reform priorities, as a level one policy priority that crosses settings and ministries
c.    shift advocacy discussion from population aging to chronic disease management, especially for the most vulnerable members of our population
d.    make frailty screening a standard of care both in legislation and in standards.
4) Encourage team-based funding models that embrace frailty as a preventive screening tool linked to care pathways and remunerate these models of care appropriately.
5) Encourage the computerization and digitization of frailty screening tools. Integrate tools into electronic medical records to support data aggregation across systems and settings, for population-level analysis.
Everyone is touched by the challenges of frailty. Given the economic challenges that face our health system, frailty provides a compelling platform for policy action. The time is now to scale up frailty screening practices across our federated health system.

 

Acknowledgements: We would like to thank our 2016 advisory committee for their generous advice and time in this project. Members included Lisa Ashley (Canadian Nurses Association), Stephen Vail (Canadian Medical Association), Sharon Baxter (Canadian Hospice Palliative Care Association) and Phil Emberley (Canadian Pharmacists Association).
Conflict of interest: None

APPENDIX

References

1.     Canadian Foundation for Healthcare Improvement. Report on Taming of the Queue 2015: Improving timely, appropriate care for patients in an aging society. 2015. http://www.cfhi-fcass.ca/NewsAndEvents/Events/Taming_of_the_Queue.aspx . Accessed December 20, 2016
2.    Public Health Agency of Canada. How healthy are Canadians? 2017 https://www.canada.ca/en/public-health/services/publications/healthy-living/how-healthy-canadians.html . Accessed December 20, 2016
3.    Canadian Institute for Health Information. Alternative Level of Care in Canada. 2009. https://secure.cihi.ca/estore/productSeries.htm?pc=PCC456 . Accessed December 20, 2016
4.    Canadian Institute for Health Information. Sources of Potentially Avoidable Emergency Department Visits. 2014 https://secure.cihi.ca/estore/productSeries.htm?pc=PCC1172 . Accessed December 20, 2016
5.    Woo J. Managing Elderly Patients with Multiple Morbidities – Are We Providing Patient-Centred Care? Hong Kong Med Diary,2008;13(9):3-4. http://www.fmshk.org/database/articles/34_1.pdf
6.    Cornwall, J. The care of frail older people with complex needs: time for a revolution. The Kings Fund. 2012. http://www.kingsfund.org.uk/publications/care-frail-older-people-complex-needs-time-revolution . Accessed December 20, 2016
7.    Canadian Foundation for Healthcare Improvement. Healthcare Priorities in Canada; A Backgrounder. 2014. https://www.cfhi-fcass.ca/sf-docs/default-source/documents/harkness-healthcare-priorities-canada-backgrounder-e.pdf . Accessed December 20, 2016
8.    Canadian Frailty Network. What is frailty? http://www.cfn-nce.ca/frailty-in-canada/ . Accessed December 20, 2016
9.    Canadian Institute for Health Information. National Health Expenditure Trends. 2015. https://secure.cihi.ca/estore/productSeries.htm?pc=PCC52 . Accessed December 20, 2016
10.    Conference Board of Canada. Future Care for Seniors: A Status Quo Forecast. 2015. http://www.conferenceboard.ca/e-library/abstract.aspx?did=7374 . Accessed December 20, 2016
11.    Turcotte, M. Family caregiving: What are the consequences? Statistics Canada Catalogue no. 75-0006-X. 2013. http://www.statcan.gc.ca/pub/75-006-x/2013001/article/11858-eng.pdf . Accessed December 20, 2016
12.    Conference Board of Canada. Home and community care in Canada: an economic footprint. 2012. http://www.conferenceboard.ca/cashc/research/2012/homecommunitycare.aspx . Accessed December 20, 2016
13.    Statistics Canada. Family caregiving: What are the consequences? 2013. http://www.statcan.gc.ca/pub/75-006-x/2013001/article/11858-eng.pdf . Accessed December 20, 2016
14.    Health Council of Canada. Seniors in need, caregivers in distress: What are the home care priorities for seniors in Canada? 2012. http://www.carp.ca/wp-content/uploads/2012/04/HCC_HomeCare_2d.pdf . Accessed December 20, 2016
15.    Statistics Canada. Dependency Ratio. 2015. http://www.statcan.gc.ca/pub/82-229-x/2009001/demo/dep-eng.htm . Accessed December 20, 2016
16.    Healthcare of Ontario Pension Plan. Seniors and Poverty: Canada’s Next Crisis? 2017. https://hoopp.com/docs/default-source/about-hoopp-library/advocacy/retirementsecurity-seniorsandpoverty-feb2018.pdf?sfvrsn=93333ffe_2 . Accessed December 20, 2016
17.    Tam P. Canada 2020 Health Summit Conference Report: Creating a Sustainable Health-care System for Canada. 2015. http://canada2020.ca/canada-2020-health-summit-report/ . Accessed December 20, 2016
18.    Canadian Institute for Health Information. Health Care Cost Drivers: The Facts. 2011. https://secure.cihi.ca/free_products/health_care_cost_drivers_the_facts_en.pdf . Accessed December 20, 2016
19.    Canadian Institute for Health Information. Prescribed Drug Spending in Canada, 2012: A Focus on Public Drug Programs. 2013. https://secure.cihi.ca/estore/productSeries.htm?pc=PCC1011 . Accessed December 20, 2016
20.    Reason B, Terner M, Moses A, McKeag BT, Webster G. The impact of polypharmacy on the health of Canadian seniors. Family Pract 2012;29(4):427-432. https://doi.org/10.1093/fampra/cmr124
21.    Canadian Pharmacists Association. The role of pharmacists in deprescribing. The Translator 2013;7(3):1-4. https://www.pharmacists.ca/cpha-ca/assets/File/education-practice-resources/Translator2013V7-3EN.pdf . Accessed December 20, 2016
22.    Wang C, Song X, Mitnitski A, et al. Effect of Health Protective Factors on Health Deficit Accumulation and Mortality Risk in Older Adults in the Beijing: Longitudinal Study of Aging. J Am Geriatr Soc 2014;62(5):821-828.
23.    Park G, Garm A. Proactively Delaying Frailty in Seniors: Community Actions and Resources Empowering Seniors (CARES). 2016. http://www.slideshare.net/bcpsqc/the-cares-quality-improvement-project-preventing-frailty-in-seniors . Accessed December 20, 2016
24.    Rolfson D, Heckman G, Bagshaw S, Robertson D, Hirdes P. Implementing frailty measures in the Canadian healthcare system. 2016. Manuscript accepted for publication.
25.    Ipsos Poll on behalf of HealthCareCAN. 2015. http://ipsos-na.com/news-polls/pressrelease.aspx?id=6888 . Accessed December 20, 2016
26.    Marchildon G. Canada: Health system review. Health Sys in Transition 2013;15(1):1-179.
27.    Canadian Institute for Health Information. Commonwealth Fund International Health Policy Survey of Older Adults. 2014. https://secure.cihi.ca/estore/productSeries.htm?pc=PCC1251 . Accessed December 20, 2016
28.    The Economist Intelligence Unit. The quality of death: Ranking end-of-life care across the world. Commissioned by the Lien Foundation. 2015. http://graphics.eiu.com/upload/eb/qualityofdeath.pdf . Accessed December 20, 2016
29.    Naylor D. Report of the Advisory Panel on Healthcare Innovation. 2015. http://healthycanadians.gc.ca/publications/health-system-systeme-sante/report-healthcare-innovation-rapport-soins/index-eng.php . Accessed December 20, 2016
30.    Conference Board of Canada. How Canada Performs: Provincial and Territorial Ranking. 2015. http://www.conferenceboard.ca/hcp/provincial/health.aspx . Accessed December 20, 2016
31.    Clegg A, Young J, Iliffe S, Rikkert MO, Rockwood K. Frailty in elderly people. Lancet 2013;381(9868):752-762.
32.    Rockwood K, Theou , Mitnitski A. What are frailty instruments for? Age and Ageing 2015;44(4):545-547.
33.    Conference Board of Canada. Federal Policy Action to Support the Health Care Needs of Canada’s Aging Population. 2015. https://www.cma.ca/Assets/assets-library/document/en/advocacy/conference-board-rep-sept-2015-embargo-en.pdf . Accessed December 20, 2016
34.    Canadian Medical Association. 2010 National Report Card Survey. Ottawa, 2010.
35.    Canadian Institute for Health Information. Physicians in Canada, 2015: Summary Report. 2016. https://secure.cihi.ca/free_products/Summary_Report_2015_EN.pdf . Accessed December 20, 2016
36.    Canada 2020. Canada 2020 Health Summit Report. 2015. http://canada2020.ca/canada-2020-health-summit-report/ . Accessed December 20, 2016
37.    Ministry of Health and Long-Term Care. Guide to the Advanced Health Links Model. 2015. http://www.health.gov.on.ca/en/pro/programs/transformation/docs/Guide-to-the-Advanced-Health-Links-Model.pdf . Accessed December 20, 2016
38.    Canada Health Infoway. What we do: Progress in Canada. 2016. https://www.infoway-inforoute.ca/en/what-we-do/progress-in-canada . Accessed December 20, 2016
39.    InterRAI. 2016. http://interrai.org/instruments.html Accessed December 6, 2016.
40.    InterRAI. 2016. http://interrai.org/licensed-software-vendors.html Accessed December 6, 2016.
41.    Heckman G, Gray L, Hirdes J. Addressing Health Care Needs for Frail Seniors in Canada: The Role of InterRAI Instruments. Can Geriatr Soc J Continuing Med Educ 2013;3(1):8-16.
42.    Straus S, Liu B. Mobilization of Vulnerable Elders in Ontario (MOVE ON) ARTIC Project Participant Information Package. Council of Academic Hospitals of Ontario. 2012. http://caho-hospitals.com/wp-content/uploads/2014/02/MOVE-ON-Participant-Information-Package-January-20121.pdf . Accessed December 20, 2016
43.    Drubbel I, Numans ME, Kranenburg G, Bleijenberg N, de Wit NJ, Schuurmans MJ. Screening for frailty in primary care: A systematic review of the psychometric properties of the frailty index in community-dwelling older people. BMC Geriatr 2014;14:27.
44.    Koller K, Rockwood K. Frailty in older adults: Implications for end-of-life care. Cleveland Clin J Med 2013;80(3):168-174.
45.    Goldstein JP, Andrew MK, Travers A. Frailty in Older Adults Using Pre-hospital Care and the Emergency Department: A Narrative Review. Can Geriatr J 2012;5(1):16-22.
46.    Lacas A, Rockwood K.  Frailty in primary care: A review of its conceptualization and implications for practice. BMC Med 2012;10(4):4.
47.    National Health Service (England). Safe, compassionate care for frail older people using an integrated care pathway. 2014. https://www.england.nhs.uk/wp-content/uploads/2014/02/safe-comp-care.pdf . Accessed December 20, 2016
48.    Canadian Foundation for Healthcare Improvement. Palliative and Therapeutic Harmonization: Optimal care, appropriate spending. 2013. http://www.cfhi-fcass.ca/WhatWeDo/on-call/path-program . Accessed December 20, 2016
49.    Morley JE. Frailty screening comes of age. J Nutr Health & Aging 2014;18(5):53–454.
50.    Canadian Home Care Association. The Way Forward: An Integrated Palliative Approach to Care, The Caregiver Voice. 2014. http://www.hpcintegration.ca/resources/the-national-framework.aspx . Accessed December 20, 2016
51.    Lee L, Heckman G, Molnar FJ, & the College of Family Physicians of Canada. Frailty: Identifying elderly patients at high risk of poor outcomes. Can Family Physician Medecin de Famille Canadien 2015;61(3):227-231.

ETHICAL AND LEGAL IMPLICATIONS OF FRAILTY SCREENING

 

L. Reid1, W. Lahey2, B. Livingstone3, M. McNally4 On behalf of Canadian Frailty Network

 

1. Dalhousie University Faculty of Medicine (Bioethics), Halifax, NS, Canada; 2. University of Kings College, Halifax, Canada; 3. University of Waterloo, Waterloo, Canada;
4. Dalhousie University Faculties of Dentistry and Medicine (Bioethics), Halifax, Canada.
Corresponding author: Mary McNally DDS, MA (Phil), Dalhousie University Faculties of Dentistry and Medicine (Bioethics),5981 University Avenue, PO Box 15000, Halifax, NS  B3H 4R2, Phone: (902) 494-1294,Fax: (902) 494-1604,Email:  mary.mcnally@dal.ca

J Frailty Aging 2018;7(4):224-232
Published online October 8, 2018, http://dx.doi.org/10.14283/jfa.2018.31

 


Abstract

Abstract: Goals of screening for frailty include (a) promoting healthy aging, (b) addressing frailty with preventive and targeted interventions, (c) better aligning social and medical responses to frailty with the needs of frail older adults and (d) preventing harms to frail older adults from excessive and inappropriate medical interventions that are insensitive to the implications of frailty. However, the medicalization of frailty and outcomes of the screening process also risk harming frail older adults and their autonomy through stereotyping and by legitimizing denial of care. This risk of harm gives rise to ethical and legal questions and considerations that this paper addresses. Frailty screening that is ethically defensible will situate and support healthcare that is consistent with people’s needs, circumstances and capacity to benefit from the care provided. We also call for an informed consent process that incorporates supported or shared decision making in order to protect the autonomy of frail older adults.

Key words: Frailty, screening, ethics, law, medicalization.


 

 

This paper explores ethical and legal concerns and opportunities associated with screening for frailty.
The goal of screening for frailty is to ensure that healthcare provided is consistent with people’s needs, circumstances and capacity to benefit. This goal aligns not only with good clinical care but also with the fundamental definition of formal justice (1) and in some jurisdictions, human rights law (2). Screening for frailty has the potential to equip us for responding positively to the magnitude of challenges that older adults will face in the coming decades: it can motivate high-value care, situate appropriate health and social system responses, and promote preventive interventions (3). Accompanying the anticipation of benefits from screening, however, are concerns that screening itself poses risks and gives rise to ethical questions about the extent to which the goals of screening must incorporate concerns with both doing good and avoiding harm – with both beneficence and non-maleficence.
The goal of providing health-related benefits to individuals, by whatever means (medical or social), is fundamental to medical practice. Beneficence also provides justification for interventions in individuals’ lives and for the use of public resources. However this goal is complemented by a duty of non-maleficence: the responsibility to minimize the harms of any intervention and to ensure that the unavoidable harms are balanced with anticipated benefits.
The duty of non-maleficence is related to the general moral principle to avoid causing harm, but it has a special interpretation in medical ethics (4). Many medical interventions carry the risks or the certainty of unintended harmful effects, including screening. For example, recent ethical analyses related to screening have been prompted in part by concerns about the modest benefits of and the prevalence of potentially harmful over-diagnoses in cancer screening (5-8). Parker et al. (9) argue that beneficence has been the focus of screening programs, to the neglect of non-maleficence and informed choice. This paper aims to consider these ethical imperatives as they are relevant to screening for frailty.
The recent call to action for healthcare systems to systematically screen for frailty is motivated by concerns that the care needs of many older adults are not being met (3). Key goals of screening articulated by proponents include (a) promoting healthy aging and preventing frailty (secondary prevention), (b) improving social and medical responses to those who are frail and (c) preventing inappropriate medicalization, in the form of excessive interventions based on guideline-driven care that may be insensitive to individuals’ capacity to benefit (10).
Defining a syndrome or state of health detriment where previously none was identified (a process typically termed medicalization) may entail both positive and negative consequences (11). In particular, medicalization may divert attention from the social determinants of health or reinforce stigmatization of vulnerable populations. These consequences are far from the intentions of frailty screening proponents. However, to maximize the benefits of frailty screening and minimize its harms, and to work in accordance with applicable legal principles, we must anticipate and address the specific ways in which the processes of medicalization may work against the goals of frailty screening proponents.
We start by considering the concept of medicalization as it applies to any definition of a clinical condition or syndrome, and then show its relevance to frailty. Concerns about medicalization, we argue, are affected by choices about how frailty is conceptualized (as a pathological condition or as a normal life stage) and by decisions about how the goals of screening are matched with frailty assessment within appropriate healthcare system settings. We then draw on recent analyses of the ethics of screening to identify specific risks of harm for the integration of frailty assessment into the healthcare system. Proponents should mitigate these risks and policy makers should consider the remaining risks in deciding on the use of frailty measurement in clinical and community decision making.
If one goal of screening is to reduce inappropriate medical management, the question of how assessment for frailty should affect medical decision making is important. Assigning people the status of “frail” raises fundamental ethical, legal and conceptual questions about the autonomy of older adults. These are concerns generally relevant to older adults but are heightened in the case of suspected or diagnosed frailty. In light of this, we consider the role that supported or shared decision making can play to assure that the results of screening promote the benefits of screening and minimize its harms, as this goal is pursued within the concept and principles of informed consent.

 

The Medicalization of Frailty and Demedicalizationof Aging

When a condition is medicalized, a disease entity is defined or its boundaries are extended such that people who were previously considered medically normal (or who had not drawn medical attention) are labeled as unhealthy. Medicalization is beneficial when it leads to appropriate responses to care needs, but it also raises a variety of specific concerns (analyzed in Verweij, 11), some of which are relevant to frailty. Medicalization was initially understood as placing common experiences of daily life (such as pregnancy, menopause and aging) under medical authority, and it was analyzed by sociologists as a kind of threat to autonomy and independence (summarized in Verweij, 11, pp. 84–87). A contemporary understanding sees medicalization as potentially destabilizing individuals’ perceptions about their own health and autonomy but also as empowering them to make claims on the healthcare system for response to the medicalized condition.
Another concern about medicalization is that it may reframe social problems as medical conditions. Social problems thus become individualized, decontextualized and positioned as objects of medical intervention. Remedies for social problems are similarly individualized, leaving relevant underlying social structures and inequalities unacknowledged and unaddressed. As detailed in an accompanying paper in this series (12), health deficits associated with frailty may be influenced more significantly by broader social determinants than by disease or illness themselves. The socially determined nature of frailty and its associated deficits suggest that responses to frailty must also consider options outside the healthcare system and therefore beyond the direct control of those diagnosing the condition (13). Medicalization of frailty could legitimize individual and social claims for frailty to be addressed primarily by the healthcare system, whether or not that system is well-suited to respond. These concerns are also raised by other papers in this series (12, 14).
Finally, while medicalization has sometimes de-stigmatized conditions previously subject to moral or religious stricture (such as addictions), it may also stigmatize the population captured under the new diagnostic label. A diagnosis of frailty runs the risk of stigmatizing older adults, negatively impacting their self-concept and bolstering pre-existing ageist attitudes (15, 16). Older adults already risk the assumption of having diminished capacity to make healthcare decisions, based on stereotypes of dependency and incapacity associated with old age (17). As Andrew et al.  (12) have pointed out, research demonstrates that frail older adults themselves are aware of the impact that labeling and perceptions about their health have on their lives (18, 19). Medicalization would present the concept of frailty as a scientifically validated construct and could, despite the intentions of proponents, heighten existing negative associations, including real or perceived paternalism and discrimination within the healthcare system (described in Hall, 20). Screening or case finding could expose a larger number of older adults to pejorative attitudes. Through the process of self-stigmatization, this could have a corrosive effect on their physical and cognitive function and self-image (15, 21, 22). In fact, many older adults do not identify with the term frailty as it is applied to them (23). If screening accentuates these risks and stereotypes, it could impair informed decision making by undermining legally mandated and prevailing ethical norms associated with meaningful choice (24).
None of these harms are intended by those who have devised measures of frailty and who advocate for screening. Rather, proponents seek to expand the appropriate care for frail older adults beyond the realm of specialist medicine and to promote a more holistic picture of health and ill health among this population. Routine care that considers only isolated physiological indications for medical intervention may cause more harm than benefit in the case of frail older adults (10). Furthermore, as medical interventions and technologies become more advanced, readily available, less invasive and less risky, their uptake as standard procedure for people of increasingly advanced age is taken for granted (25, 26). Frailty is associated with shortened life expectancy and a trajectory of declining health status over time, and it is appropriate to take this into account in decision making (10). However, the lack of clinical outcomes evidence specific to frail older adults makes it difficult for physicians to adequately advise patients and for patients and families to meaningfully choose whether or not to treat. One aim of identifying frailty is to support appropriate medical care and decision making (15, 27). In this sense, and if we conceptualize frailty as a state of increased vulnerability as opposed to a specific syndrome, we might describe the medicalization of frailty as having the goal of demedicalizing a normal life stage by making it less subject to inappropriate medical intervention. Medicalization depends on how frailty is conceptualized and measured (28). The risks of medicalization may be mitigated (i.e., demedicalization may be achieved) by conceptualizing frailty relationally and situationally, rather than ascribing a global trait to individuals. Promoting the appropriate integration of the needs of frail older adults into both the community and healthcare systems reduces inappropriate medical treatment of the specific medical conditions to which frail older adults are vulnerable and to which that medical treatment in turn renders them even more vulnerable.

 

Conceptualization and Measurement of Frailty

Frailty can be conceptualized and measured as a syndrome or as a state (29), or in relation to the normal aging process, as described below.
The original phenotypic definition of frailty (30) is an example of frailty as a syndrome, and the criteria outlined by Fried as indicating the presence of frailty are still elements of many contemporary measures (29). Alternatively, frailty can be conceptualized as a general state of increased vulnerability to adverse outcomes after exposure to stressors (15) or, as stated by Rockwood and Mitnitski (31), “a nonspecific state of increasing risk, which reflects multi-system physiological change” (p. 722). Deficit accumulation is the most common way of identifying or diagnosing frailty as a state (29). Frailty conceptualized as a syndrome is primarily binary; frailty conceptualized as a state (the deficit accumulation model) is a continuum.
Whether understood as a syndrome or state, frailty is conceptualized as a pathological condition (15) that can and should be the target of prevention in order to facilitate healthy or successful aging (32, 33).
Frailty is also conceptualized in relation to the aging process (15). While some functional decline is inevitable over the life course (15), variation exists in the rate and scale of decline experienced by older adults. Frailty in this understanding represents differing vulnerability to adverse health outcomes in people of the same chronological age (15, 27). Viewed in this way, and in contrast to conceptualizations of frailty as pathological, frailty is barely distinguished from normal aging; it indicates the rate at which people reach a stage that we will all reach if we do not die of conditions that foreshorten this decline.
While proponents for measuring frailty in clinical settings focus on the fact that either kind of measurement may be predictive (28), how we conceptualize frailty has implications for how it is both measured and managed. For example, emphasizing multidimensional functional decline and variation in decline within the aging process may prompt more holistic responses, including ameliorating the social conditions that contribute to the extent of frailty and the seriousness of the associated deficits. Conversely, viewing frailty as a pathological condition may bias our response toward medical intervention and away from social intervention. Furthermore, it may inappropriately place the burden and responsibility to cope and to protect and promote one’s own health on individual older adults and their families. This may leave unacknowledged society’s inadequate response to the needs of those who are frail. Approaches to frailty that emphasize its social dimensions may be less susceptible to reinforcing the pejorative and stigmatizing connotations of being labeled frail than approaches that view frailty as a medical condition.

