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PREDICTION OF COGNITIVE STATUS AND 5-YEAR SURVIVAL RATE FOR ELDERLY WITH CARDIOVASCULAR DISEASES: A CANADIAN STUDY OF HEALTH AND AGING SECONDARY DATA ANALYSIS

 

S. Pakzad1, P. Bourque1, N. Fallah2

 

1. École de psychologie, Université de Moncton, 18 Antonine-Maillet Avenue, Moncton, New Brunswick, Canada E1A 3E9; 2. Division of Neurology, Department of Medicine, University of British Columbia, UBC Hospital, Vancouver, British Columbia, Canada, V6T 2B5
Corresponding author: Dr. Sarah Pakzad, Ph.D. 531, Taillon, Université de Moncton, Moncton, NB E1A 3E9, Tel: (506) 858-4245; Fax: (506) 852-3125, Email: sarah.pakzad@umoncton.ca

J Frailty Aging 2021;10(1)31-37
Published online April 24, 2020, http://dx.doi.org/10.14283/jfa.2020.21

 


Abstract

Background: Given the important association between cardiovascular disease and cognitive decline, and their significant implications on frailty status, the contribution of neurocognitive frailty measure helping with the assessment of patient outcomes is dearly needed. Objectives: The present study examines the prognostic value of the Neurocognitive Frailty Index (NFI) in the elderly with cardiovascular disease. Design: Secondary analysis of the Canadian Study of Health and Aging (CSHA) dataset was used for prediction of 5-year cognitive changes. Setting: Community and institutional sample. Participants: Canadians aged 65 and over [Mean age: 80.4 years (SD=6.9; Range of 66-100)]. Measurement: Neurocognitive Frailty Index (NFI) and Modified Mini-Mental State (3MS) scores for cognitive functioning of all subjects at follow-up and mortality rate were measured. Results: The NFI mean score was 9.63 (SD = 6.04) and ranged from 0 to 33. This study demonstrated that the NFI was significantly associated with cognitive changes for subjects with heart disease and this correlation was a stronger predictor than age. Conclusion: The clinical relevance of this study is that our result supports the prognostic utility of the NFI tool in treatment planning for those with modifiable cardiovascular disease risk factors in the development of dementia.

Key words: Frailty, cardiovascular disease, mortality, cognitive deficits.


 

Introduction

While cardiovascular diseases (CVD) are associated to cognitive decline and dementia in the elderly (1, 2), much consideration has been given to the contribution of cardiovascular diseases to cognitive impairment (3). Today, CVD are established to strongly relate to cognitive impairment and dementia in the elderly (1). Interestingly, CVD can lead to Alzheimer’s disease (AD) and vascular dementia (1).
Coronary heart disease, hypertension, left ventricular hypertrophy, among others, are risk factors for heart failure (4). The association between CVD and cognitive impairment in older people is multifactorial and involves common risk factors such as atherosclerosis, hypertension, and diabetes mellitus (1). A large European longitudinal study (5) has shown that modifiable cardiovascular risk factors (CRFs) including diabetes, smoking, hypertension, and low physical activities are associated with lower cognitive test scores. A review (3)additionally underscored that traditional CRFs (e.g., high blood pressure, hyperglycemia, insulin resistance, diabetes mellitus, hyperlipidemia, cigarette smoking, heart failure, stroke, and atrial fibrillation) are also risk factors for AD. However, it is unclear whether those modifiable behaviors significantly partake in lowering the risk of developing cognitive impairment (6). As highlighted, under promising strategies for prevention of dementia (7), these risk factors need to be identified in vulnerable individuals so that appropriate preventative interventions can be undertaken to reduce the risk of developing cognitive impairment. Indeed, evidence shows that healthy cardiovascular lifestyles mitigate cognitive disorders through modifiable CVD risk factors (8).
Growing evidence shows that vascular/cerebrovascular pathology can accelerate the progression of preclinical AD and speed disease evolution (9). For instance, one study (10) note that those who have CVD are at a greater risk of developing mild cognitive impairment (MCI), and another review and synthesis, examining the pathophysiologic relationship between AD, cerebrovascular disease, and cardiovascular risk [9] showed that effectively controlling vascular risk factors serves to delay onset of AD and that the management of CRFs is associated with a reduced risk of dementia.
Frailty, an age-related fragility associated with multidimensional loss of energy, physical ability, cognition and general health reserves (11), has been positively associated with cognitive function in individuals with CVD (12) and is more prevalent in patients with CVD (13). To this end, it is noteworthy of underscoring the significance of the bidirectional relationship between frailty and both cognitive and functional reserve, and implication in neuropathology and brain reserve, motor signs of aging and the reversibility of cognitive frailty (14, 15). Hence, we need to underscore the importance of a frailty index as a prognostic tool to evaluate the risk of cognitive impairment and death.
Among those high-risk groups, individuals with cardiovascular health conditions are more vulnerable to the development of dementia (16). Thus, index of ‘frailty’ needs to be considered in cognitive decline as a modifying and prognostic variable since morbidity and mortality are related to CVD (17). The association between CVD and cognitive impairment have long been established using statistical approach whereas multidimensional approach is lagging behind that could confirm this association.
Recently, the Neurocognitive Frailty Index (NFI) was developed to create a more comprehensive measure of frailty in the elderly (11), and evidence supports its higher accuracy compared to other frailty outcome measures (11). Although evidence shows that the NFI provides higher accuracy to predict outcomes, the contribution of neurocognitive frailty indicator in improving patient prognostic in high-risk groups has not been established, that subsequently warrants further studies (11).
Given the link between CVD and dementia, there is an urgent need to examine these associations (18). Thus, a scientific group of experts convened by the Alzheimer’s Association, with scientific input from the National Institute of Neurological Disorders and Stroke and the National Heart, Lung and Blood Institute from the National Institutes of Health (2) to help in developing more accurate outcome measures and clinical criteria to examine the effects of controllable vascular risk factors for developing AD. Furthermore, the risk of developing MCI is higher for people who have CVD (19) or CRFs (20). This knowledge is important because this can help in examining ways to prevent or better manage the precursors of dementia. As noted earlier (11), studies to further evaluate the contribution of neurocognitive frailty indicators in improving prediction of patient outcomes are needed. Hence, this study evaluates the prognostic value of the NFI in the elderly with cardiovascular disease.

 

Materials and Methods

Study population

Participants’ data were drawn from the Canadian Study of Health and Aging (CSHA) (21), a study of the epidemiology of dementia in Canada. Planned in 1989, as a national longitudinal study, the CSHA followed over 10,000 elderly Canadians (from 36 communities and institutions) over a ten-year period from 1991 to 2001 and has collected a wide range of information on changing health status over time [http://www.csha.ca]. The CSHA was conducted in three waves but in this study only data from waves 1 and 2 were used: CSHA-1 (1991 to 1992) and CSHA-2 (1996 to 1997) for prediction of 5-year cognitive changes. The current analysis focused on the 997 participants who received a consensus diagnosis of no-cognitive impairment (NCI) or cognitive impairment but not dementia (CIND) on CSHA-1. Samples included those who completed neuropsychological tests at CSHA-1 and received a clinical diagnostic assessment at CSHA-1 and CSHA-2 (n=1228). The CIND category included individuals whose level of cognitive impairment was measured to be greater than the NCI group but less than the dementia group. Of the original 997 participants, 299 individuals had died in five years of follow-up, but these were included in our participant sample.

Measurement

NFI was defined as a combined score of 42 physical and cognitive elements. The physical elements included 4 domains, activities of daily living, instrumental activities of daily living, general health and comorbidities. The cognitive elements include 8 domains, short-term memory, long-term memory, verbal abstract thinking, judgment, aphasia, apraxia, agnosia, and constructional difficulty, as they were available in the dataset. Global cognitive score was obtained via the 3MS (22) at baseline and at follow-up, and mortality rate was used as an outcome measure.

Coding of Neurocognitive Frailty Index

The initial NFI (11) was determined from 42 variables, here we used 41 variables, as CVD was our chief focus. The NFI variables, which include both physical and cognitive elements, are described in our earlier study (11). Thirty-three physical components were selected from the CSHA-1 dataset at baseline (1991). Binary variables were recoded, using the ‘’0’’ to indicate the absence of the deficit, and ‘’1’’ to indicate the presence of a deficit. Particularly in Activities of Daily Living (ADL) and Instrumental Activities of Daily Living (IADL) items, data converted as score “1” for “can’t do at all” or “with some help” denoting needed help, and we assigned score “0” for “without any help”. The Self-Rated Health Question, «How is your health these days? Very good, pretty good, not too good, poor, very poor» was rated between ”0” and “1”. Each lower self-rating of health was coded to represent a larger deficit «very good=0», «pretty good=0.25», «good=0.5», «poor=0.75» and «very poor=1». For variables with dichotomous response (general health), data coded into a score between “0” where no deficit is present and “1” where the deficit is present. Overall, physical component scores varied between zero “0” to thirty-three “33”.
Similarly, we recoded the cognitive component but by recognizing an ordinal scale of variables. A simple recoding was done for the eight elements: short-term memory, long-term memory, verbal abstract thinking, judgment, aphasia, apraxia, agnosia, and constructional difficulty. A score was assigned a “0” for response “None”, “1” for “Questionable”, “2” for “Mild”, “3” for “Moderate” and “4” for “Severe”. There were eight measures in this part which means theoretically that a person could have a score between zero “0” to thirty-two “32”. Each of the 41 components added together, therefore there was a total score of NFI ranging between zero “0” and sixty-five “66”. The physical and cognitive variables description and cut points for the NFI are presented in a previous article (11).

Statistical Analysis

The parametric tests were used for normally distributed variables and nonparametric tests were used for the not normally distributed. Before applying regression models univariate tests (including chi-square, t-test, ANOVA, correlation) were used for data exploration.
Categorical NFI scores were created using classification and regression trees, using the applied Chi-squared Automatic Interaction Detector (CHAID) model. This modeling accounts for the binomial distributions in response variable. Validation of the multivariate analyses was tested with a 10-fold cross-validation; CHAID identifies ‘nodes’, or persons subgroups, that are most homogeneous with regards to probability of death. These nodes were then applied to Cox regression for further investigation. In addition to bivariate tests and decision tree, two separate Cox regression models were applied to the data to estimate chance of death in 5 years of follow-up. Cox regression was used in two formats of NFI (one for NFI as a continuous and another as a categorical variable). A p-value of 0.05 or lower was considered statistically significant. Statistical analyzes were performed using SPSS (version 23) (23) and R×64 (version 3.1) (24).

 

Results

This was a secondary analysis of the Canadian Study of Health and Aging (CSHA) dataset (21, 25). The NFI mean was 9.63 (SD=6.04) and ranged from 0 to 33. Distribution of NFI was slightly skewed to the right (skewness=0.95, SE=0.08; Kurtosis= 0.90, SE=0.16). At baseline, the mean age of sample was 80.4 years (SD=6.9; Range: 66-100). There was a clear positive, yet small association between age and NFI at baseline (rho=0.27, p<0.001, 2-tailed).

Table 1
Demographic characteristics and cognitive status of the sample as a function of NFI score categories

Note: 3MS: Modified Mini-Mental State; CSHA: Canadian Study of Health and Aging; NFI: Neurocognitive Frailty Index. Data showing mean (standard deviation) for all variables, and total number (percentage) for sex category. * NFI score defined as number of deficits for example “0-3” means zero to three deficits

 

Presented in Table 1, demographic characteristics and cognitive data were described using means and standard deviation for age, 3MS at baseline (CSHA1) and at follow-up (CSHA2), years of education, and male sex percentage, as a function of NFI scores breakdown. Here, NFI result was grouped into 4 categories based on decision trees results (Figure 1).

Figure 1
Decision trees to determine optimal stratification of NFI for predicting mortality

 

Tables 2 presents the results of two separate regression models predicting outcomes 3MS for participants with and without CVD. In both models, NFI is significantly related to 3MS at follow-up; however, NFI is stronger predictor in people with CVD than without. In the multiple linear regression analysis, adjusted for age, sex, education, and 3MS at baseline, the NFI was correlated to 3MS at follow-up (regression coefficient=-0.6), and age (p<0.05). Every additional deficit used to calculate the NFI was associated with an increased chance of cognitive decline. However, this association was stronger in people with CVD. NFI (regression coefficient=-0.79) was significantly associated with cognitive status at follow-up for people with CVD and this was stronger than age (p<0.05).

Table 2
Associations between NFI and Cognitive status (3MS at follow-up) using Multiple Linear regression for participants without CVD in Panel A and with CVD in Panel B

*Note: B: Beta coefficient; SE: standard Error.

 

Although in both groups (with and without heart problems), NFI is related to the outcome (without:R2=0.08, rho=0.32, p<0.01; with:R2=0.02, rho=0.141, p<0.05), the association between NFI and 3MS indicates different variability to predict the outcome in people with and without CVD (Table 2).
The CHAID decision tree analysis was used to determine optimal stratification groups to best identify association between NFI and mortality. Result indicated that the NFI could be grouped into four categories. Group one (category) is for the people who have an NFI score of less than 2.75 deficits and mortality rate of 7.6%. The second and third groups have a mortality rate of 21.8% and 36.3%, respectively, indicating a three to five times more mortality rate, compared to baseline. The highest risk was for people with an NFI score of more than 17.8 and mortality rate of 58%. We used this result for making 4 levels of NFI to predict mortality.
Cox regression was applied for modeling probability of mortality when NFI was considered as a continuous variable. NFI was significantly related to mortality probability with an odds ratio of 1.08. This indicates participants with a higher NFI score have a higher mortality probability. In this model, age, gender and CVD were significant (Table 3, Panel A). In the proportional hazard ratio analysis, accounting for age, gender and education as confounding variables, the value of NFI (as categorical) was more highly correlated to survival than age. Every additional deficit used to calculate the NFI was associated with an increased risk. By using results of the decision tree grouping, the NFI was significantly associated with mortality for the second group of participants in comparison to the fittest people at baseline with an NFI score between 4 and 8 deficits (HR = 2.63, 95% confidence interval (CI) 1.22-5.71; p-value = 0.014). Participants with 18+ NFI deficits showed greater risk [HR = 9.172, (95% CI 4.12-20.41)], indicating they have 9 times more chance of death compared to baseline (Table 3, Panel B). Moreover, mortality probability was greater in people with CVD (HR = 1.28) than without (Figure 2, Panel A). Although all levels of NFI were highly correlated to mortality (Figure 2, Panel B), the Kaplan-Meier curve shows increasing level of NFI, suggesting dose-response effect in relation to survival.

Table 3
Association between NFI and mortality probability using Cox proportional hazard model (Panel A). Association between NFI and mortality using Cox proportional hazard model (Panel B)

*Note: B: Beta coefficient; CI: Confidence Interval; HR: Hazard Ratio; NFI (≤3 deficits) is baseline. SE: standard Error.

 

Figure 2
Kaplan-Meier curves for the proportional survival of people with and without CVD (Panel A) also with various levels of NFI (Panel B). In Panel B, node 1 indicate NFI < 4 and node 2 for NFI between 4 and 8, node 3 related to NFI score between 9 and 17, and finally node 4 is related to NFI more than 18 score

 

Discussion

Our study aimed to predict cognitive status and 5-year survival rate in participants with CVD in the Canadian Study of Health and Aging (21) dataset using a deficit accumulation approach. The NFI, as a measure of frailty, was used to examine the association between frailty and cognition as measured by 3MS at baseline and 5 years later in individuals with and without CVD.
We have shown a significant and positive association between age and NFI, indicating that neurocognitive frailty increases with age. Additionally, the magnitude of the association for the NFI was larger than age. Furthermore, higher NFI score was associated with higher probability of mortality. Mortality was 28% greater in individuals with than without CVD.
Our study results support previous findings from various scientific groups. For example, the Alzheimer’s Association that noted CVD are present in most cases with AD, without suggesting that all individuals with AD have heart problems (9). Similarly, the Cardiovascular Health Study Cognition Study (26, 27) found subjects with mild cognitive deficits have CVD. Using the results of models from this study, baseline cognition and age were significant predictors, yet a more comprehensive prediction can be achieved by including frailty measures. Additionally, our results concur with studies examining vascular risk factors involving cognitive decline. A longitudinal study (28) found the associations between smoking and long-term blood pressure with the risk of cognitive decline. However, a review (3) underlined the need for more sensitive neuropsychological measures. Our study shows that the NFI serves as a more effective prognostic tool to evaluate the risk of developing dementia. Findings from an earlier study (29)underscore the association between vascular risk factors and cognitive function at midlife, and their different impacting pathways on brain mechanisms. Although we have shown that the NFI was highly correlated to mortality, a dose-response effect in relation to survival is noted with increasing levels of NFI. Thus, the identification of CRFs using the NFI might serve to identify those at higher risk for dementia so to provide preventative interventions.
Perhaps the most important implication of our result is the integration of physical frailty and cognitive elements as a neurocognitive frailty measure such as the NFI to better understand the relationship between CVD and cognitive decline. Indeed, in line with our findings, others (12) have noted the importance of including the frailty status in the clinical process of evaluating risk factors for dementia to optimize the treatment plan. The present study provides validation-evidence for the use of NFI with elderly individuals at risk for cognitive deficit with co-existing CVD to enhance the prognostic value of cardiovascular risk factors.

Noteworthy that our study is not without limitation, and we can underscore our use of cognitive screening tool as a possible issue. In our study the cognitive status of all subjects was assessed through the Modified Mini-Mental State test; although this test offers increased validity over the MMSE, it remains a screening tool for the evaluation of cognitive impairment.
We have established an association between CVD and cognitive deficits; however, further investigation is needed to clarify the extent and types of modifiable CVD risk factors (e.g., diabetes and hypertension) and their consequence on the cognitive functions. As previously highlighted (30) to identify individuals needing specific intervention plans and to maximize outcomes based on risk profiles using the NFI future research is warranted. Since the NFI risk profile could be useful in treatment planning, and that CVD are related to cognitive deficits as noted by several colleagues (31), maintaining vessel elasticity through physical exercise could be beneficial to those at risk of developing cognitive deficits.

 

Conflict of Interest: None to declare
Sponsors’ role: None to declare
Disclosure: The research team would like to acknowledge the access given to the Canadian Study of Health and Aging (CSHA) dataset by Dr Kenneth Rockwood, which was much appreciated.
Ethical Standards: The authors declare that the experiments and data collected comply with the current laws of the country in which they were performed.
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.

 

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SELF-RATED FRAILTY AND MORTALITY IN OLD MEN: THE MANITOBA FOLLOW-UP STUDY

 

E. Sachs1, P. St. John2,3, A. Swift1, R. Tate1,3

 

1. Department of Community Health Sciences, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Canada
2. Section of Geriatric Medicine, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Canada; 3. Centre on Aging, University of Manitoba, Canada.
Corresponding author: Elizabeth Sachs, Manitoba Follow-up Study, University of Manitoba, P218-770 Bannatyne Avenue, Winnipeg, MB, R3E 0W3, Ph.:204-298-1059, Fax: 204-789-3905, Elizabeth.Sachs@umanitoba.ca

J Frailty Aging 2021;10(1)44-48
Published online March 30, 2020, http://dx.doi.org/10.14283/jfa.2020.14

 


Abstract

Background: While a multitude of definitions and operationalizations of frailty have been developed, rarely have these considered the perspective of the older adult themselves. This knowledge gap was addressed by examining older adults’ self-rating of frailty. Objectives: To assess the validity of self-rated frailty and to determine whether self-rated frailty relates to mortality. Design: The Manitoba Follow-up Study was initiated in 1948 as a prospective cohort study of 3,983 men. Setting: Community dwelling older adult men. Participants: Survivors of the original cohort (231 men) were sent a quality of life survey in 2015. A response was received from 186 men, including 146 surveys completed by the participant himself and thus were eligible to include (completion rate of 78.4%). Measurements: The quality of life survey is sent out annually to the study participants to ascertain information about mental, physical, and social functioning. In 2015, the Clinical Frailty Scale was adapted and added to the survey as a simple self-rating of frailty. Results: The mean age of the 146 respondents in 2015 was 93.7 years (SD 2.7) Self-ratings of “moderate-severe” frailty, received from 132 men, were associated with worse measures of physical health and functional impairment, thus supporting the significance of self-rated frailty. Adjusted for age, the Hazard Ratio for mortality over the next 3 years was 3.3 (95% CI: 1.5, 7.1) for those who rated themselves as “mildly to severely frail” vs. “very fit or well, with no disease”. Conclusion: The present study has illustrated that self-rated frailty is associated with other measures of health and that self-rated frailty predicts mortality over a three-year period. These findings support the utilization of older adult’s self-ratings of frailty for new avenues of operationalizing frailty.

Key words: Frailty, self-rated, older men, mortality.