 

Implementing Frailty Measurement

Surveillance, Screening, Assessment and Case Finding

It may seem reasonable to assume that screening a population to identify health needs and acting to meet those needs is beneficial. However, decades of experience with screening for chronic disease has demonstrated that, as Wilson & Jungner (34), authors of a landmark publication on screening, suspected, this is not always the case. The harms and benefits of screening depend (among other factors) on disease dynamics, available interventions, test performance, social acceptability and costs in relation to the capacity and priorities of the health system. Ethical analysis of problems of false positives, false negatives and over-diagnosis is a new and rapidly growing field (e.g., Harris et al., 35). What proponents call “frailty screening” in fact includes case finding, surveillance, integration of frailty measures in clinical outcomes research and (ultimately) guidelines and care pathways, assessment for psychosocial needs and classic screening for primary or secondary prevention (see 36, 28). The balance of harms and benefits for any particular proposal will depend on the particular instrument, the setting, the intervention and the goal. In this section we illustrate basic principles of screening ethics to alert proponents to pitfalls that may accompany the benefits they seek.
According to Juth & Munthe (37), screening is: “The use [or promotion of the use] of medical investigation or testing methods at the initiative of health care or society for the purposes of investigating the health status of individuals, with the aim of selecting some of these for possible treatment [or preventive measures] … from a large population of people not united by previously recognized risk or symptoms of disease. (pp. 10–11)”
The core of this definition usefully distinguishes means from goals in screening, case finding or assessment so that we can ask and answer these questions: Does the process achieve its goals? At what financial, medical or social cost?
To illustrate ethical concerns with screening, assessment and case finding, we first explain the problem that arises when measurement and intervention are applied on a population-wide basis, and then focus on two proposals for the clinical application of frailty measures: a) screening or case finding with the goal of reducing inappropriate medical interventions in the older adult population, for which we will use the label “quaternary prevention” (38), and b) assessment for appropriate psychosocial support and targeted interventions (e.g., Comprehensive Geriatric Assessment). In quaternary prevention, concerns arise about cost and social acceptability; in assessment for psychosocial support, questions arise about the availability of beneficial interventions.

The Problem of Population-wide Implementation

Frailty screening proponents focus on the number of persons who could benefit from prevention or intervention (3). Success in prevention, however, depends on the actual benefits experienced by a large enough proportion of those we identify in advance as exhibiting risk factors. There may be a large gap between these numbers (the number of those whose care in retrospect we wish we could have improved and the number of those identified in advance whose care we are actually able to improve) due to the timing of screening or case finding, the performance of our screening instrument and the dynamics of the condition in question.
“Perfect” test performance cannot be assumed. False positives and false negatives are intrinsic to measurement. Frailty indices lack a gold standard diagnostic test (39). Purely formal reasoning (34, pp. 24–25) supports the concern that implementation of a measurement instrument in a lower risk population generates many false positives and increases the identification of borderline cases; this has been borne out in cancer screening (6). Wilson & Junger argue that is incumbent on healthcare providers to determine the appropriate management of borderline cases in order to avoid causing more harm than good (34, pp. 24–26). The “problem of borderline cases” that they identify encompasses what we now call over-diagnosis.
The potential for bias on the part of providers less experienced in care for older adults exacerbates this concern. Positive predictive value (the chance that a person screened as frail actually experiences the outcome of concern) for existing frailty scores is estimated at 6% to 49%, while the reliability of screen negatives is much better at 73% to 96% (40), indicating that false positives are more likely to be a problem than false negatives. Thus, a substantial number of those identified in screening or case finding as frail would not have experienced the outcome we seek to prevent or address by screening. This has two adverse consequences. First, the identification of these persons as “frail” constitutes labeling (with all its potential negative consequences, discussed above) for potentially limited individual preventive benefit. Second, in attempting to assist a group we believe to need early intervention, we may divert energy and resources from them to a much larger group who exhibit risk factors but who would not in any case have experienced the outcome we seek to prevent.

Screening or Case Finding for “Demedicalization”

Quaternary prevention goals (38)—reducing inappropriate medical interventions in frail older adults—could be met through case finding at different points within the healthcare system. In primary care, physicians and other healthcare providers can begin discussions about goals of care. In emergency departments, pathways of care can be more appropriately informed. In long-term care residences, goals of care can be updated.
There is a risk the public will perceive the goal of helping people avoid inappropriate medical care as rationing, and that this will render frailty screening socially unacceptable. Quaternary prevention may be seen as akin to age-based allocation such as QALYs (41) or “fair innings” or lifespan approaches (42, 43). Rationing withholds beneficial care from individuals, regardless of their wishes, based on societal decisions about distributing scarce resources. Basing the rationale for frailty screening on healthcare expenditures that flow to elderly people could exacerbate the perception that frailty screening is a form of rationing. Individualizing decision making, rather than excluding individuals unilaterally from medical interventions, would combat this perception. The goal of frailty screening must be clear: is it a form of rationing or does it aim for appropriate care for older adults?
If frailty assessment replaces arbitrary measures in clinical guidelines (chronological age or life stage) with measures that are directly pertinent to the individual’s needs, circumstances and capacity to benefit (24), this may result not just in “ruling out” certain medical interventions for individuals who would be harmed, but also in “ruling in” medical treatment for older adults who are not frail and who might otherwise have been denied treatment based on age-related criteria. Increased access to beneficial medical interventions for non-frail older adults may be clinically appropriate and in accordance with formal justice, but the social question of resources and priorities of the healthcare system remains. In general, screening for chronic disease is not cost saving (44). Policy makers are increasingly aware of this (45). Screening proponents must present plausible modeling to support claims that preventive measures will achieve benefits at an acceptable cost.

Assessment to Meet Social Needs

Assessment to meet social needs may take place in tertiary care (to identify needs for enhanced nursing support in the hospital or discharge planning) or in primary care (to identify the need for community-based services). Optimal value from screening for these goals depends on the availability of beneficial interventions: in tertiary care, on the availability of enhanced nursing support, personal care workers, and resources for discharge planning; in primary care, on strengthened links to the broader healthcare system and a network of community-based providers and services to support and care for those who are frail.
Attending to these links is critical, particularly given that medicalization can shift expectations from other sectors to the healthcare sector. For example, frailty indices often include nutritional status (46) and sometimes include weight loss (33). The determinants of nutritional status or weight loss could be structural (food insecurity for older adults) or social (social isolation affecting eating), compounding the physiological changes accompanying frailty. Providers and healthcare systems are generally responsive to the expectations that are shifted to them, applying the tools at their immediate disposal. For example, a good solution to weight loss in older adults identified as frail might be inter-sectoral work on food security or on social isolation. In tertiary care, this could involve addressing food quality and the social experience of eating in hospital. Instead, the response in either sector could be the “quick fix” of nutrition supplementation. The expectation of older adults themselves for a clinical solution to a clinically-defined condition may legitimize and exacerbate this dynamic.

Summary

Whether a frailty index is used for screening, case finding, or assessment, the goal is to generalize the benefits of a process that was typically performed in a specific setting with a high-risk population and resources for response (e.g., Geriatric Assessment in tertiary care) to broader settings and populations. This generalization entails a more diverse set of assessors, with a lower risk population and different resources for response. Meanwhile, depending on the dynamics of the underlying condition, the harms we intend to prevent and the setting and time frame for screening or case finding, we may or may not be able to intervene effectively to prevent the outcome we seek to prevent. Necessary interventions may originate outside the healthcare system, such as home care and social supports; even if they do exist, they may not be accessible. In a worst-case scenario, a lack of access to these types of options could result in frail older adults being triaged out of the very system that establishes their frailty status, into a social care system that may or may not be equipped to respond. Lack of operational alignment between healthcare (where the screening happens) and social care (where the responses are allocated) could prevent frailty screening from leading to appropriate and demedicalized responses to care needs even where social care exists and is properly funded.
Proponents might argue that assessment and the interventions that result from it (social support, goals of care discussions) are so low-risk that the issues we raise are inconsequential. However, screening, case finding and assessment for social needs may be socially and emotionally intrusive: they raise questions of mental capacity, ability to live safely in the community, impending death and family caregiver dynamics. They also introduce the labeling effects described above (21, 22).

 

Implementing the Results of Screening: The Role of Supported (or Shared) Decision Making

The Changing Understanding of Individual Autonomy in Relation to Informed Consent

Frailty screening that justifies a move away from excessive or harmful medical intervention toward appropriate care can be ethically defended. However, this conclusion is subject to an important caveat: the individual, not the care provider or healthcare system, must make the choice to forgo or to undergo “inappropriate” medical interventions and to opt for “appropriate” alternatives based on assessment of relative frailty. Properly designed and conducted screening sets the stage for satisfying this caveat; it allows providers to give individuals more of the information that is relevant to both the choices they must make and the care they should ask for and receive.
Screening that expands or situates choice in this way aligns with ethical obligations to respect the autonomy of individuals by equipping them to consider healthcare choices on the basis of more complete information relative to a more complete range of options (4, 47). In other words, the applicability of these obligations is a critical protection for individuals screened as frail against the risk of being arbitrarily denied treatment on that basis (24).
At the same time, frailty can connote or imply dependency and vulnerability. Screening could contribute to or reinforce tendencies to equate age with the incapacity that (in Canadian law) triggers the delegation of decision making to a substitute decision maker, typically a family member (17). If this were to happen, screening results (including false positives) could legitimize substituted decision making in place of a more robust engagement with the elements and process of informed choice that maximize a frail individual’s existing capacity and autonomy.
Conversely, older adults (especially those who are frail) sometimes are attributed more capacity for decision making than they objectively possess. One factor contributing to this may be a temptation to avoid the complications of finding and dealing with a substitute decision maker. Another factor is the unwillingness of health providers (or their institutions) to risk real or perceived coercion of individuals. Coercion violates a key responsibility associated with informed choice. Yet if individuals who truly lack sufficient decision making capacity for the choices they face are left to decide on their own to avoid risking coercion or its appearance (48), this too can be harmful. Older adults who are cognitively impaired, and who have more autonomy in decision making foisted upon them than they are equipped to exercise, risk making decisions that are not appropriately responsive to the implications of their frailty or that are unacceptably determined by those implications.
Inappropriate assumption of either incapacity or capacity jeopardizes attainment of one of screening’s ultimate goals: protecting the frail elderly from both harmful medical entanglement and denial of care. This dilemma reflects the binary quality of capacity determination, which providers face in applying the concept of informed choice (as it is generally configured) to individuals who are decisionally compromised (49). Individuals are either assumed to be fully capable or fully incapable relative to each decision that must be made. This reflects the origins of the concept of capacity in the traditional and individualistic liberal model; each individual is conceptualized as rational, self-sufficient and materially and emotionally independent (50). An extension of this is the notion that vulnerability and dependency are the antithesis of autonomy (50).
In healthcare, individuals have been recognized as able to act intentionally, with understanding and without controlling interferences, for the purposes of healthcare decision making (4). This has been the basis for displacing paternalistic models of decision making (49). However, it may also have left healthcare providers with inadequate options for navigating individuals’ vulnerability and dependency in ways that respect their continuing but diminished capacity and therefore their autonomy.
More relational accounts of personhood and autonomy are gaining ground (50-52) in which personhood is understood to be socially constituted and situated, embodied and relative (50). The relational self is imagined as individual but also interdependent and constructed in relationships with others (52). Autonomy is conceptualized as a process in which one’s sense of self is confirmed in interactions and experiences, as opposed to being entirely individuated and free of constraint (51, 52). Conceiving of autonomy in this way opens possibilities for those with limited capacity to still exercise autonomy and retain selfhood (52).
Capacity itself can also be re-imagined as relative or situational, and this has important implications for the autonomy of those who are decisionally impaired. In the disability literature, the notion that disability is a property of individual bodies is replaced with the idea that disability, including mental disability, is socially constructed and perpetuated based on disabling social structures and barriers (50, 53). Under this view of disability, experiences of both capacity and limitation are relative and socially situated, in that they depend on the socially constructed environment in which an individual lives, the supports available to that individual and the kinds of capacities that are valued and differences that are tolerated in a given social context (50).
Frail older adults experience similar limits in capacity and ability based on the interaction of physical and cognitive impairments with social circumstances and supports. Their dependency, like that of people who are mentally or physically disabled, is relational and situational as opposed to based purely on physical, mental, or emotional deficits (54).

Models of Decision Making for Frail Older Adults

Frailty assessment will call upon individuals to make more complex choices, from a wider range of choices, than might have been offered in the absence of an identification of frailty. If the anticipated benefits of screening are to be ethically realized, decision making that is truly informed will be required by and on behalf of those who are determined to be frail or on the frailty spectrum. While decision making that both respects individual autonomy and is sufficiently supportive is of concern for many older adults, we argue that the issue is of particular relevance in the case of older adults who have been deemed frail. As outlined, a diagnosis of frailty may bring with it especially damaging assumptions of dependency or incapacity. The implications of these assumptions must be taken into consideration.
In the face of the dependency and vulnerability that accompanies a diagnosis of frailty, frameworks for decision making cannot rely on liberal conceptions of individualized autonomous agency. Informed decision making in this context is most likely to be supported or shared decision making. Typically these approaches are called for on normative grounds, as a right of individuals to have their capacity and therefore their autonomy respected to the fullest extent possible (49). We argue they are also called for on more functional grounds, as potentially necessary enablers of the decision making that is responsive to the results of screening, including those which prevent or minimize undue entanglement in potentially harmful medical intervention.
The standard response to concerns about capacity is to move to a substituted decision making model. In substituted decision making, a person makes decisions on behalf of an individual on the basis of their understanding of how that individual would want to be treated (55). The substitute decision maker is ideally the person with the most in-depth knowledge of the individual’s will, values and preferences. Theoretically, this equips the substitute decision maker to make the same decision the individual would make if the individual were able to decide (55). If a suitable substitute decision maker cannot be determined, decisions are made according to the individual’s “best interests” (55, 56). Both substituted decision making and the best interests approach run the risk of coercion if they result in decisions that an individual would not have wanted (55-57). Even more fundamentally, however, these models of decision making may compromise individuals’ basic freedom and human rights (55) if they are applied to individuals who would be capable of making decisions for themselves. They may also result in decision making that favors either a family member’s concern to ensure that everything that can be done is done for their loved one or a caregiver’s concern about their capacity to care for their loved one.
In the Canadian literature, particularly in the disabilities context, supported decision making models are a favored alternative both to substituted decision making and the alternative of leaving the decisionally impaired with more choice and responsibility than they are equipped to handle. Supported decision making is called for in Article 12 (3) of the Convention on the Rights of Persons with Disabilities (58). The Convention requires that State Parties provide the supports necessary for people with disabilities to exercise the legal capacity they are deemed to enjoy, on terms of equality with all others (56, 59). On a supported decision making model, legal capacity is at no point lost, and it is not dependent on mental capacity (48). While frailty is considered to be distinct from disability (60), discussions of decision making and capacity in the literature on disability may give the closest analogy to frailty, given the lack of literature pertaining specifically to decision making among frail older adults.
The objective of supported decision making is to provide individuals with the support and accommodation they require to make their own decisions. Support persons are guided by the individual’s will, the individual remains central in the decision making process and the individual retains the final say (48, 56, 61). In the context of frail older adults, the more specific rationale for maximizing autonomy and informed choice through supported decision making has two components. It can protect vulnerable individuals (as well as their providers and care systems) from being denied options on the basis of their frailty status. It can also support those individuals in avoiding the harmful medical entanglement, which is one critical ethical rationale for frailty screening.
Models of supported decision making therefore are sensitive to conceptions of autonomy, competence and capacity as contextual or relational and as existing on a spectrum, rather than as individual attributes that are either present or absent. A variety of supported decision making models encompass myriad forms of support such as single support persons, support groups, circles, boards or networks (often a person or persons with whom individuals have a trusting and caring relationship). However, several commonalities are apparent between most models. First, individuals are able to choose and change their support person or persons at will (56, 61). Second, supported decision making typically involves presenting information to individuals in a form that best facilitates their understanding, helping them appreciate the information, apply it to the decision at hand given their beliefs and values and facilitate communication of their decision to others (56, 57, 59, 61, 62). Third, the role of the supporting person or persons is to support decision making by the individual, not to decide for the individual, and to make sure decisions are respected and implemented (49, 56, 59).
Shared decision making is another recognized alternative to substituted decision that also avoids leaving a decisionally impaired person to decide on their own. Andrew et al. (12) suggest shared decision making may be useful in the case of frail older adults. Like supported decision making, it aims to be respectful and protective of individual autonomy (63). It is seen, particularly in the medical profession, as an ethical option in the era of informed choice, patient centered care and evidence-based medicine (64-66). This model of decision making has been linked to improved individual outcomes, both clinical or functional and in terms of individual satisfaction, from as early as the 1960s (67, 68). Shared decision making is a joint process between an individual, one or more healthcare professionals and often a family member or significant other (67). The individual communicates their values, views and desire for information to the healthcare professional, who in turn brings to the table their expertise in evidence-based treatment options (67). The individual, healthcare professional and any other parties involved work together and engage in two-way information sharing and dialogue about treatment options, risks, alternatives and the individual’s values in order to reach a decision that is mutually acceptable given the presence of clinical choice (65, 67). Individuals participate in decision making to the extent they desire, and their preference for more or less participation in the decision making process is part of the information the individual shares with the healthcare professional. It is these principles, not a specific set of protocols or practices, that characterize shared decision making (67).
Supported decision making aims to facilitate individuals’ ability to decide and to communicate their decisions whatever their apparent decisional impairment, while shared decision making is designed to address different individual preferences in their role in the informed consent process (49). Both models carry risks that have to be recognized and considered. As described above, the key justification for treating individuals as autonomous decision makers is to prevent the paternalism of earlier eras (49). With both supported and shared decision making, attending to the inherent vulnerability and dependency of individuals runs the risk that support or sharing could lapse into persuasion or even coercion (48, 49). At an even more basic level, it is recognized that healthcare practitioners bring their own values into the decision making process and hence may unintentionally impose their own values and opinions about the best course of treatment onto individuals (67). Furthermore, both shared and supported decision making require healthcare providers with strong communication skills and a willingness and ability to actively listen and to deliberate (49, 68). This places a significant burden of responsibility on the shoulders of providers and the healthcare system. Constraints on time and resources may render such intensive decision making processes difficult to implement and to follow.

 

Conclusions

In broad terms, screening for frailty, in the right context and with careful planning for interventions, has the potential to align the care offered to older adults by providers and systems (formal and informal) with older adults’ actual needs, circumstances and capacities to benefit. Frailty screening therefore has the capacity to increase equity in health and social care. Screening also has the potential to harm older adults by accentuating stigmatization and stereotyping, legitimizing denial of care and unnecessarily medicalizing the aging process. Responding appropriately to the needs of individuals identified by screening as “frail” requires an informed consent process that incorporates shared or supported decision making and that maximizes autonomy to the greatest possible extent. Shared or supported decision making approaches may also be crucial to achieve the quaternary prevention goals that are key to the ethical defensibility of screening for frailty.

 

Funding: Acknowledgement to the Canadian Frailty Network for funding support.
Conflict of interest: None

 

References

1.    Aristotle. Bk. V, Chap. VI. Aristotle’s Nicomachean Ethics (SD Collins, RC Bartlett, Trans.). 2011. University of Chicago Press, Chicago. (Original work published in 350 BCE).
2.    Law V. Canada (Minister of Employment and Immigration) 1999; 1 SCR 497.
3.    Muscedere M, Andrew M, Bagshaw S, et al. Screening for frailty in Canada’s health care system: A time for action. Can J Aging 2016. http://dx.doi.org/ 10.1017/ S0714980816000301. Accessed 27 May 2018
4.    Beauchamp TL, Childress JF. Principles of Biomedical Ethics, 7th ed. 2013. Oxford University Press, Oxford.
5.    Biller-Andorno N, Jüni P. Abolishing mammography screening programs? A view from the Swiss medical board. N Engl J Med 2014;370:1965-1967.
6.    Prasad V, Lenzer J, Newman DH. Why cancer screening has never been shown to “save lives”—and what we can do about it. BMJ 2016;352:h6080.
7.    Brodersen J, Schwartz LM, Heneghan C, et al. Overdiagnosis: What it is and what it isn’t. BMJ Evid Based Med 2018;23:1-3.
8.    Rogers WA, Mintzker Y. Getting clearer on overdiagnosis. J Eval Clin Pract 2016;22:580-587.
9.    Parker L, Carter S, Williams J, et al. Avoiding harm and supporting autonomy are under-prioritised in cancer-screening policies and practices. Eur J Cancer 2017;85:1-5.
10.    Mallery L, Moorhouse P. Respecting frailty. J Med Ethics 2011;37:126-128.
11.    Verweij M. Preventive medicine between obligation and aspiration. 2000. Springer Netherlands, Dordrecht.
12.    Andrew M, Dupuis-Blanchard S, Maxwell C, et al. Social and societal implications of frailty, including impact on Canadian healthcare systems. 2018. Manuscript accepted for publication.
13.    Andrew M, Dupuis-Blanchard S, Maxwell C, et al. Social and societal implications of frailty including impact on Canadian health care systems. 2016. Manuscript accepted for publication.
14.    Rolfson D, Heckman G, Bagshaw S, et al. Implementing frailty measures in the Canadian healthcare system. 2018. Manuscript accepted for publication.
15.    Bergman H, Ferrucci L, Guralnik J, et al. Frailty: An emerging research and clinical paradigm—issues and controversies. J Gerontol A Biol Sci Med Sci 2007;62:731-737.
16.    Richardson S, Karunananthan S, Bergman H. I may be frail but I ain’t no failure. Can Geriatr J 2011;14:24-28.
17.    Hall M. Mental capacity in the (civil) law: Capacity, autonomy, and vulnerability. McGill Law J 2012;58:61-94.
18.    Grenier A. Construction of frailty in the English language, care practice and the lived experience. Aging Soc 2012;27:1-21.
19.    Warmoth K, Lang IA, Phoenix C, et al. Thinking you’re old and frail: a qualitative study of frailty in older adults. Ageing Soc 2015;36:1483-1500.
20.    Hall M. Old age (Or, do we need a critical theory of law and aging?). Windsor Rev Legal Soc Issues 2014;35:1-21.
21.    Levy B. Improving memory in old age through implicit self-stereotyping. J Pers Soc Psychol 1996;71:1092-1107.
22.    Levy B, Ashman O, Dror I. To be or not to be: The effects of aging stereotypes on the will to live. Omega (Westport) 1999-2000;40:409-420.
23.    National Voices. I’m still me: A narrative for coordinated support for older people. 2014. http://www.nationalvoices.org.uk/publications/our-publications/im-still-me. Accessed 27 May 2018
24.    McNally M, Lahey W. Frailty’s place in ethics and law: Some thoughts on equality and autonomy and on limits and possibilities for aging citizens. In: Theou O, Rockwood K (eds) Frailty in Aging. Biological, Clinical and Social Implications. Interdiscipl Top Gerontol Geriatr 2015;41:174-185. Karger, Basel.
25.    Kaufman S, Shim J, Russ A. Revisiting the biomedicalization of aging: Clinical trends and ethical challenges. Gerontologist 2004;44:731-738.
26.    Shim J, Russ A, Kaufman S. Risk, life extension and the pursuit of medical possibility. Sociol Health Illn 2006;28:479-502.
27.    Lacas A, Rockwood K. Frailty in primary care: A review of its conceptualization and implications for practice. BMC Med 2012;10:4.
28.    Rolfson D, Heckman G, Bagshaw S, et al. Implementing frailty measures in the Canadian health care system. 2016. Manuscript accepted for publication.
29.    Theou O, Walston J, Rockwood K. Operationalizing frailty using the frailty phenotype and deficit accumulation approaches. In: Theou O, Rockwood K (eds) Frailty in Aging. Biological, Clinical and Social Implications. Interdiscipl Top Gerontol Geriatr 2015;41:66-73. Karger, Basel.
30.    Fried L., Tangen C., Walston, J, et al. Frailty in older adults: Evidence for a phenotype. J Gerontol A Biol Sci Med Sci 2001;56:M146-M157.
31.    Rockwood K, Mitnitski A. Frailty in relation to the accumulation of deficits. J Gerontol A Biol Sci Med Sci 2007;62:722-727.
32.    Clegg A, Young J, Iliffe S, et al. Frailty in elderly people. [Erratum in: Lancet, 382] [9901]. Lancet 2013; 381:752-762.
33.    Morley J, Vellas B, van Kan G, et al. Frailty consensus: A call to action. JAMDA 2013;14;392-397.
34.    Wilson J, Jungner G. Principles and practice of screening for disease. Geneva: World Health Organization. Public Health Papers No. 34. 1968. http://whqlibdoc.who.int/php/ WHO_PHP_34.pdf. Accessed 27 May 2018
35.    Harris RP, Sheridan SL, Lewis CL, et al. The harms of screening: A proposed taxonomy and application to lung cancer screening. JAMA Intern Med 2014;174:281-285.
36.    Rockwood K, Theou O, Mitnitski A. What are frailty instruments for? Age Ageing 2015;44:545-547.
37.    Juth N, Munthe C. The ethics of screening in health care and medicine: Serving society or serving the Patient?. 2012. Springer, New York.
38.    Jamoulle M. Quaternary prevention, an answer of family doctors to overmedicalization. Int J Health Policy Manag 2015;4:61-64.
39.    Rockwood K. What would make a definition of frailty successful? Age Ageing 2005;34:432-434.
40.    Pijpers E, Ferreira I, Stehouwer CD, Nieuwenhuijzen Kruseman AC. The frailty dilemma. Review of the predictive accuracy of major frailty scores. Eur J Intern Med 2012;23:118-123.
41.    Harris J, Regmi S. Ageism and equality. J Med Ethics 2012;38:263-266.
42.    Bognar G. Fair innings. Bioethics 2015;29:251-261.
43.    Tsuchiya A. QALYs and ageism: Philosophical theories and age weighting. Health Econ 2000;9:57-68.
44.    Maciosek MV, Coffield AB, Edwards NM, Flottemesch TJ, Goodman MJ, Solberg LI. Priorities among effective clinical preventive services: Results of a systematic review and analysis. Am J Prev Med 2006;31:52-61.
45.    Ahern J, Jones MR, Bakshis E, Galea S. Revisiting Rose: Comparing the benefits and costs of population-wide and targeted interventions. Milbank Q 2008; 86:581-600.
46.    de Vries NM, Staal JB, van Ravensberg CD, Hobbelen JS, Olde Rikkert MG, Nijhuis-van der Sanden MW. Outcome instruments to measure frailty: A systematic review. Ageing Res Rev 2011;10:104-114.
47.    Peppin P. (2011). Informed Consent. In: Downie J, Caulfield T, Flood C (eds) Canadian Health Law and Policy, 4th ed. LexisNexis Canada Inc, Canada.
48.    Richardson G. Mental disabilities and the law: From substitute to supported decision-making? Curr Legal Probs 2012;65:333-354.
49.    Peisah C, Sorinmade OA, Mitchell L, Hertogh CM. Decisional capacity: Toward an inclusionary approach. Int Psychogeriatr 2013;25:1571-1579.
50.    Dodds S. Depending on care: Recognition of vulnerability and the social contribution of care provision. Bioethics 2007;21:500-510.
51.    Dekkers W. Autonomy and dependence: Chronic physical illness and decision-making capacity. Med Health Care Philos 2011;4:185-192.
52.    Perkins MM, Ball MM, Whittington FJ, Hollingsworth C. Relational autonomy in assisted living: A focus on diverse care settings for older adults. J Aging Stud 2012;26:214-225.
53.    Oliver M. The social model of disability: Thirty years on. Disab Soc 2013;28:1024-1026.
54.    Shakespeare, T. The social model of disability. In: Davis LJ (ed) The disability studies reader. 2006. Routledge, New York, pp 230-257.
55.    Devi N, Bickenbach J, Stucki G. Moving towards substituted or supported decision-making? Article 12 of the Convention on the Rights of Persons with Disabilities. ALTER – Eur J Disabil Res 2011; 5:249-264.
56.    Morrissey F. The United Nations Convention on the Rights of Persons with Disabilities: A new approach to decision-making in mental health law. Eur J Health Law 2012;19:423-440.
57.    Series L. Relationships, autonomy and legal capacity: Mental capacity and support paradigms. Int J Law Psychiatry 2015;40:80-91.
58.    United Nations. Convention on the Rights of Persons with Disabilities. Department of Public Information. 2006. http://www.un.org/disabilities/documents/convention/ convention_accessible_pdf.pdf . Accessed 27 May 2018
59.    Bach M, Kerzner L. A new paradigm for protecting autonomy and the right to legal capacity. Law Commission of Ontario. 2010. https://www.lco-cdo.org/wp-content/uploads/2010/11/disabilities-commissioned-paper-bach-kerzner.pdf. Accessed 27 May 2018
60.    Sternberg SA, Wershof Schwartz A, Karunananthan S, et al. The identification of frailty: A systematic literature review. J Am Geriatr Soc 2011;59:2129-2138.
61.    Jameson JM, Riesen T, Polychronis S, et al. Guardianship and the potential of supported decision making with individuals with disabilities. Res Pract Persons Severe Disabl 2015;40:36-51.
62.    Devi N. Supported decision-making and personal autonomy for persons with intellectual disabilities: Article 12 of the UN convention on the rights of persons with disabilities. J Law Med Ethics 2013; 41:792-806.
63.    Lipman H, Kalra A, Kirkpatrick J. Foundations of medical decision-making for older adults with cardiovascular disease. J Geriatr Cardiol 2015;12:335-339.
64.    Godolphin W. Shared decision-making. Health Q 2009;12:e186-e190.
65.    Whitney S, McGuire A, McCullough L. A typology of shared decision making, informed consent, and simple consent. Ann Intern Med 2003;140:54-59.
66.    Politi M, Wolin K, Legare F. Implementing clinical practise guidelines about health promotion and disease prevention through shared decision making. J Gen Intern Med 2013;28:838-844.
67.    Charles C, Gafni A, Whelan T. Shared decision-making in the medical encounter: What does it mean? (Or it takes at least two to tango). Soc Sci Med 1997;44:681-692.
68.    Gravel K, Légaré F, Graham I. Barriers and facilitators to implementing shared decision-making in clinical practise: A systematic review of health professionals’ perceptions. Implement Sci 2006;1:n.p.

PREVALENCE OF FRAILTY AND MOBILITY LIMITATION IN A RURAL SETTING IN FRANCE

 

M. CESARI, L. DEMOUGEOT, H. BOCCALON, B. VELLAS

 

Institut du Vieillissement, Gérontopôle, Centre Hospitalier Universitaire de Toulouse, Toulouse, France, INSERM Unit 1027, Toulouse, France

Corresponding author: Matteo Cesari, MD, PhD, Institut du Vieillissement, Université de Toulouse; 37 Allées Jules Guesde, 31000 Toulouse, France. Phone: +33 (0)5 6114-5628; Fax: +33 (0)5 6114-5640; email: macesari@gmail.com

J Frailty Aging 2012;1(4):169-173
Published online February 14, 2016, http://dx.doi.org/10.14283/jfa.2012.26


Abstract

Background: The prevalence of frailty is variable according to the adopted operational definition, the tested population, and the setting where it is explored. Objective: To estimate the prevalence of frailty and mobility disability in community-dwelling persons aged 60 years and older. Design: Cross-sectional analyses. Setting: The rural area of Labastide-Murat (France). Participants: All community-dwelling persons aged 60 years and older living in the area and answering to the study survey (n=572/1022). Measurements: The study questionnaire included questions defining mobility disability (as ability to walk 400 meters and climb up 2 flights of stairs) and frailty (according to the FRAIL instrument and a modified version of the original definition proposed by Fried and colleagues). Results: Mean age of participants was 72.4 years old. Mobility disability was reported by 47 (8.3%) participants. The sedentariness criterion of frailty was the most prevalent in the present population. Overall, according to the FRAIL instrument, 77.6%, 14.0%, and 8.3% could be defined as robust, pre-frail/frail, and disabled, respectively. When the alternative definition of frailty mirroring the definition proposed in the Cardiovascular Health Study was adopted, the prevalence of frailty increased and showed gender-specific differences (p=0.02). Conclusions: A relevant number of older persons living in rural areas experiences physical impairments and presents an increased risk for major negative health-related events. These results may support the ongoing clinical and research actions aimed at preventing the functional decline in elders.

Key words: Screening, frailty, elderly, prevention, disability.


 

Introduction

The growing number of older persons in our societies has led to an exponential increase of healthcare costs. To adequately face the consequences of such demographic and socio-economical modifications, public health authorities are growingly demanding to quantify the number of older persons at increased risk of negative health-related events in the general population. Such estimates are needed in order to optimize the distribution of resources, especially towards areas already experiencing limited access to healthcare services.

Among the clinical conditions imposing the heaviest burdens on public health, physical disability is surely one of the most relevant. It has been estimated that 19.6% of older persons presenting functional dependence accounts for almost half (46.3%) of total healthcare expenditures in the United States (1).

Since disability is commonly considered as an irreversible condition, the only way to effectively counteract it is by preventively acting (2). In this context, the “frailty syndrome” (the age-related accumulation of deficits) (3) may indeed represent the ideal target for interventions because preceeding the onset of disability and, more importantly, still amenable of being reversed (4).

In the present study, we present results from a survey conducted in the rural area of Labastide-Murat (Department du Lot, France). The survey was specifically aimed at estimating the prevalence of frailty and mobility disability among community-dwelling persons aged 60 years and older.

 

Methods

Since 2011, the Causse de Labastide-Murat (a rural area in the South-Western France, population about 3,000 inhabitants) was selected as site of a governmental preventive program aimed at developing interventions against physical disability and age-related conditions. In this context, between February and April 2012, all the persons aged 60 years and older living in one of the fifteen villages in the area and registered to the local electoral roll (n=1,022) received a questionnaire by mail from the Institut du Vieillissement, Université de Toulouse (Toulouse, France), scientific partner of this project. In the joint cover letter, it was asked to complete the questionnaire and return it using the enclosed pre-paid envelop. For persons who did not answer at this first contact, attempts were made to reach them over the phone to administer the questionnaire. Moreover, local healthcare professionals took part at directly solicitating their patients at taking part in the survey. The data management of the study (including treatment of confidential information) was approved by the Commission Nationale de l’Informatique et des Libertés (Paris, France).

Questionnaire

The questionnaire, self-completed by participants (if needed with the help of proxies) or administered by study investigators over the phone, mainly consisted of seven different questions (Table 1). Two questions (A and B) were specifically aimed at identifying individuals with mobility disability. These two questions, composing the “disability domain” of the questionnaire, have been previously adopted in geriatric research to defined the mobility disability outcome (5, 6). Mobility disability represents a preliminary stage of the disabling process and has been used as primary outcome of studies about prevention of disability (7, 8). For the present analyses, the presence of mobility disability was defined as “a lot of difficulties” or “inability” at performing both these tasks.

Table 1 Details of the questionnaire adopted in the survey

Five additional questions (C-G) were aimed at assessing signs, symptoms, or conditions commonly considered as possible components of the frailty syndrome (9). For the present analyses, fatigue was defined as the concurrent presence of answers “3” or “4” at the items D and E of the questionnaire, and sedentariness by a level “1” or “2” at the item F of the questionnaire.

The “frailty domain” of the questionnaire is largely based on the 5-item FRAIL instrument (acronym of Fatigue, Resistance, Aerobic, Illnesses, and Loss of weight), recently proposed by Morley (10) and the International Association of Nutrition and Aging (11). This instrument (questions C-F) was developed to promote and facilitate the screening of frailty in the clinical setting. Morever, since it is only composed by a series of questions, it can also be easily administered without the need of having the subject on site (for example, it can be completed over the phone or by mail). In details, questions A and B are singularly used to define the “aerobic” and “resistance” component of the FRAIL instrument. The questions C, D, and E of the questionnaire mirror the original criteria (i.e., fatigue and involuntary weight loss) proposed in the Cardiovascular Health study (9). The question F about the number of clinical conditions is a specific component of frailty introduced by the FRAIL instrument (10, 11). In the present analyses, participants presenting one or two frailty criteria are considered as “pre-frail”, and those with three or more criteria as “frail”.

The questionnaire sent to the population of the Labastide-Murat area contained an additional item in the frailty domain (i.e., question G) measuring sedentariness. This question has been previously used in the “Invecchiare in Chianti” (Aging in the Chianti area, InCHIANTI) study to quantify the self-reported amount of physical activity among community-dwelling older persons (12). Although the sedentariness criterion is not part of the FRAIL instrument, it was included in the adopted questionnaire because commonly considered in different operative definitions of frailty (9, 13).

As alternative approach in the assessment of frailty in the present survey, we also explored a definition more closely mirroring the original phenotype proposed by Fried and colleagues (9). Thus, in this second definition, we modified the FRAIL instrument by replacing the number of diseases criterion with the sedentariness one. In this way, compared to the original definition tested in the Cardiovascular Health Study (9), the assessment of physical performance measures (i.e., gait speed and hand grip strength)  is here replaced with two self-reported items (i.e., item A and B, respectively).

Finally, the study questionnaire was completed by main sociodemographic data (i.e., age, gender), contact information (i.e., address, phone number) of participants, and a final question about their availability for possible future research activities.

Statistical analysis

Data are presented as percentages, or means ± standard deviations (SD). Spearman’s correlation analysis was performed to test the relationship between the two tested frailty definitions. SPSS software (version 16.0 for Mac, SPSS Inc., Chicago, IL) was used.

 

Results

Main characteristics of subjects accepting to participate at the survey (n=572, 56.0% of the entire population of the area) are presented in Table 2. Mean age of participants was 72.4 (SD 8.6, minimum 60, maximum 101) years old, with no significant differences between men and women. Persons participating at the survey were significantly (p=0.02) younger than subjects who did not complete the questionnaire (n=450; mean age 73.8 (SD 10.2) years).

Mobility disability (defined as concurrent presence of “a lot of difficulties” or “inability” to walk 400 meters and climbing up two flights of stairs) was reported by 47 (8.3%) participants with a non-significant (p=0.57) higher prevalence among women (9.0%) compared to men (7.6%).

 

Table 2 Main characteristics of the study sample according to gender. Resuls are shown as percentages, or mean ± standard deviations

CHS: Cardiovascular Health Study; FRAIL instrument: fatigue, aerobic impairment, resistance impairment, number of diseases, weight loss; Modified CHS definition: fatigue, aerobic impairment, resistance impairment, sedentariness, weight loss; For both definitions, pre-frail status is defined by the presence of 1 or 2 criteria, and frailty by the presence of 3 or more criteria; * Analyses for the frailty domain variables are performed only in participants with no mobility disability

The sedentariness criterion of frailty (not included in the FRAIL instrument, but commonly considered in other operative definitions) was the most prevalent in the present population (22.7%). It was the only one showing a significant difference between men and women (16.7% versus 28.6%, respectively; p=0.001).     

Overall, 77.6%, 14.0%, and 8.3% could be defined as robust (absence of frailty and mobility disability), pre-frail/frail (according to the FRAIL instrument), and disabled (on the basis of mobility capacity), respectively. No significant gender difference was reported. If analyses were restricted to individuals aged 65 years and older (n=453, 79.2% of participants), the prevalence of robustness, (pre-)frailty, and mobility disability changed into 74.6%, 14.7%, and 10.7%, respectively. Again, no significant gender difference was reported.

When the alternative definition of frailty replacing the number of diseases criterion with sedentariness was adopted (thus more closely mirroring the definition proposed in the Cardiovascular Health Study), the prevalence of frailty increased and showed gender-specific differences. In fact, 58.2%, 32.3%, and 9.5% of women and 70.7%, 21.3, and 8.0% of men (p=0.02) were robust, pre-frail/frail, and mobility disabled, respectively. Similar results were reported when analyses were reperformed in individuals aged 65 years and older (women: robustness 53.8%, pre-frailty/frailty 34.4%, mobility disability 11.8%; men: robustness 67.8%, pre-frailty/frailty 22.9%, mobility disability 9.3%; p=0.02). As expected, the two frailty operative definitions (i.e., FRAIL instrument and the modified version of the index proposed by Fried and colleagues) were highly correlated each other (Spearman’s r=0.801, p<0.001).

Discussion

In the present study, we report results of a survey aimed at estimating the prevalence of frailty and mobility disability in community-dwelling older persons living in a rural area in France. Our findings show that a relevant number of older persons experiences physical impairments and presents an increased risk for major negative health-related events in rural settings, too. In fact, besides of one tenth of disabled participants, we detected about 15-30% of the population (according to the adopted definition) presenting common features of frailty.

The novelty of the present findings mainly reside in:
1) the rural area where the survey was conducted,
2) the target of the study (i.e., general population), and
3) the adoption of novel instruments to measure the health status of the older person.

To our knowledge, no other study has previously measured the prevalence of frailty and disability in older persons living in the countryside. This may be particularly important from a public health viewpoint since persons living in rural areas are generally more difficult to be reached by healthcare services. Therefore, the availability of the data from such understudied populations may help at better distributing resources, especially aimed at facing the increase of age-related conditions.

The present study was conducted in community-dwelling older persons living in a specific geographical area. This means that our findings may indeed support actions for primary prevention against disability. The evaluation of frailty in a clinical setting (e.g., outpatient clinics, hospital, nursing homes) may alter the estimate of such geriatric syndrome and also impair the capacity of preventively acting against disability. Differently, the screening of frailty in the general population might provide the basis to reverse the disabling cascade at its very early phases (14).

Several instruments are currently available to detect frailty in older persons. However, their routine implementation in the clinical setting is often limited by their need of special devices (e.g., stop-watch, dynamometer) or because considered time-consuming. The questionnaire adopted in our survey is based on a series of questions that can be easily answered even by persons at very advanced age. This may imply that a wider diffusion of this instrument may facilitate the detection of frailty and promote prompt interventions against disability. Moreover, from the questionnaire we adopted, we were able to retrieve two different operational definitions of frailty (i.e., the FRAIL instrument and a phenotype mirroring the one proposed by Fried and colleagues in the Cardiovascular Health Study).

Some limitations of the present study need to be mentioned. It is possible that our estimates are underestimating the real prevalence of frailty and disability in our population. In fact, the frailest and dependent individuals might have had more difficulties at completing the questionnaire compared to subjects with better health status, as also suggested by the younger age of respondents. Moreover, the concept of frailty and mobility disability may largely overlap as both representing preliminary phases of the disabling process.Therefore, it is possible that some participants presenting mobility disability may not yet experience a complete loss of function in activities of daily living, but still be frail. The modified version of the Fried and colleagues’ definition we used in the present analyses has not yet been validated, and the FRAIL questionnaire has only been validated among middle aged African-Americans (15). Further studies are needed to specifically evaluate the inner structures and characteristics of these instruments, hopefully in different populations and settings. Finally, the representativeness of our sample might be limited, thus cautiousness is required before directly translating our findings to different settings and populations.

In conclusion, our findings provide prevalence data about frailty and mobility disability in a rural area. These results may support the ongoing actions taken by public health authorities in France as well as worldwide aimed at preventing the functional decline of our aging societies. These preliminary data may also help the design of interventional studies specifically aimed at counteracting the disabling cascade and reversing the frailty syndrome in community-dwelling older persons.

Acknowledgements: The present study was possible thanks to the enthustiatic support received by the populations, local authorities (majors, Communauté de Communes du Causse de Labastide-Murat, Agence Regionale de Santé) and public health professionals (general practitioners, nurses, physical therapists, pharmacist, social assistants, home care personnel) of the following participating towns: Beaumat, Blars, Caniac du Causse, Fontanes du Causse, Frayssinet, Ginouillac, Labastide-Murat, Lunegarde, Montfaucon, Saint Sauveur La Vallée, Senaillac Lauzes, Seniergues, Soulomes, Vaillac. Special thanks also go to Mrs. Joelle Schlama and Constance de Seynes (Gérontopôle of the Centre Hospitalier Universitaire de Toulouse) for their crucial help in the conduction of the survey. The present study is part of a Chair of Excellence of the Agence Nationale de Recherche assigned to Dr. Cesari.

References

1.    Fried TR, Bradley EH, Williams CS, Tinetti ME. Functional disability and health care expenditures for older persons. Arch Intern Med. 2001;161:2602-2607.
2.    Cesari M. The multidimentionality of frailty: many faces of one single dice. J Nutr Health Aging. 2011;15:663-664.
3.    Rockwood K, Mitnitski A. How might deficit accumulation give rise to frailty? J Frailty Aging. 2012;1:7-10.
4.    Cesari M. Frailty and aging. J Frailty Aging. 2012;1:3-5.
5.    Houston DK, Neiberg RH, Tooze JA, Hausman DB, Johnson MA, Cauley JA et al. Low 25-Hydroxyvitamin D Predicts the Onset of Mobility Limitation and Disability in Community-Dwelling Older Adults: The Health ABC Study. J Gerontol A Biol Sci Med Sci. 2012
6.    Cesari M, Kritchevsky SB, Nicklas BJ, Penninx BW, Holvoet P, Koh-Banerjee P et al. Lipoprotein peroxidation and mobility limitation: results from the Health, Aging, and Body Composition Study. Arch Intern Med. 2005;165:2148-2154.
7.    Pahor M, Blair SN, Espeland M, Fielding R, Gill TM, Guralnik JM et al. Effects of a physical activity intervention on measures of physical performance: Results of the lifestyle interventions and independence for Elders Pilot (LIFE-P) study. J Gerontol A Biol Sci Med Sci. 2006;61:1157-1165.
8.    Fielding RA, Rejeski WJ, Blair S, Church T, Espeland MA, Gill TM et al. The Lifestyle Interventions and Independence for Elders Study: design and methods. J Gerontol A Biol Sci Med Sci. 2011;66:1226-1237.
9.    Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J et al. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci. 2001;56:M146-56.
10.    Morley JE. Frailty: diagnosis and management. J Nutr Health Aging. 2011;15:667-670.
11.    Morley JE, Abbatecola AM, Argiles JM, Baracos V, Bauer J, Bhasin S et al. Sarcopenia with limited mobility: an international consensus. J Am Med Dir Assoc. 2011;12:403-409.
12.    Patel KV, Coppin AK, Manini TM, Lauretani F, Bandinelli S, Ferrucci L et al. Midlife physical activity and mobility in older age: The InCHIANTI study. Am J Prev Med. 2006;31:217-224.
13.    Ensrud KE, Ewing SK, Taylor BC, Fink HA, Cawthon PM, Stone KL et al. Comparison of 2 frailty indexes for prediction of falls, disability, fractures, and death in older women. Arch Intern Med. 2008;168:382-389.
14.    Subra J, Rouge-Bugat ME, Balardy L, Bismuth S, Gillette Guyonnet S, Vellas B et al. Gait speed: a new «vital sign» for older persons in primary care. J Frailty Aging. 2012;1:90-91.
15.    Morley JE, Malmstrom TK, Miller DK. A Simple Frailty Questionnaire (FRAIL) Predicts Outcomes in Middle Aged African Americans. J Nutr Health Aging. 2012;16:601-608.