 

 

Introduction

Frailty has been considered to be a loss of reserve capacity and resistance to stressors (1). This condition has been associated with an increased risk of adverse health outcomes including increased risk of falls, disability, hospitalization, and mortality (1–3). Frailty is a growing public health concern. In 2016, 18% of the United Kingdom’s (UK) population was aged 65 years or older (4). It was estimated that by 2036, this proportion would increase to 24% (4). Similarly, the Canadian older adult population is expected to comprise up to 25% of the population by 2036 (5). The fastest growing segment of this population, the oldest old (80+ years old), are at increased risk for the detrimental effects of frailty (1, 6, 7). Therefore the multifaceted clinical and societal consequences of frailty are expected to increasingly impact the provision and financial implementation of health policy and service provision (8).
Many definitions and models of frailty have been developed. From a medical researcher and clinician perspective, the most popular models are:1) Fried et al.’s (1) frailty phenotype, which considers frailty as a biological syndrome; and 2) Rockwood and Mitnitski’s (9) accumulation of deficits model which views frailty as a state of risk determined by the burden of deficits in multiple domains acquired over time (2, 10). A universal definition of frailty has yet to be agreed upon (11–13), and efforts to reach consensus have had limited success (14).
The study of successful aging experienced similar conceptual and operational challenges (15). In response, Swift and Tate (16) found that lay definitions of successful aging – definitions from the perspective of the older adult – were much more complete than researcher-generated definitions. In light of this parallel research in the area of successful aging, perhaps lay definitions of frailty may be much more comprehensive than researcher-generated conceptualizations.
However, this approach has been minimally explored. Grenier (17) conducted a study exploring lived experiences of frailty of older women. The women interviewed discussed times when they experienced vulnerability and uncertainty, thus “feeling frail” as opposed to elaborations of physical characteristics (17). St. John, McClement, Swift, and Tate (18)explored older men’s definitions of frailty. It was found that 56% of participants did not think that they were frail (18). The participants were also asked to provide their own definition of frailty (18), which did not fully align with clinical definitions (18). Of the responses that did align with a clinical definition, the most popular definition was “frailty as a disability” (18, 19).

Self-rated Frailty

Self-rated health has been well recognized as a valid indicator of health and an important predictor of mortality and well-being (20–22). Analogous to how self-rated health has a strong positive gradient with risk of mortality (20–22), it may be that self-rated frailty (SRF) may also exhibit a relationship with mortality and well-being. This approach has been minimally explored in the literature. With these considerations, we sought to explore the utility of SRF in a Canadian older adult population. The objectives of the present study were to assess the validity of self-rated frailty and to determine whether and how SRF relates to mortality.

 

Methods

Sample

The Manitoba Follow-up Study (MFUS) is the longest running prospective investigation of cardiovascular disease and aging in Canada. Currently in its 72nd year, this prospective cohort study examines health and well-being in a cohort of Second World War Royal Canadian Air Force aircrew recruits. The cohort was sealed on July 1, 1948 with 3,983 men (23). Further cohort details are available elsewhere (23). The present analysis has used primary data collected from MFUS. In 1996, a quality of life survey was designed and mailed to study participants to ascertain core information about each man’s mental, physical, and social functioning apart from physician diagnosed disease (23). Deemed the Successful Aging Questionnaire (SAQ), the self-administered questionnaire captured several aspects of health, well-being, and functional status (24). The construction of the SAQ drew from several pre-existing sources, most notably the RAND SF-36 (25). Several open-ended qualitative questions assessing successful aging and frailty have since been added to the SAQ (24).

The 2015 SAQ was mailed to 231 members. Of the surveys returned, 23 surveys were received blank, marked “moved” or “deceased.” 148 were filled out by the MFUS member himself without any outside assistance, however 2 additional responses were excluded as the response received was not pertinent (i.e. something other than the SAQ). Therefore, 146 men were included for this analysis at Tinitial. This process was repeated in 2016 and 2017 (Tfinal). A detailed description of the methods of that study are available elsewhere (18).

Measures

Information of interest included limitations with basic activities of daily living (BADL), limitations with instrumental activities of daily living (IADL), mental health (MCS), physical health (PCS), and the study member’s self-rating of frailty (16, 23, 24). Self-rated frailty was measured using a version of the 7-point Clinical Frailty Scale used in Canadian Study of Health and Aging (26, 27), which we modified for self-report. This scale asked participants to “Please rate YOUR frailty on this scale”. Available responses range from 1= very fit to 7= severely frail. This scale is available in Appendix 1.

Data Analysis

Data was analyzed using SAS (version 9.4) in a secure location on the University of Manitoba campus. Hypothesis testing was conducted at the p ≤ 0.05 level of significance. If a SAQ was not returned by the member, the member was excluded from analysis for that year. Responses to the SRF question were coded as ‘missing’ if the SAQ was returned but this question was not answered, or if the given answer was not one of the options available (i.e. a written answer, question crossed out, or “see previous”). Missing answers were excluded from analysis. A questionnaire containing more than one indicated response was assigned the most severe rating.
Following preliminary descriptive analysis, the utility of using a self-rating to measure frailty was investigated by determining how the self-rated Clinical Frailty Scale related to other global measures of health. Specifically, the mean and standard deviation of the measures of health and function assessed within the SAQ (MCS, PCS, IADL, and BADL) were calculated within the categories of self-rated frailty (levels 1-7). These means were then compared with ANOVA. It was expected that if self-rating was a useful measure of frailty, then MFUS members with lower self-rated frailty scores (i.e. less frail) would have better measures of health (higher MCS and PCS scores) and fewer limitations (lower BADL and IADL scores). “Age” was also tested to see if it is significantly related to self-rated frailty. Additionally, the self-rated frailty score from Tinitial and mortality data at Tfinal was used to investigate the relationship between SRF and mortality. A Kaplan-Meier curve illustrated the survival of each grouping of the self-rated frailty scores. Cox proportional hazard modeling illustrated the contributions of self-rated frailty to mortality. Other factors included in the modeling included age, marital status, PCS, and MCS.

 

Results

The final samples sizes used for analysis were 146 (Tinitial) and 87 (Tfinal). Response rates were 80.5% (Tinitial) and 80.6% (Tfinal). The mean age of the participants was 93.7 (SD 2.7) (Tinitial), and 94.6 (2.7) (Tfinal).

Validity of SRF

The results of the ANOVA that compared the measures of health within categories of the SRF scores are presented by Table 1. After exclusions, the remaining sample sizes were 132 responses at Tinitial. Groups 6 and 7 were combined during analysis, as there were fewer than 5 members reported in group 7. There were statistically significant mean differences for PCS, IADL, and BADL across the six categories of SRF. To determine which group means differ significantly, a post hoc Tukey test was performed. No significant difference in mean PCS was apparent for SRF groups 1 and 2, group 2 was different from group 4, and the mean in each of the first 5 SRF groups differed from the mean PCS of the most frail, group 6 and 7. Mean IADL of the most frail men, group 6 and 7, differed significantly from all other SRF groups. Similarly, the mean BADL of the most frail men in groups 5, 6 and 7 differed significantly from all other SRF groups. These results indicate that the least frail men (groups 1 and 2) reported significantly better physical health than the most frail men (group 6&7). The least frail and most frail men also reported significantly different physical health than men who reported mild frailty (groups 3 and 5). Therefore a gradual gradient of declining physical health with increasing SRF was observed. This gradual change can also be seen within the IADL and BADL variables. Therefore we concluded that increased SRF scores generally correspond with worse health and increased activity limitations as measured by other accepted measures of health (PCS, IADL, BADL).

Table 1
Mean and Standard Deviation of Measures of Functional Status (TInitial) Within Categories of Self-rated Frailty Scores

Notes. MCS=Mental Component Score, PCS=Physical Component Score, IADL=Instrumental Activities of Daily Living score, BADL=Basic Activities of Daily Living score. Several health measures use smaller sample sizes because of missing data (i.e. unanswered questions throughout the SAQ); these instances have been indicated as such. MCS/PCS variables were scored so that a lower score indicates worse health. IADL and BADL variables were scored so that a lower score indicated fewer limitations.

 

Mortality

This study also sought to determine whether and how self-rated frailty relates to mortality. Figure 1 is a Kaplan-Meier curve displaying the survival of each grouping of the self-rated frailty scores (log rank χ2 test: 16.2, 3 df, p<0.001). Cox proportional hazard modeling was used to illustrate the contributions of SRF to mortality. Marital status, MCS score, and PCS score were not significant in multivariable modelling. Table 2 illustrates that the hazard of dying for men who reported a SRF of Mildly-severely frail was 3.3 (95% CI: 1.5, 7.1) times than that of men who reported a SRF of Very fit-well with no disease, when adjusted for age. When men in the Very fit-well with no disease rating were compared to all men in the less fit categories, the age-adjusted hazard ratio was 2.9 (95% CI: 1.6, 5.2). Therefore self-identified frail men have a significantly increased risk of mortality than men who rate themselves as less frail.

Table 2
Age-adjusted Hazard Ratios (95% CI) for Total Mortality Associated with Categories of Self-rated Frailty at TInitial

Figure 1
Survival Probability by Self-rated Frailty Group. This figure displays the Kaplan-Meier curve survival of each self-rated frailty grouping. Log rank χ2 test: 16.2, 3 df, p<0.001

 

Discussion

We explored the utility of SRF in response to conceptual and operation difficulties within the literature. We found that in a cohort of older Canadian men, increased SRF was associated with worse health and increased functional limitations, as measured by other accepted measures of health. We also found that men who self-rated themselves as frail had a significantly increased risk of mortality compared to men who self-rated as less frail. Therefore, in addition to utilizing scales and comprehensive assessments of frailty clinicians and researchers are urged to consider their patient’s self-perceptions of their own experience of frailty.

Strengths

There are several strengths to this study. First, the SAQ used by the present study has been in use at MFUS since 1996, using the same methodology with few deviations since its implementation (15). Furthermore, the men involved with MFUS are familiar with the questionnaire and answering open ended questions. Second, the SAQ is a self-administered questionnaire that has captured several aspects of health, well-being, and functional status (24), and is based on well-established measures such as the SF-36. Third, the data provided by MFUS was unique as studies with participants over the age of 90 are unusual (18, 23).

Limitations

There are several limitations to the present study. First, the sample was made of very old Canadian men (23, 28). Their experiences of frailty may include factors that have a cultural or gendered perspective, impacting the applicability of the present study’s results to older women and to older men from other cultures (18). Secondly these men were born within only a few years of each other, have resided mostly within Canada for most of their lifespan, and have had the common experience of having served in the Royal Canadian Air Force during the Second World War (15, 18). This may limit the generalizability of results to populations outside this demographic.

Implications

As the population ages, an increasing proportion of older adults are expected to be affected by frailty (8). The operational and conceptual definitions in the literature exhibit lack of consensus, limiting the effectiveness of our approach. The present study has provided support that SRF is most closely associated with factors of physical health and functional limitations. Therefore the implications of the present study support that SRF is most closely associated with physical factors or experiences of frailty. In this manner the present study has provided evidence to support operational or conceptual approaches to frailty that consider factors of physical health, such as Fried et al.’s (1) phenotype of frailty or the use of physical components of health in frailty indices (9). Additionally, Table 1 indicates a lack of association between chronological age and severity of SRF score. Although the age range of the sample is narrow, this may show the importance of considering other factors than just age in clinical decision making.
The British Geriatrics Society Fit for Frailty report has acknowledged the importance of identifying the impact frailty has on care provision (29). However, a hesitancy to use the term “frail” when engaging with older adults has been noted (18). This discomfort may be due to fear of offence thereby impacting the physician-patient relationship, the displeasure of delivering bad news, or concern that the patient might internalize a sick role. This study has shown that a self-rating was a useful measure of frailty. Furthermore, the hazard of dying for men who reported a SRF of 5, 6, or 7 at TInitial (group 5,6,7. Mildly-severely frail) was 3.3 (95% CI: 1.5, 7.1) times greater than men who reported a SRF of 1 or 2 (when adjusted for age). As such, the consequences of becoming frail warrant reconsideration on the discussion of SRF with older adults.

 

Conclusion

In closing, the growing impact of frailty has far reaching implications on the provision and financial implementation of health policy and service provision (8). While several definitions and operationalizations of frailty have been developed, the current researcher-generated definitions of frailty might not fully address the issue. Analogous to the usefulness of self-rated health, using older adult’s self-ratings of frailty may present new avenues of operationalizing frailty. The present study addressed these issues through investigation of the utility of self-rated frailty using data collected from the Manitoba Follow-up Study.
The analyses of the present study showed that increased ratings of SRF scores generally correspond with worse health and increased limitations as measured by other accepted measures of health (PCS, IADL, BADL). It was also found that self-identified frail men have a significantly increased risk of mortality than non-frail men. The implication of these results is that SRF may provide an alternative method that may not be as affected by feasibility concerns during clinical application. Additionally, this project adopted the perspective of the older adult, which was lacking from the current literature.

 

Funding: This work was funded by the Canadian Institute for Health Research [grant number PJT-152874] and charitable donations from the participants and families of MFUS members. 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: We express our gratitude to the participants of MFUS and their families for their continued involvement in the study.
Conflict of interest: None declared by the Authors.
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.

 

SUPPLEMENTARY MATERIAL

 

References

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PREDICTIVE FACTORS OF IN-HOSPITAL MORTALITY IN OLDER ADULTS WITH COMMUNITY-ACQUIRED BLOODSTREAM INFECTION

 

D. Angioni1, M. Hites2, F. Jacobs2, S. De Breucker1

1. Department of Geriatric Medicine, CUB-Erasme, Brussels, Belgium; 2. Infectious Diseases Clinic, CUB-Erasme, Brussels, Belgium.
Corresponding author: Davide Angioni, Hopital Erasme, Gériatrie, Belgium, davideangioni2@gmail.com

J Frailty Aging 2020;9(4)232-237
Published online January 6, 2020, http://dx.doi.org/10.14283/jfa.2019.45

 


Abstract

Objectives: To assess the prevalence of intra-hospital mortality and associated risk factors in older people aged 75+, admitted with blood stream infections (BSI). Design: Single center retrospective study performed in an 850-bed of the academic hospital of the Université Libre de Bruxelles. Setting and Participants: From January 2015 to December 2017, all inpatients over 75 years old admitted with BSI were included. Measures: Demographical, clinical and microbiological data were collected. Results: 212 patients were included: median age was 82 [79-85] years and 60 % were female. The in-hospital mortality rate was 19%. The majority of microorganisms were Gram-negative strains, of which Escherichia coli was the most common, and urinary tract infection was the most common origin of BSI. Compared to patients who survived, the non-survivor group had a higher SOFA score (6 versus 3, p<0.0001), a higher comorbidity score (5 versus 4, p<0.0001), more respiratory tract infections (28 vs 6 %, p < 0.0001) and fungal infections (5 vs 1 %, p = 0.033), bedridden status (60 vs 25 %, p < 0.0001), and healthcare related infections (60 vs 40 %, p = 0.019). Using Cox multivariable regression analysis, only SOFA score was independently associated with mortality (HR 1.75 [95%IC 1.52-2.03], p<0.0001).Conclusions and Implications: BSI in older people are severe infections associated with a significant in-hospital mortality. Severity of clinical presentation at onset remains the most important predictor of mortality for BSI in older people. BSI originating from respiratory source and bedridden patients are at greater risk of intra-hospital mortality. Further prospective studies are needed to confirm these results.

Key words: Blood stream infections, older adults, mortality, bacteremia, SOFA.


 

 

Introduction

Bloodstream infection (BSI) is a common cause of hospitalization and mortality in older people (1). The incidence of BSI increases with age (2), due to multiple factors such as immune senescence, comorbidity, malnutrition, and environmental factors (3). The diagnosis of BSI in frail old people remains a challenge because of the high frequency of atypical clinical presentations (4). Geriatric symptoms such as delirium, drowsiness, loss of appetite, weight loss, falls, and incontinence may be in the foreground in the absence of specific symptoms of infection (5). Inflammatory biomarkers like C – reactive protein may be useful but lacks specificity and the use of procalcitonin as a specific biomarker of infection is still debated (6). Urinary tract infections are the most common source of bacteremia in the majority of studies, followed by respiratory infections. Gram-negative are more common than Gram-positive organisms. They are responsible for 40% to 60% of BSI in older people (1). Among them, Escherichia coli spp is the most common pathogen found, accounting for 40% of community-acquired BSI, and 10-20% of healthcare-associated BSI (7-11). In the last 20 years, the incidence of BSI has increased in the general population, and in-hospital case-fatality ratio has decreased (12). Four studies evaluated intra-hospital mortality rate in patients over 75 years of age, ranging from 15 to 56%. (11, 13-15). None of these studies assessed the mortality rate as a primary objective, and population and clinical characteristics varied considerably from one study to another.
The main objective of this study was to assess the prevalence of intra-hospital mortality in patients older than 75 years old admitted with BSI. The secondary objectives were to evaluate the characteristics of BSI and to identify risk factors for in-hospital mortality.

 

Materials and methods

Setting and design

We performed a single center retrospective study in the 850 beds of the academic hospital of the Université Libre de Bruxelles, Brussels, Belgium. From January 2015 to December 2017, we included all inpatients over 75 years admitted for BSI (bacteremia and fungemia), in whom positive blood cultures were obtained within the first 48 hours after admission. We identified the patients on the basis of laboratory reports generated by the microbiology department. We excluded patients whom blood cultures were positive for a germ considered as a contaminant, patients having signed an opt-out declaration (written declaration of refusal to participate in a clinical study) and patients for whom the medical files were incomplete. Source of infection was determined according to CDC definitions (16). The local Ethical Committee (Comité d’Ethique Hospitalo-Facultaire Erasme-ULB) approved the study (P2017/125) but waived the need for informed consent because of its retrospective nature.

Clinical data

We evaluated patient-related risk factors, BSI-related risk factors and environmental risk factors of BSI.
Patient’s factors were age (years), gender (Male = 1, Female = 0), Sequential Organ Failure Assessment (SOFA) score (from 0 to 24 points) (17), Charlson Comorbidity Index (from 0 to 37 points) (18), and immunosuppression (Yes = 1; No = 0). Immunosuppression was defined as HIV patients with lymphocytes CD4+<200/mm³, patients taking immunosuppressive drugs in a context of organ transplant or autoimmune pathology, patients taking at least 7.5 mg of prednisolone for more than 3 months, and/or neutropenia with <500 neutrophils/mm3. Others factors were: active solid or hematological tumor (Yes = 1; No = 0), chronic renal insufficiency (Yes = 1; No = 0), defined in patients with a glomerular filtration rate (GFR) < 60 mL/min according to CKD- EPI and/or under dialysis, severe dementia (Yes = 1; No = 0), defined according to medical data recorded, and bedridden patients (Yes = 1; No = 0), defined as patients unable to get out of bed for more than 3 days during hospitalization.
Factors related to BSI were: adequate antibiotic therapy initiated in the first 48h of BSI onset (according to the antibiogram) (Yes = 1; No = 0); Health care-associated bloodstream infection (HCA-BSI) (Yes = 1; No = 0) defined as those having at least one of the following characteristics (19): having been discharged from an acute care hospital within the last 30 days, receiving hemodialysis or any kind of intravenous therapy provided by a hospital-dependent facility within 30 days prior to the BSI, residence in a long-term care facility. Other factors were: BSI with multi-resistant bacteria (BMR- BSI) (Yes = 1; No = 0), defined as a bacterium resistant to at least 3 classes of antibiotics including a third-generation cephalosporin (20), the microorganisms responsible for the BSI divided in four categories (GRAM-positive strains, GRAM-negative strains, fungal and polymicrobial infections) (Yes = 1; No = 0), the source of BSI divided in five categories (urinary, respiratory, intra-abdominal, other sources and unknown sources) (Yes = 1; No = 0) and the need for a surgical or endoscopic treatment for source control (Yes = 1; No =0).
Environmental factors were: residence in a nursing home before hospitalization (Yes = 1; No = 0), recent hospitalization in the last 30 days (Yes = 1; No = 0) and treatment with any antibiotic in the previous 30 days (Yes = 1; No = 0). The survivor group was defined as patients who were discharged from the hospital alive. The non-survivor group was defined as patients who died during hospitalization, regardless of the cause and the length of hospitalization.

Statistical analysis

Analyses were conducted using Stata-12 software (Stata Corp LLC, College Station, TX, USA). Descriptive results were reported as number and percentage (categorical variables). Continuous variables were expressed as mean ± SD or median (interquartile range, IQR). Comparison of the clinical characteristics differences in both groups (Survivors and Non-survivors patients) were performed using Chi2 test or Fischer’s test for categorical variables, non-paired Student’s t test or Mann Whitney test respectively for parametric and non-parametric continuous variables.
Patient survival was calculated by the Kaplan Meier method. The association between patient mortality and independent variables was estimated by univariate Cox proportional hazards model.
Thereafter, factors significantly associated with mortality were identified using univariable Cox’s regression. Multivariable models were built using all significant variables detected in the univariable Cox’s regression. Because of the number of variables allowed in the final model (cases=40), a first model was selected by using a forward stepwise procedure. Only one variable was selected and the final model was presented with the significant variable (SOFA) and isolates.
We have presented the HRs with 95% confidence interval derived from the Cox model and p-value corresponding to the Wald’s test. The proportional hazards assumption for variables in the final Cox model was tested graphically for categorical variables and by using interaction with time for quantitative variable.
Two-sided p-values < 0.05 were considered as statistically significant.