BIOMARKERS OF SARCOPENIA IN CLINICAL TRIALS RECOMMENDATIONS FROM THE INTERNATIONAL WORKING GROUP ON SARCOPENIA

 

M. CESARI1, R.A. FIELDING2, M. PAHOR3, B. GOODPASTER4, M. HELLERSTEIN5, G. ABELLAN VAN KAN1, S.D. ANKER6,7, S. RUTKOVE8, J.W. VRIJBLOED9, M. ISAAC10, Y. ROLLAND1, C. M’RINI11, M. AUBERTIN-LEHEUDRE12, J.M. CEDARBAUM13, M. ZAMBONI14, C.C. SIEBER15, D. LAURENT16, W.J. EVANS17, R. ROUBENOFF18, J.E. MORLEY19, B.VELLAS1 FOR THE INTERNATIONAL WORKING GROUP ON SARCOPENIA

 

1.  Institut du Vieillissement, Gérontopôle and INSERM Unit 1027, Université de Toulouse, Toulouse, France; 2. Nutrition, Exercise Physiology, and Sarcopenia Laboratory, Jean Mayer USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA, USA; 3. Department of Aging and Geriatric Research, Institute on Aging, University of Florida, Gainesville, FL, USA; 4. Division of Endocrinology and Metabolism, University of Pittsburgh, Pittsburgh, PA, USA; 5. Department of Nutritional Sciences and Toxicology, University of California at Berkeley, San Francisco, CA, USA; 6. Department of Cardiology, Campus Virchow-Klinikum, Charité Universitätsmedizin Berlin, Germany; 7. Centre for Clinical and Basic Research, IRCCS San Raffaele, Rome, Italy; 8. Harvard Medical School, Boston, MA, USA; 9. Neurotune AG, Schlieren, Switzerland; 10. Human Medicine Special Areas, Scientific Advice Section, European Medicines Agency, London, UK; 11. Institut Mérieux, Lyon, France; 12. Départment de kinanthropologie, Université du Quebec, Montreal, Canada; 13. Clinical Research Operations, Neuroscience & Neuromuscular Disorders, Cytokinetics Inc., South San Francisco, CA, USA; 14. Department of Medicine, University of Verona, Verona, Italy; 15. Institute for Biomedicine of Aging, Friedrich-Alexander-University Erlangen-Nürnberg, Nürnberg, Germany; 16. Novartis Institutes for Biomedical Research, Basel, Switzerland; 17. Muscle Metabolism DPU, Metabolic Pathways CEDD, GlaxoSmithKline, Research Triangle Park, NC, USA; 18. Musculoskeletal Translational Medicine, Novartis Institutes for Biomedical Research, Cambridge, MA, USA; 19. University School of Medicine and GRECC, VA Medical Center, St. Louis, MO, USA.

Corresponding author: Matteo Cesari, MD, PhD, Institut du Vieillissement, Gerontopôle; Université de Toulouse. 37 Allées Jules Guesde, 31000 Toulouse, France. Phone: +33 (0)5 6114-5628; Fax: +33 (0)5 6114-5640; Email: macesari@gmail.com. Alternative address for correspondence: Roger Fielding, PhD, Jean Mayer USDA Human Nutrition Research Center on Aging; Tufts University. 711 Washington Street, 02111 Boston, MA, USA. Email: roger.fielding@tufts.edu

J Frailty Aging 2012;1(3):102-110
Published online February 16, 2012, http://dx.doi.org/10.14283/jfa.2012.17


Abstract

Sarcopenia, the age-related skeletal muscle decline, is associated with relevant clinical and socioeconomic negative outcomes in older persons. The study of this phenomenon and the development of preventive/therapeutic strategies represent public health priorities. The present document reports the results of a recent meeting of the International Working Group on Sarcopenia (a task force consisting of geriatricians and scientists from academia and industry) held on June 7-8, 2011 in Toulouse (France). The meeting was specifically focused at gaining knowledge on the currently available biomarkers (functional, biological, or imaging-related) that could be utilized in clinical trials of sarcopenia and considered the most reliable and promising to evaluate age-related modifications of skeletal muscle. Specific recommendations about the assessment of aging skeletal muscle in older people and the optimal methodological design of studies on sarcopenia were also discussed and finalized. Although the study of skeletal muscle decline is still in a very preliminary phase, the potential great benefits derived from a better understanding and treatment of this condition should encourage research on sarcopenia. However, the reasonable uncertainties (derived from exploring a novel field and the exponential acceleration of scientific progress) require the adoption of a cautious and comprehensive approach to the subject.

Key words: Biomarkers, sarcopenia, elderly, skeletal muscle, imaging, screening, follow-up, assessment, aging, consensus paper.

 

The present article is jointly published in the Journal of Frailty & Aging and in the Journal of Cachexia, Sarcopenia and Muscle


 

Introduction

One of the most recognized changes in body composition with senescence is the loss of skeletal muscle mass. This loss occurs even among physically active older persons and was originally termed «sarcopenia» for the Greek words «flesh» and «loss» (1). The age-related loss in skeletal muscle mass is associated with substantial social and economic costs and is characterized by impairments in strength, limitations in function, and ultimately physical disability and institutionalization (2-4). In consideration of the increased awareness of this syndrome and the continued rapid development of therapeutic strategies to slow or reverse sarcopenia, the International Working Group on Sarcopenia was convened to address issues related to the successful conduct of clinical trials in this area (5). This task force, consisting of geriatricians and scientists from academia and industry, met again in Toulouse, France in June of 2011, to discuss the current state of the art in the development of biomarkers to be utilized in clinical trials on sarcopenia. The purpose of this meeting was to gain an understanding of the currently available parameters that could be utilized in clinical trials of sarcopenia and to discuss future research needs in this area. Specific topics that were addressed include: review of current consensus definitions of sarcopenia, the importance of muscle performance and quality, biomarkers in other clinical states and chronic diseases, potential biomarkers for sarcopenia, applications in clinical trials, and recommendations for future studies.

Definition of sarcopenia

Since the advent of the term «sarcopenia» in 1989, there has been a dramatic increase in publications in this area and clinical interest in this condition (6). Originally described as the age-related decrease in skeletal muscle mass (7), until very recently there has been a lack of consensus on the operational definition of sarcopenia without clinically appropriate correlates for this syndrome. In the past two years, a number of academic societies have put forward operational definitions of sarcopenia (8-11). Although each consensus definition has some distinct features, there is general agreement among these groups on the definition of sarcopenia. A summary of consensus sarcopenia definitions is presented in Table 1. The characteristics of sarcopenia highlighted in these reports include: an objective measure of muscle or fat free mass using dual energy x-ray absorptiometry (DXA) or computed tomography (CT), a reliable measure of muscle strength, and/or an objective test of physical functioning. Although the sequence of events and specific recommendations differ somewhat, the general approaches proposed require that patients be identified with measured deficits in physical function for which sarcopenia may be the cause, and subsequently quantification of muscle strength and mass to definitively confirm the diagnosis.

Table 1 Summary of consensus sarcopenia definitions

Definition of biomarker

A biomarker is defined as “a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention”(12). Hence, biomarkers support the diagnosis, facilitate the tracking of changes over time, and help clinical and therapeutic decision-making processes. Taking this definition into account, the functional, biological, or imaging-related parameters considered in the present document will be hereby generally referred to with the term «biomarker».

There are currently numerous parameters that are potentially able to track the age-related skeletal muscle decline. Depending on the parameter chosen to define sarcopenia, different information might be obtained. Such variability depends on the specific characteristics of each parameter and the mechanisms measured by the parameter. The intrinsic (e.g., accuracy, specificity, sensitivity) and extrinsic (e.g., cost, availability, time to be performed) properties of each biomarker will largely drive its use in research trials, making it more suitable for screening, baseline evaluation, and/or definition of outcomes (Table 2).

Table 2 Possible biomarkers to be used in trials on sarcopenia

* The importance of all these biomarkers in the evaluation of sarcopenia will largely depend on the study hypotheses, the specific aims, and/or the target population. – : Not recommended for this use; + : may be of use, but severely limited; ++ : suitable for this use; +++ : recommended for this use

The use of biomarkers in a given study must be «fit for purpose». Thus, several different biomarkers may be required to support different aspects of the development of a therapeutic intervention. For example, biomarkers for detection and diagnosis may not be the same as those that ideally track disease progression. Likewise, for new therapeutic agents, a single assay may not suffice as a biomarker reflecting both target engagement and the pharmacodynamic effects of a drug.

Muscle quantity versus muscle quality

Although muscle mass can objectively define the presence of sarcopenia, several components of skeletal muscle function are not adequately captured by simply measuring mass or cross-sectional area. It is now clear that there is a certain degree of divergence between changes in muscle mass and alterations in muscle performance. The well-described decline in skeletal muscle mass in older adults is a critical determinant of age-related weakness, which is defined as a reduction in maximal voluntary joint torque or power. Yet, it is now clear that the relationship between force production capability and muscle size in older adults is less robust than it is in young people (13). Indeed, longitudinal studies have demonstrated that the age-related decline in muscle strength far exceeds the observed changes in muscle mass or size, particularly in weight-stable individuals (14, 15). Furthermore, longitudinal studies indicate that maintenance or even gain of muscle mass may not prevent weakness in older adults (15, 16). In addition, a number of age-related changes in force production capability is not readily explained by a reduction in muscle mass, including decreased specific force (force per cross sectional area) (17, 18) and slower rate of isometric force production (expressed relative to peak torque or to body weight) (19, 20). Furthermore, voluntary weight loss leads to reductions in muscle mass/size with no declines in muscle strength (21). It is also noteworthy that pharmacologic interventions that increase muscle mass/size do not necessarily improve voluntary strength. Similarly, physical activity interventions that increase muscle strength do not necessarily augment muscle size (22, 23). Noticeably, gains in muscle strength secondary to increased physical activity generally precede measurable changes in skeletal muscle mass/size.

The progressive muscle atrophy with aging is associated with a loss of overall muscle force and changes in force and power generation of the remaining muscle fibers (24). However, several additional physiological mechanisms that accompany the phenomenon of sarcopenia may directly influence muscle function and force production with advancing age. Recent evidence has shown that adipose tissue accumulation around and between muscle fibers concomitant with reductions in muscle cross-sectional area occurs with aging, and that this skeletal muscle attenuation is inversely associated with muscle performance (18, 25). Age-related changes in the nervous system may also play a substantial role in the decline in muscle power generation (26). These include loss of motor neurons and concomitant remodeling of motor units through collateral reinnervation (27), impairment of neuromuscular activation observed as decreased maximal motor unit firing rates (28-30) and uncoordinated patterns of intermuscular neural activation (31). Finally, changes in individual muscle fiber composition and intrinsic contractile properties may influence the decline in muscle force among older adults. For instance, cross-sectional observations suggest that reductions in muscle torque may be related to changes in fiber composition and, in particular, to the preferential atrophy of type II (fast-twitch) fibers with aging (32). Specific changes in the intrinsic ability of aged muscle to generate force have also been observed (33). Decreases in specific force (force normalized per cross sectional area) and unloaded shortening velocity in type I and IIA fibers have been reported in older males compared with young controls (32, 34). Conversely, recent longitudinal data have demonstrated that, despite reductions in whole muscle cross-sectional area, single muscle fiber contractile function is preserved with advancing age as existing fibers may compensate and partially correct these deficits, therefore maintaining optimal force-generating capacity (14).

Although precise and valid measures of muscle mass are important components of sarcopenia assessment, these gross measures of muscle size do not adequately account for the dynamic components (force, power, activation) of muscle function that are responsible for performing activities of daily living. Future trials on sarcopenia adopting clinically meaningful endpoints should evaluate these key biomarkers of muscle function through the use of state-of-the-art methodologies.

Quantitative assessment of sarcopenia

The bidimensional definition of sarcopenia simultaneously includes a functional parameter (i.e., muscle performance) and a quantitative index (i.e., muscle mass). Therefore, techniques aimed at capturing the objective amount of skeletal mucle mass are required. Multiple methodologies are currently available to accomplish this task (35).

DXA is the most commonly used imaging technique for several reasons. First of all, because it is commonly available in clinical and research settings, being relatively inexpensive, sufficiently precise, and well-accepted by older persons. Second, the initial operative definition of sarcopenia proposed by Baumgartner and colleagues (3) was based on appendicular lean mass measured by DXA. Later on, DXA was used to provide alternative definitions of sarcopenia based on the fat-adjusted residual method (36). Nevertheless, it cannot be ignored that the first operative definition is dated more than 10 years, and during this time several steps forward have been made in refining imaging techniques as well as understanding the sarcopenia phenomenon.

The identification of the “gold standard” for the quantitative evaluation of muscle mass in clinical trials (which is currently lacking) should be based on criteria of accuracy (i.e., the degree of conformity of a measure to a standard or a true value), precision (i.e., the degree of refinement with which an operation is performed or a measurement stated), reproducibility (i.e., the quality of being reproducible under the same operating conditions over a period of time, or by different operators), sensitivity to change (i.e., the degree of being modified by interventions), and accessibility (i.e., its usual availability in research and clinical centers).

DXA currently represents the more accessible technique for body composition assessment. It may accurately provide estimates of lean, fat, and bone tissues in the entire body or in specific regions. Moreover, it is inexpensive and quick to be performed. The radiation exposure associated with DXA is low and highly acceptable (about 1 mrem, a quantity similar to that of a 3-day background). The main limitations of this imaging approach reside in some analytical differences across manufacturers and models, and the risk of biased results due to the low differentiation between water and bone-free lean tissue.

CT accurately measures a direct physical property of the muscle (e.g., cross-sectional area and volume). It also allows the evaluation of muscle density (a parameter related to intramyocellular lipid deposits) as well as subcutaneous and intramuscular adipose tissue deposition. The radiation exposure associated with this technique is higher (i.e., about 15 mrem) than with DXA.

Magnetic resonance imaging (MRI) presents a high agreement with CT and provides similar measures. It does not involve radiation exposure, and also has the additional capacity of multiple slice acquisition, thus rendering 3D volumetric estimates. The lack of radiation exposure makes MRI the method of choice for many studies where ethics committee or national authority approval is more difficult to obtain for CT. The major limitations of this methodology reside in the higher technical complexity and costs, and in the inapplicability to subjects with older models of implanted metal devices (e.g., joint prostheses, pace-makers, etc.). Both CT and MRI may be limited in the ability to accomodate very obese individuals.

Finally, it needs to be emphasized that imaging provides information only about one of the two sarcopenia dimensions. As discussed earlier, changes in muscle function and quantity do not necessarily follow similar trajectories with aging (37). Therefore, interventions able to increase lean mass may not necessarily produce parallel gains in strength and vice versa (38). To overcome this issue and include the two components of sarcopenia in the same variable, it has been proposed to compute an index of skeletal muscle quality derived from the ratio between strength and mass (15, 39, 40).

One of the most recently developed techniques which might find larger application in the near future for the evaluation of sarcopenia is the electrical impedance myography (EIM) (41). This is a noninvasive, painless approach based on the surface application and measurement of a high-frequency, low-intensity electrical current applied to specific muscles. EIM detects changes in the conductivity and permittivity of skeletal muscle caused by alterations in muscle composition and structure. EIM is repeatable and sensitive to skeletal muscle changes in patients with amyotrophic lateral sclerosis (42). Moreover, its changes over time may also have clinical relevance as they are predictive of survival in animal models of amyotrophic lateral sclerosis (43). Finally, it is also noteworthy that the EIM phase shows a consistent inverse relationship with age (44).

An alternative method to measure skeletal muscle size is by ultrasonography. This technique has shown to be a valid (versus MRI-based measurements) and highly reliable way for assessing cross-sectional areas of large individual human muscles (45). It is particularly useful in mobility-impaired subjects who cannot easily be transported to scanners such as CT or MRI machines.

Also remarkable is the development of mass isotopomer distribution analysis based on the evaluation of protein and proteome synthesis rate obtained by heavy water labeling (46, 47). Although this technique can still be considered suitable mainly for research settings, its flexibility and the large amount of information it provides about a wide spectrum of proteins make it extremely promising.

Other techniques are also available to detect sarcopenia, but their limited validation, low accuracy, and difficult large-scale implementation discourage their use. For example, bioeletrical impedance analysis (BIA) is a popular, very simple and low-cost technique, but its results are far from being accurate. The BIA technique is based on the notion that tissues rich in water and electrolytes are less resistant to the electrical passage than adipose tissue. The BIA is therefore based on a single body resistance parameter (not a direct measure of skeletal muscle), and its results can be easily altered by fluid retention and health status in general. For these reasons, a recent consensus paper by the Society of Sarcopenia, Cachexia and Wasting Disorders has discouraged the use of BIA for the assessment of sarcopenia (9).

Definition of critical thresholds

There is still resistance to accept sarcopenia as a clinical condition despite its well-established relationship with major health-related negative events (in particular, mobility and physical disability) (8). This issue might (at least partly) be explained by the current lack of clinically relevant thresholds that distinguish normal from abnormal values of skeletal muscle mass.

Several approaches can be adopted to identify critical cut-points. A paradigmatic example potentially lending support to the operative definition of sarcopenia might be provided by the approach previously adopted to identify osteoporosis on the basis of bone mineral density. In fact, approaches that have been developed for bone and osteoporosis may serve well for skeletal muscle and sarcopenia. The clinical definition of a specific condition (which will consequently lead to the indication for treatment) might be based on:

1)    A parallel clinical diagnosis. For osteoporosis, diagnosis can be obtained by evaluating the presence of vertebral fractures or deformities at the X-ray examination. Vertebral fractures indicate decreased bone strength, regardless of bone mineral density. It is well-established that patients with vertebral fractures present an increased risk of new events, and therefore require treatment. This approach is legitimate and may well work, but may find some limitations when applied in primary prevention.

2)    A biological assessment. Given its well-established association with fracture risk, bone mineral density may represent the key parameter on which to rely to determine the presence or absence of osteoporosis. However, bone mineral density (like any other biological marker) exists as a continuous variable, does not present a clear threshold, and is parallel to gradients of risk. Although necessary to provide clinical relevance to biological markers, any categorization will lead to a loss of information and will inevitably introduce an “arbitrary” decision. For the definition of osteoporosis, the cut-off defining the disease was arbitrarily set by a committee which judged the -2.5 standard deviations at the T-score as an adequate match between risk and prevalence. One major problem with the bone definition that should not be repeated for sarcopenia is the inclusion of osteopenia. Osteopenia (defined by a bone mineral density T-score ranging between -1 and 2.5 SDs) encompasses about 50% of the female healthy population, and has led to confusion and concerns among policy-makers regarding the validity of a construct that cannot really be considered abnormal. An approach consistent with this model has also been adopted in the definition of other clinical conditions such as anemia (48).

3)    The risk of adverse clinical outcomes. The indication to treatment of a specific condition (e.g., osteoporosis) might be based on the evaluation of risk of events (i.e., fractures) resulting from the assessment of multiple factors (which may even not include bone mineral density) (49). This approach will not be exclusively based on the single evaluation of a (potentially inaccurate and/or arguable) biomarker, but on a more comprehensive screening and on cost-effectiveness analyses (e.g., treat if the 10-year risk is exceeding a critical threshold). With this rationale, the FRAX (50) and QFractureScores(51) algorithms were recently developed to guide osteoporosis treatment.

In summary, the presence of sarcopenia might be determined by 1) relying on a clinical diagnosis closely related to skeletal muscle decline (e.g., mobility disability) after exclusion of secondary causes, 2) a representative scientific committee identifying a critical threshold for a biological parameter directly representative of skeletal muscle health, and/or 3) developing a risk index to guide treatment.

Biological markers of sarcopenia

Given the syndromic nature of sarcopenia, intervention strategies aimed at preventing/treating its process might need to target multiple risk factors. In this context, several biological markers have been shown to be associated with skeletal muscle mass, strength and function, thus representing potential markers for the effect of the studied interventions. Such a list is quite long, and each biomarker identifies a specific mechanism contributing the age-related skeletal muscle decline, although they are not specific to muscle and many are likely to turn out to be only weakly associated with clinically relevant outcomes. The most common markers are inflammatory biomarkers [e.g., C-reactive protein (52, 53), interleukin-6 (52-54), and tumor necrosis factor-α (52, 54)], clinical parameters [e.g., hemoglobin (55, 56), serum albumin (57, 58), urinary creatinine (59)], hormones [e.g., dehydroepiandrosterone sulfate (60), testosterone (61), insulin-like growth factor-1 (62), and vitamin D (63-65)], products of oxidative damage [e.g., advanced glycation end-products (66), protein carbonyls (67, 68), and oxidized low-density lipoproteins (69)], or antioxidants [e.g., carotenoids (70, 71), and α-tocopherol (70)].

Other promising biomarkers have been identified in the last years and may represent useful parameters to more directly explore sarcopenia because they are closely related to skeletal muscle changes. For example, plasma concentrations of procollagen type III N-terminal peptide (P3NP) represent an interesting marker of skeletal muscle remodeling (72, 73). P3NP is a fragment released by the cleavage of procollagen type III to generate collagen III (a protein produced in soft connective tissues, skin, and muscle). Preliminary studies have also suggested an interesting role played by biomarkers specifically linked to the neuromuscular junction in evaluating skeletal muscle modifications (74, 75).

Clinical outcome measures of sarcopenia

Ultimately, the goal of clinical trials for sarcopenia treatments will require the evaluation of clinical benefit. In fact, clinical measures can also be considered as biomarkers as they reflect the impact of the pathological process of sarcopenia on the patient’s health. The assessment of measures of muscle strength (e.g., hand grip), muscle power (e.g., leg extension power), and physical performance [e.g., Short Physical Performance Battery (4) and gait speed tests] comprise important indices of the individual’s physical function. In addition, functional outcome measures will need to be developed in order to help understand the impact of any treatment-related quantitative gains in performance on the person’s daily life.

Recommendations

diflucan for bv acquire diflucan

Adoption of comprehensive operative definitions

The lack of a unique operative definition of sarcopenia and the numerous methodological issues could potentially hinder efforts to study sarcopenia and to develop effective treatments. Such difficulties should not hamper the process of exploring this syndrome which severely affects the health status of millions of older persons. The current ambiguities can be easily overcome by adopting flexible and comprehensive approaches in the design of studies, for example by avoiding reliance on a single parameter or technique to evaluate age-related skeletal muscle decline. The adoption of a variety of assessment approaches in combination is agreeable. Although this might lead to the risk of conflicting results (and increase the need of resources), it will serve to 1) capture different domains of the sarcopenia syndrome, 2) provide useful insights about the pathophysiological process underlying this phenomenon, and 3) facilitate the development and use of the findings in future and more definitive studies. In this context, it is noteworthy the lack of studies simultaneously testing different techniques measuring skeletal muscle (e.g., MRI, CT, DXA, etc) in relationship with clinically meaningful outcomes. Such studies might greatly help in the standardization of instruments and in the adoption of an univocal direction in the study of sarcopenia.

MRI and CT scan to be equally considered as “gold standard” imaging techniques

It is now clear that to be adequately assessed, the sarcopenia phenomenon cannot merely rely on the evaluation of the contractile part of skeletal muscle. The close relationship between lean mass and adipose tissue in determining age-related decline of skeletal muscle is evident (38, 76, 77). Therefore, techniques allowing the simultaneous evaluation of fat and muscle should be preferred. DXA, CT and MRI are the most important assessment instruments. CT and MRI should be considered the “gold standard” techniques. The balance of pros and cons for both CT and MRI does not allow a clear indication on which of the two should be preferred. Resources, instrument availability, and need of details will represent the factors guiding the investigator’s preference for one over the other. On the other hand, DXA should not be discarded, and still represents the instrument more likely to promote the “clinical relevance” of sarcopenia. For its characteristics, DXA may be an extremely interesting methodology to be used for preliminary screening. Moreover, its use in combination with either CT or MRI will help drive the research in the field towards more clinical aspects. While imaging and other biomarkers will be valuable tools for initial proof of concept studies, assessment tools for evaluating the effect of treatments on outcomes reflecting clinical benefit will be required to support eventual pivotal studies.

Adequate length of study

To evaluate the efficacy of a specific intervention on sarcopenia, it is necessary that the follow-up will be sufficiently long to allow the hypothesized modifications of biomarkers. Surely, not all biomarkers will be similarly influenced by the intervention. Such variations will depend on multiple factors, including the population characteristics, the type and strength of the tested intervention, and the sensibility of the biomarker to changes. However, six months have been generally indicated as the minimum timeframe to expect changes in imaging parameters.

Sarcopenia is a “work in progress”

The study of sarcopenia is still in its infancy, but we have clearly acknowledged the great potential benefits arising from the understanding and treatment of this condition at both person and population levels. Taking together the uncertainties of exploring a novel field with the exponential acceleration of scientific progress, it is currently difficult to provide long-lasting statements, recommendations, and guidelines. It is likely that what seems reasonable today will be confounded by several studies in the near future. For this reason, extreme caution is needed to avoid jeopardizing the future development of research in the field. It is important to consider the study of sarcopenia as a “work in progress”, always amenable to changes and redirections. After all, the first Phase II trials in this syndrome are just starting, and this is the appropriate time to raise doubts and pose questions. With time, a stronger foundation for sarcopenia research will be developed that will ultimately lead to larger scale and more definitive studies. In this context, it is critical that an ongoing dialogue be initiated and sustained amongst investigators with an interest in age-dependent decline of muscle.

Acknowledgements: Dr. Fielding’s contribution is based upon work supported by the US Department of Agriculture, under agreement No. 58-1950-7-707.

Members of the International Working Group on Sarcopenia: Gabor Abellan Van Kan, France; Sandrine Andrieu, France; Stefan D. Anker, Germany; Patricia Anthony, Switzerland; Christian Asbrand, Germany; Mylène Aubertin-Leheudre, Canada; Sebastien Barbart-Artigas, Canada; Olivier Benichou, France; Cécile Bonhomme, France; Pascale Borensztein, France; Denis Breuillé, Switzerland; Sergio Castro Henriquez, Chile; Jesse M. Cedarbaum, USA; Matteo Cesari, France; Patricia Chatelain, France; Wm. Cameron Chumlea, USA; Richard V. Clark, USA; Capucine De Meynard, France; William J. Evans, USA; Gary Fanjiang, USA; Luigi Ferrucci, USA; Roger A. Fielding, USA; Philippe Garnier, France; Sophie Gillette-Guyonnet, France; Bret Goodpaster, USA; Marie-Françoise Gros, France; Luis Miguel F. Gutierrez Robledo, Mexico; Marc Hellerstein, USA; Kelly Krohn, USA; Maria Isaac, United Kingdom; Didier Laurent, Switzerland; Menghua Luo, USA; Hélène Matheix-Fortunet, France; Inge Mohede, The Netherlands; John E. Morley, USA; Christine M’Rini, France; Ramon Navarro, France; Bruno Oesch, Switzerland; Reinhard Ommerborn, Germany; Marco Pahor, USA; Patrick Ritz, France; Yves Rolland, France; Daniel Rooks, USA; Ronnen Roubenoff, USA; Fariba Roughead, Switzerland; Seward Rutkove, USA; Cornel C. Sieber, Germany; Michèle Storrs-Malibat, France; Stephanie Studenski, USA; Yannis Tsouderos, France; Bruno Vellas, France; Sjors Verlaan, The Netherlands; Stephan Von Haehling, Germany; J. Willem Vrijbloed, Switzerland; Sander Wijers, The Netherlands; Mauro Zamboni, Italy.

Conflicts of interest: MC has received consultancy fees from Sanofi-Aventis and Pfizer; RAF is consultant with Merck, Eli Lilly, Cytokinetics, DMI, Kraft Foods, and Unilever; MH is stockholder, chairmen of scientific advisory board and consultant for KineMed, Inc.; SA is consultant with Brahms, Vifor, Professional Dietetics, PsiOxus, Takeda, receives research support from Vifor, BG Medicine, and has received fees for speaking at meetings from Brahms, Vifor; SR has equity in and receives consulting income from Convergence Medical Devices, Inc; WV is employee and shareholder of Neurotune AG; YR receives support from Lactalis, Lundbeck, Lilly, Nutricia, Servier, Cheisi, Ipsen, Novartis; JMC is employee and shareholder of Cytokinetics, Inc; MZ has received a fee from Abbot for a conference; DL and RR are employed by Novartis; WJE is employed by GlaxoSmithKline; JEM is consultant and stokeholder of Mattern Pharmaceuticals and consultant for Sanofi-Aventis; BV is consultant and member of Advisory Board with Novartis, Servier, Nestlè. MP, BG, GAVK, MI, CMR, MAL, CCS have no conflict of interest to declare.

Disclaimer: Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the Authors and do not necessarily reflect the position of the supporting organizations or agencies.

 

References

1.    Rosenberg IH. Sarcopenia: origins and clinical relevance. J Nutr. 1997;127:990S-991S.
2.    Frontera WR, Hughes VA, Lutz KJ, Evans WJ. A cross-sectional study of muscle strength and mass in 45- to 78-yr-old men and women. J Appl Physiol. 1991;71:644-650.
3.    Baumgartner RN, Koehler KM, Gallagher D, Romero L, Heymsfield SB, Ross RR et al. Epidemiology of sarcopenia among the elderly in New Mexico. Am J Epidemiol. 1998;147:755-763.
4.    Guralnik JM, Ferrucci L, Simonsick EM, Salive ME, Wallace RB. Lower-extremity function in persons over the age of 70 years as a predictor of subsequent disability. N Engl J Med. 1995;332:556-561.
5.    Chumlea WC, Cesari M, Evans WJ, Ferrucci L, Fielding RA, Pahor M et al. Sarcopenia: designing phase IIB trials. J Nutr Health Aging. 2011;15:450-455.
6.    Pahor M, Cesari M. Designing Phase II B Trials in Sarcopenia: The Best target Population. J Nutr Health Aging. 2011;15:725-730.
7.    Evans WJ. What is sarcopenia? J Gerontol A Biol Sci Med Sci. 1995;50 Spec No:5-8.
8.    Fielding RA, Vellas B, Evans WJ, Bhasin S, Morley JE, Newman AB et al. Sarcopenia: an undiagnosed condition in older adults. Current consensus definition: prevalence, etiology, and consequences. International working group on sarcopenia. J Am Med Dir Assoc. 2011;12:249-256.
9.    Morley JE, Abbatecola AM, Argiles JM, Baracos V, Bauer J, Bhasin S et al. Sarcopenia with limited mobility: an international consensus. J Am Med Dir Assoc. 2011;12:403-409.
10.    Cruz-Jentoft AJ, Baeyens JP, Bauer JM, Boirie Y, Cederholm T, Landi F et al. Sarcopenia: European consensus on definition and diagnosis: Report of the European Working Group on Sarcopenia in Older People. Age Ageing. 2010;39:412-423.
11.    Muscaritoli M, Anker S, Argilés J, Aversa Z, Bauer J, Biolo G et al. Consensus definition of sarcopenia, cachexia and pre-cachexia: Joint document elaborated by Special Interest Groups (SIG) «cachexia-anorexia in chronic wasting diseases» and «nutrition in geriatrics». Clinical nutrition (Edinburgh, Scotland). 2010;
12.    Group BDW. Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin Pharmacol Ther. 2001;69:89-95.
13.    Clark BC, Manini TM. Sarcopenia =/= dynapenia. J Gerontol A Biol Sci Med Sci. 2008;63:829-834.
14.    Frontera WR, Reid KF, Phillips EM, Krivickas LS, Hughes VA, Roubenoff R et al. Muscle fiber size and function in elderly humans: a longitudinal study. J Appl Physiol. 2008;105:637-642.
15.    Goodpaster BH, Park SW, Harris TB, Kritchevsky SB, Nevitt M, Schwartz AV et al. The loss of skeletal muscle strength, mass, and quality in older adults: the health, aging and body composition study. J Gerontol A Biol Sci Med Sci. 2006;61:1059-1064.
16.    Hughes VA, Frontera WR, Wood M, Evans WJ, Dallal GE, Roubenoff R et al. Longitudinal muscle strength changes in older adults: influence of muscle mass, physical activity, and health. J Gerontol A Biol Sci Med Sci. 2001;56:B209-17.
17.    Morse CI, Thom JM, Davis MG, Fox KR, Birch KM, Narici MV. Reduced plantarflexor specific torque in the elderly is associated with a lower activation capacity. Eur J Appl Physiol. 2004;92:219-226.
18.    Goodpaster BH, Carlson CL, Visser M, Kelley DE, Scherzinger A, Harris TB et al. Attenuation of skeletal muscle and strength in the elderly: The Health ABC Study. J Appl Physiol. 2001;90:2157-2165.
19.    Klass M, Baudry S, Duchateau J. Age-related decline in rate of torque development is accompanied by lower maximal motor unit discharge frequency during fast contractions. J Appl Physiol. 2008;104:739-746.
20.    Laroche DP, Knight CA, Dickie JL, Lussier M, Roy SJ. Explosive force and fractionated reaction time in elderly low- and high-active women. Med Sci Sports Exerc. 2007;39:1659-1665.
21.    Wang X, Miller GD, Messier SP, Nicklas BJ. Knee strength maintained despite loss of lean body mass during weight loss in older obese adults with knee osteoarthritis. J Gerontol A Biol Sci Med Sci. 2007;62:866-871.
22.    Fiatarone MA, O’Neill EF, Ryan ND, Clements KM, Solares GR, Nelson ME et al. Exercise training and nutritional supplementation for physical frailty in very elderly people. N Engl J Med. 1994;330:1769-1775.
23.    Reid KF, Callahan DM, Carabello RJ, Phillips EM, Frontera WR, Fielding RA. Lower extremity power training in elderly subjects with mobility limitations: a randomized controlled trial. Aging Clin Exp Res. 2008;20:337-343.
24.    Brooks SV, Faulkner JA. Skeletal muscle weakness in old age: underlying mechanisms. Med Sci Sports Exerc. 1994;26:432-439.
25.    Borkan GA, Hults DE, Gerzof SG, Robbins AH, Silbert CK. Age changes in body composition revealed by computed tomography. J Gerontol. 1983;38:673-677.
26.    Aagaard P, Suetta C, Caserotti P, Magnusson SP, Kjaer M. Role of the nervous system in sarcopenia and muscle atrophy with aging: strength training as a countermeasure. Scand J Med Sci Sports. 2010;20:49-64.
27.    Lexell J. Evidence for nervous system degeneration with advancing age. J Nutr. 1997;127:1011S-1013S.
28.    Kamen G, Sison SV, Du CC, Patten C. Motor unit discharge behavior in older adults during maximal-effort contractions. J Appl Physiol. 1995;79:1908-1913.
29.    Clark DJ, Patten C, Reid KF, Carabello RJ, Phillips EM, Fielding RA. Impaired voluntary neuromuscular activation limits muscle power in mobility-limited older adults. J Gerontol A Biol Sci Med Sci. 2010;65:495-502.
30.    Clark DJ, Patten C, Reid KF, Carabello RJ, Phillips EM, Fielding RA. Muscle performance and physical function are associated with voluntary rate of neuromuscular activation in older adults. J Gerontol A Biol Sci Med Sci. 2011;66:115-121.
31.    Hakkinen K, Newton RU, Gordon SE, McCormick M, Volek JS, Nindl BC et al. Changes in muscle morphology, electromyographic activity, and force production characteristics during progressive strength training in young and older men. J Gerontol A Biol Sci Med Sci. 1998;53:B415-23.
32.    Larsson L, Grimby G, Karlsson J. Muscle strength and speed of movement in relation to age and muscle morphology. J Appl Physiol. 1979;46:451-456.
33.    Martin JC, Farrar RP, Wagner BM, Spirduso WW. Maximal power across the lifespan. J Gerontol A Biol Sci Med Sci. 2000;55:M311-6.
34.    Frontera WR, Suh D, Krivickas LS, Hughes VA, Goldstein R, Roubenoff R. Skeletal muscle fiber quality in older men and women. Am J Physiol Cell Physiol. 2000;279:C611-8.
35.    Pahor M, Manini T, Cesari M. Sarcopenia: clinical evaluation, biological markers and other evaluation tools. J Nutr Health Aging. 2009;13:724-728.
36.    Newman AB, Kupelian V, Visser M, Simonsick E, Goodpaster B, Nevitt M et al. Sarcopenia: alternative definitions and associations with lower extremity function. J Am Geriatr Soc. 2003;51:1602-1609.
37.    Lauretani F, Russo CR, Bandinelli S, Bartali B, Cavazzini C, Di Iorio A et al. Age-associated changes in skeletal muscles and their effect on mobility: an operational diagnosis of sarcopenia. J Appl Physiol. 2003;95:1851-1860.
38.    Delmonico MJ, Harris TB, Visser M, Park SW, Conroy MB, Velasquez-Mieyer P et al. Longitudinal study of muscle strength, quality, and adipose tissue infiltration. Am J Clin Nutr. 2009;90:1579-1585.
39.    Hairi NN, Cumming RG, Naganathan V, Handelsman DJ, Le Couteur DG, Creasey H et al. Loss of muscle strength, mass (sarcopenia), and quality (specific force) and its relationship with functional limitation and physical disability: the Concord Health and Ageing in Men Project. J Am Geriatr Soc. 2010;58:2055-2062.
40.    Newman AB, Haggerty CL, Goodpaster B, Harris T, Kritchevsky S, Nevitt M et al. Strength and muscle quality in a well-functioning cohort of older adults: the Health, Aging and Body Composition Study. J Am Geriatr Soc. 2003;51:323-330.
41.    Rutkove SB. Electrical impedance myography: Background, current state, and future directions. Muscle Nerve. 2009;40:936-946.
42.    Rutkove S. Electrical impedance myography as a biomarker for ALS. Lancet Neurol. 2009;8:226; author reply 227.
43.    Wang LL, Spieker AJ, Li J, Rutkove SB. Electrical impedance myography for monitoring motor neuron loss in the SOD1 G93A amyotrophic lateral sclerosis rat.Clin Neurophysiol 2011;122:2505-2511.
44.    Aaron R, Esper GJ, Shiffman CA, Bradonjic K, Lee KS, Rutkove SB. Effects of age on muscle as measured by electrical impedance myography. Physiol Meas. 2006;27:953-959.
45.    Reeves ND, Maganaris CN, Narici MV. Ultrasonographic assessment of human skeletal muscle size. Eur J Appl Physiol. 2004;91:116-118.
46.    Busch R, Kim YK, Neese RA, Schade-Serin V, Collins M, Awada M et al. Measurement of protein turnover rates by heavy water labeling of nonessential amino acids. Biochim Biophys Acta. 2006;1760:730-744.
47.    Price JC, Holmes WE, Li KW, Floreani NA, Neese RA, Turner SM et al. Measurement of human plasma proteome dynamics with (2)H(2)O and liquid chromatography tandem mass spectrometry. Anal Biochem. 2012;420:73-83.
48.    Wintrobe MM. Blood of normal men and women: erythrocyte counts, hemoglobin and volume of packed red cells of two hundred and twenty-nine individuals. Bull Johns Hopkins Hosp. 1933;53:118-130.
49.    Kanis JA, McCloskey EV, Johansson H, Strom O, Borgstrom F, Oden A. How to decide who to treat. Best Pract Res Clin Rheumatol. 2009;23:711-726.
50.    Kanis JA, Johnell O, Oden A, Johansson H, McCloskey E. FRAX and the assessment of fracture probability in men and women from the UK. Osteoporos Int. 2008;19:385-397.
51.    Hippisley-Cox J, Coupland C. Predicting risk of osteoporotic fracture in men and women in England and Wales: prospective derivation and validation of QFractureScores. BMJ. 2009;339:b4229.
52.    Cesari M, Penninx BW, Pahor M, Lauretani F, Corsi AM, Rhys Williams G et al. Inflammatory markers and physical performance in older persons: the InCHIANTI study. J Gerontol A Biol Sci Med Sci. 2004;59:242-248.
53.    Schaap LA, Pluijm SM, Deeg DJ, Visser M. Inflammatory markers and loss of muscle mass (sarcopenia) and strength. Am J Med. 2006;119:526.e9-526.17.
54.    Visser M, Pahor M, Taaffe DR, Goodpaster BH, Simonsick EM, Newman AB et al. Relationship of interleukin-6 and tumor necrosis factor-alpha with muscle mass and muscle strength in elderly men and women: the Health ABC Study. J Gerontol A Biol Sci Med Sci. 2002;57:M326-32.
55.    Cesari M, Penninx BW, Lauretani F, Russo CR, Carter C, Bandinelli S et al. Hemoglobin levels and skeletal muscle: results from the InCHIANTI study. J Gerontol A Biol Sci Med Sci. 2004;59:249-254.
56.    Penninx BW, Pahor M, Cesari M, Corsi AM, Woodman RC, Bandinelli S et al. Anemia is associated with disability and decreased physical performance and muscle strength in the elderly. J Am Geriatr Soc. 2004;52:719-724.
57.    Baumgartner RN, Koehler KM, Romero L, Garry PJ. Serum albumin is associated with skeletal muscle in elderly men and women. Am J Clin Nutr. 1996;64:552-558.
58.    Visser M, Kritchevsky SB, Newman AB, Goodpaster BH, Tylavsky FA, Nevitt MC et al. Lower serum albumin concentration and change in muscle mass: the Health, Aging and Body Composition Study. Am J Clin Nutr. 2005;82:531-537.
59.    Proctor DN, O’Brien PC, Atkinson EJ, Nair KS. Comparison of techniques to estimate total body skeletal muscle mass in people of different age groups. Am J Physiol. 1999;277:E489-95.
60.    Valenti G, Denti L, Maggio M, Ceda G, Volpato S, Bandinelli S et al. Effect of DHEAS on skeletal muscle over the life span: the InCHIANTI study. J Gerontol A Biol Sci Med Sci. 2004;59:466-472.
61.    Morley JE, Baumgartner RN, Roubenoff R, Mayer J, Nair KS. Sarcopenia. J Lab Clin Med. 2001;137:231-243.
62.    Perrini S, Laviola L, Carreira MC, Cignarelli A, Natalicchio A, Giorgino F. The GH/IGF1 axis and signaling pathways in the muscle and bone: mechanisms underlying age-related skeletal muscle wasting and osteoporosis. J Endocrinol. 2010;205:201-210.
63.    Ceglia L. Vitamin D and skeletal muscle tissue and function. Mol Aspects Med. 2008;29:407-414.
64.    Mastaglia SR, Seijo M, Muzio D, Somoza J, Nunez M, Oliveri B. Effect of vitamin D nutritional status on muscle function and strength in healthy women aged over sixty-five years. J Nutr Health Aging. 2011;15:349-354.
65.    Cesari M, Incalzi RA, Zamboni V, Pahor M. Vitamin D hormone: A multitude of actions potentially influencing the physical function decline in older persons. Geriatr Gerontol Int. 2011;11:133-142.
66.    Dalal M, Ferrucci L, Sun K, Beck J, Fried LP, Semba RD. Elevated serum advanced glycation end products and poor grip strength in older community-dwelling women. J Gerontol A Biol Sci Med Sci. 2009;64:132-137.
67.    Howard C, Ferrucci L, Sun K, Fried LP, Walston J, Varadhan R et al. Oxidative protein damage is associated with poor grip strength among older women living in the community. J Appl Physiol. 2007;103:17-20.
68.    Semba RD, Ferrucci L, Sun K, Walston J, Varadhan R, Guralnik JM et al. Oxidative stress and severe walking disability among older women. Am J Med. 2007;120:1084-1089.
69.    Cesari M, Kritchevsky SB, Nicklas BJ, Penninx BW, Holvoet P, Koh-Banerjee P et al. Lipoprotein peroxidation and mobility limitation: results from the Health, Aging, and Body Composition Study. Arch Intern Med. 2005;165:2148-2154.
70.    Semba RD, Blaum C, Guralnik JM, Moncrief DT, Ricks MO, Fried LP. Carotenoid and vitamin E status are associated with indicators of sarcopenia among older women living in the community. Aging Clin Exp Res. 2003;15:482-487.
71.    Semba RD, Lauretani F, Ferrucci L. Carotenoids as protection against sarcopenia in older adults. Arch Biochem Biophys. 2007;458:141-145.
72.    Bhasin S, He EJ, Kawakubo M, Schroeder ET, Yarasheski K, Opiteck GJ et al. N-terminal propeptide of type III procollagen as a biomarker of anabolic response to recombinant human GH and testosterone. J Clin Endocrinol Metab. 2009;94:4224-4233.
73.    Chen F, Lam R, Shaywitz D, Hendrickson RC, Opiteck GJ, Wishengrad D et al. Evaluation of early biomarkers of muscle anabolic response to testosterone. J Cachex Sarcopenia Muscle. 2011;2:45-56.
74.    Bolliger MF, Zurlinden A, Luscher D, Butikofer L, Shakhova O, Francolini M et al. Specific proteolytic cleavage of agrin regulates maturation of the neuromuscular junction. J Cell Sci. 2010;123:3944-3955.
75.    Butikofer L, Zurlinden A, Bolliger MF, Kunz B, Sonderegger P. Destabilization of the neuromuscular junction by proteolytic cleavage of agrin results in precocious sarcopenia. FASEB J. 2011;25:4378-4393.
76.    Rolland Y, Lauwers-Cances V, Cristini C, Abellan van Kan G, Janssen I, Morley JE et al. Difficulties with physical function associated with obesity, sarcopenia, and sarcopenic-obesity in community-dwelling elderly women: the EPIDOS (EPIDemiologie de l’OSteoporose) Study. Am J Clin Nutr. 2009;89:1895-1900.
77.    Zamboni M, Mazzali G, Fantin F, Rossi A, Di Francesco V. Sarcopenic obesity: a new category of obesity in the elderly. Nutr Metab Cardiovasc Dis. 2008;18:388-395.

FRAILTY AND NUTRITION: WHAT WE HAVE LEARNED FROM RESEARCH AND CLINICAL PRACTICE ON THE MINI NUTRITIONAL ASSESSMENT

 

Y. GUIGOZ

 

Corresponding auhtor: Chemin du Raidillon, CH-1066 Epalinges, Switzerland, Email: yves.guigoz@gmail.com

J Frailty Aging 2012;1(2):52-55
Published online February 14, 2012, http://dx.doi.org/10.14283/jfa.2012.10


Abstract

In this short communication, we review the relationship between frailty and malnutrition risk in the elderly. Frailty is a term used for elderly at increased risk of adverse outcomes, including disability, falls, hospitalization, need for long-term care, and mortality. The Mini Nutritional Assessment (MNA) was designed and validated in a series of studies to assess nutritional status of elderly, as integral part of the comprehensive geriatric assessment, with a 2-steps screening process; when the MNA-SF classify a person at risk, the full MNA should be completed. The MNA and MNA-SF are sensitive, specific, and accurate in identifying nutrition risk. Increased risk of malnutrition, a common condition in the elderly, is closely associated with many potential contributors of frailty. The maintenance of optimal physical and cognitive performances depends on the early screening of critical conditions to develop preventive targeted interventions; the MNA supports such preventive action.

Key words: Frailty, nutrition, mini nutritional assessment, disability, screening.


 

Frailty is a dynamic state affecting an individual who experiences losses in one or more domains of human functioning (physical, psychological, social). It is caused by the influence of a range of variables and increases the risk of adverse outcomes, including disability, falls, hospitalization, need for long-term care, and mortality (1). The frailty phenotype is determined by the presence of at least three of the following five components: weight loss, exhaustion, weakness, slow walking speed, and/or sedentariness (2). Weakness is often the first warning sign, whereas weight loss and exhaustion are more likely to usually characterize the onset of frailty (3). Physical impairment is a major contributor to frailty in community-dwelling older persons (4), and gait speed reduction has shown to identify subjects at increased risk of adverse outcomes (5).