 

Results

Two hundred and twelve (60 %) patients with BSI were included, and 143 patients (40 %) were excluded because of positive blood cultures considered as contaminants. The most common pathogen considered as a contaminant was Staphylococcus epidermidis spp (48%) found in single bottle. The median age of the study group was 82 [79-85] years and two thirds were female. Forty patients died (19%) after an average of 11 [5-10] days after admission. Figure 1 shows the survival curve: sixty (40 %) died within the first week of hospitalization, twenty-five (62%) within the first 15 days, and thirty- five (87%) within the first 30 days of hospitalization. Table 1 shows the characteristics of survivor et non-survivor groups. SOFA score and comorbidity according to the Charlson Comorbidity Index was higher in the non-survivor group. Fourteen (35%) of them were admitted at least once to Intensive Care Unit. Twenty-five (62%) died while receiving antibiotic therapy. The survival rate was equal with or without source control of bacteremia, either by endoscopy (p=0.628), or by surgical treatment (p=0.103). No difference was seen between groups for the number of adequate empirical antibiotherapy (p=0.194).

Table 1 Descriptive results in total, Non Survivor and Survivor groups

Table 1
Descriptive results in total, Non Survivor and Survivor groups

SOFA : Sequential Organ Failure Assessment. Continuous data are expressed in medians ([IQR] or means ± SD. Categorical data are expressed in total numbers (percentages)

Figure 1 Kaplan-Meier curve

Figure 1
Kaplan-Meier curve

 

The majority of causative microorganisms were Gram-negative strains with E. coli as the most frequently isolated bacteria; urinary tract infection was the most common origin of BSI (Tables 1, 2). Cox univariate regression analysis identified the following risk factors for in- hospital mortality: the SOFA score, the Charlson Comorbidity Index, the status of being bedridden, the healthcare related infections, and the respiratory source (Table 3). On the other hand, infections caused by Escherichia coli (HR 0.36 [CI 95% 0.16-0.77], p=0.009) were found to be protective factor in terms of mortality. Using Cox multiple regression analysis, only the SOFA score was independently significantly associated with mortality.

Table 2 List of microbiological isolates found in blood cultures

Table 2
List of microbiological isolates found in blood cultures

ESBL = Extended-Spectrum ß-Lactamase

Table 3 Univariate Cox regression model

Table 3
Univariate Cox regression model

HR = Hasard Ratio. 95%CI = 95% Confidence Interval; SOFA : Sequential Organ Failure Assessment.

Table 4 Multiple cox regression model (cases=40/ n=212)

Table 4
Multiple cox regression model (cases=40/ n=212)

aHR = adjusted Hasard Ratio. 95%CI = 95% Confidence Interval; SOFA : Sequential Organ Failure Assessment

Discussion

We described the factors associated with mortality inpatients older than 75 years old with community-acquired bloodstream infections, hospitalized in medical and surgical units in an academic center. The patient population included in our study has similar characteristics to patients older than 75 years old hospitalized in acute care units in Belgium, in terms of age, sex and length of stay (30). The most common source of BSI was urinary tract infection, as has been shown in many studies (7-10, 13-15). Urinary tract infection (UTI) is the most frequent bacterial infection in old people (21). Although bacteremia is classically considered as a marker of severe disease (22), two studies demonstrated that the prognosis of UTI associated with bacteremia is not worse than UTI without bacteremia in old patients (23, 24). We found multi-drug resistant gram-negative strains in 16% of cases, mainly E. Coli and K. Pneumoniae producing extended-spectrum ß-lactamase (ESBL). Only 2% were associated with Methicillin-resistant Staphylococcus Aureus and no patients presented BSI related to Vancomycin-resistant Enterococci (VRE) or Carbapenemase-producing Klebsiella species (KPC). The presence of multi drug resistant and classically healthcare associated bacteria in this population is due to the the fact that Gram negative strains producing ESBL are an emerging cause of community infections (25) and to the fact that many patients included in our study presented one or more risk factors like living in a nursing home (18%), a history of recent hospitalization (23%) or recent antibiotherapy (18%). The source of anaerobic BSI, essentially Clostridium species (1%) and Bacteroides species (2%), and polymycrobial BSI (8%) originated from intra-abdominal in almost all cases.
We also found a high prevalence of BSI due to abdominal infections, partially due to a large and active medico-surgical digestive department in our hospital. The prevalence of BSI from unknown source varies from one study to another, depending on the definitions used to describe the presence of an infection (1). In our study, the source was identified in 87% of cases, which is equivalent to other studies (7, 9, 13-15). The unknown source might also reflect the fact that clinicians limit investigations and privilege an empirical strategy, because of old age itself or because of pre-defined therapeutic limitations. Gram-negative strains were responsible of two thirds of BSI. In the literature, Gram-negative strains are more common than Gram-positive pathogens in BSI of older patients (15). The risk of colonization with Gram-negative microorganisms increases with age, functional status, nursing home residency, hospitalization and respiratory disease.

The mortality associated with BSI was significant (19%) but lower than what is reported in other studies (11, 13-15). Mortality varies according to the characteristics of the population studied. For example, Blots et al. described a mortality rate of 56% but they considered only nosocomial BSI in patients hospitalized in intensive care units (11). The low mortality rate might also be due to the systematic co-management of patients with BSI in our hospital: the infectious diseases physician is immediately informed by the microbiology laboratory if a patient has positive blood cultures. Patients are then examined and antibacterial treatment is immediately reviewed: treatment is started, maintained if considered appropriate, or adapted to the pathogen identified in blood cultures (26).
More than one third of the non-survivor group died after the end of the antibiotic treatment, suggesting that the risk of mortality from BSI is not only a direct consequence of infection but also the consequence of the complications from the infection during hospitalization (anorexia, weakness, bedbound status, cardiac failure, altered neurological status, etc.). Although we found different patient-related, BSI-related and environmental-related risk factors associated with hospital mortality in Cox univariate analysis like bedridden status, respiratory infection, only SOFA score was found to be an independent risk factor of mortality. The SOFA score is an organ dysfunction/failure and morbidity estimation tool predicting the clinical outcomes in critically ill patients (17). In our study, a median score of 6 in the non-survivor group means a significant dysfunction of at least 2 systems. We hypothesize that the severity of the clinical presentation at the onset remains the most important predictor of mortality for BSI in older people, as already described in other studies (1, 7, 11, 13). Since this score has been validated for critically ill ICU patients, future studies are needed to assess the prognosis of patients with BSI.
The association between poor functional status and mortality in BSI has already been described; it may reflect both the poor condition of the patient prior to the infection and the severity of the infection itself (10, 27, 28). Gavazzi et al. demonstrated that an ADL score <2 was associated with 30-day mortality in nosocomial BSI (27). In Belgium, Reunes et al. found that increased age and bedridden status were independent risk factors for death in nosocomial BSI (10). Based on this, we suggest that early mobilization in case of bacteremia could influence the rate of mortality of these patients.
The respiratory source has also been described in other studies as an independent risk of mortality in BSI (7, 11). In case of pneumonia, the yield of blood cultures increases significantly with the severity of pneumonia (29).
There are several study limitations that should be acknowledged. First, this is a retrospective study; geriatric syndromes like depression, malnutrition or functional status were therefore not systematically assessed. For the same reason, information on therapeutic limitations and vaccination status were lacking in the medical files. Second, it is a single center study, limiting its external validity.

 

Conclusion and implications

Our study confirms that BSI in older people are severe infections associated with a significant in-hospital mortality. The severity of clinical presentation assessed by the SOFA score at admission remains the most important predictor of mortality for BSI in older people. We highlight that BSI originating from pneumonia are the most lethal and that bedridden patients are at greater risk of in-hospital mortality. On the other hand, urinary BSI are the most common but are less dangerous. Further multi-centric, long-term prospective studies are needed to better identify the patients older than 75 years old with a BSI at risk of dying during their hospitalization.

 

Acknowledgements: Professor Doctor Christian Melot, (Emergency Department, CUB Erasme, Brussels, Belgium) and Professor Judith Racapé (Biostatistics Department, CUB Erasme, Brussels, Belgium)
Conflicts of Interest: The author(s) declare(s) that there is no conflict of interest regarding the publication of this article.
Ethical standards: All procedures followed were in accordance with the ethical standards.

 

SUPPLEMENTARY MATERIAL

 

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ASSOCIATION BETWEEN THE USE OF ANGIOTENSIN-BLOCKING MEDICATIONS WITH HIP FRACTURE AND DEATH IN OLDER PEOPLE

 

C. Shea1, M.D. Witham1,2

 

1. School of Medicine, University of Dundee, Dundee, United Kingdom; 2. AGE Research Group, NIHR Newcastle Biomedical Research Centre, Newcastle University and Newcastle upon Tyne Hospitals Trust, Newcastle, United Kingdom
Corresponding author: Professor Miles Witham, AGE Research Group, NIHR Newcastle Biomedical Research Centre, Newcastle University, Newcastle NE4 5PL, UK. Email: Miles.Witham@newcastle.ac.uk. Tel: 0044 191 208 1317

J Frailty Aging 2020;9(2)107-110
Published online October 30, 2019, http://dx.doi.org/10.14283/jfa.2019.38

 


Abstract

It is unclear if angiotensin blocking drugs (angiotensin converting enzyme inhibitors and angiotensin receptor blockers) reduce or increase the risk of falls and fractures. We retrospectively analysed routinely-collected, linked health and social care data for patients aged 65 and over from Tayside, Scotland, including hospital discharge diagnoses, biochemistry, deaths, care package provision and community prescribing. We conducted unadjusted and adjusted Cox regression analyses for time to hip fracture and time to death, for any exposure to angiotensin blocking drugs and for time-dependent exposure to angiotensin blocking drugs. We analysed data on 16782 patients. Angiotensin blocking drug use was associated with an exposure-dependent lower risk of hip fracture (hazard ratio 0.988 [95%CI 0.982-0.994] per year of exposure; p<0.001) and death (hazard ratio 0.986 [95%CI 0.983-0.989] per year of exposure; p<0.001). These findings call into question the appropriateness of stopping angiotensin blocking drugs for older people at risk of falls.

Key words: Hip fracture, ACE inhibitor, angiotensin receptor blocker, mortality.


 

Introduction

Falls are a key consequence of the frailty syndrome, and hip fracture is a particularly feared consequence of falling. Controversy continues in the extent to which antihypertensive medication might contribute to falls risk; such medications are frequently stopped in older people who fall because of a perception that such medications increase falls risk by reducing blood pressure and hence cerebral perfusion. However some classes of medication, notably angiotensin-converting enzyme inhibitors (ACEi) and angiotensin receptor blockers (ARBs) have been shown to have beneficial effects on muscle function in preclinical and some clinical studies (1-4). This raises the possibility that such medications could in fact be protective against falls, with recent systematic reviews supporting this possibility (5). There is currently uncertainty from observational studies as to whether ACEi and ARBs reduce or increase the risk of fractures, in part because analyses to date have not been able to adjust for functional status at baseline (6-13). In addition, some studies have not been able to account for cumulative exposure to these medications, instead restricting analyses to baseline exposure with consequent indication bias. The aim of this analysis was therefore to test the association between ACEi / ARB use and hip fracture using a large, routinely collected, linked health and social care dataset.

 

Methods

We performed a retrospective analysis of linked routinely-collected health and social care data. We studied patients aged 65 and over within the Tayside health board area, Scotland, UK (population 400,000), who had contact with Dundee city council social services between 2002 and 2012. The date of first social services assessment was taken as the inception date for each individual in the analysis cohort. We derived cumulative exposure to ACEi or ARB (in days) and any exposure including prior to the inception date from community prescribing records of prescriptions that were encashed with pharmacists. Deaths were obtained from Scottish General Register Office records. Hip fractures and comorbidity diagnoses were derived from hospital discharge records using ICD-10 codes. Hip fracture was diagnosed by the presence of ICD-10 codes S72.0, S72.1, S72.2, S72.8 or S72.9. Biochemical covariates were obtained from routinely collected biochemistry data analysed at Ninewells Hospital, Dundee. The need for a care package was used as a proxy for functional impairment; these data were obtained from Dundee City Council social work records. The methods used for sourcing and assembling these linked data have been described previously (14). All data were held with the Dundee Health Informatics Centre safe haven environment. Local Caldicott Guardian (data protection officer) approval was in place for all studies involving this linked dataset; the need for research ethics approval was waived as the analysis was performed using data collected as part of routine clinical care with no additional patient contact, which had then been anonymised prior to release to the research team.

We conducted unadjusted and adjusted Cox regression analyses for time to hip fracture and time to death, starting with any previous exposure to ACEi/ARB and then adding time-dependent exposure to ACEi/ARB. Such an approach allows the effect of drug exposure to be at least partly separated from the presence of the drug as a marker of underlying disease. Although hip fracture was our primary outcome of interest, we analysed all-cause mortality as well to ensure that any benefit on hip fracture was not counterbalanced by an adverse effect on mortality rates. We additionally conducted competing risks analysis using the method of Fine and Gray (15) to test whether accounting for all-cause mortality as a competing risk modified the association between ACEi/ARB use and hip fracture. Analyses were adjusted for baseline age, sex, deprivation, comorbidities, previous hip fracture, creatinine, bisphosphonate therapy, calcium and vitamin D therapy and receipt of a care package as a proxy for impaired physical function. These factors were chosen as they were available in linked, routinely collected clinical data and have all been shown to influence the risk of hip fracture or the risk of death. We also conducted exploratory subgroup analyses for age (above and below median for the study population), sex, and the presence of a package of social care as a surrogate for frailty. All analyses were conducted in SPSS v24 (IBM, New York, USA) with the exception of competing risks analyses, which were conducted using the stccreg function in STATA version 14 (STATAcorp, Texas, USA). A two-sided p value of <0.05 was taken as significant for all analyses.

 

Results

A total of 16782 patients were included in the analysis. The mean age was 74.2 years and 9957 (59%) were female. 8354 (50%) had been exposed to ACEi/ARB therapy at any point prior to death, fracture or censoring. Table 1 shows the baseline details of the cohort. Users of ACEi/ARB were more likely to be male, had more comorbid disease, and were more likely to be in receipt of a care package than those who had never used ACEi or ARB. Table 2 shows the risk of hip fracture and of death in unadjusted and adjusted analyses. Analyses of any exposure to ACEi/ARB and time-dependent analyses accounting for cumulative exposure to ACEi/ARB are presented, showing that in adjusted analyses, cumulative exposure to ACEi or ARB was associated with an exposure-dependent reduction in the risk of hip fracture, and that this effect was in addition to the lower risk of hip fracture seen in those that had previously used ACEi or ARB. Similar, but more marked effects were seen for all-cause mortality. Unadjusted competing-risks models showed a hazard ratio for hip fracture of 0.992 (95%CI 0.986 to 0.998; p=0.006) per year of exposure to ACEi/ARB; in adjusted competing-risks analyses, the effect did not reach significance (HR 0.996; 95%CI 0.991 to 1.002; p=0.21).

Table 1 Baseline details of analysis cohort

Table 1
Baseline details of analysis cohort

ACEi: Angiotensin converting enzyme inhibitor. ARB: Angiotensin receptor blocker

Table 2 Cox proportional hazards models showing effect of ACEi / ARB exposure on risk of hip fracture or death

Table 2
Cox proportional hazards models showing effect of ACEi / ARB exposure on risk of hip fracture or death

*adjusted for age, sex, deprivation, comorbidities, previous hip fracture, creatinine, bisphosphonate therapy, calcium and vitamin D therapy, receipt of care package; ACEi: Angiotensin converting enzyme inhibitor. ARB: Angiotensin receptor blocker

 

Exploratory subgroup analyses for age above or below the median (74 years), sex, or the presence or absence of a package of care are shown in Table 3. The hazard ratio for death per year of exposure to ACEi/ARB was lower for those receiving a package of care, and this difference was significant (p=0.03) on formal interaction testing; no other significant differences were seen between subgroups.

Table 3 Exploratory subgroup analyses – effect of cumulative exposure in adjusted time-dependent analyses

Table 3
Exploratory subgroup analyses – effect of cumulative exposure in adjusted time-dependent analyses

Adjusted for age, sex, deprivation, comorbidities, previous hip fracture, creatinine, bisphosphonate therapy, calcium and vitamin D therapy, receipt of care package with the exception of the subgroup variable under test; *Hazard ratio per year of ACEi/ARB exposure

 

Discussion

In this analysis, exposure to ACEIs/ARBs was associated with an exposure-dependent reduction in the risk of hip fractures and death. The analysis sample reflects a real-world sample due to the use of routinely collected data, was able to use time-dependent exposure to medication, and was also able to adjust for a range of comorbidities, biomarkers, and importantly, was able to adjust for daily function using a proxy measure (the need for social care). The results are in accord with the majority of previous observational studies (6-13), and are also supported by findings from the Hypertension in the Very Elderly (HYVET) trial, which found a lower rate of fractures in older people with hypertension treated with thiazides and ACE inhibitors (16), but differ from a post-hoc analysis of the Antihypertensive and Lipid Lowering to prevent Heart Attack (ALLHAT) trial, where ACEi did not confer a lower risk of fracture compared to calcium channel blockers, and were inferior to thiazides (17). Given the known beneficial effects of thiazides on bone mineral density, these results are not incompatible however. Recent observational data from the Womens Health Initiative studies suggests variable risk with time; short-term increased risks may be balanced by longer-term benefits to ACEi use (18).
ACE inhibitors may reduce fracture rates via a range of different biological mechanisms. Although such medications are often stopped in patients with orthostatic hypotension, the evidence that modern, long-acting ACEi and ARBs cause orthostatic hypotension is very limited (19); the underlying vascular disease that such agents are used to treat is as likely to explain any such association in observational studies. On the contrary, ACEi and ARB are known to improve endothelial function and vascular stiffness (20, 21), and thus could in fact mediate improvements in vascular tone and control that would mitigate orthostatic hypotension. ACEi have also been postulated to directly improve skeletal muscle function; observational data suggests that those taking ACEi exhibit slower declines in walk speed, and 20 weeks of ACEi therapy improved six-minute walk distance in older people with functional impairment (2). ACEi do not appear to improve postural sway in older people at risk of falls however (4). Finally, it is possible that ACEi and ARB may have beneficial effects on bone mineral density, although observational studies to date suggest that this benefit may be confined to African-American men (22, 23).
Observational studies cannot ascertain causality, even with adjustment for multiple confounders, and our findings may still be due to residual confounding. We could not adjust directly for frailty, but relied on a surrogate measure of functional impairment. We did not compare the effect of ACEi/ARB use with use of other classes of antihypertensive medication, although dissecting out effects for different classes in patients on multiple medications would be challenging. We were also unable to adjust for some important covariates, such as blood pressure, as these data were not available in electronic form in the routinely collected datasets available for this analysis. Similarly, data on cognition were not available in electronic form, and would not have been recorded as part of routine care for many of those studied in this analysis. Some diagnoses, such as a diagnosis of hypertension, are not well recorded on hospital discharge codes and we could not therefore adjust for such covariates. Despite these limitations, our findings, in conjunction with previous observational and trial evidence, at least call into question the appropriateness of avoiding or discontinuing ACEi/ARBs in older people at risk of falls and fracture.

 

Funding: Chief Scientist Office, Scottish Government, grant number SCPH/10
Conflicts of interest: The authors declare that they have no conflicts of interest
Acknowledgements: None
Ethical standards: This analysis used anonymised, routinely collected healthcare data and thus did not require separate ethics approval. Please see text for details.
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.

 

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THE ASSOCIATION OF FRAILTY WITH HOSPITALIZATIONS AND MORTALITY AMONG COMMUNITY DWELLING OLDER ADULTS WITH DIABETES

 

J. Ferri-Guerra1,2, R. Aparicio-Ugarriza1,2, D. Salguero1,2, D. Baskaran1, Y.N. Mohammed1,2, H. Florez1,2, J.G. Ruiz1,2

 

1. Miami VA Healthcare System Geriatric Research, Education and Clinical Center (GRECC), Miami, USA; 2. University of Miami Miller School of Medicine, Miami, USA.
Corresponding author: Corresponding author: Jorge G. Ruiz, MD, VA GRECC Associate Director for Clinical Affairs, Bruce W. Carter Miami VAMC, GRECC (11GRC), 1201 NW 16th Street, Miami, Florida 33125, Telephone: (305) 575-3388 /Fax: (305) 575-3365, Mail: j.ruiz@miami.edu, ORCID: 0000-0003-3069-8502

J Frailty Aging 2020;9(2)94-100
Published online October 4, 2019, http://dx.doi.org/10.14283/jfa.2019.31

 


Abstract

Background: Diabetes (DM) is associated with an accelerated aging that promotes frailty, a state of vulnerability to stressors, characterized by multisystem decline that results in diminished intrinsic reserve and is associated with morbidity, mortality and utilization. Research suggests a bidirectional relationship between frailty and diabetes. Frailty is associated with mortality in patients with diabetes, but its prevalence and impact on hospitalizations are not well known. Objectives: Determine the association of frailty with all-cause hospitalizations and mortality in older Veterans with diabetes. Design: Retrospective cohort. Setting: Outpatient. Participants: Veterans 65 years and older with diabetes who were identified as frail through calculation of a 44-item frailty index. Measurements: The FI was constructed as a proportion of healthcare variables (demographics, comorbidities, medications, laboratory tests, and ADLs) at the time of the screening. At the end of follow up, data was aggregated on all-cause hospitalizations and mortality and compared non-frail (robust, FI≤ .10 and prefrail FI=>.10, <.21) and frail (FI≥.21) patients. After adjusting for age, race, ethnicity, median income, history of hospitalizations, comorbidities, duration of DM and glycemic control, the association of frailty with all-cause hospitalizations was carried out according to the Andersen-Gill model, accounting for repeated hospitalizations and the association with all-cause mortality using a multivariate Cox proportional hazards regression model. Results: We identified 763 patients with diabetes, mean age 72.9 (SD=6.8) years, 50.5% were frail. After a median follow-up of 561 days (IQR=172), 37.0% they had 673 hospitalizations. After adjustment for covariates, frailty was associated with higher all-cause hospitalizations, hazard ratio (HR)=1.71 (95%CI:1.31-2.24), p<.0001, and greater mortality, HR=2.05 (95%CI:1.16-3.64), p=.014. Conclusions: Frailty was independently associated with all-cause hospitalizations and mortality in older Veterans with diabetes. Interventions to reduce the burden of frailty may be helpful to improve outcomes in older patients with diabetes.

Key words: Frailty, diabetes mellitus, hospitalizations, mortality, older adults.


 

Introduction

Frailty is a state of vulnerability to stressors, characterized by multisystem decline that results in a diminished intrinsic reserve (1). Frailty is associated with higher morbidity, mortality and healthcare utilization. Research evidence suggests bidirectional relationship between diabetes and frailty (2). Older adults with frailty demonstrate a high prevalence of risk factors associated with diabetes including obesity, inactivity, declining renal function (3, 4). On the other hand, diabetes may contribute to a higher risk for frailty as a result of the high prevalence of cardiovascular risk factors (5). The bidirectional association between frailty and diabetes and their combined effects may be particularly deleterious for older persons (2, 5).
Several explanations for the relationship between frailty and diabetes mellitus have been proposed. Frailty and diabetes share some of the same mechanisms: insulin resistance, low grade inflammation, oxidative stress, stem cell dysfunction, mitochondrial dysfunction, and sarcopenia (6-8). Comorbid medical and mental health conditions often coexist in both frailty and diabetes including but not limited to obesity, cardiovascular disease, sleep apnea, depression and cognitive impairment (5, 9, 10). Hypoglycemia in older people with diabetes may be a particularly important contributor to frailty risk and similarly frailty may predispose older people with diabetes to hypoglycemia (8).
Both frailty (11) and diabetes (12) are prevalent in the Veteran than the general US population. Diabetes (13) and frailty (14) are both independently associated with a higher risk for all-cause hospitalizations in older adults. Furthermore, mortality is also higher in frailty and diabetes. In these patients, the concurrence of frailty and diabetes may further increase the effects of individual conditions on clinical outcomes that may lead to higher healthcare utilization and mortality. Previous studies have shown that older adults with either frailty (14) or diabetes (15) are high utilizers of healthcare including hospitalizations. Although Veterans receiving care at Department of Veterans Affairs (VA) Medical Centers have an increased risk for hospitalizations, there is, however, no data regarding the effects on hospitalizations in Veterans with coexistent frailty and diabetes. Thus, the aim of this study was to determine the effects of frailty on all-cause hospitalization and mortality in older adults with diabetes at a VA medical center.

 

Methods

Study Setting

This research is a retrospective cohort study that was conducted at a tertiary care VA Medical Center, a US government-run healthcare institution. The study is part of a clinical demonstration quality improvement project looking at identifying Veterans with frailty.

Identification of Patients

Our project team identified community-dwelling Veterans 65 years and older with diabetes coming to the VA Medical Center outpatient clinics from January 2016 to August 2017, and patients were follow-up until October 2018. We identified patients with diabetes, diagnosed between October 2, 1996, and July 17, 2017. Trained research associates collected patient data from the electronic health record and the VA Corporate Data Warehouse (CDW) including demographic information; vital signs and BMI, physical and mental health conditions; laboratory data, sensory problems and functional status. We used the zip code and race to obtain the patients annual median household income based on 2014 Census tract data as a parameter for social status classification. Information on physical health conditions was used to calculate an age-adjusted Charlson comorbidity index (CCI) (16).

Frailty Assessment

Data collected for each patient from reviews of the VA electronic health record and the VA Corporate Data Warehouse (CDW) was used to calculate a frailty index (FI) which included 44 items. Each patient’s FI had a minimum of 30 of the 44 items. The 44 items in the FI belonged to 7 major categories (supplementary materials): socio-demographic (4 items), vital signs and other measurements (3 items), physical and mental health conditions (20 items), laboratory data (10 items), sensory problem (1 item) and functional status (6 items). The 44-item frailty index used in this study is based on the deficit accumulation conceptual framework that assumes that frailty is the result of interacting physical, functional psychological, and social factors (17). Unlike the frailty phenotype, which is the most widely used conceptual model in the field, the deficit accumulation approach does not rely on predetermined variables (18). Each patient’s FI was calculated by dividing the number of items present (19). We chose the cut-off of 0.21, which was most recently used by Orkaby et al. as part of a large VA study on the prevalence of frailty in the VA (11). This resulted in a score between 0-1, where higher scores represent higher frailty. The patients were stratified as non-frail (FI is <0.21) and frail (FI is ≥ 0.21).
Hospitalization Ascertainment
Patients were followed from January 2016 to August 2017 until October 2018 for VA all-cause hospitalizations following the initial assessment of frailty. We recorded the total number of hospital admissions during the previous one year and for the follow up period. The primary reasons for hospitalizations were assessed using ICD 9 and 10 codes assigned by trained staff after discharge.

Mortality

All-cause mortality was identified through official sources including VHA facilities, death certificates, and National Cemetery Administration data available from the VA CDW. There is high agreement (91-99%) between dates of death recorded in the CDW and dates of death recorded in external sources that feed the VHA Vital Status File (20). The last day of follow-up was October 31th, 2018.

Data Analysis

Baseline characteristics are presented as frequency (percent) for categorical variables, and as mean+SD for continuous variables. We report descriptive statistics of age, race, ethnicity, median income, marital status, body-mass index (BMI), and age-adjusted CCI, duration of diabetes, DM with complication, number of medications, use of insulin or sulfonylureas, metformin, level of glycemia control,  previous and during follow-up hospitalizations. All variables were checked for normality of distribution using the Kolmogorov-Smirnov test. All values showed no-normal distribution. Mann-Whitney U and Kruskal-Wallis test (for non-normally distributed variables) and Chi-Square were run to evaluate the differences between non-frail and frail. The association of frailty with all-cause hospitalizations in older adults with diabetes was determined with the Andersen-Gill model, accounting for repeated hospitalizations. Patients were censored if they died without having a hospital admission. Univariate and multivariate analyses were conducted adjusting for age, race, ethnicity, median household income, BMI, age-adjusted CCI, diabetes complications, duration of diabetes, use of insulin or sulfonylureas, metformin, level of glycemia control, and all-cause hospitalizations in the previous year. Four models were constructed to assess the role of the covariates in the association between frailty and all-cause hospitalization: Model 1 was adjusted for age, race, ethnicity, BMI and Median Household Income. Model 2 was adjusted for the covariates in Model 1 and age-adjusted CCI. Model 3 was adjusted for the covariates in Models 1-2 and diabetes complications, duration of diabetes, use of insulin or sulfonylureas, metformin and level of glycemia control. Model 4 was adjusted for the covariates in the previous models and for hospitalizations in the previous year. The proportional hazard assumption was tested using scaled Schoenfeld residuals and was found to be valid. Cox regression analysis was performed to calculate the hazard ratios and 95% confidence intervals (CIs) of survival for frailty on all-cause mortality. We built 4 models to assess the role of the covariates in the association between frailty and all-cause mortality as described for all-cause hospitalizations. To assess the robustness of our results, sensitivity analyses were performed in which we dichotomized subgroups of older Veterans with frailty by age (<75 and ≥ 75 years old), race (White vs. African American), and with history of hospitalizations in the previous year (Yes vs. No). We did not have to exclude variables having a high collinearity among themselves. Associations were considered significant if p<0.05. Follow up duration was calculated as follows: (October 31th, 2018 – frailty assessment date)/365. All analyses were performed using the SPSS 25.0 for Windows (SPSS, Inc., Chicago, Illinois) and SAS for Windows version 3.71 (SAS Institute Inc., Cary, North Carolina). All statistical tests were two-tailed, and statistical significance was assumed for a p-value <0.05.

 

Results

Patient Characteristics

Table 1 shows participant characteristics. 763 participants were included in the study. Patients were 98.3% male, 56.7% White, 77.1% non-Hispanic and the mean age was 72.9 (SD= 6.8) years. Compared with the non-frail, older adults with diabetes were less likely to be married, have more end-organ damage, longer duration of diabetes, more multimorbidity and use of medications, more likely to be taking insulin or sulfonylureas, less likely to be on metformin and have more hospitalizations in the previous year (Table 1).

Table 1 Participant Characteristics

Table 1
Participant Characteristics

*Diabetes with End organ damage: patients diagnosed with one or more of the following diagnosis: retinopathy, neuropathy and nephropathy. SD = standard deviation; n = number of participants. BMI= body mass index; FU= follow-up; Mann-Whitney U and Kruskal-Wallis test (for non-normally distributed variables) and Chi-Square for continuous variables and categorical variables, respectively. Significant differences between frailty groups are in bold (p< .05).

 

Hospitalizations

There were 673 all-cause hospitalizations over a median follow-up period of 561 days (IQR= 172) with the range between 0 and 12 hospitalizations. The leading causes for hospitalization were cardiovascular, infectious and renal diagnoses representing 137 (21%), 71 (11%) and 69 (10%) of the total respectively. The year before evaluation of frailty, 239 patients (31.3%) had at least one hospitalization and 524 (68.7%) did not have any hospitalizations. Over the follow up period, 481 participants (63.0%) did not have any hospitalizations; whereas, 282 (37.0%) had at least 1 hospitalization (data are not shown).
As shown in Table 2, using the Andersen-Gill model fully adjusted for covariates, frailty was significantly associated with higher risk for hospitalizations compared to non-frail patients, adjusted HR=1.71 (95%CI:1.31–2.24), p<.0001. There were some differences appeared after conducting sensitivity analysis in the subgroup of older Veterans with diabetes and frailty. In terms of age, there were no associations between frailty and all-cause hospitalizations in participants 75 years of age and older after adjustment for all covariates (Table 3), HR=.86 (95%CI:.59-1.23), p=.399. There were significant associations of frailty with lower risk for all-cause hospitalizations in African American participants after adjusting for covariates: HR=.61 (95%CI:.41-.91), p=.015 (Table 3). After dividing the groups into those with and those without hospitalizations in the previous year, there were significant differences in those participants with previous hospitalizations HR=3.37 (95%CI:2.43-4.66), p<.0001 (Table 3).

Table 2 Association of Frailty with All-Cause Hospitalizations and Mortality in Older Veterans with Diabetes (n = 763)

Table 2
Association of Frailty with All-Cause Hospitalizations and Mortality in Older Veterans with Diabetes (n = 763)

Model 1 was adjusted for age, race, ethnicity, BMI and Median Household Income. Model 2 was adjusted for the covariates in Model 1 and Charlson Comorbidity Index. Model 3 was adjusted for the covariates in Models 1-2 and diabetes complications, duration of diabetes, use of insulin or sulfonylureas, metformin and level of glycemia control. Model 4 was adjusted for the covariates in the previous models and for hospitalizations in the previous year. Significant associations are in bold (p< .05).

Table 4 Association of All-Cause Mortality with Age Group (American (n=165) and Prior Hospitalizations (No (n=207) vs. Yes (n=178)) in Patients with Frailty and Diabetes (n = 385)

Table 3
Association of All-Cause Mortality with Age Group (American (n=165) and Prior Hospitalizations (No (n=207) vs. Yes (n=178)) in Patients with Frailty and Diabetes (n = 385)

Model 1 was adjusted for age, (except age group: <75y and ≥75y), race (except for race group: White vs African American), ethnicity, BMI and Median Household Income. Model 2 was adjusted for the covariates in Model 1 and Charlson Comorbidity Index. Model 3 was adjusted for the covariates in Models 1-2 and diabetes complications, duration of diabetes, use of insulin or sulfonylureas, metformin and level of glycemia control. Model 4 was adjusted for the covariates in the previous models and for hospitalizations in the previous year (except for Prior hospitalizations: Yes Prior and No prior). Significant associations are in bold (p< .05).

 

Mortality

Over the follow-up period, 81 deaths occurred. Table 2 displays the association between mortality and frailty in older Veterans with diabetes. After adjusting for all covariates, (Model 4), frailty increased the risk of all-cause mortality during follow up, HR=2.05 (95%CI:1.16-3.64), p=.014. During sensitivity analyses, frailty did not show association with all-cause mortality in participants 75 years of age and older after adjustment for covariates HR=1.39 (95%CI:.79-2.46), p=.248 (Table 3). There was not association of frailty with all-cause mortality in African Americans after adjustment for covariates: HR=.67 (95%CI:.34 – 1.32),  p=.244.  Furthermore, frailty was significantly associated with higher all-cause mortality in those with previous hospitalizations after adjustment: HR=3.36 (95%CI:1.87-6.06), p<.0001 (Table 3).

Table 4 Association of All-Cause Mortality with Age Group (<75 y (n=261) vs. ≥75 y (n=124)), Race (White (n=220) vs. African American (n=165) and Prior Hospitalizations (No (n=207) vs. Yes (n=178)) in Patients with Frailty and Diabetes (n = 385)

Table 4
Association of All-Cause Mortality with Age Group (American (n=165) and Prior Hospitalizations (No (n=207) vs. Yes (n=178)) in Patients with Frailty and Diabetes (n = 385)

Model 1 was adjusted for age, (except age group: <75y and ≥75y), race (except for race group: White vs African American), ethnicity, BMI and Median Household Income. Model 2 was adjusted for the covariates in Model 1 and Charlson Comorbidity Index. Model 3 was adjusted for the covariates in Models 1-2 and diabetes complications, duration of diabetes, use of insulin or sulfonylureas, metformin and level of glycemia control. Model 4 was adjusted for the covariates in the previous models and for hospitalizations in the previous year (except for Prior hospitalizations: Yes Prior and No prior). Significant associations are in bold (p< .05).

 

Discussion

In this study, we investigated whether frailty was associated with risk for either all-cause hospitalizations or mortality in older adults with diabetes. The overall analysis showed an association between frailty in older adults with diabetes and a higher risk for all-cause hospitalization and mortality after adjustment for known confounders. There were, however, differences between subgroups of participants with frailty. African Americans with frailty had a lower risk for all-cause hospitalizations that whites. Older adults with frailty and history of hospitalizations in the previous year demonstrated a higher risk for both all-cause hospitalizations and mortality.
The independent contribution of frailty to a higher risk for all-cause hospitalizations and mortality in older people with diabetes may be related to several factors associated with this syndrome. Frailty may shape the presentation of type 2 diabetes by increasing the risk of hypoglycemia (8). Weight loss and sarcopenia which are often part of the frailty syndrome, may be further exacerbated by concurrent anorexia of aging potentially leading to the normalization of glycemic control and an increased risk for recurrent and sometimes severe hypoglycemia (21), which may lead to cardiovascular complications. Cardiovascular diagnoses represented the leading cause of hospitalization in our sample of older Veterans with diabetes. Older individuals with diabetes and frailty may be especially susceptible to the physiological effects of hypoglycemia on the cardiovascular system potentially contributing to a higher rate of hospitalizations. It has been proposed that older adults with diabetes and frailty may benefit from less aggressive targets for glycemic control (21). The increased inflammatory and coagulation abnormalities characteristic of frailty may also worsen the microvascular effects of diabetes (22) resulting in a higher rate of complications and in turn a risk for higher rate of subsequent hospitalizations and poor clinical outcomes including death. In our study complications of renal disease were amongst the leading causes of hospitalization. In older adults, frailty may mediate the link between diabetes and disability which in itself is associated with a higher risk for all-cause hospitalization (23) and mortality (24). Falls, delirium, dementia and other geriatric syndromes often coexist with frailty sharing mechanisms that may also jointly contribute to an increase risk for hospitalizationsand mortality (25). Cognitive impairment, which is often underrecognized, often occurs and coexists patients with both diabetes and frailty, and is a known risk factor for hospital admissions and readmissions (26) and mortality in in patients with diabetes (27). That frailty has an additive effect to that of diabetes on all-cause hospitalizations and mortality in older adults with an already increased risk for healthcare utilization and decreased survival is particularly noteworthy. Identifying frailty could facilitate clinical decision making and potentially contribute to the implementation of clinical interventions aimed at reducing poor clinical outcomes and hospitalization risk in older patients with diabetes.
Although some studies have addressed the issue of previous hospitalizations in older adults with frailty, none has specifically looked at the coexistence of both frailty and diabetes in subgroups of older adults with frailty. This analysis reveals some evidence of the level of heterogeneity in all-cause hospitalizations and mortality older people with diabetes and frailty.The lack of differences in hospitalizations between the two age subgroups may just be function of the smaller sample size. However, another explanation may be related to the characteristics of the subgroups (supplementary materials). The over 75 years old group shows characteristics that may explain the lower rate of hospitalizations we observed namely a higher proportion of Whites, a higher median household income, and a lower rate of diabetes complications. On the other hand, factors that may offset such advantages in the older group include a longer duration of diabetes and higher levels of multimorbidity. African American race is  independently associated with frailty (28) but differences in all-cause hospitalizations suggest that all things being equal, African Americans with frailty are less likely to be hospitalized. More research is needed regarding race-based differences in clinical and healthcare utilization outcomes of individuals with frailty and the specific factors that may contribute to this differential. Older adults with diabetes and a history of hospitalizations represent a high risk group for future hospitalizations (29). In hospitalized older adults, frailty was independently associated with a higher rate of complications and mortality risk (30). In frail older adults with diabetes hospitalization may further compromise their medical condition as the may be particularly vulnerable to the effects of hospitalization. In terms of mortality, our results are consistent with previous studies showing that African Americans with diabetes have similar (31) or even lower mortality that Caucasians (32, 33). The explanations vary and may include reporting bias, lack of adjustment for socio-economic status (lower for African Americans), better access to care in an integrated healthcare system such as the VA (33) and differences in the prevalence of diabetes-related complications between these two groups (32-34). Although several studies have shown that African Americans have higher rates of kidney disease related mortality (32), Whites have higher rates of coronary heart disease than African Americans (33, 34) potentially leading to competing risks that may offset the effects of ESRD-related mortality on African Americans effectively canceling out any possible mortality differences.
Strengths of this study include a relatively large sample of older adults with documented diabetes diagnoses, complete evaluation of frailty, inclusion of complete healthcare data from electronic health records, adjustment for multiple covariates associated with increased risk for all-cause hospitalization and mortality, and a long period of follow up. There are a few limitations. We used a convenience sample of predominantly male Veterans at one medical center, and the ethnic, racial, educational, and socio-economic composition as well as the structure of the healthcare system may be different from other healthcare settings in the US.  Future cohort studies should include larger, more diverse and randomly selected samples from varied geographic locations and healthcare systems.
This study indicated overall associations of frailty with higher risk for all-cause hospitalization and mortality in older adults with diabetes. Frailty appears to have an additive effect beyond that of diabetes on hospitalizations and mortality. Developing interventions aimed at reducing hospitalization risk in older adults with diabetes may start with the identification of frailty followed by the management of this syndrome in these individuals. Further research is needed with random sampling in a broader spectrum of healthcare settings to better understand what roles frailty might play in healthcare utilization, mortality and other clinical outcomes of older adults with diabetes.

 

Funding: This material is the result of work supported with resources and the use of facilities at the Miami VA Healthcare System GRECC.
Conflict of interest: The authors declare none.
Ethical standards: A protocol of this study was submitted to and approved by the Institutional Review Board as a VA quality improvement project.