The Mini Nutritional Assessment (MNA) was designed and validated in a series of studies to assess nutritional status of older persons (i.e., which geriatric patients are at risk for malnutrition?) (6-9), to become integral part of the comprehensive geriatric assessment (CGA) (10, 11). The MNA is composed of 18 items (questions) grouped in four sections, all together providing a multidimensional nutritional assessment of the older subject: anthropometric assessment (weight, height, arm and calf circumferences, and weight loss), general assessment (six questions related to lifestyle, medication, and mobility), dietary assessment (eight questions related to number of meals, food and fluid intake, and autonomy of feeding), and subjective assessment (self-perception of health and nutrition) (6, 7). Six questions, showing the strongest correlations with the results of the MNA full version, are used to constitute the MNA-Short Form (MNA-SF). When the MNA-SF classifies a person as at risk of malnutrition, the subject should undergo the complete MNA assessment (8). Recently, the MNA-SF has shown to be correlated with calf circumference (particularly useful when body mass index, BMI, is not available/doable) and to improve the detection of malnutrition (9). Moreover, it has been independently validated in different healthcare settings (12).

Frailty and Malnutrition

Interestingly, the MNA includes several aspects of frailty such as low BMI, weight loss, low food intake, strength/muscle mass (mobility and calf circumference), and neuropsychological problems (depression and cognitive function). The prevalence of malnutrition is related to the level of disability, and gradually increases from community-dwelling older patients to hospitalized and institutionalized elders (11, 13-15). It is noteworthy that older persons at risk of malnutrition are often identified as being also frail (16-18), and this risk is closely linked with the healthcare needs (19, 20). Malnourished frail subjects are at higher risk of adverse clinical outcomes, independently of the healthcare setting (21-26). Moreover, the MNA score is well correlated with cognitive decline (27-30) and depressive symptoms (31-34). Thus, the increased risk of malnutrition, a common condition in the elderly, is closely associated with many potential contributors of frailty (10, 15, 35-37).

The risk of malnutrition is associated with lower food intakes (38, 39), low weight, or weight loss (35, 40, 41). In community-dwelling older persons, it is often due to a reduction of food intake because of a loss of appetite (42, 43). Furthermore, weight loss has been indicated as a marker of cognitive decline (44, 45), which is also significantly associated with frailty (35, 46, 47).

The presence of chronic diseases in the elderly characterizes a chronic inflammatory state, which affects lean body mass, protein metabolism, and immune response. Malnutrition and the risk for malnutrition determine lower muscle mass/strength and worse functional capacity (34, 48-50).

The assessment of nutritional status by the evaluation of serum proteins is potentially misleading because protein concentrations are affected by inflammation (51-54). For example, hypoalbuminemia is associated with increased inflammatory biomarkers (often due to concurrent chronic diseases) (55, 56). Interestingly, it has been suggested that the risk of malnutrition assessed by the MNA may be detected before albumin concentrations decline, in particular through the evaluation of decreased food intakes (7, 57). Moreover, the relationships between malnutrition with immunological parameters (57-59) and inflammatory markers also underlie the complex scenario of cachexia (52, 60-62). It is possible that the link existing between chronic inflammation and malnutrition may become the specific target for interventions in the next future (63, 64).

Mini Nutritional Assessment and Comprehensive Geriatric Assessment

Under specific conditions, (the risk of) malnutrition is not an isolated problem, but part of a polymorbidity (52). This implies that MNA should be regarded as a component of the CGA in which it is well integrated (65-67). This is particularly evident in cancer patients (37, 68-70), probably because of the relevance of the frailty condition during chemotherapy, but can also be easily applied to other conditions (71-73).

The new version of the MNA-SF should be more increasingly used in the evaluation of older persons, especially among institutionalized patients. The maintenance of optimal physical and cognitive performances depends on the early screening of critical conditions to develop preventive targeted interventions. The MNA supports such preventive action making possible the early identification of subjects at risk of malnutrition before relevant weight changes occur (7, 31, 74).

In community-dwelling older persons aged 85 years and older, low comorbidity, low risk of malnutrition (assessed by the MNA), and low risk of falls were associated with successful aging (75). In the New Mexico Aging Process Study, the mean MNA score of elders in good or excellent health status was 27 compared to those frail individuals reporting a mean MNA score of 25 (74), well above the malnutrition risk score of ≤23.5. These data support the important role played by adequate nutrition at advanced age, and indicate the need of always to consider its evaluation in the clinical and research setting.

In conclusion, the standardized and global use of the MNA is in line with the acknowledgment of adequate nutrition as a crucial component of the wellbeing in geriatrics. The MNA is a well-validated and broadly used instrument. To date, the risk of malnutrition is still too poorly recognized, although widely indicated as a key factor to detect. Further research is needed to improve and optimize interventions, specifically adapted to special age-related conditions (such as Alzheimer’s disease), which are particularly difficult to explore and burdening for public health.

References

1.    Fried LP, Ferrucci L, Darer J, Williamson JD, Anderson G. Untangling the Concepts of Disability, Frailty, and Comorbidity: Implications for Improved Targeting and Care. J Gerontol A Biol Sci Med Sci 2004;59(3):M255-M63.
2.    Fried LP, Tangen CM, Walston J, et al. Frailty in Older Adults. J Gerontol A Biol Sci Med Sci 2001;56(3):M146-M57. doi: 10.1093/gerona/56.3.M146.
3.    Xue QL, Bandeen-Roche K, Varadhan R, Zhou J, Fried LP. Initial Manifestations of Frailty Criteria and the Development of Frailty Phenotype in the Women’s Health and Aging Study II. J Gerontol A Biol Sci Med Sci 2008;63(9):984-90.
4.    Brown M, Sinacore DR, Binder EF, Kohrt WM. Physical and Performance Measures for the Identification of Mild to Moderate Frailty. J Gerontol A Biol Sci Med Sci 2000;55(6):M350-M5. doi: 10.1093/gerona/55.6.M350.
5.    Abellan Van Kan G, Rolland Y, Andrieu S, et al. Gait speed at usual pace as a predictor of adverse outcomes in community-dwelling older people an International Academy on Nutrition and Aging (IANA) Task Force. J Nutr Health Aging 2009;13(10):881-9. doi: 10.1007/s12603-009-0246-z.
6.    Guigoz Y, Vellas B, Garry PJ. Assessing the nutritional status of the elderly: The Mini Nutritional Assessment as part of the geriatric evaluation. Nutr Rev 1996;54(1 Pt 2):S59-S65. doi: 10.1111/j.1753-4887.1996.tb03793.x.
7.    Vellas B, Guigoz Y, Garry PJ, et al. The Mini Nutritional Assessment (MNA) and its use in grading the nutritional state of elderly patients. Nutrition 1999;15(2):116-22. doi: 10.1016/S0899-9007(98)00171-3
8.    Rubenstein LZ, Harker JO, Salva A, Guigoz Y, Vellas B. Screening for undernutrition in geriatric practice: developing the short-form mini-nutritional assessment (MNA-SF). J Gerontol A Biol Sci Med Sci 2001;56(6):M366-M72.
9.    Kaiser MJ, Bauer JM, Ramsch C, et al. Validation of the Mini Nutritional Assessment Short-Form (MNA(R)-SF): A Practical Tool for Identification of Nutritional Status. J Nutr Health Aging 2009;13(9):782-8. doi: 10.1007/s12603-009-0214-7.
10.    Vellas B, Villars H, Abellan G, et al. Overview of the MNA–Its history and challenges. J Nutr Health Aging 2006;10(6):456-63; discussion 63-65.
11.    Guigoz Y. The Mini Nutritional Assessment (MNA) review of the literature–What does it tell us? J Nutr Health Aging 2006;10(6):466-85.
12.    Kaiser MJ, Bauer JM, Uter W, et al. Prospective Validation of the Modified Mini Nutritional Assessment Short-Forms in the Community, Nursing Home, and Rehabilitation Setting. J Am Geriatr Soc 2011;59(11):2124-8. doi: 10.1111/j.1532-5415.2011.03659.x.
13.    Kaiser MJ, Bauer JM, Rämsch C, et al. Frequency of Malnutrition in Older Adults: A Multinational Perspective Using the Mini Nutritional Assessment. J Am Geriatr Soc 2010;58(9):1734-8. doi: 10.1111/j.1532-5415.2010.03016.x.
14.    Soini H, Suominen MH, Muurinen S, Strandberg TE, Pitkälä KH. Malnutrition according to the mini nutritional assessment in older adults in different settings. J Am Geriatr Soc 2011;59(4):765-6. doi: 10.1111/j.1532-5415.2011.03339.x.
15.    Cereda E. Mini Nutritional Assessment. Curr Opin Clin Nutr Metab Care 2012;15(1):29-41. doi: 10.1097/MCO.0b013e32834d7647.
16.    Serra-Prat M, Palomera E, Clave P, Puig-Domingo M. Effect of age and frailty on ghrelin and cholecystokinin responses to a meal test. Am J Clin Nutr 2009;89(5):1410-7. doi: 10.3945/ajcn.2008.27076.
17.    Mirarefin M, Sharifi F, Fakhrzadeh H, et al. Predicting the Value of the Mini Nutritional Assessment (MNA) as an Indicator of Functional Ability in Older Iranian Adults (Kahrizak Elderly Study). J Nutr Health Aging 2011;15(3):175-80. doi: 10.1007/s12603-011-0032-6.
18.    Morley JE. Assessment of malnutrition in older persons: A focus on the mini nutritional assessment. J Nutr Health Aging 2011;15(2):87-90. doi: 10.1007/s12603-011-0018-4.
19.    Laniece I, Couturier P, Drame M, et al. Incidence and main factors associated with early unplanned hospital readmission among French medical inpatients aged 75 and over admitted through emergency units. Age Ageing 2008;37(4):416-22. doi: 10.1093/ageing/afn093.
20.    Jürschik P, Torres J, Sola R, Nuin C, Botigue T, Lavedan A. High rates of malnutrition in older adults receiving different levels of health care in lleida, catalonia: an assessment of contributory factors. J Nutr Elder 2010;29(4):410-22. doi: 10.1080/01639366.2010.521043.
21.    Van Nes MC, Herrmann FR, Gold G, Michel JP, Rizzoli R. Does the mini nutritional assessment predict hospitalization outcomes in older people? Age Ageing 2001;30(3):221-6. doi: 10.1093/ageing/30.3.221.
22.    Kagansky N, Berner Y, Koren-Morag N, Perelman L, Knobler H, Levy S. Poor nutritional habits are predictors of poor outcome in very old hospitalized patients. Am J Clin Nutr 2005;82(4):784-91.
23.    Espaulella J, Arnau A, Cubi D, Amblas J, Yanez A. Time-dependent prognostic factors of 6-month mortality in frail elderly patients admitted to post-acute care. Age Ageing 2007;36(4):407-13. doi: 10.1093/ageing/afm033.
24.    Dramé M, Fierobe F, Lang P, et al. Predictors of institution admission in the year following acute hospitalisation of elderly people. J Nutr Health Aging 2011;15(5):399-403. doi: 10.1007/s12603-011-0004-x.
25.    Donini L, de Felice M, Savina C, et al. Predicting the outcome of long-term care by clinical and functional indices: The role of nutritional status. J Nutr Health Aging 2011;15(7):586-92. doi: 10.1007/s12603-011-0030-8.
26.    Vischer UM, Frangos E, Graf C, et al. The prognostic significance of malnutrition as assessed by the Mini Nutritional Assessment (MNA) in older hospitalized patients with a heavy disease burden. Clin Nutr 2012;31(1):113-7. doi: 10.1016/j.clnu.2011.09.010.
27.    Dumont C, Voisin T, Nourhashémi F, Andrieu S, Koning M, Vellas B. Predictive factors for rapid loss on the mini-mental state examination in Alzheimer’s disease. J Nutr Health Aging 2005;9(3):163-7.
28.    Vellas B, Lauque S, Gillette-Guyonnet S, et al. Impact of nutritional status on the evolution of Alzheimer’s disease and on response to acetylcholinesterase inhibitor treatment. J Nutr Health Aging 2005;9(2):75-80.
29.    Spaccavento S, Del Prete M, Craca A, Fiore P. Influence of nutritional status on cognitive, functional and neuropsychiatric deficits in Alzheimer’s disease. Arch Gerontol Geriatr 2009;48(3):356-60. doi: 10.1016/j.archger. 2008.03.002.
30.    Chen CC-H, Chiu M-J, Chen S-P, Cheng C-M, Huang G-H. Patterns of cognitive change in elderly patients during and 6 months after hospitalisation: A prospective cohort study. Int J Nurs Stud 2011;48(3):338-346. doi: 10.1016/j.ijnurstu.2010.03.011.
31.    Toliusiene J, Lesauskaite V. The nutritional status of older men with advanced prostate cancer and factors affecting it. Support Care Cancer 2004;12(10):716-9. doi: 10.1007/s00520-004-0635-0.
32.    Johansson Y, Bachrach-Lindstrom M, Carstensen J, Ek AC. Malnutrition in a home-living older population: prevalence, incidence and risk factors. A prospective study. J Clin Nurs 2009;18(9):1354-64. doi: 10.1111/j.1365-2702.2008.02552.x.
33.    Chen CC, Dai YT, Yen CJ, Huang GH, Wang C. Shared risk factors for distinct geriatric syndromes in older Taiwanese inpatients. Nurs Res 2010;59(5):340-7. doi: 10.1097/NNR.0b013e3181eb31f6.
34.    Kaburagi T, Hirasawa R, Yoshino H, et al. Nutritional status is strongly correlated with grip strength and depression in community-living elderly Japanese. Public Health Nutr 2011;14(11):1893-9. doi: 10.1017/S1368980 011000346.
35.    Johansson L, Sidenvall B, Malmberg B, Christensson L. Who will become malnourished? A prospective study of factors associated with malnutrition in older persons living at home. J Nutr Health Aging 2009;13(10):855-61. doi: 10.1007/s12603-009-0242-3.
36.    Pilotto A, Rengo F, Marchionni N, et al. Comparing the Prognostic Accuracy for All-Cause Mortality of Frailty Instruments: A Multicentre 1-Year Follow-Up in Hospitalized Older Patients. PLoS ONE 2012;7(1):e29090. doi: 10.1371/journal.pone.0029090.
37.    Johnson M. Chemotherapy treatment decision making by professionals and older patients with cancer: a narrative review of the literature. Eur J Cancer Care 2012;21(1):3-9. doi: 10.1111/j.1365-2354.2011.01294.x.
38.    Tur JA, Colomer M, Monino M, Bonnin T, Llompart I, Pons A. Dietary intake and nutritional risk among free-living elderly people in Palma de Mallorca. J Nutr Health Aging 2005;9(6):390-6.
39.    Neumann SA, Miller MD, Daniels LA, Ahern M, Crotty M. Mini Nutritional Assessment in geriatric rehabilitation: Inter-rater reliability and relationship to body composition and nutritional biochemistry. Nutr Diet 2007;64(3):179-85. doi: 10.1111/j.1747-0080.2007.00146.x.
40.    Thompson Martin C, Kayser-Jones J, Stotts N, Porter C, Froelicher ES. Nutritional Risk and Low Weight in Community-Living Older Adults: A Review of the Literature (1995-2005). J Gerontol A Biol Sci Med Sci 2006;61(9):927-34.
41.    Cereda E, Valzolgher L, Pedrolli C. Mini nutritional assessment is a good predictor of functional status in institutionalised elderly at risk of malnutrition. Clin Nutr2008;27(5):700-5. doi: 10.1016/j.clnu.2008.06.001.
42.    Wilson M-MG, Thomas DR, Rubenstein LZ, et al. Appetite assessment: simple appetite questionnaire predicts weight loss in community-dwelling adults and nursing home residents. Am j Clin Nutr 2005;82(5):1074-81.
43.    Castel H, Shahar D, Harman-Boehm I. Gender Differences in Factors Associated with Nutritional Status of Older Medical Patients. J Am Coll Nutr 2006;25(2):128-34.
44.    Guérin O, Andrieu S, Schneider SM, et al. Characteristics of Alzheimer’s disease patients with a rapid weight loss during a six-year follow-up. Curr Opin Clin Nutr Metab Care 2009;28(2):141-6. doi: 10.1016/j.clnu.2009.01. 014.
45.    Soto ME, Secher M, Gillette-Guyonnet S, et al. Weight Loss and Rapid Cognitive Decline in Community-Dwelling Patients with Alzheimer’s Disease. J Alzheimers Dis 2012;28(3):647-54. doi: 10.3233/jad-2011-110713.
46.    Jacobs J, Cohen A, Ein-Mor E, Maaravi Y, Stessman J. Frailty, cognitive impairment and mortality among the oldest old. J Nutr Health Aging 2011;15(8):678-82. doi: 10.1007/s12603-011-0096-3.
47.    Shatenstein B. Frailty and cognitive decline: Links, mechanisms and future directions. J Nutr Health Aging 2011;15(8):665-6. doi: 10.1007/s12603-011-0337-5.
48.    Norman K, Smoliner C, Valentini L, Lochs H, Pirlich M. Is bioelectrical impedance vector analysis of value in the elderly with malnutrition and impaired functionality? Nutrition 2007;23(7-8):564-9. doi: 10.1016/j.nut.2007.05.007.
49.    Chevalier S, Saoud F, Gray-Donald K, Morais JA. The Physical Functional Capacity of Frail Elderly Persons Undergoing Ambulatory Rehabilitation is Related to Their Nutritional Status. J Nutr Health Aging 2008;12(10):721-6. doi: 10.1007/BF03028620.
50.    Ferdous T, Cederholm T, Razzaque A, Wahlin A, Nahar Z. Nutritional status and self-reported and performance-based evaluation of physical function of elderly persons in rural Bangladesh. Scand J Public Health 2009;37(5):518-24. doi: 10.1177/1403494809102778.
51.    Mueller C. Inflammation, Old Age, and Nutrition Assessment. Topics in Clin Nutr2008;23(2):131-8. doi: 10.1097/01.TIN.0000318909.16241.da.
52.    Venzin RM, Kamber N, Keller WCF, Suter PM, Reinhart WH. How important is malnutrition? A prospective study in internal medicine. Eur J Clin Nutr 2007;63(3):430-6. doi: 10.1038/sj.ejcn.1602948.
53.    McMillan DC. Systemic inflammation, nutritional status and survival in patients with cancer. Curr Opin Clin Nutr Metab Care 2009;12(3). doi: 10.1097/MCO.0b013e32832a7902.
54.    Vischer UM, Giannelli SV, Weiss L, Perrenoud L, Frangos E, Herrmann FR. The prevalence, characteristics and metabolic consequences of renal insufficiency in very old hospitalized diabetic patients. Diabetes Metab 2011;In Press, Corrected Proof. doi: 10.1016/j.diabet.2010.08.007.
55.    Gehring N, Imoberdorf R, Wegmann M, Ruhlin M, Ballmer PE. Serumalbumin–a qualified parameter to determine the nutritional status? Swiss Med Wkly 2006;136(41-42):664-9.
56.    Vischer UM, Perrenoud L, Genet C, Ardigo S, Registe-Rameau Y, Herrmann FR. The high prevalence of malnutrition in elderly diabetic patients: implications for anti-diabetic drug treatments. Diabet Med 2010;27(8):918-24. doi: 10.1111/j.1464-5491.2010.03047.x.
57.    Calderon Reyes ME, Ibarra Ramirez F, Garcia J, Gomez Alonso C, Rodriguez-Orozco AR. Evaluación nutricional comparada del adulto mayor en consultas de medicina familiar  [Compared nutritional assessment for older adults at family medicine settings.]. Nutr Hosp 2010;25(4):669-75.
58.    Schiffrin EJ, Guigoz Y, Perruisseau G, et al. MNA and immunity: nutritional status and immunological markers in the elderly. Nestle Nutr Workshop Ser Clin Perform Programme 1999;1:22-33, discussion 34. doi: 10.1159/00006 2948,
59.    Hudgens J, Langkamp-Henken B, Stechmiller JK, Herrlinger-Garcia KA, Nieves C, Jr. Immune function is impaired with a mini nutritional assessment score indicative of malnutrition in nursing home elders with pressure ulcers. JPEN J Parenter Enteral Nutr 2004;28(6):416-22. doi: 10.1177/01486071040 28006416
60.    Slaviero KA, Read JA, Clarke SJ, Rivory LP. Baseline nutritional assessment in advanced cancer patients receiving palliative chemotherapy. Nutr Cancer 2003;46(2):148-57. doi: 10.1207/S15327914NC4602_07  
61.    Kuikka LK, Salminen S, Ouwehand A, et al. Inflammation markers and malnutrition as risk factors for infections and impaired health-related quality of life among older nursing home residents. J Am Med Dir Assoc 2009;10(5):348-53. doi: 10.1016/j.jamda.2009.02.007.
62.    Gioulbasanis I, Georgoulias P, Vlachostergios PJ, et al. Mini Nutritional Assessment (MNA) and biochemical markers of cachexia in metastatic lung cancer patients: Interrelations and associations with prognosis. Lung cancer  2011;74(3):516-20. doi: 10.1016/j.lungcan.2011.05.009.
63.    Pepersack T. Outcomes of continuous process improvement of nutritional care program among geriatric units. J Gerontol A Biol Sci Med Sci 2005;60 (6):787-92. doi: 10.1093/gerona/60.6.787.
64.    Thomas DR. But Is It Malnutrition? J Am Med Dir Assoc 2009;10(5):295-7. doi: 10.1016/j.jamda.2009.03.004.
65.    Riccio D, Solinas A, Astara G, Mantovani G. Comprehensive geriatric assessment in female elderly patients with alzheimer disease and other types of dementia. Arch Gerontol Geriatr 2007;44(Supplement 1):343-53. doi: 10.1016/j.archger.2007.01.047.
66.    Dechamps A, Diolez P, Thiaudiere E, et al. Effects of Exercise Programs to Prevent Decline in Health-Related Quality of Life in Highly Deconditioned Institutionalized Elderly Persons: A Randomized Controlled Trial. [Article]. Arch Intern Med 2010;172(2):162-79.
67.    Ernsth Bravell M, Westerlind B, Midlöv P, et al. How to assess frailty and the need for care? Report from the Study of Health and Drugs in the Elderly (SHADES) in community dwellings in Sweden. Arch Gerontol Geriatr 2011;53(1):40-5. doi: 10.1016/j.archger.2010.06.011.
68.    Extermann M, Hurria A. Comprehensive Geriatric Assessment for Older Patients With Cancer. J Clin Oncol 2007;25(14):1824-31. doi: 10.1200/JCO. 2007.10.6559.
69.    Terret C. How and why to perform a geriatric assessment in clinical practice. Ann Oncol 2008;19(suppl 7):vii300-vii3. doi: 10.1093/annonc/mdn478.
70.    Retornaz F, Seux V, Pauly V, Soubeyrand J. Geriatric assessment and care for older cancer inpatients admitted in acute care for elders unit. Crit Rev Oncol Hematol 2008;68(2):165-71. doi: 10.1016/j.critrevonc.2008.04.001.
71.    Melis RJF, van Eijken MIJ, Teerenstra S, et al. Multidimensional Geriatric Assessment: Back to the Future A Randomized Study of a Multidisciplinary Program to Intervene on Geriatric Syndromes in Vulnerable Older People Who Live at Home (Dutch EASYcare Study). J Gerontol A Biol Sci Med Sci 2008;63(3):283-90.
72.    Kono A, Kanaya Y, Fujita T, et al. Effects of a Preventive Home Visit Program in Ambulatory Frail Older People: A Randomized Controlled Trial. J Gerontol A Biol Sci Med Sci 2012;67A(3):302-9. doi: 10.1093/gerona/glr176.
73.    Tolea MI, Ferrucci L, Costa PT, et al. Personality and Reduced Incidence of Walking Limitation in Late Life: Findings From the Health, Aging, and Body Composition Study. J Gerontol B Psychol Sci Soc Sci 2012;In Press. doi: 10.1093/geronb/gbs001.
74.    Scheirlinckx K, Vellas B, Garry PJ. The MNA score in people who have aged successfully. Nestle Nutr Workshop Ser Clin Perform Programme 1999;1:61-5. doi: 10.1159/000062951.
75.    Formiga F, Ferrer A, Megido MJ, Chivite D, Badia T, Pujol Rn. Low Co-Morbidity, Low Levels of Malnutrition, and Low Risk of Falls in a Community-Dwelling Sample of 85-Year-Olds Are Associated with Successful Aging: The Octabaix Study. Rejuvenation Res 2011;14(3):309-14. doi: 10.1089/rej.2010.1131.