SUPPLEMENTAL MATERIAL

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PSOAS AND PARASPINOUS MUSCLE MEASUREMENTS ON COMPUTED TOMOGRAPHY PREDICT MORTALITY IN EUROPEAN AMERICANS WITH TYPE 2 DIABETES MELLITUS

 

B.M. TUCKER1, F.C. HSU2, T.C. REGISTER3, J. XU4, S.C. SMITH4, M. MUREA1, D.W. BOWDEN4, B.I. FREEDMAN1, L. LENCHIK5

1. Department of Internal Medicine, Section on Nephrology, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA; 2. Department of Biostatistical and Data Sciences, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA; 3. Department of Pathology, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA; 4. Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA; 5. Department of Radiology, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA.
Corresponding author: Barry I. Freedman, MD, Internal Medicine – Nephrology, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, NC 27157-1053, Phone: 336-716-6461, Fax: 336-716-4318, bfreedma@wakehealth.edu

J Frailty Aging 2019;in press
Published online March 22, 2019, http://dx.doi.org/10.14283/jfa.2019.5

 


Abstract

Background: Appendicular skeletal muscle mass index and muscle attenuation (density) are negatively associated with mortality in European-derived populations. Objectives: The present analyses assessed association between axial skeletal muscle density and muscle index with mortality in European Americans with type 2 diabetes mellitus (T2D). Design: Single-center observational study. Setting: Diabetes Heart Study. Participants:  839 European Americans with T2D. Methods: Computed tomography-measured psoas and paraspinous muscle mass index (cross sectional area/height2) and radiographic density (Hounsfield Units) were assessed in all participants. A Cox proportional hazards model was computed. The fully-adjusted model included covariates age, sex, body mass index, smoking, alcohol use, diabetes duration, insulin use, hormone replacement therapy (women), prevalent cardiovascular disease (CVD), hypertension, and coronary artery calcified atherosclerotic plaque mass score. Deaths were recorded in the National Death Index data through December 31, 2015. Results: Participants included 428 women and 411 men with median (25th, 75th quartile) age 62.8 (56.1, 69.1) years and diabetes duration 8.0 (5.0, 14.0) years. After 11.9 (9.4, 13.3) years of follow-up, 314 (37.4%) of participants were deceased. In the fully-adjusted model, psoas muscle density (hazard ratio [HR] 0.81, p<0.001), psoas muscle index (HR 0.82, p=0.008), and paraspinous muscle density (HR 0.85, p=0.003) were inversely associated with mortality. Paraspinous muscle index was not significantly associated with mortality (HR 0.90, p=0.08). Results did not differ significantly between men and women. Conclusions: In addition to established risk factors for mortality and CVD, higher psoas muscle index, psoas muscle density, and paraspinous muscle density were significantly associated with lower all-cause mortality in European Americans with T2D.

Keywords: European American, mortality, muscle, computed tomography, type 2 diabetes.


 

Introduction

The relationship between sarcopenia and type 2 diabetes mellitus (T2D) appears to be reciprocal, where individuals with sarcopenia are at a higher risk for developing T2D and individuals with T2D are at a higher risk for developing sarcopenia (1). In 2016, the International Conference on Frailty and Sarcopenia Research (ICFSR) Task Force concluded that individuals with T2D provide a useful target population for sarcopenia trials (2). To date, very few studies of T2D cohorts have examined the relationship between sarcopenia and health outcomes (3-6).
Studies of sarcopenia increasingly use computed tomography (CT) to evaluate skeletal muscle size and muscle density (7).  These CT-derived muscle metrics have been shown to be independent risk factors for morbidity and mortality (8, 9). The loss of skeletal muscle mass and infiltration of muscle with fat (i.e., myosteatosis) increase with aging, as well as in patients with endocrine disorders (diabetes mellitus, hypogonadism, growth hormone deficiency, hyperthyroidism, hypercortisolism, and vitamin D deficiency), chronic obstructive pulmonary disease, congestive heart failure, advanced kidney disease, cirrhosis, cancer, rheumatoid arthritis and HIV infection (10). Given increasing prevalence of T2D and increasing use of CT for the evaluation of sarcopenia, the relationships between CT-derived muscle metrics and mortality in individuals with T2D require further study.
Recently, inverse associations were reported between CT-derived psoas and paraspinous muscle indices with mortality in middle-aged African American men with T2D in the African American-Diabetes Heart Study (AA-DHS) (6). These associations were independent from the presence and severity of cardiovascular disease (CVD) risk factors; however, they were not observed in women (6). It is unclear whether CT-derived measures of muscle health associate with mortality in individuals with T2D from other ancestral populations. As such, the present analyses assessed relationships between psoas and paraspinous muscle mass index and muscle density with long-term mortality in a cohort of European Americans with T2D from the Diabetes Heart Study (DHS).

 

Methods

Study sample

The DHS is a single-center Wake Forest School of Medicine (WFSM) study. Detailed recruitment criteria have been reported (11). In brief, sibling pairs with T2D lacking advanced kidney disease were recruited from the community and endocrinology clinics in Winston-Salem, North Carolina from 1998 through 2005. T2D was defined as clinically-diagnosed diabetes developing after the age of 35 years and initially treated with diet, exercise or oral agents in the absence of diabetic ketoacidosis or insulin therapy alone. Participants underwent interviews for medical history and health behaviors, fasting blood and urine collection, and CT imaging for calcified atherosclerotic plaque in the coronary arteries, carotid arteries and aorta using multi-detector scanners with cardiac gating on chest scans. The study was approved by the WFSM Institutional Review Board and all participants provided written informed consent. This report is limited to self-reported European American participants. Vital status was assessed through Dec 31, 2015 using the National Death Index. Causes of death were listed on death certificates; however, all-cause mortality was selected as the primary outcome since specific causes could not be adjudicated for accuracy.

Psoas and paraspinous muscle imaging

Skeletal muscle index and radiographic density (attenuation) were assessed on CT. The proximal and distal borders of the psoas and paraspinous muscles were determined on a single 2.5 mm slice thickness image at the level of the L4 pedicle. Using picture archiving and communication system (PACS) software, the free-hand region of interest tool was used to define the periphery of the muscles without Hounsfield Units (HU) thresholding for measurement of mean muscle attenuation in HU and muscle cross-sectional area (CSA). Lower CT-measured skeletal muscle densities by this approach reflect greater intermuscular and intramuscular fat content. CT scans with left-right asymmetry between muscles due to scoliosis, degenerative diseases, or prior surgery, as well as scans with internal or external artifacts were excluded (N=27). The skeletal muscle index (SMI) was calculated by dividing muscle CSA by height squared. This index is commonly used instead of CSA in CT studies of sarcopenia to adjust for different body sizes. The muscle segmentation process was standardized by following the Standard Operating Procedures manual. CT measurements of muscle were performed by a musculoskeletal radiology fellow, following specialized training. CT images with measurements were archived and validated for measurement accuracy by a musculoskeletal radiology faculty member, with extensive expertise in quality assurance of body composition phenotypes (LL).

Statistical analyses

Demographic and laboratory characteristics of participants were contrasted by survival status using the unadjusted Cox proportional hazards models with sandwich-based variance estimation for familial relationships. The primary outcome was time to death, determined by the interval between the date of study enrollment and death. Study participants who were known to be alive as of December 31, 2015 were censored.
Kaplan-Meier curves were constructed to estimate the survival probabilities by muscle phenotype group (skeletal muscle density and skeletal muscle mass index in the psoas and paraspinous distributions). The four muscle phenotypes were standardized and categorized into three groups: Z ≤-1, Z >-1 and Z ≤1, and Z >1. The comparison of survival curves was calculated using the Cox proportional hazards model due to the correlated data structure.
Cox proportional hazards models with sandwich-based variance estimation were subsequently fitted to evaluate the associations between muscle phenotypes and mortality. Covariates were selected to limit confounding effects and ensure that reported effects were not due to other measured variables not accounted for in the model.
Association results were presented for an unadjusted model, a minimally-adjusted model accounting for age, sex, smoking status, alcohol use, diabetes duration, insulin use, and hormone replacement therapy (women), as well as a fully-adjusted model with all covariates in the minimally-adjusted model plus body mass index (BMI), self-reported prevalent CVD (angina, myocardial infarction, coronary artery bypass surgery, coronary angioplasty, stroke or carotid endarterectomy), coronary artery calcified atherosclerotic plaque, and hypertension. To determine whether the associations between muscle phenotypes and mortality were the same in men and women, interactions between sex and muscle phenotypes were added to the model.  If the interactions were significant, the sex specific analyses would be performed. Because there are 4 muscle phenotypes, the significance level was set at 0.0125 (p=0.05/4) based on a strict Bonferroni correction. All analyses were performed using SAS version 9.4 (SAS Inc., Cary, NC).

 

Results

The full sample of 839 European American DHS participants with T2D from 428 families were included in these analyses, 49% were men (N=412) and 51% (N=427) women. The median (25th, 75th quartile) age of the cohort at recruitment was 62.8 (56.1, 69.1) years with diabetes duration 8.0 (5.0, 14.0) years. After 11.9 (9.4, 13.3) year median follow-up, 314 (37.4%) of participants had died.
Table 1 displays baseline demographic and clinical data in this cohort, based upon vital status as of December 31, 2015. Participants who had died during follow-up were older at recruitment with longer durations of T2D and they had higher rates of baseline CVD, smoking, hypertension and insulin use. Significant differences in body mass index or use of statins, aspirin, angiotensin converting enzyme inhibitors (ACEi)/angiotensin receptor blockers (ARB), or oral hypoglycemic agents were not observed between those living and deceased at the end of follow-up.

Table 1 Baseline demographic and clinical characteristics of the Diabetes Heart Study cohort*

Table 1
Baseline demographic and clinical characteristics of the Diabetes Heart Study cohort*

*Data presented as median (25th, 75th percentile) for continuous variables or % for categorical variables; ^p-value calculated using the unadjusted Cox proportional hazards models with sandwich-based variance estimation; ACEi angiotensin converting enzyme inhibitor; ARB angiotensin receptor blocker

Table 2 displays baseline biochemical and CT imaging results in participants. Similar levels of glycemic (fasting blood sugar and HbA1c) and lipid (LDL-cholesterol, HDL-cholesterol and triglycerides) control were present in those alive and those who had died. In contrast, markedly higher levels of subclinical atherosclerosis (CT-detected calcified atherosclerotic plaque in the coronary arteries, carotid arteries and aorta) were present in participants who died during follow-up. In addition, baseline psoas and paraspinous muscle densities and psoas muscle index were significantly lower in participants who died, with a trend toward a lower paraspinous muscle index (p=0.08).

Table 2 Baseline biochemical and radiological characteristics of the Diabetes Heart Study cohort*

Table 2
Baseline biochemical and radiological characteristics of the Diabetes Heart Study cohort*

*Data presented as median (25th percentile, 75th percentile) for continuous variables or % for categorical variables; CP calcified plaque; HU Hounsfield Units.

Figure 1 shows survival curves based on the three groups (Z score ≤ -1, -1 < Z score ≤ 1, and Z score >1) for each muscle phenotype. The survival curves were different between the three groups for psoas density, psoas index, and paraspinous density (all p≤ 0.008). Participants with  Z scores >1 tended to have the highest survival probability over time, those with Z scores between -1 and 1 had the medium survival probability, and those with Z scores ≤ -1 had the lowest survival probability. Survival curves were not different between the three groups for paraspinous index.

Figure 1 Kaplan-Meier survival curves for muscle phenotype groups (Z score ≤-1, -11) for: (a) psoas muscle density, (b) psoas muscle index, (c) paraspinous muscle density, and (d) paraspinous muscle index

Figure 1
Kaplan-Meier survival curves for muscle phenotype groups (Z score ≤-1, -1< Z score ≤1, and Z score >1) for: (a) psoas muscle density, (b) psoas muscle index, (c) paraspinous muscle density, and (d) paraspinous muscle index

FIG 2 FREEDMAN

Older participants, women, and participants with lower diastolic blood pressure (DBP), higher blood urea nitrogen, and lower CKD-EPI estimated glomerular filtration rate (eGFR) were more likely to have lower psoas muscle density (Supplementary Tables 1-2).  Older participants, women, non-smokers, and participants with lower BMI, longer diabetes duration, lower DBP, lower serum creatinine, and higher HDL cholesterol were more likely to have lower psoas muscle indices (Supplementary Tables 3-4).
Table 3 contains results of the Cox proportional hazards models for association between the four skeletal muscle phenotypes and mortality. In the full model (Model 3) considering age, sex, smoking, alcohol use, diabetes duration, insulin use, hormone replacement therapy (women), BMI, prior CVD, coronary artery calcified atherosclerotic plaque and hypertension as covariates, psoas muscle density (HR per SD = 0.81, 95% confidence interval [CI] 0.73-0.90, p  < 0.001), psoas muscle index (HR per SD = 0.82, 95% CI 0.72-0.95, p = 0.008), and paraspinous muscle density (HR per SD = 0.85, 95% CI 0.76-0.94, p = 0.003) remained inversely associated with mortality. Paraspinous muscle index (HR per SD = 0.90, 95% CI 0.80-1.01, p = 0.08) was not significantly associated with mortality.
Table 4 presents the association between categorized muscle metrics and mortality. Subgroup analyses based on sex did not reveal significantly different relationships between psoas and paraspinous muscle phenotypes and mortality between men and women (data not shown).

Table 3 Relationships between muscle phenotypes and mortality in the Diabetes Heart Study cohort

Table 3
Relationships between muscle phenotypes and mortality in the Diabetes Heart Study cohort

Hazard ratios (HR), 95% confidence intervals (CI) and P values were obtained with Cox proportional hazard models with sandwich-based variance estimation. Model 1, unadjusted; Model 2 adjusted for age, sex, smoking, alcohol use, diabetes duration, insulin use, and estrogen supplementation (women). Model 3 adjusted for all variables in Model 2, plus body mass index, prior cardiovascular disease, coronary artery calcified atherosclerotic plaque, and hypertension.

Table 4 Relationships between muscle phenotypes and mortality in the Diabetes Heart Study cohort

Table 4
Relationships between muscle phenotypes and mortality in the Diabetes Heart Study cohort

Hazard ratios (HR), 95% confidence intervals (CI) and P values were obtained with Cox proportional hazard models with sandwich-based variance estimation. Model 1, unadjusted; Model 2 adjusted for age, sex, smoking, alcohol use, diabetes duration, insulin use, and estrogen supplementation (women). Model 3 adjusted for all variables in Model 2, plus body mass index, prior cardiovascular disease, coronary artery calcified atherosclerotic plaque, and hypertension. 

 

Discussion

The present study assessed associations between CT-derived psoas and paraspinous skeletal muscle indices and muscle densities with all-cause mortality in a large cohort of European Americans with T2D. The DHS cohort has been intensively phenotyped for CVD risk factors and had median 11.9 year follow-up with linkage to the National Death Index (12). Significant inverse associations were detected between psoas muscle index, psoas muscle density, and paraspinous muscle density with all-cause mortality. Relationships were robust to adjustment for BMI as well as clinical and subclinical CVD (CT-derived coronary artery calcified atherosclerotic plaque).
An increased risk of T2D in older adults with sarcopenia has been observed in various studies, most notably in an 11-year follow-up of the Health, Aging and Body Composition (Health ABC) study and a 6-year follow-up of the English Longitudinal Study of Ageing (13, 14). Conversely, the increased risk of sarcopenia in older adults with increased insulin resistance has been shown in a 5-year follow-up of the Osteoporotic Fractures in Men (MrOs) study (15). While the mechanisms underlying the reciprocal relationship between sarcopenia and T2D are complex, age-related inflammation of adipose tissue and skeletal muscle likely play major roles (16).
The clinical relevance of sarcopenia in individuals with T2D is especially important to determine owing to the high background of cardiovascular morbidity and mortality in this population. In a landmark study using the AGES-Reykjavik cohort, Murphy et al. (3) concluded that increased mortality in T2D was mediated by the smaller muscle CSA on CT images of the thigh. The present study showed increased mortality associated with lower psoas muscle index in T2D (CSA, adjusted for height) on CT of the abdomen, a more commonly imaged region on clinical CT examinations. Using a somewhat different methodology, CT-derived skeletal muscle index at L3 predicted 5-year mortality in a small cohort with T2D and limb amputations (5).
The present study employed the same methodology for measuring muscle phenotypes on CT as the AA-DHS (6). Compared to European Americans in the current study, African Americans in AA-DHS had higher psoas density (54.0 vs 49.3 HU), higher paraspinous density (40.7 vs 31.6 HU), higher psoas index (4.0 vs 3.6), and higher paraspinous index (8.1 vs 7.7).  In contrast to African Americans with T2D, where only muscle indices were inversely associated with mortality (6), muscle densities were more strongly predictive of mortality in European Americans with T2D.
CT-derived muscle density is a measure of myosteatosis, a phenotype that captures aspects of muscle quality, rather than muscle quantity. Because insulin sensitivity has ancestral/ethnic determinants, differences in ectopic fat deposition including myosteatosis in African Americans compared to European Americans with T2D were not unexpected.  Prior studies have shown higher muscle mass in African American compared to European American men and women (17). This is why serum creatinine-based equations to estimate the GFR need to account for African American ancestry. In addition, myosteatosis has been reported to be higher in African Americans than European Americans and this finding is hypothesized to contribute to the increased risk for type 2 diabetes (and hypertension) in African Americans (18-22).  Since African Americans had higher baseline muscle density than European Americans, a longer follow-up interval in AA-DHS would likely be necessary to show association between lower muscle density and mortality.
Although studies of sarcopenia in cohorts with T2D are few, prior studies of CT-derived muscle metrics and mortality in non-T2D cohorts likely included some individuals with T2D, although the true percentage may not have been known or reported. In general, CT-derived muscle mass has been used to predict mortality more often than CT-derived muscle density. In a cohort of 274 hip-fracture patients followed for 8 years, CT-derived muscle index and muscle attenuation had comparable ability to predict survival (23). But in several cancer cohorts that used both muscle phenotypes, CT-derived muscle density was a better predictor of survival than muscle mass (8).
Similar to results in AA-DHS, the present findings confirmed that the psoas muscle index was inversely associated with mortality in T2D. However, in European Americans the paraspinous muscle index only showed a trend towards a similar protective relationship (p=0.11). Similar differences in the ability of the psoas versus the paraspinous muscle metrics to predict mortality have been observed in cohorts with T2D (23). While the psoas muscles are composed of more type II (fast-twitch) fibers, the paraspinous muscles are composed of more type I (slow-twitch) fibers. Muscle fiber types are differentially affected by aging and disease, with the proportion of slow-twitch fibers correlating with insulin responsiveness (24, 24). The muscle fiber type may even dependent on the location of analysis, with more slow-twitch fibers cranially and more fast-twitch fibers caudally (25). The differences in the relative proportion of muscle fiber type may help explain the difference in the observed muscle-mortality relationship in African Americans compared to European Americans with T2D.
In contrast to results in African Americans with T2D from the AA-DHS where the muscle indices were inversely associated with mortality only in men and no relationships were seen in women (6), the results in European Americans with T2D were consistent in men and women. This may relate to a longer follow-up period in the current study (11.9 years) compared to AA-DHS (7.1 years), older ages at baseline and greater numbers of deaths.
This study has strengths and limitations. Limitations include that routine PACS software was used, without thresholding of muscle as performed in other reports. While this approach may change disease prevalence, prior studies have shown it to be valid in predicting important health outcomes, including mortality in over 23,000 trauma patients (26). In addition, markers of physical function commonly used in sarcopenia studies were not obtained (such as gait speed or grip strength). Integrating measures of physical function with CT-derived muscle metrics may improve prognostication. Finally, the findings in this retrospective study do not prove causality; underlying disease processes that caused the changes in muscle metrics may have led to the increased mortality. Despite these limitations, our results showed that CT-derived muscle metrics were significant prognostic markers for long-term mortality in men and women with T2D. Strengths include use of routine PACS software to measure psoas and paraspinous muscle metrics, more generalizable to clinical workflows than methods using specialized segmentation software and tissue thresholding. The DHS cohort was extensively phenotyped for risk factors that determine mortality, including vascular calcification, medication use, and markers of glycemic control. In the Framingham Heart Study, which used the same methodology to measure muscle as the present study, paraspinous muscle density was associated with metabolic risk factors but the association was lost after adjustment for BMI (27). In the present study, the fully-adjusted models included BMI and CVD risk factors and association between muscle density and mortality persisted.
If future studies support our findings, the implications for research and patient care would be substantial. CT-derived muscle metrics could be incorporated into intervention studies evaluating diet, exercise, and pharmacologic agents to improve muscle metrics in patients with T2D. CT scans obtained during routine clinical care of patients could be used to opportunistically screen for sarcopenia. Interventions may then be prescribed to improve CT-measured muscle metrics, thereby improving survival.
In conclusion, although the interaction of adipose tissue and skeletal muscle is well established and the relationship of sarcopenia and T2D is increasingly recognized, studies of CT-derived muscle metrics in T2D cohorts are lacking. In this large cohort of European American men and women with T2D, atrophy and fatty infiltration of skeletal muscle determined from CT images was significantly associated with mortality, independent from major CVD risk factors.

Acknowledgements: The authors thank the study investigators, study staff, and participants for their continued participation.
Funding: This work was supported by grants from the National Institutes of Health R01 DK071891 (BIF), HL67348 (DWB), and the Wake Forest Claude D. Pepper Older Americans for Independence Center P30 AG21332 (TCR and LL).
Conflict of interest: No conflicts exist.
Ethical standards: All study participants provided written informed consent. DHS is approved by the Wake Forest School of Medicine IRB.

 

SUPPLEMENTARY TABLE

 

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NOVEL FRAILTY SCREENING QUESTIONNAIRE (FSQ) PREDICTS 8-YEAR MORTALITY IN OLDER ADULTS IN CHINA

 

L. Ma1,2,3, Z. Tang1,3, P. Chan1,3,4, J.D. Walston2,5

 

1. Department of Geriatrics, Beijing Geriatric Healthcare Center, Xuanwu Hospital, Capital Medical University, Beijing Institute of Geriatrics, Key Laboratory on Neurodegenerative Disease of Ministry of Education, Beijing 100053, China; 2. Division of Geriatric Medicine and Gerontology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21224, USA; 3. National Clinical Research Center for Geriatric Disorders, Beijing 100053, China; 4. Department of Neurology and Neurobiology, Xuanwu Hospital, Capital Medical University, Beijing 100053, China; 5. Older Americans Independence Center, Center on Aging and Health, Johns Hopkins University, Baltimore, Maryland 21205, USA.
Corresponding author: Dr. Jeremy D. Walston, Division of Geriatric Medicine and Gerontology, Johns Hopkins University School of Medicine, Baltimore, Maryland 21224, USA. E-mail: jwalston@jhmi.edu; Dr. Zhe Tang, Beijing Geriatric Healthcare Center, Xuanwu Hospital, Capital Medical University, 45 Changchun Street, Xicheng District, Beijing 100053, China. Tel: 86-010-63162077. E-mail: tangzhe@sina.com.

J Frailty Aging 2019;8(1):33-38
Published online November 23, 2018, http://dx.doi.org/10.14283/jfa.2018.38

 


Abstract

Background: Although frailty status greatly impacts health care in countries with rapidly aging populations, little is known about the frailty status in Chinese older adults. Objectives: Given the increased health care needs associated with frailty, we sought to develop an easily applied self-report screening tool based on four of the syndromic frailty components and sought to validate it in a population of older adults in China. Design: Prospective epidemiological cohort study. Setting: Community-dwelling residents living in Beijing, China. Participants: 1724 community-dwelling adults aged ≥60 years in 2004 with an 8-year follow up. Measurements: We developed a simple self-report frailty screening tool—the Frailty Screening Questionnaire (FSQ)—based on the modified Fried frailty components. The predictive ability for outcome was assessed by age and sex adjusted Cox proportional hazards model. Results: According to FSQ criteria, 7.1% of the participants were frail. Frailty was associated with poor physical function, fractures, falls, and mortality. Both frailty and pre-frailty were associated with a higher mortality rate: frailty—hazards ratio (HR), 3.94, 95% confidence interval (CI), 3.16–4.92, P<0.001; pre-frailty—HR, 1.89; 95% CI, 1.57–2.27, P <0.001; adjusted models for this variable did not affect the estimates of the association. Among the four frailty components, slowness was the strongest predictor of mortality. The combination of the four components provided the best risk prediction. Conclusions: FSQ is a self-report frailty measurement tool that can be rapidly performed to identify older adults with higher risk of adverse health outcomes.

Key words: Frailty, physical function, mortality.


 

Introduction

One of the most commonly recognized risk states for adverse outcomes in older adults is frailty. Quality frailty measurement should be able to identify frailty and those frail older adults that might respond to treatment, predict adverse outcomes, and ideally be able to identify those with a common biological underpinning (1). Frailty is most often defined as a geriatric syndrome, resulting from a cumulative decrease in multiple physiological systems and consequent reduction in physical reserve and defense ability (2, 3). It is often accompanied by increased vulnerability to adverse outcomes including falls, disability, and mortality (2). The most common definition of frailty was proposed by Fried, who considered the clinical phenotype of frailty as a well-defined syndrome with biological underpinnings (3). The Fried frailty detection identifies frailty by evaluating symptoms and signs associated with biological aging, including shrinking, exhaustion, weakness, slowness, and low levels of activity (3). Other main frailty concepts are often measured by cumulative comorbidities or “deficits” (4). The deficit model assesses accumulated declines in multiple domains with regard to diseases, and physical, psychological, and social functions, and comprehensively captures comorbidity and disability.
Although the original Fried’s physical frailty phenotype scale remains the most validated and utilized method, self-report information on Fried components also showed good predictive ability (5, 6). Hence, a self-report frailty detection tool may provide an alternative method for rapidly screening large populations of frail adults. Although dozens of other measurement tools for frailty have been reported, frailty detection methods are recommended to be matched to a particular need or environment to be most effective (7).
When attempting to identify frailty detection methods applicable to Chinese older adults, it is evident that available screening tools present two major limitations. First, most are time-consuming and are difficult to apply in busy medical practices with large populations. Second, no tool to date has been developed specific for Chinese elders. Given the large number of older outpatients in Chinese health care settings, the use of a standardized subjective evaluation of frailty would likely be readily accepted and adopted by busy clinicians. To address the current lack of an easy-to-use, valid, reliable screening measure of physical frailty consistent with original conceptual and biological model, we developed and validated a simple frailty pre-screening tool for outpatient settings—the Phenotypic Frailty Screening Questionnaire (FSQ).

 

Methods

Participants

Data were from the Beijing Longitudinal Study of Aging, a longitudinal study funded by the United Nations Population Fund (UNPFA CPR/90/P23) in 1992 (8). A cross-sectional survey comprising 1865 adults aged ≥60 years was conducted based on sample data from the fourth census of Beijing in 2004. Well-established statistical sampling techniques, which included clustering, stratification, and random selection were applied. Details of the sampling scheme were described elsewhere (9, 10). 1724 participants completed the frailty assessment. Data were collected on the following aspects: socioeconomic and demographic characteristics, physical health (self-report history of chronic disease and clinical syndromes), physical function, life behavior and social function, neuropsychological health, and medical condition. The definitions of cognitive impairment and depression appear in our previous publication (11). The mortality data for all subjects were collected every year until the end of December 2012. Mortality ascertainment was 100% complete. Instances of death were confirmed by family members or neighborhood or village committees. This study was approved by the ethics review board of Xuanwu Hospital, Capital Medical University and all the participants provided informed consent.

FSQ assessment

The FSQ scale was developed to represent four of five components of the Fried criteria: slowness, weakness, inactivity, and exhaustion (Table S1). Slowness received a score of 1 if participants had difficulty walking 250 meters. Weakness received a score of 1 if participants had difficulty in lifting or carrying something weighing 5 kilograms. Exhaustion received a score of 1 for participants who responded yes to either “Everything I did was an effort” or “I could not get going” in the past week. Inactivity was measured by asking participants how many hours they had spent on weekly exercise; subjects who responded <3 hours/week scored 1 point. The FSQ total score is 0–4. A score of 0 was considered robust; 1–2 was considered pre-frail; and a score of ≥3 indicated frailty.

Physical function

We assessed physical function by means of the balance test, chair-stand test, activities of daily living (ADL), and instrumental activities of daily living (IADL) as well as in terms of fractures and falls.

Statistical methods

Statistical analyses were performed by SPSS 11.5 (SPSS, Inc., Chicago, IL, USA). Chi-square tests were conducted for discrete variables, and analysis of variance and Student t tests were used to compare means of the groups for continuous variables with Tukey post hoc tests. We evaluated survival using Kaplan-Meier curves stratified for different sex and age-groups. A Cox proportional hazards model was used to evaluate the effect of covariates (age, sex, and frailty) on mortality after testing for the proportionality assumption. We considered P <0.05 statistically significant.

Results

Using the FSQ in the Beijing Longitudinal Study of Aging population, 194 participants were identified as frail and the prevalence was 11.3% (weighted, 7.1%). The prevalence of pre-frailty was 32.5% (weighted, 29.5%). Frailty was associated with female gender, rural residency, older age, and lower socioeconomic status. Higher prevalence of frailty was observed among participants who were not married, those with a history of heavy physical labor occupation, and those with poor health or life satisfaction (Table 1). For both men and women, the prevalence of frailty increased with age and was higher among rural residents (Table S2). Frailty was more common in subjects with chronic diseases (Table S3).

Table 1 Demographic characteristics of the frailty status defined by FSQ

Table 1
Demographic characteristics of the frailty status defined by FSQ

**P<0.01.

 

The prevalence of frailty components according to the FSQ included slowness, 15.3%; weakness, 19.0%; inactivity, 23.0%; and exhaustion, 21.9%. The prevalence of 0, one, two, three, and four components was 56.3%, 22.9%, 9.6%, 7.9%, and 3.4%, respectively. The prevalence of the four components was higher among women than among men (Table S4).
Compared with robust subjects, frail and pre-frail status was associated with poor balance and chair-stand performance, ADL dependency, IADL dependency, fracture and falls, even after adjustment for sex (but not fractures in male) (Table 2). Among both men and women, being frail or pre-frail was associated with 8-year mortality. The four components showed a higher mortality rate in the overall, female, and male samples (Table S5). Frailty and each of the four components were associated with mortality in every age-group (except inactivity in 60-69 years group and exhaustion in ≥ 80 years group) (Table S6).  Figures S1, S2 and S3 present Kaplan-Meier curves for the proportional survival of participants with different frail statuses in the different age- and sex groups. The unadjusted associations were significant for the predictive association of frailty and pre-frailty with mortality; after adjusting for age and sex, the 8-year mortality hazard ratio was 2.131–3.444 and 1.318–1.972, respectively, for frailty and pre-frailty. Each component could predict mortality—even after adjusting for age and sex. Slowness was the strongest predictor and exhaustion the weakest predictor. Combined, the four components offered best risk prediction for mortality than the single component (Table 3; Figure S4).

Table 2 Characteristics of physical functions in different sex according to the FSQ

Table 2
Characteristics of physical functions in different sex according to the FSQ

a: have a fracture in last two years. b: fall twice in the last year; Abbreviations: ADL, activities of daily living; IADL, instrumental activities of daily living.

Table 3 Predictive models of mortality at 8-year follow-up

Table 3
Predictive models of mortality at 8-year follow-up

Reference: Robust. Model 1: Unadjusted Cox proportional hazard analysis. Model 2: Adjusted Cox proportional hazard analysis. Adjusted for age and sex. Abbreviations: HR, hazard ratio; CI, confidence interval.

 

Discussion

The FSQ is an easy to use self-report tool developed in a Chinese population. It was loosely derived from the phenotypic frailty detection method. This was developed in part because of the need for quick pre-screening tool for frailty in busy Chinese outpatient practices where up to 10 patients an hour may be seen. This study found the weighted prevalence of frailty based on the FSQ to be 7.1%. This is similar to the prevalence of frailty as measured using the Fried phenotype criteria in China (7.0%) (12). Most data on the prevalence of frailty in the Chinese population have been based on frailty index conceptual model. We previously reported the prevalence of frailty based on frailty index to be 8.8% in the China Comprehensive Geriatric Assessment Study and 9.1% in the Beijing Longitudinal Study of Aging II (13, 14). In this study, the prevalence of frailty based on the FSQ was found to be higher in women and increased with advancing age, consistence with previous studies (3, 13, 15–17). One meta-analysis confirmed the pattern of sex differences in frailty and mortality to be a “male-female health-survival paradox” (18).
We found that slowness was the highest predictor among the four components. This result constitutes a response to the question as to which component of the phenotype model is more informative with regard to frailty assessment. Another investigation found gait speed to be the best indicator of frailty and that the combination of gait speed and physical activity was the most informative among the Fried components (19). Several studies have determined gait speed to be the preeminent frailty screening tool (20–22). Gait speed is a simple, acceptable measurement that can be easily performed in a routine clinic. The present investigation provides evidence that among the four components, self-report slowness is also the most important indicator for mortality in older subjects.
We compared the new tool with other instruments reported in the literature in terms of the following five aspects: population; frailty components; ease of application; primary use; and validity (Table S7). Among the eleven self-report instruments in the comparison, the FSQ was one of only two tools which were based on physical frailty and showed validity in outcome prediction in a large population.
The present study’s strengths include the large sample and completeness of the long-term follow-up. The Beijing Longitudinal Study of Aging is based on a large population-based cohort using clustering, stratification, and random-selection sampling techniques; thus, it can be taken to be representative of older Chinese people (8). Moreover, in the 8-year follow-up, mortality ascertainment was 100% complete. The present study also addressed the question as to which component of the phenotype model was more important. As shown in Table S7, the FSQ is quick to use by non-specific staff, and it is available from routinely comprehensive geriatric assessment data. Last and the most important, this study shows that the FSQ is feasible for a Chinese population. To the best of our knowledge, the FSQ tool is the only assessment tool based on the frailty phenotype designed for screening frailty in a Chinese population.
Our study also has several limitations. One of the main limitations is the lack of objective measurements. Hence, we were unable to evaluate the five-item Fried Phenotype in comparative analyses. Future studies on validation of FSQ with measured Fried phenotype should be conducted. Second, we did not take into account potential changes in frailty status between visits. A scoring system is needed to capture the dynamic nature of frailty so that it can be used as an outcome and intervention measurement (23). Third, we demonstrated that the four self-report Fried frailty components do not play the same role in predicting mortality, and the total level of frailty is not equivalent to the sum of those components. Future studies should weigh those components, characterize the trajectories of frailty, and examine cross-cultural validation.

 

Conclusions

FSQ is a useful quick and feasible self-report frailty tool that has been demonstrated to predict mortality in Chinese old adults. To our knowledge, this study is the first to report the prevalence of frailty and long-term prognosis using a self-report version of the Fried phenotype in a large longitudinal Chinese population. The FSQ was gathered using information provide by participants; it is associated with physical function, chronic disease, fracture, falls, and mortality, and it shows a good agreement with prior studies in China using Fried phenotype. The results of this study may ease frailty screening in older Chinese population by offering a very simple way to identify frailty and related risk of mortality in older adults. This in turn may facilitate targeted comprehensive geriatric assessment for the frail subset of patients as has previously been recommended in the United Kingdom (24). In addition, it may facilitate the development of novel interventions to better manage frailty and slow  declines in health status.

 

Funding: This work was supported by United Nations Population Fund (CPR/90/P23) and Milstein Medical Asian American Partnership Foundation Project Award in Geriatrics.
Acknowledgement: We acknowledge all the people who participated in the cohort study.
Presented as poster in Society for Epidemiologic Research 51 Annual Meeting (SER2018), Baltimore, Maryland, USA, June 19-22, 2018.
Conflict of Interest: None.

SUPPLEMENTARY MATERIALS

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IMPACT OF SARCOPENIA ON ONE-YEAR MORTALITY AMONG OLDER HOSPITALIZED PATIENTS WITH IMPAIRED MOBILITY

 

M. POURHASSAN1, K. NORMAN2, M. J MÜLLER3, R. DZIEWAS4, R. WIRTH1

 

1. Department of Geriatric Medicine, Marien Hospital Herne, Ruhr-University Bochum, Germany; 2. Charité Research Group on Geriatrics, Charité – Universitätsmedizin Berlin, Germany; 3. Institute of Human Nutrition and Food Science, Christian-Albrechts-University, Kiel, Germany; 4. Department of Neurology, University Hospital Münster, Germany
Corresponding author: Dr. oec. troph. Maryam Pourhassan, Marien Hospital Herne, Department of Geriatric Medicine, University Hospital Ruhr-University Bochum, Germany, Hölkeskampring 40, D- 44625 Herne, Germany, Tel: +4923234992416, Email: maryam.pourhassan@ruhr-uni-bochum.de

J Frailty Aging 2018;7(1):40-46
Published online September 20, 2017, http://dx.doi.org/10.14283/jfa.2017.35

 


Abstract

Objectives: However, the information regarding the impact of sarcopenia on mortality in older individuals is rising, there is a lack of knowledge concerning this issue among geriatric hospitalized patients. Therefore, aim of the present study was to investigate the associations between sarcopenia and 1-year mortality in a prospectively recruited sample of geriatric inpatients with different mobility and dependency status. Design and setting:  Sarcopenia was diagnosed using the criteria of the European Working Group on Sarcopenia in Older People (EWGSOP). Hand grip strength and skeletal muscle mass were measured using Jamar dynamometer and bioelectrical impedance analysis, respectively. Physical function was assessed with the Short Physical Performance Battery. Dependency status was defined by Barthel-Index (BI). Mobility limitation was defined according to walking ability as described in BI. The survival status was ascertained by telephone interview. Results: The recruited population comprised 198 patients from a geriatric acute ward with a mean age of 82.8 ± 5.9 (70.2% females). 50 (25.3%) patients had sarcopenia, while 148 (74.7%) had no sarcopenia. 14 (28%) patients died among sarcopenic subjects compared with 28 (19%) non-sarcopenic subjects (P=0.229). After adjustment for potential confounders, sarcopenia was associated with increased mortality among patients with limited mobility prior to admission (n=138, hazard ratio, HR: 2.52, 95% CI: 1.17-5.44) and at time of discharge (n=162, HR: 1.93, 95% CI: 0.67-3.22). In a sub-group of patients with pre-admission BI<60 (n=45), <70 (n=73) and <80 (n=108), the risk of death was 3.63, 2.80 and 2.55 times higher in sarcopenic patients, respectively. In contrast, no significant relationships were observed between sarcopenia and mortality across the different scores of BI during admission and at time of discharge. Conclusion: Sarcopenia is significantly associated with higher risk of mortality among sub-groups of older patients with limited mobility and impaired functional status, independently of age and other clinical variables.

Key words: Geriatric hospitalized patients, sarcopenia, mortality, walking limitation, Barthel-Index.


 

 

Introduction

Sarcopenia is a geriatric syndrome determined by progressive loss of muscle mass and muscle strength or muscle performance with ageing and disease. It is a major risk factor for adverse health outcomes and has important consequences in terms of falls and fractures, functional decline, impaired mobility, hospital admissions, morbidity and mortality (1-3). In older adults, muscle mass and muscle strength are reported to decline at mean annual rate of approximately 1% and 3%, respectively (4). However, the rate of decline shows great variance according to different factors such as physical activity and disease.
Some previous studies have demonstrated the association between sarcopenia with mortality in geriatric patients (5-7). However, multiple studies have systematically investigated the decline in muscle function and muscle strength as strong predictors of disability and mortality among this population (8-11). The current definition of sarcopenia proposed by the European Working Group on Sarcopenia in Older People (EWGSOP) comprises both, the loss of muscle mass and low muscle strength or poor physical performance (12). There are several published data to date available regarding the prognostic impact of sarcopenia on survival in nursing home and community-dwelling older persons (13, 14) according to the definition of the EWGSOP-criteria (12). By contrast, there is a lack of knowledge concerning this issue among geriatric hospitalized patients. One of the major challenges in this field is to differentiate muscle weakness induced by acute disease from weakness induced by sarcopenia.
In a recent multicenter observational study of 770 older hospitalized patients, Vetrano and colleagues (15) demonstrated that sarcopenia, as defined according to EWGSOP-criteria, was the major predictor of in-hospital (hazard ratio 3.45; CI: 1.35-8.86) and 1-year mortality (hazard ratio 1.59; CI: 1.10-2.41). In addition, using the EWGSOP- suggested criteria, results of a prospective observational study of 103 older patients admitted to a geriatric unit have shown that sarcopenic patients had a higher risk of death in the short-term (16). Both studies have measured sarcopenia directly after hospital admission, when the overlap of muscle weakness due to sarcopenia with the consequences of acute disease is assumingly substantial.
Despite the fact that major disability and impaired mobility are supposed to be a consequence of sarcopenia, it is still unknown if the prognostic prevalence of sarcopenia for mortality depends on mobility or dependency status among older hospitalized patients. Previous studies in older hospitalized individuals did not stratify the patients according to walking difficulty or functional status (15, 17-19). In addition, some studies have measured sarcopenia at time of hospital admission, a time at which the contribution of acute disease to the loss of muscle strength and function cannot be differentiated from that of sarcopenia (15, 16). We hypothesize that sarcopenia may be predictive of mortality among older hospitalized patients, especially in those with impaired mobility. We therefore conducted this study to investigate the associations between sarcopenia and 1-year mortality in a prospectively recruited sample of geriatric inpatients and in particular, those with different mobility and dependency status according to the Barthel-Index.

 

Methods

Study participants

All patients who consecutively admitted between May and October 2012 to the geriatric acute care ward were prospectively recruited into the study. The study population suffered from the typical variety of geriatric acute care conditions including infections, fractures and neurodegenerative diseases. Exclusion criteria were severe dementia, palliative situation and age < 65 years. Description of the methodology has been reported in greater detail elsewhere (20). The study was approved by the ethics committee of the University of Münster.

Geriatric Assessment

Nutritional status was evaluated using the Mini Nutritional Assessment Short Form (MNA-SF) (21) and subjects were stratified as malnourished (0-7 points), at risk of malnutrition (8-11 points) and having normal nutritional status (12-14 points). Self-caring capacity was assessed using Barthel-Index (BI) on admission and at time of discharge (22). In addition, pre-admission functional status was determined by interviewing the patients and their relatives, where necessary (pre-admission BI). The points range of the German version of the BI is 0-100 pts., with 100 pts. indicating independency in all activities of daily living (ADL; (22)).
Mobility status was defined according to walking ability as described by the BI. In brief, mobility score of 15 represents a person who can sit or walk at least 50 m independently without a walker or help but may use any aid i.e. stick. Mobility scores of 10 (walks at least 50 m with a walker or with help of one person), 5 (walking the distances in the living area with help; wheelchair independent) and 0 (patients does not meet these requirements, immobile) were considered as mobility limitation. In addition, we classified patients according to their ADL as follow: BI score <60, <70, <80 and <90. Furthermore, cognitive status was assessed using the MMSE (23) and depression was investigated by the use of the Geriatric Depression Scale (GDS-4) (24). Comorbidity was determined using the Charlson Comorbidity Index (25).

Functional status

Hand grip strength (HGS) was tested with a Jamar dynamometer (Lafayette Instrument Company, Lafayette, IN). The Short Physical Performance Battery (SPPB) was applied for evaluating lower extremity function (26).