COMPONENTS OF THE RISK INSTRUMENT FOR SCREENING IN THE COMMUNITY (RISC) THAT CORRELATE WITH PUBLIC HEALTH NURSES’ PERCEPTION OF RISK

 

P. LEAHY-WARREN1, R. O’CAOIMH2, C. FITZGERALD2, A. COCHRANE3, A. SVENDROVSKI4, U. CRONIN2, E. O’HERLIHY2, N. CORNALLY1, Y. GAO2, E. HEALY5, E. O’CONNELL6, G. O’KEEFFE7, S. COVENEY7, J. MCGLYNN7, C. FITZGERALD2, R. CLARNETTE8, D. W. MOLLOY2

 

1. School of Nursing & Midwifery, University College Cork, Ireland; 2. Centre for Gerontology and Rehabilitation, University College Cork, St Finbarr’s Hospital, Cork City, Ireland; 3. Department of Psychology, Maynooth University, Maynooth, Co Kildare, Ireland; 4. UZIK Consulting Inc., Toronto, ON, Canada; 5. Centre for Public Health Nursing, Ballincollig and Bishopstown, Co Cork, Ireland; 6. Centre for Public Health Nursing, Mahon and Ballintemple, Cork City, Ireland; 7. Health Service Executive, Ireland; 8. School of Medicine and Pharmacology, University of Western Australia, Crawley, Australia.

Corresponding author: Dr Rónán O’Caoimh, Centre for Gerontology and Rehabilitation, University College Cork, St Finbarr’s Hospital, Douglas road, Cork City, Ireland, Email: rocaoimh@hotmail.com, Phone: +353214901461, Fax: +3534901635

J Frailty Aging 2015;4(3):149-154
Published online June 11, 2015, http://dx.doi.org/10.14283/jfa.2015.56


Abstract

Background: Functional decline and frailty are common in community-dwelling older adults, leading to an increased risk of adverse outcomes. Objective: To examine the factors that public health nurses perceive to cause risk of three adverse outcomes: institutionalisation, hospitalisation, and death, in older adults, using the Risk Instrument for Screening in the Community (RISC). Design: A quantitative, correlational, descriptive design was used. Setting and Participants: A sample of 803 community-dwellers, aged over 65 years receiving regular follow-up by public health nurses. Procedure and Measurements: Public health nurses (n=15) scored the RISC and the Clinical Frailty Scale (CFS) on patients in their caseload. We examined and compared correlations between the severity of concern and ability of the caregiver network to manage these concerns with public health nurses’ perception of risk of the three defined adverse outcomes. Results: In total, 782 RISC scores were available. Concern was higher for the medical state domain (686/782,88%) compared with the mental state (306/782,39%) and activities of daily living (595/782,76%) domains. Concern was rated as severe for only a small percentage of patients. Perceived risk of institutionalisation had the strongest correlation with concern over patients mental state,(r=0.53), while risk of hospitalisation,(r=0.53) and death,(r=0.40) correlated most strongly with concern over the medical state. Weaker correlations were found for the other domains and RISC scores. The CFS most strongly correlated with the ADL domain,(r=0.78). Conclusion: Although the prevalence of concern was high, it was mostly rated as mild. Perceived risk of institutionalisation correlated most with concern over the ability of caregiver networks to manage patients’ mental state, while risk of hospitalisation and death correlated with patients’ medical state. The findings suggest the importance of including an assessment of the caregiver network when examining community-dwelling older adults. Validation of the RISC and public health nurses’ ratings are now required.

Key words: Screening, frailty, risk, adverse outcomes, public health nurses.


 

Introduction

Population ageing represents a considerable challenge for health and social care systems that are already struggling to meet the needs of the increasing numbers of older people, particularly during a period of economic adversity (1). The gains in longevity are undoubtedly something to be celebrated, but there has been a concomitant rise in the prevalence of disease and chronic conditions, including heart disease, arthritis, diabetes and dementia (2). As a result, there is growing international interest in reducing demand on acute hospitals services and residential care provision, by developing cost-effective interventions to support older people living in their own homes (3). The effectiveness of any intervention relies on the prompt identification of those older people who are likely to experience frailty and/or decline in function that may lead to adverse outcomes such as institutionalisation, hospitalisation, (admission to nursing home) or death. Once properly targeted, interventions can then reduce the burden on services through the allocation of resources, according to need.

Multidimensional interdisciplinary comprehensive geriatric assessment (CGA) is one of the key features of modern geriatric care (4). The assessment process facilitates the development of a coordinated and integrated plan for treatment and follow-up. CGA has been shown to improve outcomes for hospitalised older adults (4) and those assessed in the community (5). The success of CGA and subsequent patient management rely on the expertise of a dedicated team, including medical, nursing, physiotherapy, and occupational therapy specialists (4). It may not be possible or cost-effective to offer CGA to all older adults. It could be more appropriate, in the first instance, to identify those older adults at high risk of adverse outcomes and refer these individuals for more specialised assessment and management.

A variety of short screening tools have been developed to identify older adults at risk of functional decline, across a number of settings, for example, during hospitalisation (6), routine visits to the physician (7), and through self-reported postal surveys (8).  Other tools have attempted to predict the likelihood of adverse outcomes in the emergency room, including unplanned re-admissions (9).  The predictive validity and generalisability of these tools has not been fully determined however, and currently there is no comprehensive screening instrument that is widely available, or used in the community, to simultaneously measure risk of hospitalisation, institutionalisation and death (10).

Multiple biological, social and psychological factors are associated with an increased risk of adverse outcomes (8), (9), (10), which can be grouped into three discrete domains: mental state, activities of daily living (ADL) and medical (including physical) state. These three domains form the basis of the short screening tool – the Risk Instrument for Screening in the Community (RISC) (11), formerly known as the Community Assessment of Risk Screening Tool (CARST). The RISC was developed as part of a comprehensive screening, triage assessment and treatment programme – i.e. Community Assessment of Risk and Treatment Strategies (CARTS) [www.collage-ireland.eu]. The CARTS programme uses two tools – (i) the RISC and (ii) the more comprehensive assessment tool for those screened who are at greater risk of adverse outcomes – the Community Assessment of Risk Instrument (CARI) (12), (13). 

The RISC identifies the presence and severity of concern in the three domains and quantifies the risk of adverse outcomes: hospitalisation, institutionalisation and death. Individuals scoring as medium or high risk are referred for a more detailed assessment. The RISC includes an assessment of the ability of the individuals’ caregivers, formal and informal, to manage their care needs. Formal caregivers refer to health care professionals, such as Public Health Nurses (PHNs) and home help. Informal caregivers includes family, friends, neighbours etc. PHNs visit patients in their home and may be in the best position to screen older community dwellers (14), (15), (16), (17).

This study examines the correlations between PHNs ratings of concern, in the three domains of the RISC, their interpretation of the severity of the risk of the three adverse outcomes, and their perceived view of the ability of an individuals’ caregiver network to manage these concerns.

Methods

A quantitative correlational descriptive design was used.

Sample

A convenience sample of older adults (n=803), aged 65 years and older, under regular follow-up by PHNs, in two community areas in a Southern County in Ireland, were assessed. Those living in nursing homes, or other long-term care institutions were excluded.

Outcome measure

The Risk Instrument for Screening in the Community (RISC)

The RISC has three domains: (i) Mental state (i.e. cognition, mood, psychiatric issues and behaviours), (ii) Activities of Daily Living (basic ADL; self-care activities such as grooming, dressing, mobility, feeding and instrumental ADL; managing shopping, finances, medication management) and (iii) Medical (including physical) state (medical problems, medical conditions, falls, nutrition and environment). A space is provided for the assessor to include other issues where necessary (see Appendix 1).

The scoring of each domain follows three steps. First, ‘cause for concern’ is scored dichotomously (i.e. yes or no). If there is no concern, the rater simply moves on to the next domain. If there is concern, the rater scores the ‘severity of concern’ on a scale of 1-3 (mild, moderate or severe). The ability of the caregiver network to manage the care needs on a five-point Likert scale of 1-5 (1 = can manage, 5 = absent/liability), is then scored.  The caregiver network represents all of the formal and informal resources, and services that are available to the person.  The effectiveness of the caregiver network and severity of concern, are taken into account when completing the global risk scores. The risk of the three adverse outcomes (institutionalisation, hospitalisation, and death) occurring in the next year, are then scored on a Likert scale from 1-5 (1 = minimal /rare, 5 = extremely likely/certain).

Frailty Measure – Clinical Frailty Scale

The Clinical Frailty Scale (20) is a nine-point scale that stratifies older adults according to their level of frailty. Scores range from one (very fit) to nine (terminally ill).

Data Collection

Training program

PHNs received a short face-to-face training workshop (approximately 4 hours) that was developed to establish optimal understanding of the tool and to maximise inter-rater reliability (IRR), between the trainees (18). At the start of the training, the PHNs were asked to score six standard cases; two low-risk, two medium-risk and two high-risk. The programme explains the concept of risk, the different elements of the instrument, and scoring criteria using the six sample cases as examples. Upon completion of training, PHNs were requested to score six different cases (two low-risk, two medium-risk and two high-risk). Their scores were correlated against those generated by a panel of experts to facilitate an evaluation of each trainee’s performance and understanding of the instrument. Those who “passed” were certified, and those who required further training were identified. High levels of IRR have been demonstrated in Ireland (18) and in Australia (13).

The PHNs (n=15) who successfully completed the training and were certified accordingly, completed a desk-based clinical assessment from patient records of older adults, within their caseload (n=803), using the RISC tool and the Clinical Frailty Scale (20). Ethical approval was obtained in advance, from the Cork Research and Ethics Committee of the Cork University Hospitals, Ireland.

Data Analysis

The Statistical Package for Social Sciences (SPSS 17.0 for Windows) was used for storage, analysis and presentation of data. A data coding framework was designed and pre-coded data from all questions on the scales were tabulated and entered into SPSS. Data were analysed using descriptive and inferential statistics. Normality was tested using the Shapiro–Wilk test. The majority of the data were not normally distributed and were analysed using Spearman (non-parametric) correlation coefficients.

Results

Demographic Characteristics

The RISC was scored on 803 patients living in urban/suburban districts of Cork City and County; 516 were female, (64%) and 287 male (36%).  Their mean age was 79.8 years (SD: 7.4). Females were significantly older than males (p = 0.04) with mean ages of 80.2 years, standard deviation (SD) = 7.4 and 79 (SD = 7.5) respectively. The majority of the patients (n=723, 90%) lived in their own homes, and nearly half lived alone (n = 374; 47.4%).  Other variables were collected and are reported elsewhere (12).

Risk Instrument for Screening in the Community scores

The RISC score was available for 782 out of 803 patients, with 21 patients, who had not been reviewed within the last six months, excluded. The frequency scores for the RISC are presented in Table 1. There was a higher rate of concern for the medical state, with 686/782 (88%) deemed to have issues relating to this domain, compared to the other two domains: 306/782 (39%) for mental state and 595/782 (76%) for ADL, although this concern was rated as severe for only a small percentage, 26/782, (3%) of the total sample. The ratings suggest that overall, the caregiver network was perceived to manage care across all three domains, with only 1% of the total sample considered to either be unable to manage (“cannot manage”) or “absent/liability”. The majority of the sample was perceived to be at minimal/low risk of each of the three adverse outcomes.

Table 1 Frequency scores (percentage %) according to each component of the Risk Instrument for Screening in the Community, (n=782)

The Clinical Frailty Scale 

The Clinical Frailty Scale was available for 784 patients (97%), 426 (54.3%) of whom scored >5 and were categorized as frail.  A further 171 (21.8%) scored 4, i.e. demonstrating vulnerability to frailty, while 187 (23.9%) scored three or less, i.e., classified as very fit, well or managing well (robust).

Correlational analysis

The relationship between Global Risk scores (institutionalisation, hospitalisation, and death) and the other measures (severity of concern and caregiver network) across the three domains (Mental State, ADLs and Medical state), were examined using Spearman’s correlation coefficients (Table 2).

Table 2 Correlations between global risk scores and other measures

All correlations are significant (p<0.01); ADL = Activities of Daily Living.

Risk of Institutionalisation had the highest correlation with the severity of concern and the perceived ability of the caregiver network to manage that risk, for the patients’ mental state (r = 0.53, and 0.54 respectively, p <0.001), ADL (r = 0.47 and 0.55 respectively, p <0.001), and the frailty measure (r = 0.42, p <0.001). It seems that as concern over these areas increases, so does the strain on the caregivers, leading to the possible need for long-term care placement. Risk of Hospitalisation had the highest correlation with severity of concern for the medical state (r = 0.53, p <0.001) and with the frailty measure (r = 0.43, p <0.001). There was a moderate correlation between Risk of Death and the severity of concern over the patients’ medical state (r  = 0.40, p <0.001). Weaker correlations were found for the other domains and RISC scores. All of the correlations reached statistical significance at the 0.001 level.

The Clinical Frailty Scale most strongly correlated with the ADL domain. In particular, it correlated strongly and significantly with both the severity of the concern for ADL  (r = 0.78, p <0.001) and concern over the ability of the caregiver network to manage a patients’ ADL (r = 0.70, p <0.001). Correlation with the other domains were significantly weaker (p <0.001), than for ADL, see Table 3.

Table 3 Correlations between components of the Risk Instrument for Screening in the Community (RISC) and Clinical Frailty Scale, n = 784

All correlations are significant (p<0.01); ADL = Activities of Daily Living.

  

Discussion

This paper presents the internal correlations of the RISC, a new multi-modal risk screening instrument, for use in community dwelling older adults. The results of the internal correlations contribute to an understanding of the relationship between factors associated with adverse outcomes in older adults, and the perception of risk as quantified by PHNs. The results suggest that PHNs’ concerns related to patients’ mental and functional (ADL) states were associated with a perceived risk of institutionalisation. This risk is likely to be exacerbated if the caregiver network is experiencing difficulties managing these areas. These findings are consistent with other studies that have suggested that predictors of nursing home placement are mainly based on underlying cognitive and/or functional impairment, and associated lack of support and assistance in daily living (21). In the current study, perceived risk of hospitalisation and death were most strongly correlated with concerns over patients’ medical state. This is intuitive as medical conditions are more likely to place an individual at risk of hospitalisation and of dying, than institutionalisation. 

This study is one of the first to examine the perceived impact of a patients’ existing caregiver network on their perceived risk of adverse outcomes. In general only a small number of caregiver networks were felt to be failing. Concerns over a patient’s caregiver network’s ability to manage mental and functional (ADL) states correlated most strongly with their perceived risk of institutionalisation. Correlations between the caregiver network and other adverse outcomes were much weaker, albeit statistically significant. There are a number of possible explanations for the lower correlations between the caregiver network and risk of hospitalisation/death. First, informal caregivers who are frequently older themselves (22) may not have the necessary skills or abilities to manage complex medical conditions in the home.  Second, the focus of home-care services is often on the completion of household tasks and/or personal care (24), rather than meeting medical needs. Finally, while it is possible to deliver professional and effective acute medical care in the home (25), it is not readily available in many settings. Other studies have suggested that effective discharge planning and continuity of care post-discharge, can reduce unplanned hospital re-admissions for older adults, although factors associated with re-admission are not well understood (23, 26, 27). The on-going prospective study validating the RISC and following this cohort of older adults, may help to clarify some of these factors.

There was a high prevalence (54%) of frailty defined by the Clinical Frailty Scale, among this cohort of older adults. This could be expected as the sample was taken from the existing caseloads of the PHNs and as such, these older adults are more likely to have medical and other co-morbidities, than a cross-sectional sample of all community-dwelling older adults. The Clinical Frailty Scale produced moderate correlations across all adverse outcomes, mainly correlating with components of the ADL domain. This would be expected given that the Clinical Frailty Scale primarily assesses ADL. In addition, it does not include an assessment of the caregiver network and may as a result, under- or over-estimate the risks.

This study has a number of limitations. This study was a retrospective review of patients’ PHN records and depended upon patients having been reviewed recently. Some demographic data, including 21 RISC and 19 CFS scores were not available, which may have led to bias. It is however important to note that the PHNs involved in this study were very familiar with the older adults they assessed, and the RISC was designed with this in mind. Previous analysis of a risk register suggested that PHNs needed a quick screening tool to support their judgements in terms of allocating resources to those most in need (28). Other professionals, who have less knowledge about a particular client, may need to gather additional information before using the RISC. Likewise, the patients included (those under active follow-up) were more likely to be at greater risk of adverse outcomes than a cross-sectional community sample. This may also have created bias. Furthermore, the study is limited by the analytical techniques employed. While correlations are useful in examining the interaction between different components of instruments, they do not establish predictive ability. Future research is now required to operationalise the domains and scoring system and add to its usability across other groups of health and social care providers. A one-year follow-up study that measures the predictive validity of the tool by recording the rates of institutionalisation, hospital admission and mortality amongst the original sample, is now underway.

In summary, the RISC is a promising new screening tool that will assist healthcare professionals working in the community to identify older adults at risk of adverse outcomes. Those at medium or high risk can be referred for a more detailed assessment and resources allocated to prevent or delay these events. The inclusion of an assessment of the caregiver network may help to direct interventions that help older adults to receive appropriate levels of support to meet their needs and remain in their own homes for as long as possible (29). This study proposes that the efficacy of the caregiver network is as important in contributing to risk of nursing home placement as the actual underlying concerns over patient’s mental and functional states and as such, it should be taken into account when screening frail older adults in the community.

Acknowledgements: This research has been carried with the support of the Health Service Executive of Ireland (South), Atlantic Philanthropies, and the Centres for Public Health Nursing in Ballincollig & Bishopstown and Mahon & Ballintemple in County Cork.

Conflict of interest: The authors report no conflict of interest.

References

1. Global elderly care in crisis. The Lancet. 2014. Editorial, 383, 927

2. Christensen C, Dobhlhammer G, Rau R, Vaupel J. Ageing populations: the challenges ahead. Lancet. 2009;374:1196–208.

3. Rostgaard T, Glendinning C, Gori C, et al. Livindhome: Living independently at Home: Reforms in home care in 9 European countries. SFI – Danish National Centre for Social Research: Copenhagen. 2011.

4. Ellis G, Whitehead M, O’Neill D, Langhorne P, Robinson D.Comprehensive geriatric assessment for older adults admitted to hospital. Cochrane Database Systematic Review. 2011;6:7.

5. Stuck A, Egger M, Hammer A, Minder C, Beck J.Home visits to prevent nursing home admission and functional decline in elderly people: systematic review and meta-regression analysis. J Am Med Assoc. 2002;287(8):1022-8.

6. Sutton M, Grimmer-Somers K, Jeffries L. Screening tools to identify hospitalised elderly patients at risk of functional decline: a systematic review. Int J Clin Prac. 2008;62(12):1900–1909.

7. Corapi K, McGee H,  Barker M. Screening for frailty among seniors in clinical practice. Nat Clin Prac Rheum. 2006;2:9.

8. Chrischilles E, Rubenstein L, Van Gilder R, Voelker M, Wright K. Risk factors for adverse drug events in older adults with mobility limitations in the community setting. J Am Geriatr Assoc. 2007;55(29): 34-1.

9. Grafa C, Giannellia S, Herrmanna F, Sarasinb P, Michela L, Zekrya D.  Identification of older patients at risk of unplanned readmission after discharge from the emergency department. Swiss Med Weekly. 2012;141: 1-9.

10. Kansagara D, Englander H, Salanitro A, et al. Risk prediction models for hospital readmission: a systematic review. J AmMed Assoc. 2011;306: 1688-1698.

11. O’ Caoimh R, Gao Y, Svendrovski A, et al. Screening for markers of frailty and perceived risk of adverse outcomes using the Risk Instrument for Screening in the Community (RISC). BMC Geriatr. 2014; 14:104 doi: 10.1186/1471-2318-14-104. 

12. O’ Caoimh R, Gao Y, Healy E, O’Connell E, O’Keefe G, Molloy W. Screening for Markers of Frailty and Perceived Risk of Adverse Outcomes Using The Community Assessment of Risk Tool (CART). Irish Journal of Medical Science. [Abstract]. 2013;182(S6):230.  

13. Clarnette RM, Ryan JP, O’ Herlihy E, et al. The Community Assessment of Risk Instrument:  Investigation of Inter-rater Reliability of an Instrument Measuring Risk of Adverse Outcomes. J Frailty and Aging. 2014;In press.

14. Office of the Nursing and Midwifery Services Director. Report on Current Public Health Nursing Services, Report prepared by Patricia O’Dwyer, Project Officer to the Expert Advisory Group on Public Health Nursing Services. 2012. Dublin: Health Service Executive.

15. Sherman H, Forsberg C, Karp A, Tornkvist L. The 75-year-old persons’ self-reported health conditions: a knowledge base in the field of preventive home visits. J Clin Nurs., 2012;21:3170–3182.

16. Dorresteijn T, Rixt Zijlstra G, Van Haastregt J, Vlaeyen J,  Kempen G.Feasibility of a nurse-led in-home cognitive behavioral program to manage concerns about falls in frail older people: A process evaluation. ResNurs & Health. 2013;36(3):257-270.

17. Lupari M, Coates V, Adamson G, Crealey G. ‘We’re just not getting it right’- how should we provide care to the older person with multi-morbid chronic conditions? J Clin Nurs. 2011;20(9/10):1225-1235.

18. O’Caoimh R, Healy E, O Connell E, Gao Y, Molloy D.W. The Community Assessment of Risk Tool (CART): Investigation of Inter-Rater Reliability for a New Instrument measuring risk of Adverse Outcomes in Community Dwelling Older Adults. Irish Journal of Medical Science. [Abstract]. 2012;181(S7):227.

19. Active and Healthy Ageing reference site COLLAGE (COLLaboration on AGEing) (http://www.collage-ireland.eu/initiatives/specific-action-group-members/c2/)

20. Rockwood K, Song X, MacKnight C, et al.  A global clinical measure of fitness and frailty in elderly people. Can Med Assoc J.  2005;173(5):489-495.

21. Luppa M, Luck T, Weyerer S, König H., Brähler E,  Riedel-Heller S.  Prediction of institutionalization in the elderly. A systematic review. Age and Ageing. 2010;39(1):31-8.

22. TILDA. Fifty Plus in Ireland 2011. First results from the Irish Longitudinal Study on Ageing (TILDA). TCD: Dublin. 2011.

23. Coffey A, McCarthy GM. Older people’s perception of their readiness for discharge and postdischarge use of community support and services. Int J Older People Nurs. 2013;8(2):104-15.