Muscle mass assessment

Impedance parameters were measured with Bioelectrical Impedance Analysis (BIA) using the Data Input Nutrigard M device (Data Input GmbH, Darmstadt, Germany) applying an alternating electric current of 800 mA at 50 kHz. Skeletal muscle mass (SM) was calculated from raw data obtained from BIA according to the equation of Janssen et al (27). SM index (SMI) was also determined as SM/height in m2.

Definition of sarcopenia

Sarcopenia was defined according to the criteria of the European Working Group on Sarcopenia in Older People (EWGSOP) (12) using the following cutoff points: SMI based on 2 SDs below the mean of young adults (SMI in men < 8.87 kg/m2 and women < 6.42 kg/m2 (28), HGS in men < 30 kg and women < 20 kg) (29) and impaired physical performance defined as SPPB of 8 or lower (26).

Time of assessment

All measurements and assessments were performed within two days before hospital discharge, except MMSE, GDS-4 and BI on admission and BI prior to admission which were assessed soon after hospital admission. The rationale behind this was to minimize the contribution of acute disease to reduced muscle strength and function. Moreover, BIA results would have been highly influenced by changes of fluid distribution due to acute disease on admission.

Mortality outcome

The survival status was ascertained with a telephone interview after one year with the patients’ proxies or the patient, if competent. The day of discharge from hospital was considered as the follow-up starting point. We asked for mortality and the day of death. No information regarding causes of death was collected.

Statistical analysis

The statistical analysis was performed with SPSS statistical software (SPSS Statistics for Windows, IBM Corp, Version 23.0, Armonk, NY, USA). Continuous variables are expressed by their means and standard deviations (SDs) for normally distributed variables and median values with interquartile ranges (IQR) were reported for non-normally distributed data. Categorical variables are expressed as absolute numbers and relative frequencies (%). A group comparison was performed using the t-test for continuous data with normal distribution, the Mann-Whitney U test for continuous variables with non-normal distribution and Pearson Chi-square test for categorical variables.
Cox proportional hazard models and 95% confidence intervals (CI) adjusted for potential confounders were used to evaluate the association of sarcopenia with survival in total population, in a subgroup of patients with mobility limitation and in those classified according to the BI score <60, <70, <80 and <90. As a result, model 1 was adjusted for age and gender and model 2 was adjusted for age, gender and Charlson-Index. Due to similarities of some items of MNA-SF (i.e. mobility and calf circumference) with sarcopenia definition, nutritional status was not included into the model, to avoid the consequences of auto-correlation. Kaplan-Meier curves of survival were analyzed to explore the impact of sarcopenia on 1-year mortality. Differences between curves were evaluated with the log-rank test. A P value <0.05 was accepted as the limit of significance.

 

Results

Characterization of study population

Socio-demographic, functional, cognitive and clinical characteristics of study participants stratified according to presence of sarcopenia are summarized in Table 1. The recruited population comprised 198 patients with a mean age of 82.8 ± 5.9, predominantly females (70.2%). According to MNA-SF, the prevalence of patients at risk of malnutrition and malnourished subjects were 39.9% and 33.8%, respectively. In addition, 99 (50.0%) subjects did not show cognitive deficits, 76 (38.4%) were mildly or questionably cognitively impaired and 23 (11.6%) subjects displayed a moderate cognitive impairment. Fifty patients (25.3%) had a diagnosis of sarcopenia. Further, compared to non-sarcopenic subjects, sarcopenic patients were more often malnourished and at risk of malnutrition, and displayed a lower body weight, BMI, calf circumference, SM and SMI.

Table 1 Characteristics of the study population according to presence of sarcopenia and mortality

Table 1
Characteristics of the study population according to presence of sarcopenia and mortality

BMI, body mass index; SM, skeletal mussle mass; SMI, SM index measured as SM/height in m2; SMI cutoff, SMI in men < 8.87 kg/m2 and women < 6.42 kg/m2; MNA-SF, Mini Nutritional Assessment Short Form; HGS, hand grip strength; HGS cutoff, hand grip strength cutoff in men < 30 kg and women < 20 kg; SPPB, Short Physical Performance Battery; BI, Barthel-Index; MMSE, Mini-Mental State Examination; GDS, Geriatric Depression Scale; Values are given as mean±SD, median (interquartile range) or number (%); *P<0.001 difference between sarcopenia and no saropenia, **Pre-sarcopenia

A total of 42 deaths (26 females and 16 males, P=0.182) occurred during the 1-year follow-up, in which 14 patients had sarcopenia. Patients who died during follow-up, had significantly lower SPPB, higher rate of comorbidity and poorer nutritional status than those who survived.  Kaplan-Meier survival curves showed that there was no significant difference in death rates between sarcopenic and non-sarcopenic patients (log rank P=0.149). Moreover, in total population, there was no association between sarcopenia and 1-year mortality in the unadjusted model (HR: 1.59, 95% CI: 0.84-3.3; P=0.152) according to Cox proportional hazard. Following an adjustment for the potential confounders, the association between sarcopenia and 1-year mortality remained non-significant (HR: 1.54, 95% CI: 0.81-2.95; P = 0.185). Since in whole population, no statistically significant association between sarcopenia and mortality were found, further analyses were performed in population sub-groups.

Association between sarcopenia and 1-year mortality in sub-groups of patients with poor mobility

A total of 138 (70%) older patients (99 females, 72%) complained of walking difficulty (mobility scores of ≤10) prior to admission, of which 30 patients (30/138; 21.7%) had sarcopenia. In addition, a total of 30 patients (30/138; 21.7%) died after 1 year, of which 11 (36.7%) were sarcopenic. A total of 162 older individuals (119 females, 73.5%) had poor mobility at time of discharge, of which 37 patients (37/162; 24.1%) were sarcopenic. Moreover, a total of 35 patients (35/162; 21.6%) died after 1 year, of which 13 (37.1%) had sarcopenia.
Results from unadjusted and adjusted Cox proportional hazard are shown in Table 2. In the unadjusted model, a significant association between sarcopenia and mortality (P=0.017) was observed among older patients with poor mobility prior to admission. Following an adjustment for age and gender, the association between sarcopenia and mortality remained significant (P=0.025) . In the fully adjusted model for potential confounders, patients with sarcopenia displayed higher mortality rates compared with the subjects without sarcopenia (P=0.018). Similarly, such an association was consistent either in unadjusted model and adjusted models among older subjects with difficulty in walking at time of discharge. Concomitantly, Charlson-Index was also a significant predictor of mortality in both groups. In order to determine the impact of sarcopenia on 1-year mortality, the Kaplan-Meier survival curves were analyzed. The survival curves differed significantly in the log rang test both prior to admission (P=0.013, Figure 1a) and at time of discharge (P=0.027, Figure 1b).

Table 2 Association between sarcopenia and 1-year mortality according to Cox regression models adjusted for potential confounders in sub-group of older patients with mobility limitation

Table 2
Association between sarcopenia and 1-year mortality according to Cox regression models adjusted for potential confounders in sub-group of older patients with mobility limitation

Model 1: adjusted for age and gender; Model 2: adjusted for age and gender and Charlson-Index; HR, hazard ratio; CI = confidence interval; *P<0. 05; **P<0.01

 

Figure 1 Kaplan-Meier survival curves for mortality stratified by sarcopenia status in a) older patients with mobility limitation prior to admission (n=138), b) older individuals with mobility limitation at time of discharge (n=162) and c) a sub-group of older patients with pre-admission Barthel-Index < 60 (n=45)

Figure 1
Kaplan-Meier survival curves for mortality stratified by sarcopenia status in a) older patients with mobility limitation prior to admission (n=138), b) older individuals with mobility limitation at time of discharge (n=162) and c) a sub-group of older patients with pre-admission Barthel-Index < 60 (n=45)

 

Association between sarcopenia and 1-year mortality in sub-groups of patients stratified according to the BI

The association between sarcopenia and mortality was also assessed comparing different subgroups with distinct scores of the BI in pre-admission, during admission and at time of discharge (Table 3). The prognostic impact of sarcopenia on mortality was statistically significant in a sub-group of patients with pre-admission BI<60, BI<70 and BI <80 whereas such an association was not observed across the different scores of the BI during admission and at time of discharge. Kaplan-Meier curves along with their respective log-rank tests demonstrated significant differences in mortality between patients with sarcopenia and without sarcopenia with pre-admission BI<60 (P=0.011, Figure 1c), pre-admission BI<70 (P=0.019) and pre-admission BI<80 (P=0.016).

Table 3 Association between sarcopenia and 1-year mortality according to Cox regression models adjusted for potential confounders in sub-group of older patients stratified according to Barthel-Index (BI)

Table 3
Association between sarcopenia and 1-year mortality according to Cox regression models adjusted for potential confounders in sub-group of older patients stratified according to Barthel-Index (BI)

HR, hazard ratio; CI = confidence interval; *P<0.05

Discussion

The results of this study demonstrate that the prevalence of sarcopenia in older hospitalized patients, as defined according to EWGSOP-criteria, was 25.3%. Although numerous studies have investigated the prevalence of sarcopenia in various populations, there are few data involving acute care geriatric patients. Using similar criteria that we used in the current study, Rossi et al. (19) and Cerri et al. (16) have shown that prevalence of sarcopenia were 26% and 21.4%, respectively, in older persons admitted to an acute care ward. By contrast, in a cohort of 342 acutely ill older hospitalized patients, Gariballa and colleagues (30) found a prevalence of 10%, and in a sample of 1787 community-dwelling older persons, Patel et al. (31) demonstrated a prevalence of 7.8%. However, it has to be noted that in those studies, muscle mass was estimated from anthropometric measurements (i.e. mid-arm circumference). Consequently, discrepant results may have been a result of different methods and cut-off points used to assess muscle mass and muscle performance to define sarcopenia or different patients’ characteristics (19, 20). These discrepancies highlight the necessity to establish and use a consensus definition of diagnostic criteria across different studies, such as those proposed by the EWGSOP.
In addition, this study has two major findings concerning sarcopenia and mortality in older hospitalized patients.Sarcopenia was more relevant to mortality, independent of age and other clinical variables, when patients had lower self-caring capacity before acute disease (in particular, pre-admission BI < 60 and BI < 70), and this is also true for older patients with poor mobility before acute disease and even at time of discharge. Indeed, compared with non-sarcopenic patients, risk of death (hazard ratio) was significantly higher if sarcopenic patients have BI < 80 and mobility limitation. Impairment of functional status due to an acute disease and inability to remain functionally stable after an acute illness may explain the association of sarcopenia with mortality depending on functional status prior to admission and at time of discharge in our study. By contrast, no significant association between sarcopenia and mortality was observed in subjects with reduced functional status on admission. However, it is not surprising since BI on admission is substantially influenced by the effect of acute disease and does not reflect patient’s permanent functional status.
Interestingly, in our population, no significant differences in total BI score (prior to admission, on admission and at time of discharge) between sarcopenic and non-sarcopenic patients were observed. This is surprising, as reduction in muscle strength and physical function presumably impact the ability to perform the ADL. We therefore classified the patients according to BI and repeated the analysis after excluding those with normal mobility. The results showed that sarcopenic and non-sarcopenic patients significantly differed at pre-admission BI < 70 (P=0.005) and < 80 (P=0.007), at which the risk of death was 2.80 and 2.55 times higher in sarcopenic compared with non-sarcopenic participants. Moreover, not SMI but SPPB was significantly different between survivors and non-survivors confirming existing evidence that not primarily mass but function is relevant for mortality and other adverse outcomes. These findings indicate the need for assessment, recognition and monitoring of older hospitalized patients with impaired ADL and limited mobility, since the impact of sarcopenia on mortality seems to be stronger when the patients have functional limitation.
To the best of our knowledge, this study is the first to determine the association of sarcopenia with mortality in a group of geriatric hospitalized patients stratified according to their mobility and ADL status. Several previous studies which investigated the association of sarcopenia with mortality in nursing home or hospitalized patients did not stratify patients for functional and mobility status (15, 17-19, 32), exept the reccent study by Landi and colleagues, which showed similar results to this study, but in a cohort of community living older persons (33). The International Working Group on Sarcopenia (IWGS) (34) proposed that sarcopenia should always be investigated in patients with limited mobility or in subjects who cannot rise from a chair without any help and our sub-analysis seems to support this suggestion. A recent study by Maeda et al. (35) who stratified 778 older medical patients according to their mobility status, has demonstrated that the prevalence of sarcopenia highly increased in patients with limited mobility (walk with help, 76.1%; wheelchair, 89.4%; and immobile, 91.7%) compared with those who were able to walk independently (57.9%, P<0.001). However, the authors have not investigated the association between sarcopenia and mortality.

Study limitations and strength

Some limitations of the present study need to be mentioned. The use of BIA to measure muscle mass in older hospitalized patients is controversial. BIA is not the gold standard for assessing muscle mass. However, BIA measurements are regarded as sufficient, when analyzing patient groups (36, 37). Further, in previous studies of hospitalized and community-dwelling older people (38, 39), BIA was considered to be a valid, reliable and easy to use method. In addition, the cut-off values of BIA-derived SMI used in this study were based on the cut-offs proposed by EWGSOP criteria (12). These cut-offs were developed  in an Asian population (28) which displays lower lean body mass at a given BMI than do white subjects (40) which might raise some concern. However, the agreement between Asian cut-offs and cut-offs obtained from European population have already been confirmed (41-43). Moreover, mobility status was defined according to walking ability as described by the BI which may be imprecise. Nevertheless, previous study in patients with stroke (44) has demonstrated that measurement of mobility as measured by the BI is reliable and percentage agreements were generally high for total BI (77%) and walking (95%). Furthermore, no information regarding causes of death was collected due to the unreliability of such information obtained by telephone interview. Finally, the relatively small sample size of total and sub-groups population may have limited the value of our results. The major strength of present study is that this is, to the best of our knowledge, the only study in hospital patients measuring sarcopenia at the end of hospital stay to minimize the effect of acute disease on muscle weakness.

 

Conclusion

Taking all individuals, the present study found no significant differences in 1-year mortality between sarcopenic and non-sarcopenic older patients. However, presence of sarcopenia is associated with higher risk of mortality among those subjects with mobility limitation and dependency, independently of age, sex and comorbidity. Our data underline the need for assessment, early recognition and treatment of sarcopenic older patients with impaired activities of daily living to limit long term functional decline and mortality risk. In addition, prospective studies with larger sample size, stratification for mobility and ADL status and assessment of the causes of mortality are needed.

 

Funding: The study received no financial support
Conflicts of interest: The authors declare no conflict of interest.
Ethical standard: The authors declare that the study procedures comply with current ethical standards for research involving human participants in Germany. The study protocol had been approved by the ethical committee of the University of Münster.
Statement of authorship: The study was designed by MP and RW. Data were obtained by RW. Statistical analysis was performed by MP. MP, KN, MJM, RD and RW prepared the manuscript. All authors read and approved the final manuscript.

 

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13.    Landi, F., et al., Sarcopenia and mortality among older nursing home residents. J Am Med Dir Assoc, 2012;13(2):121-6.
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17.    Martinez, B.P., et al., Frequency of sarcopenia and associated factors among hospitalized elderly patients. BMC Musculoskelet Disord, 2015;16:108.
18.    Sousa, A.S., et al., Sarcopenia among hospitalized patients – A cross-sectional study. Clin Nutr, 2015;34(6):1239-44.
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20.    Smoliner, C., C.C. Sieber, and R. Wirth, Prevalence of sarcopenia in geriatric hospitalized patients. J Am Med Dir Assoc, 2014;15(4):267-72.
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CAN WE PREDICT MORBIDITY AND MORTALITY OF PATIENTS AGED 75 YEARS AND OLDER UNDERGOING CYSTECTOMY?

 

F. ATALLAH1, P. LETOCART1, B. MALAVAUD2, M. AHMAD1, M. MAZEROLLES1, V. MINVILLE1,3

 

1. Department of Anesthesiology and Intensive Care, Rangueil Hospital, University Hospitals of Toulouse, France; 2. Department of Urology, Rangueil Hospital, University Hospitals of Toulouse, France; 3. INSERM U 1048, équipe n°3, Institut des Maladies Métaboliques et Cardiovasculaires (I2MC), Toulouse, France.

Corresponding author: Fouad Atallah, MD, Department of anesthesiology and Intensive Care Medicine, University Hospitals of Toulouse, France, email: atallah.f@chu-toulouse.fr

J Frailty Aging 2017;6(2):72-75
Published online March 1, 2017, http://dx.doi.org/10.14283/jfa.2017.5

 


Abstract

Radical cystectomy is associated with a high postoperative mortality and morbidity in older patients. We aimed to define the predictive value of comorbidity scores and determine the prognostic factors of postoperative complications. Preoperative associated morbidities were collected and graded according to the American Society of Anesthesiologists (ASA) score system, the Adult Comorbidity Evaluation (ACE) scale and the Charlson comorbidity index. Surgical complications were graded according to Clavien classification. Early and late complications were recorded. Data are from 49 consecutive patients aged ≥ 75 years who had an open surgery for bladder cancer. The most commonly associated conditions were smoking, renal insufficiency, and arterial hypertension. Incidence of early and late complications was 49% and 16%, respectively. Four and 25 death events occurred during the early and late follow-up, respectively. The incidence of morbidity and mortality were not related to ASA, ACE or Charlson scores. Preoperative malnutrition, renal insufficiency, higher need of perioperative blood transfusions, and prolonged ileus were identified as risk factors of postoperative morbidity. Late complications seemed related to low weight.

Key words: Elderly, cystectomy, comorbidity score, morbidity, mortality.


 

Introduction

Bladder cancer is a urological malignancy, common in older patients. Radical cystectomy is a first-line treatment for invasive bladder tumor. Recent studies indicate this as a complicated surgery characterized by high mortality and morbidity (1). Anesthetic and surgical advancements have allowed offering this surgery to increasingly elderly patients. In the geriatric population, the outcome is generally determined by the interaction of patients’ physiological state and the challenges introduced by the surgery (2). We undertook this study for evaluating the incidence of morbidity and mortality following radical cystectomy in patients aged 75 years and older, defining the predictive value of comorbidity scores, and determining the prognostic factors of postoperative morbidity and mortality.

 

Methods

Over a period of 30 months, we collected data of the 49 consecutive patients aged ≥75 years who had an open surgery for bladder cancer. The surgical techniques consisted of a cysto-prostatectomy in male patients, and an anterior pelvectomy in female patients with ileal conduit. In order to reduce the need for post-operative morphine analgesia, 65% of patients were given regional analgesia. General anesthesia was maintained using total intravenous anesthesia (TIVA) with propofol and remifentanil. All patients were monitored using the BISpectral index. Comorbidities were classified according to the ASA score (American Society of Anesthesiology) and according to two oncology scores: the ACE score (Adult Comorbidity Evaluation) (3) and the Charlson score (4). The pre-operative cardiovascular risk was assessed using the cardiac risk index of Lee (5). Moderate renal failure was defined as a glomerular filtration rate, calculated with MDRD formula, <60 ml/min/1.73m2. Intraoperative data collected included: duration of anesthesia and surgery, use of epidural analgesia, technique of anesthetic maintenance, hemodynamic instability (systolic blood pressure <80mmHg for over 20 consecutive minutes and/or use of norepinephrine), blood loss, transfusion if needed, quantity of fluid replacement and level of hemoglobin in the post-anesthesia care unit (PACU). Post-operatively we documented morphine consumption during the first 48 hours post-operatively, date when patient resumed oral alimentation, duration of stay in the step-down unit, total duration of hospital stay and perioperative mortality/morbidity. Post-operative complications were considered early if they appeared in the 30 days following surgery and late if they appeared after 30 days. Complications were identified using the Clavien classification (6). Follow-up was recorded at 6 and 12 months.
Statistical analyses were carried out using Statview, version5  (SAS Institute Inc). Correlations between quantitative variables were assessed using the Spearman test, qualitative variables were tested using Fisher’s test. Comparisons between quantitative variables between classes were done by a t-test. Data are presented as mean +/- SD. Test results were considered significant for P-values <0.05.

 

Results

Demographic data are presented in Table 1. A statistically significant correlation was established between the following comorbidity scores: ASA and ACE (r=0.53; p<0.001), ASA and Charlson (r=0.56; p<0.001) and ACE and Charlson (r=0.51; p<0.001). On the other hand, there was no significant correlation between ASA, Charlson, ACE and Lee scores, and early morbidity/mortality defined according to the Clavien classification. The mean duration of surgery was 258 (±73) minutes. Mean blood loss was estimated at 514 (±460) ml whilst the mean fluid replacement, including red blood cells concentrates was 3,470 (±1,335) ml. Eighteen patients received blood transfusion perioperatively with a median of three units of red cells concentrates (2-6). 16% of the study subjects presented an intraoperative hemodynamic instability. There were no unexpected intraoperative events, such as a vascular or digestive injury. The mean hemoglobin in the PACU was 10.2 (±1.8) g/dL. Mean hemoglobin was 11.5 (±1.1) g/dL at the first day. Eleven patients required post-operative transfusion. The mean consumption of morphine in the first 48 hours following surgery was 11.6 (±12.2) mg. The mean duration for resuming alimentation was 6.8 (±3.6) days. Mean stay in the step-down unit was 6 (±3) days and the mean hospital stay was 21 (±12) days.