24. Montgomery P, Mayo-Wilson E, Dennis J. Personal assistance for older adults (65+) without dementia. Cochrane Database of Systematic Reviews. 2008;Issue 1. doi:10.1002/14651858.CD006855.pub2

25. Shepperd S, Doll H, Broad J, et al. Cochrane Database of Systematic Reviews. 2011;Issue 8. doi:10.1002/14651858.CD000356.pub3

26. Nyweide D, Anthony D, Bynum J. Continuity of care and the risk of preventative hospitalisation in older adults. J Am Med Assoc: Internal Medicine. 2013;173(20):1879-95. 

27. Coffey A, McCarthy GM. Older people’s perception of their readiness for discharge and post discharge use of community support and services. Int J Older People Nurs. 2013;8(2):104-15. 

28. O’Caoimh R, Healy E, O Connell E, Molloy D.W. Stratification of the Risk of Adverse Outcomes for Irish, Community Dwelling, Older Adults: Use of a Risk Register. Irish Journal of Medical Science. [Abstract]. 2012;181(S7):295.

29. Cutchin M, Coppolan S, Talley V, Svihula J, Catelier D, Shank K. Feasibility and effects of preventative home visits for at-risk older people: design of a randomised controlled trial. BMC Geriatr. 2009;9:54. 

Appendix 1 Risk Instrument for Screening in the Community (RISC) Score Sheet

FRAILTY AND NOVEL TECHNOLOGIES – A STEP AHEAD

 

E. KELAIDITI

Gérontopôle, Centre Hospitalier Universitaire de Toulouse, France. 

Corresponding author: Dr Eirini Kelaiditi, PhD. Institut du Vieillissement, Gérontopôle. Université de Toulouse III-Paul Sabatier. 37 Allées Jules Guesde, 31000 Toulouse, France. Tel: +33 (0) 5 6114-5668. Fax: +33 (0) 5 61145640. Email: e.kelaiditi@gmail.com 

J Frailty Aging 2015;4(2):90-92
Published online April 28, 2015, http://dx.doi.org/10.14283/jfa.2015.52


Abstract

Dependence and disability are almost inevitable consequences of population aging. As these conditions are considered irreversible, a growing interest has been directed towards the identification of related conditions that are still amenable to preventive interventions. In this context, frailty has attracted an increasing scientific interest. Frailty is characterized by decreased homeostatic reserves and diminished resistance to stressors. The frail elderly constitutes a complex population in terms of assessment, monitoring, adherence to recommendations, and follow-up. The use of novel technologies may be considerably helpful for both clinical and research purposes. In particular, technologies may support interventions preventing disability, improving the quality of life, and enhancing the wellbeing of frail people. Traditional assessment instruments can be complemented or replaced by mobile devices measuring and monitoring frailty domains (e.g., physical performance, cognitive function, physical activity, nutritional status). Novel technologies have indeed the potential to benefit, assess, monitor, and support frail older people to live independently and improve their quality of life.

 

Key words: Aging, prevention, information and communication technologies, assessment, screening.


Population aging is leading to a considerable increase in age-related pathological conditions, including dependence and disability. In 2010, more than 3 hundred million persons were disabled worldwide, and such estimate is projected to almost double by 2050, with considerable increases in costs for the healthcare system (1, 2). As dependence and disability are considered as almost irreversible conditions, a growing interest has been pointed to the identification of those health profiles that, although characterized by increased risk of negative events, may still be amenable to preventive interventions against disability. In this context, frailty has attracted a significantly increasing scientific interest (3).

Frailty is a multidimensional condition characterized by decreased homeostatic reserves and diminished resistance to stressors (4). It is also a consequence of cumulative decline in multiple physiological systems, and is associated with a greater risk of adverse health outcomes, such as falls, hospitalization, institutionalization and mortality (5). This concept is frequently adopted to indicate a status of pre-disability, characterized by potential reversibility.

Frail elders constitute a complex population in terms of screening, assessment, monitoring, and follow-up. It is likely that technologies may indeed play a role in supporting healthcare professionals and researchers in this context. In other words, the questionnaires, scales, and assessment tools usually completed by healthcare professionals may be complemented and supported by the implementation of novel technologies. An overview with few examples of novel technologies that can be used for the screening, assessment and follow-up of frail older persons is presented in this brief report. The list of examples is not exhaustive but indicative, just an example for indicating the high potentialities of this innovative and promising field.

The identification of frail older people in the routine clinical care and research is a difficult but important task. For example, electronic screening may be supported by technologies using large healthcare databases and sources to identify frail older persons in primary care (6).

Current literature on the use of novel technologies for the assessment and follow-up of older people is exponentially growing. For example, a mobile device characterized by a wide range of features (accelerometer sensors, wireless communication capabilities, and processing capacities among others) has recently been developed in order to support the frailty assessment (7). It provides information on anthropometric characteristics, nutritional, functional and cognitive status of the individual, potentially supporting the assessment of the healthcare professional. Its objective results are indeed shown to be consistent with results of the standard clinical evaluation. Another example of technologies used to measure physical performance may be represented by electronic walk-ways (e.g., www.protokinetics.com), which provide objective measures of movement patterns (i.e., gait analysis) and facilitate the identification of age-related abnormalities. A development of traditional instruments capturing some frailty criteria/domains (e.g., dynamometers for the measurement of muscle strength) are also evolving into more informative devices (8).

The use of technologies has also been proposed during the clinical interview of the older person for better understanding some specific features of his/her health status. For example, Marsh and colleagues (9) have validated the use of animation videos as examples for improving the assessment of mobility and activities of daily living. This approach uses a computer-based program displaying video clips constructed from computer animations. After viewing each video clip of the animated task, participants are asked a question about their ability to perform the same task. This method appeared to have a significant impact at improving the accuracy in the reporting of older adults’ self-reporting of ability related to mobility.

In relation to the assessment of physical activity, commercial mobile applications currently available to promote physical activity among adults are numerous (10). These applications are able to monitor physical activity on a daily basis and even provide person-specific recommendations for maintaining a good health status and a healthy body weight (11). 

Assessment and monitoring of nutritional intake and status is also very important for the frail elderly. In this case, long food frequency questionnaires could be replaced by mobile devices using applications that take a picture of the consumed meals. Then, through a dedicated online-based service, an analysis of the food can be conducted by specific software, and images converted into nutritional data (i.e., macro- and micro-nutrients composition). This information can be made directly accessible to dietitians, which may then provide personalized recommendations (12).

Furthermore, the improvement of domains other than physical function and nutrition may also positively affect the health status of frail older persons. In other words, multiple domains may generate and significantly enhance the frailty condition (e.g., vision impairment, hearing loss). In this context, visually impaired individuals could benefit from mobile applications designed to read out the text in a document or an image (e.g., https://itunes.apple.com/us/app/saytext/id376337999?mt=8). Telephone devices including a monitor, which “reads” the voice of the caller and translates it to text, may reduce the isolation of individuals with hearing impairment (e.g., www.CaptionCall.com).

Once screening and assessment are complete, a main issue with frailty is the follow-up and the identification and evaluation of adherence to recommendations of the frail elderly. New technologies could be particularly helpful in this direction. An example of the use of novel technologies in improving the adherence to recommendations during the follow-up of patients is the use of a pedometer-based behavioral change program, which appeared to increase physical activity and performance of frail elders (13). Another example of how much technologies may help in this field, may be the use of mobile applications supporting the monitoring of compliance of older frail people to medications (e.g., http://seniornet.org/blog/). In addition, identification of adherence to recommendations with the use of technologies might be evaluated by the electronic check of renewal of drug prescriptions of pharmacies.

As a future perspective, the concept of a “smart home” (for example, equipped with sensors, actuators, and/or biomedical monitors) could be a promising way for improving the assistance at home of frail and disabled elders, potentially allowing greater functional independence, maintaining good health, and preventing negative adverse outcomes (including social isolation). In fact, these types of infrastructures usually operate in a network connected to a remote data center, which may promote the early diagnoses and anticipate healthcare procedures (14).

The use of novel technologies for preventing disability in frail people is yet limited and challenging. Indeed, while a lot of applications are targeted to younger populations, less are specific to older persons, especially if frail. Nevertheless, the aging population may represent an ideal target group of persons, which may greatly benefit from scientific advancements in this field. By stating this, we are not underestimating the costs and efforts of the extension of technologies to advanced age individuals. For example, randomized-controlled trials using technologies are particularly challenging due to the complexity of the population, the reduced willingness of consider technological devices as part of their daily life, lack of consensus on technology definitions, and the poor standardization of the assessment tools. However, despite of such evident barriers, it is noteworthy that the number of people aged 65 years and older using the Internet is rapidly rising (15). And, of course, the large scale implementation of technologies in the field of frailty is subject to still-to-come positive results (especially for what concerns cost-effectiveness) from clinical trials.

In conclusion, frailty is a clinical condition determining an increased risk of adverse health-related events and requiring a complex, multidimensional evaluation. Novel technologies present interesting potentialities to support the complex research and clinical activities around this syndrome. We are just at the very beginning of an exciting new field of development for geriatrics and gerontology.

Conflict of interest: Dr. Kelaiditi has no conflict of interest to declare.

 

References

1. World Alzheimer Report 2013. ADI 2013.

2. Fried TR, Bradley EH, Williams CS, Tinetti ME. Functional disability and health care expenditures for older persons. Arch Intern Med 2001;161(21):2602-7. 

3. Cesari M, Abellan Van Kan G, Ariogul S, et al. The European Union Geriatric Medicine Society (EUGMS) working group on “Frailty in older persons”. J Frailty Aging 2013;2(3):118-120.

4. Rodríguez-Mañas L, Féart C, Mann G, et al. Searching for an Operational Definition of Frailty: A Delphi Method Based Consensus Statement. The Frailty Operative Definition-Consensus Conference Project. J Gerontol A Biol Sci Med Sci 2013;68(1):62-7.

5. Clegg A, Young J, Iliffe S, Rikkert MO, Rockwood K. Frailty in elderly people. Lancet 2013;381(9868):752-62.

6. Drubbel I, Numans ME, Kranenburg G, et al. Screening for frailty in primary care: a systematic review of the psychometric properties of the frailty index in community-dwelling older people. BMC Geriatr 2014;14:27. doi: 10.1186/1471-2318-14-27.

7. Fontecha J, Hervás R, Bravo J, Navarro FJ. Mobile and Ubiquitous Approach for Supporting Frailty Assessment in Elderly People. J Med Internet Res 2013;15(9):e197.

8. Matsui Y, Fujita R, Harada T, et al. A new grip strength measuring device for detailed evaluation of muscle contraction among the elderly. J Frailty Aging 2014;3(3):142-147.

9. Marsh AP, Ip EH, Barnard RT, Wong YL, Rejeski WJ. Using video animation to assess mobility in older adults. J Gerontol A Biol Sci Med Sci 2011;66(2):217-27.

10. Middelweerd A, Mollee JS, van der Wal C, Brug J, Te Velde SJ. Apps to promote physical activity among adults: a review and content analysis. Int J Behav Nutr Phys Act 2014;11(1):97.

11. Hebden L, Cook A, van der Ploeg HP, Allman-Farinelli M. Development of smartphone applications for nutrition and physical activity behavior change. JMIR Res Protoc 2012;1(2):e9.

12. Zhu F, Bosch M, Woo I, et al. The Use of Mobile Devices in Aiding Dietary Assessment and Evaluation. IEEE J Sel Top Signal Process 2010;4(4):756-766.

13. Yamada M, Mori S, Nishiguchi S, et al. Pedometer-based behavioral change program can improve dependency in sedentary older adults: A randomized controlled trial. J Frailty Aging 2012;1(1):39-44. 

14. Chan M, Campo E, Estève D, Fourniols JY. Smart homes – current features and future perspectives. Maturitas 2009;64(2):90-7.

15. Macfarlane H, Kinirons MT, Bultitude MF. WWW. Do not forget older people. Age Ageing 2012;41(6):807-10. 

USE OF BIOMARKERS

 

L. RODRÍGUEZ-MAÑAS

 

Getafe University Hospital, Spain.

Corresponding author: Prof. Leocadio Rodríguez Mañas, Jefe de Servicio de Geriatría, Hospital Universitario de Getafe, Ctra. de Toledo, Km. 12,5, 28905-Getafe, Spain, Phone: 00 34 916839360 (ext. 6412), Fax: 00 34 916839210, e-mail: leocadio.rodriguez@salud.madrid.org 

J Frailty Aging 2015;4(3):125-128
Published online June 25, 2015, http://dx.doi.org/10.14283/jfa.2015.46


Abstract

Expanding the concept of frailty to the clinical settings has raised the concern about the accuracy of the current definitions for identifying frail individuals (not populations). The usual tools to assess frailty show, among other characteristics, a low sensitivity and a low Positive Predictive Value. One approach to overcome this challenge is using biological biomarkers to improve those characteristics, making feasible and accurate the assessment of frailty in clinical settings. Many biomarkers of frailty have been identified but few of them have been assessed as clinical markers with controversial results. Taking into account that frailty is caused by the failure in different systems, it is worthy to check if the combination of several of these biomarkers could be of help. In this effort, the EU-funded project FRAILOMIC is trying to assess the ability of different sets of biomarkers for improving the accuracy of classical definitions in determining the risk, the diagnosis and the prognosis of frailty.

Key words: Frailty, aging, elderly, screening, assessment.


 

The traditional way of assessing frailty has been based until now in using several instruments that measure performance-based tasks jointly to the assessment of indicators of nutrition and physical activity. This approach has been rather successful in the epidemiological settings, allowing the demonstration of frailty as an important population-based risk factor for several adverse outcomes. But it looks insufficient in clinical settings, where the individual risk is the matter and where the characteristics of the instruments as diagnostic tools must be refined (1).

From a clinical point of view, to detect frailty is of outstanding importance in preventing disability. When the frailty threshold has been surpassed and the disability has emerged, recovery from disability is unlikely (2), especially as the age of the patient, the degree of disability or its duration increase (3). Although the usual spontaneous evolution is to progress from non-frail to frail and disabled, a significant percentage of people improves in terms of functional status (4), with no clearly identified predictive factors of this evolution. However, some results from the Women´s Health and Aging Study II (WHAS II) suggest that some ill-defined characteristics could predict a differential risk (5). But to make accurate diagnosis of frailty is not only an issue of interest for risk prediction purposes, targeting those patients who will benefit from a specific approach compared to others who are not going to benefit from it, becoming frailty one of the cornerstones of decision-making in elderly patients. To know which patients will respond to the different treatments now available, mainly exercise and nutrition (6-8), is also of interest. Mainly when it looks that we are now in the border of an era where multimodal and pharmacological interventions targeting frailty will be available (9,10).

Although it is well known that the evolution from frailty to disability and its clinical consequences depends on several factors, including genetic and other biological factors, their utility as biological biomarkers (BMs) of frailty and of the risk to become frail, to develop disability and to respond to treatment, remains far from desirable for the day to day clinical practice. In fact, there are no studies addressing these issues.

 

Biological biomarkers of frailty

The most accepted physiological framework to explain frailty and its consequences was proposed by Walston and Fried (1999). Its fundamentals are sarcopenia and the energetic misbalance. They also established a feed-back between them: the so-called “frailty cycle”. This cycle stems from the physiological changes associated with ageing, producing an imbalance between anabolism and catabolism. This state embraces multiple systems and especially those related to hormonal changes and the development of a pro-inflammatory state: the changes in sexual hormones (low testosterone in males but also high estradiol in women), the dysfunction of GH-IGF-1 axis, the increase in the ratio cortisol/DHEA-s, the combination of several hormonal deficits, and the increase in IL-6, IL-1 and TNF alpha circulating levels (Penninx et al., 2004), CRP and D-Dimer (Walston et al., 2002) and pro-inflammatory cytokines (Leng et al., 2002). These findings suggest that changes associated with sarcopenia and with the balance between production and use of energy may be among the most relevant factors associated with frailty: dysregulation of inflammatory cytokines and hormones, oxidative stress, nutrition, physical inactivity and mithochondrial dysfunction. In addition, the role of vascular disease (atherosclerosis) has been underscored by several authors (Strandberg & Pitkäla, 2007). The presence of clinical cardiovascular disease, but also subclinical cardiovascular disease has been shown to be associated to frailty. The possibility of detecting early biomarkers of vascular (endothelial) dysfunction rises as an important clue to the detection of early stages of frailty as to an improved diagnoses and/or prognostic capacity.

In addition to these classical BMs, more recently other BMs have come to the field of frailty. As an example, the role for Hypoxia Inducible Factors proteins-HIF (and mainly α subunits of HIF) and their signaling pathway has emerged as a main control of key pathways that are essential for cell physiology, in a variety of processes related with human ageing. The relevance of HIFα resides in the pivotal role played by their target genes in several of the above mentioned physiological systems involved in frailty: the cell metabolism and energy balance (e.g., NOS2, PHD3, GLUT1, GLUT4, GAPDH, PGK1, trasferrin, etc.), angiogenesis and cell proliferation (e.g., VEGF, TGFα, TGFβ3, IGF2, OCT3/4), the length of the telomere (hTERT), etc. At the same time HIF-1α has been identified as an important modifier of longevity in animal models (11). In addition other potential BMs have also raised as it is the case of a common signature of miRNA expression in 7 different human aging model systems (12) or the telomere length. Within this complex framework, the “omic sciences” represent a significant aid to the study of potential BMs by allowing a comprehensive approach from the genome to the metabolome (Fig. 1).

Figure 1 The “omic” sciences

In summary, early detection of subclinical changes or deficits at the molecular, cellular, and or physiologic level is key to preventing or delaying the development of frailty, and its consequences too. However, data evaluating the role of these substances in providing significant support to the clinical diagnosis of frailty or any of its associated risks are scarce.

Table 1 Non-classical Biomarkers of frailty assessed in the Frailomic Initiative

The FRAILOMIC Initiative

It is noteworthy that one of the main characteristics of frailty is that its pathophysiological routes embrace several physiological systems (13). However, until now the approach to the study of the relationships between BMs and frailty has been done one by one, ignoring the multiplicity of relationships that probably account for the role of BMs in determining frailty. In addition, the usual tools to diagnose frailty used in the epidemiological studies show some characteristics (including a low sensitivity and a low positive predictive value) (14) that do not allow to use them for clinical purposes. These are some of the gaps that the recently launched Frailomic Initiative will try to fill (15). The principal aim of the Frailomic Initiative is to develop validated sets of measures comprising both classical and omics-based laboratory biomarkers (Table 1) to predict the risk of frailty, improve the diagnostic accuracy of frailty in day-to-day clinical practice, and assess the benefits of a prognostic forecast of frailty on the onset of disability and other adverse outcomes. In order to identify predictive biomarkers, the European Union-funded Frailomic Initiative follows an “omics” approach (genomics, transcriptomics, proteomics and metabolomics), using existing large datasets from previous “omics” initiatives. These studies have created a wealth of data that, so far, have not been used in the field of frailty research until now. In addition, the participation of well-established cohorts aimed to the study of the processes of aging, frailty and disability will allow a tight assessment, due to the excellent phenotyping of the functional characteristics of those populations.

The approach taken by the Frailomic Initiative will allow clinicians to go beyond the traditional disease-based approach to healthcare strategy and toward a strategy based on comprehensive quality-of-life, since the impact will be on reducing disability. Secondary objectives of the Frailomic Initiative include assessing interactions among putative biomarkers, nutrition, exercise and their effects on the natural history of frailty. In addition, the potential therapeutic usefulness of identifying frailty status in special older populations such as those with metabolic syndrome, diabetes and cardiovascular disease will be examined. The Frailomic Initiative aims to provide useful tool kits for care providers that will allow them to assess the risk of an older individual for developing frailty (i.e., “risk biomarkers”) as well its identification (i.e., “diagnostic biomarkers”), clinical course (i.e., “prognostic biomarkers”), and likely response to treatment (i.e., “predictive biomarkers”) thus bridging the gaps between epidemiology and clinical practice.

Conflict of interest: None

Acknowledgements: Supported by the Grant Nº 305483-2 of the 7thEU Health Program

References

1. Rodriguez-Mañas L, Fried LP. Frailty in the clinical scenario. Lancet. 2014 Nov 6. pii: S0140-6736(14)61595-6. doi: 10.1016/S0140-6736(14)61595-6.

2. Ferrucci L1, Cavazzini C, Corsi A, Bartali B, Russo CR, Lauretani F, et al. Biomarkers of frailty in older persons. J Endocrinol Invest. 2002;25(10 Suppl):10-5.

3. Fried LP, Guralnik JM. Disability in older adults: evidence regarding significance, etiology, and risk. J Am Geriatr Soc 1997; 45: 92-100.

4. Xue QL, Walston JD, Fried LP, Beamer BA. Prediction of risk of falling, physical disability, and frailty by rate of decline in grip strength: the women’s health and aging study. Arch Intern Med. 2011; 171: 1119-21.

5. Xue QL, Bandeen-Roche K, Varadhan R, Zhou J, Fried LP. Initial manifestations of frailty criteria and the development of frailty phenotype in the Women’s Health and Aging Study II. J Gerontol A Biol Sci Med Sci. 2008; 63: 984-90.

6. Pahor M, Guralnik JM, Ambrosius WT, Blair S, Bonds DE, Church TS, et al. Effect of structured physical activity on prevention of major mobility disability in older adults: the LIFE study randomized clinical trial. JAMA. 2014; 311: 2387-96.

7. Cesari M, Vellas B, Hsu FC, Newman AB, Doss H, King AC, et al. A physical activity intervention to treat the frailty syndrome in older persons-results from the LIFE-P study. J Gerontol A Biol Sci Med Sci. 2015 Feb;70(2):216-22.

8. Kelaiditi E1, van Kan GA, Cesari M. Frailty: role of nutrition and exercise. Curr Opin Clin Nutr Metab Care. 2014; 17: 32-9.

9. Rodríguez-Mañas L, Bayer AJ, Kelly M, Zeyfang A, Izquierdo M, Laosa O, et al. An evaluation of the effectiveness of a multi-modal intervention in frail and pre-frail older people with type 2 diabetes–the MID-Frail study: study protocol for a randomised controlled trial. Trials. 2014; 15: 34.

10. Vellas B, Pahor M, Manini T, Rooks D, Guralnik JM, Morley J, et al. Designing pharmaceutical trials for sarcopenia in frail older adults: EU/US Task Force recommendations. J Nutr Health Aging. 2013; 17: 612-8.

11. Leiser SF, Begun A, Kaeberlein M. HIF-1 modulates longevity and healthspan in a temperature-dependent manner. Aging Cell. 2011; 10: 318-26

12. Hackl M, Brunner S, Fortschegger K, Schreiner C, Micutkova L, Mück C, et al. miR-17, miR-19b, miR-20a, and miR-106a are down-regulated in human aging. Aging Cell. 2010; 9: 291-6

13. Fried LP, Xue QL, Cappola AR, Ferrucci L, Chaves P, Varadhan R, et al. Nonlinear multisystem physiological dysregulation associated with frailty in older women: implications for etiology and treatment. J Gerontol A Biol Sci Med Sci. 2009 Oct;64(10):1049-57.

14. Garcia-Garcia FJ, Carcaillon L, Fernandez-Tresguerres J, Alfaro A, Larrion JL, Castillo C, Rodriguez-Mañas L. A new operational definition of frailty: the Frailty Trait Scale. J Am Med Dir Assoc 2014; 15: 371.e7-371.e13.

15. Lippi G, Jansen-Duerr P, Viña J, Durrance-Bagale A, Abugessaisa I, Gómez-Cabrero D, et al. Laboratory biomarkers and frailty: presentation of the FRAILOMIC initiative. Clin Chem Lab Med 2015 DOI 10.1515/cclm-2015-0147