Table 1 Demographic data

Table 1
Demographic data

Abbreviations: Body Mass Index = BMI; Glomerular Filtration Rate = GFR; Chronic obstructive pulmonary disease = COPD; American Society of Anesthesiologists = ASA; Adult Comorbidity Evaluation = ACE.

 

The rate of early post-operative complications was 49%: 13 patients had surgical complications and 11 patients had medical complications. The rate of late complications was 62%, mainly due to pyelonephritis secondary to stomal stenosis, intestinal occlusion and one deep vein thrombosis.
Table 2 shows the relationship between different patient characteristics, comorbidity scores, and incidence of early and late complications. A prolonged hospital stay was related to early morbidity; hospital stay was 16.8 ± 4.8 days for patients without complications and 25.6 ± 11.9 days for those with complications (p=0.001). A multivariate analysis demonstrated that low BMI (p=0.03) and ileus (re-alimentation >7 days; p = 0.04) were independent predictive factors of complications (Clavien ≥ 2) during the 30 postoperative days. Furthermore, there were four post-operative deaths (8%), three due to aspiration pneumonia secondary to ileus and one due to peritonitis and septic shock. Late complications were only related to a low weight. Twenty-one patients deceased between 30 days and 12 months: survival rate at 6 months was 73% and survival rate at 12 months was only 49%.

Table 2 Relationship between patients’ characteristics and the incidence of early and late complications

Table 2
Relationship between patients’ characteristics and the incidence of early and late complications

* Late complications concerned 45 patients as four patients died in the early postoperative period. Abbreviations: Body Mass Index = BMI; Glomerular Filtration Rate = GFR; American Society of Anesthesiologists = ASA; Adult Comorbidity Evaluation = ACE.

 

Discussion

In our study, although patients presented severe comorbidities reflected by elevated scores (ASA ≥ 3, ACE ≥ 2, and Charlson ≥ 6), a relationship between these different scores and early morbidity-mortality was not found. Early morbidity was associated with prolonged ileus. Its pathophysiology is not fully understood. Autonomic nervous dysfunction with a hyperactivity of the sympathetic nervous system, inflammatory and hormonal responses to the surgery trauma and to the manipulation of the bowel have an inhibitory effect on the gastrointestinal tract. It has been demonstrated that early oral feeding does not increase the incidence of complications but enhances anastomotic and wound healing as well as patient rehabilitation. The most prevalent current practice is initiating early oral intake, before clinical resolution of postoperative ileus (7). Post-operative ileus is still one of the most common post-operative complications after cystectomies, up to 32%. Several publications have shown some benefits of an early rehabilitation program: early mobilization, early re-alimentation, epidural analgesia, peri-operative intravenous xylocaine and early removal of naso-gastric tube (8).
Even though we could not demonstrate a correlation between comorbidity scores and morbidity-mortality in elderly patients, some clinical risk factors were determined. Malnutrition (indicated by a low weight and BMI) was associated with early morbidity. It is responsible for sarcopenia and immune deficiency in elderly patients who often cumulate several pathologies. It also leads to delayed wound healing and bedsores and is clearly associated with higher post-operative morbidity (9). It is recommended to detect and treat malnutrition pre-operatively.
Early morbidity was also related to blood transfusion. The increased need for blood transfusion is usually associated with a difficult and hemorrhagic surgery frequently accompanied by periods of low cardiac output and augmented visceral manipulation, resulting in multiple organ damage (10). The presence of a significant relationship between morbidity and blood transfusion, as well as the duration of ileus, demonstrates the importance of performing minimally invasive surgeries.
Chronic renal failure was also associated with morbidity. Renal failure is responsible for anemia, altered phosphorus-calcium metabolism, malnutrition, cardiovascular diseases and altered pharmacokinetics of certain medications. This relationship had also been demonstrated post-operatively in patients of different ages and an odd ratio of 3.2 for renal failure was also reported (11).
Perioperative mortality has decreased considerably in the last years due to the improvement of perioperative patients’ management (12). In our study, there were no intra-operative deaths and four early post-operative deaths; three of them were consequent to post-operative ileus and regurgitation.
Elderly people are a heterogeneous population. Clinical risk factors have to be determined individually for each patient. In each individual situation, this assessment must then be part of a decision-making process in the preoperative phase by different specialists: surgeons, anesthesiologists, geriatric specialists, primary care physicians, oncologists, cardiologists, physical therapists, nutritionists and social workers. This will provide care optimization for the elderly patient by deciding the best therapeutic management for each individual patient in order to maintain postoperatively his quality of life as much as possible. Anesthesiologists have to participate in this preoperative assessment and conduct a smooth intra-operative and postoperative care in the best way to help this frail category of patients recovering and resuming an independent life as soon as possible (13, 14).
The main limitation of our study resides in its observational and monocentric design. In our study, comorbidity scores could not predict morbidity in patients aged 75 years and older that underwent cystectomy for bladder cancer. However, preoperative malnutrition, renal insufficiency, need for blood transfusion and prolonged ileus were identified as risk factors of postoperative morbidity. A preoperative multidisciplinary team including geriatric approach to define the best therapeutic line for each patient according to his comorbidities is recommended.

 

Acknowledgments: The authors thank Reza Shams (M.D.) for reviewing the manuscript.
Conflict of interest: None

 

References

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9.    Gregg JR, Cookson MS, Phillips S, et al. Effect of preoperative nutritional deficiency on mortality after radical cystectomy for bladder cancer. J Urol 2011;185:90–96
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11.    Kauffman EC, Ng CK, Lee MM, et al. Critical analysis of complications after robotic-assisted radical cystectomy with identification of preoperative and operative risk factors. BJU Int 2010;105:520–527
12.    Donat SM, Siegrist T, Cronin A, Savage C, Milowsky MI, Herr HW. Radical cystectomy in octogenarians–does morbidity outweigh the potential survival benefits? J Urol 2010;183:2171–2177
13.    Jaklitsch M, Billmeier S. Preoperative evaluation and risk assessment for elderly thoracic surgery patients. Thorac Surg Clin 2009;19:301–312
14.    Kurrek MM, Barnett S, Minville V. Considerations for the Perioperative Care of Elderly and Frail Patients. J Frailty Aging 2014;3:230–233

NEIGHBORHOOD PERCEPTION AND OBESITY IN AGED MEXICAN AMERICANS

 

C. SIORDIA1, J. SAENZ1

 

1. Community Health and Preventive Medicine, University of Texas Medical Brach, Galveston, Texas, USA.

Corresponding author: Dr. Carlos Siordia, Community Health and Preventive Medicine, University of Texas Medical Branch, Galveston, Texas, 77555. Phone: (409) 772-1128, Fax: (409) 772-5272, E-mail: Carlos.Siordia@utmb.edu


Abstract

Background: Hypotheses on the relationship between neighborhood perception and obesity (as measured by body mass index) seem to generally posit that a positive neighborhood perception may be related with behaviors that positively moderate body weight. Objective: To determine if and how there is an association between positive neighborhood perception and obesity—while accounting for frailty- and disability-related factors. Design: Cross-sectional study from Wave-5 of the Hispanic Established Population for the Epidemiological Study of the Elderly (HEPESE). Setting: Data files housed by the Sociomedical Division in the department of Community Health and Preventive Medicine at the University of Texas Medical Branch in Galveston, Texas. Participants: A total of 889, aged 75-90 community-dwelling Mexican Americans in the Southwest United States. Measurements: Body mass index (BMI=Kg/m2), neighborhood perception, grip strength, gait speed, depression symptomatology, chronic conditions, presence of limitations with basic and instrumental basic activities of daily living (ADLs), and other health and demographic variables are used in logistic regressions predicting the likelihood of being obese (BMI > 30 Kg/m2) versus being of normal weight (BMI 18.5-25.4 Kg/m2). Results: The odds of being obese increase: as the level of positive neighborhood perception increases; grip strength increases; and with having any limitations with basic-ADLs. Conclusions: These findings provide evidence that a positive neighborhood perception need not always be accompanied with a reduced risk of being obese. Because functional limitations in older ages may influence how positive neighborhood perception affects BMI, more research is needed.

Key words: Frailty, disability, mortality, mexican elderly.


 

Introduction

Investigations on the relationship between neighborhood perception (NP) and body mass index (BMI=Kg/m2) continue to grow as interest on the topic increases and data become available. In a study by Fish and colleagues (1), the association between perceived neighborhood safety and BMI was investigated using over two-thousand adults with a mean age of 40 who resided in Los Angeles, California between the years of 2000 and 2001. About one-third of their sample was “White,” about half of it (55%) was made up of Latinos/as, and 54% self-identified as being first-generation immigrants. Our study differs in that we only have Mexican Americans (MA) aged 75 and above in our sample.

Fish and colleagues (1) begin their well-written report by pointing out that unhealthy weight levels are a major public health concern. Although a recent publication, using National Health and Nutrition Examination Survey (NHANES) data, on the prevalence of obesity in United States (US) adults age 20 and over shows a leveling off of the increasing obesity trend, they report that in 2009-2010 the obesity prevalence for all US adults was at 36% (2). Others continue to argue that obesity in the US is an epidemic (3). Tables in the Flegal et al (2) report show that 37% of males and 45% of females amongst MAs age > 20 have BMIs > 30 Kg/m2—with 4% of males and 7% of females having BMIs > 40 Kg/m2. It is important to note that only 1.1% of their weighted sample was made up of MAs age 60 and above—equaling an unweighted total of only 292 cases. Our study fills this gap by making use of an analytic sample with almost nine-hundred MAs.

Fish and colleagues (1) point out that investigating obesity is important because it has been linked to many health outcomes. For example, investigations have found that excess weight increases the risk of experiencing many chronic diseases, including cardiovascular diseases (4) and diabetes (5). Obesity has also been found to influence life expectancy (6) and to directly affect the psycho-emotional health of the individual (7). In general, it could be said that being obese increases the risk for developing undesirable health conditions (e.g., diabetes). Our investigation contributes to this line of research by investigating the relationship between several health and demographic factors with obesity.

Fish and colleagues (1) correctly highlight the fact that obesity is directly connected with the US’ financial well being and policy issues because it impacts health care costs. For example, some have calculated that about 9% of the total annual US medical expenditures in 1998 ($92.6 billion in 2002 dollars) were due to overweight- and obesity-attributable medical spending (8). Others have used microsimulations to estimate lifetime costs for seventy-year-olds and found that the obese will spend $39,000 more on health care than those of normal weight and that Medicare will spend approximately 34% more on an obese person than on a normal-weight person (9). More recently, research found that elderly men who are overweight or obese at age 65 had 6-13% more lifetime health care expenditure than their normal-weight male-counterparts and females who were overweight or obese at age 65 had 11-17% more lifetime health care expenditure than their normal-weight female-counterparts (10). As is clear, obesity has important health and financial implications.

Despite the many efforts to highlight the obesity epidemic in the US, our understandings of what factors are related to being overweight remain inconclusive. While there is much  research on the effects of NP on outdoor physical activity (e.g., walking), research on the relationship between NP and BMI remains limited. Our project fills this gap in the literature. Although investigations on how NP is related to body weight abound, no theoretical premises on the mechanisms between context and obesity have been established. This is why Fish and colleagues (1) explain that the mechanisms through which neighborhood attributes lead to weight gain are not well characterized. We hope our investigation and closing “theory discussion” contribute towards a better characterization of how NP is related to BMI.

Researchers have convincingly argued that increasing our understanding of how NP is related to BMIs may help advance our ability to delineate the mechanisms through which macro-level phenomenon affect micro-level BMI. Because ethnicity is important when understanding micro-level health outcomes (11), insight on if/how MAs have been disproportionally affected by the social structures involved in the US obesity epidemic may reveal what factors significantly impact how they regulate their body weight. For example, factors such as low-access to healthy food or facilities for physical activity, residing in lower socioeconomic areas, and economic disparities associated with poor food choices may be important determinants of obesity (3). While previous research has advanced our knowledge of how micro-level outcomes may be influenced by macro-level variables, it is unfortunate that standard methodological approaches often fail to address the role which previous contexts may play in shaping these relationships. Our project offers in closing a detailed discussion on how inter-context variability may play a role in body weight.

Fish and colleagues (1) succinctly argue that “perceived neighborhood safety” is an important mechanism through which the contextual characteristics may influence obesity. They explain that perceiving the neighborhood of residence as unsafe may increase the risk of obesity through a number of mechanisms. For example, reduced outdoor physical activity as a result of not feeling save outside the home (12, 13), and the potential for increases in stress-eating behaviors (14)—where eating may reduce distress presumably via central opiods (15), may all increase the odds of being obese. The implicit argument is that NP, a self-reported subjective (16) measure of context, has the ability to capture important characteristics in the person’s environment that are related to their body weight.

Before moving on, it is important to note that we are only interested in the individual-level subjective measure of neighborhood. In the paper by Fish and colleagues (1), they include Census tract level measures and refer to them as “neighborhood” measures. We use the term neighborhood in a different way and under different circumstances. The term neighborhood has as of yet to be scientifically defined.  We use the term because it was used during the administration of the questions from which we derived our measures of NP. We do not advance that calculations explicitly aggregated by Census geographical polygons be referred to as “neighborhood measures.”

Fish and colleagues (1) explain that the limited research on NP of safety and body weight is inconclusive.  For example, one study found that perceiving the neighborhood as unsafe was related with greater odds of being obese (17), while another found no significant statistical association (18).  Fish and colleagues find that perceiving the neighborhood as unsafe is related with greater odds of being obese (1). A related article explains that perceived neighborhood safety and walking outdoors are related (19). Others have noted that the perception of the built environment is also an important factor related to walking and thus body weight (20).

Our study approaches the same topic with a different NP measure. In contrast to Fish and colleagues (1) where perceived safety is the NP-factor of interest, our investigation measures an aged adult’s “positive” NP. Our study also differs in that it focuses on contrasting those of “normal” weight to those consider to be “obese” as measured by BMI. Positivity in a person’s NP is measured by asking them about their: (1) satisfaction with their neighborhood, (2) their view on neighbors willingness to help, (3) if they feel their area can be described as being a close-knit community, and (4) if they think people in their neighborhood can be trusted. Thus, our study captures a positive and subjective measure of neighborhood perception in contrast to the “negative” and subjective measure used by Fish and colleagues (1). In a sense, we are covering the same topic from the other side of the coin.

Research with positive NP scales (measuring respondents evaluations of the “aesthetic” quality of their environment) abound where the outcome of interest is physical activity—not BMI. For example, a study investigating neighborhood aesthetics and convenience levels by asking questions such as “How would you rate the general friendliness of the people?” found that those who report positive changes in NP were more likely to increase their walking (21).  Fortunately, the authors point out that NP with acceptable psychometric properties is lacking (21).
Similar studies have found an association between positive-NP and physical activity in a sample from Japan (22). Another semi-related study on neighborhood attachment where NP was assessed with statements such as my place “is the ideal neighborhood to live in,” found that NP was related to higher levels of fruit and vegetable intake (23).  A study with a sample from Nigeria found that a poor evaluation in the aesthetic quality of the neighborhood was related to higher levels of overweight in adults who reside in low SES neighborhoods (24). Others have even used multilevel models with a sample of Canadian adults to show that living in areas with high “material deprivation” is associated with higher BMIs (25). We were unable to locate a single article where a positive NP scale was used to model body weight.

In terms of theory, the argument is that neighborhood and body weight in older ages are linked (26). Some have explained that perceptions of neighborhood are believed to be intertwined with the concept of social cohesion (27)—a concept found to be related to outdoor physical activity (28). Investigators have asked that researchers contribute towards the development of a comprehensive list of measurable indicators for studying the relationship between context and body weight, where NP is given as a candidate for understanding the mechanisms that influence body weight (29). Our exploratory study pays heed to their call and investigates if a positive NP scale offers value towards the development of more scientific and measurable indicators. We compliment this main effort by offering a relevant and important discussion on the theory of how a person’s “history of contexts” can play a role in how their current habitat influences their current BMI.

Because demographic factors may influence NP—and such perceptions can be linked to body weight related behaviors (30)—we explore how positive NP is related to the likelihood of being obese in a sample of MAs. Effects on BMI can come from person-composition, group-characteristic or location-attribute factors. Our models only include person-level measures. Our positive NP measure varies by and is subjective to each individual. As such, it is treated as a person-level compositional characteristic.  We complement and extend the competent efforts of Fish and colleagues (1) and others by exploring how a positive NP scale is related to the odds of being obese in a group of aged MAs.

Our general research question in this exploratory analysis is: Are positive NP and the odds of being obese related in a group of aged MAs? Our specific aim is to investigate the statistical relationship between NP and the odds of being obese versus of normal weight—while controlling for other demographic and health factors. We give a hypothesis to help orient the reader. While our hypothesis is not born out of existing research, in theory one would expect the odds of being obese to decrease as positive NP increases, thus: We hypothesized that as the level of positive NP increases, the odds of being obese will decrease. We posit such a hypothesis because we could argue that a positive perception of the habitat would be related with greater levels of outdoor participation—where high body weight may be reduced.

Our hypothesis is labeled as “tentative” due to uncertainty. Previous research has focused on non-aged adults as opposed to our very special analytic sample in which all have at least one chronic health condition and are overweight in general. Thus, we explore the obesity-NP relationship while accounting for our study subject’s level of “weakness” (as measured by grip strength) and “slowness” (as measured by their gait speed)—frailty related measures (31). These two are frailty-related components and have been linked to various health outcomes (32). From our measures, an individual’s physical performance (33) is only measured by their grip strength and gait speed. Thus, when we say an individual is “more frail” than another, we simply mean that on their physical performance, they have weaker grip strength or slower gait speed.

Because a person’s stage along the disablement process may also affect the relationship between NP and BMI (34) and because we distinguish frailty from disability (35), we include measures of disability. In general, the disablement process describes how a person’s health conditions affect his/her ability to function in daily living and how environmental factors influence these person-level mechanisms. We assess our subjects’ “stage” in the disablement process by measuring their psycho-emotional status, quantity of chronic health conditions, and the presence of any limitations with activities of daily living.

Because all of the subjects in our analytic sample show evidence of some frailty and some degree of disability, we could expect that even though they may have a positive view of their neighborhood, their personal conditions would limit their ability to be physically engaged with outdoors activities. We first discuss our data source, measures, and statistical methods. Then, we review findings and conclude by outlining some limitations with our project, suggestions for future research, and a detailed theoretical discussion on how context(s) can affect the impact of future contexts on individuals.

Subjects and Methods

Subjects

Observations of aged MAs were obtained from the Hispanic Established Population for the Epidemiological Study of the Elderly (HEPESE), an ongoing study of a sample living in the Southwestern (Arizona, California, Colorado, New Mexico, and Texas) United States (36). Over the last two decades, HEPESE data has been used by various investigators to study a wide range of health outcomes in aged Mexican Americans (37). We make use of Wave-5 data which was collected during 2004-2005.

Dependent Variable

We initially ran regressions comparing: (1) underweight (BMI 14.0-18.4 Kg/m2) versus normal (BMI 18.5-25.4 Kg/m2), (2) overweight (BMI 18.5-25.4 Kg/m2) versus normal, and (3) obese (BMI 25.5-30.4 Kg/m2) versus normal. Because our NP scale was only significant in the obese versus normal comparison, our report only includes these findings. Among the full sample (n=1,547) at Wave 5 between age 75 and 90 with BMIs between 14 and 45, there were 19 who were underweight, 481 who were normal weight, 612 who were overweight, and 435 who were obese. Because of potential data issues with few cases at the extreme lower end of BMIs and extreme high end of age, we omit them from our investigation. Our analytic sample is made up of 889 aged Mexican Americans—all tables below reflect only statistics on this sample.

Positive Neighborhood Perception Scale

Although a series of other questions are available from the dataset (see Appendix 1), only four Wave-5 HEPESE items were used in the creation of our neighborhood perception (NP) scale. The questions used in the HEPESE Wave-5 neighborhood survey questions were inspired by those used in other surveys (38-40). In Wave-5, HEPESE respondents were asked the following questions regarding their perception of the neighborhood they lived in:  All things considered, would you say you are very satisfied, satisfied, neither satisfied nor dissatisfied, dissatisfied, or very dissatisfied with your neighborhood as a place to live? They were allowed to answer using 5 “satisfied” categories in a Likert scale (see Appendix 1). Those who respond “very satisfied” get a “1” on the first items and those from “satisfied” to “very dissatisfied” get a zero.

Respondents were then instructed that surveyors were “going to read [the respondent] some statements, which may or may not be true about [their] neighborhood.”  Respondents were then given the following sub-question sections: this is a close-knit neighborhood; people around here are willing to help their neighbors; people in this neighborhood can be trusted. They were allowed to answers using 5 “agree” categories in a Likert scale (see Appendix 1). Those who responded “strongly agree” get a “1” on these three items and those from “agree” to “strongly disagree” get a zero.  All non-valid answers (i.g., don’t know, refused, or missing) got no score (i.e., are recorded as missing). Because values of “1” capture very satisfied and strongly agree, our NP scale consequently represents extremely-positive responses from the addition of all these four items. We abstain from labeling the scale with the “extreme” term so as to improve the flow of the discussion. However, readers should note that those responding with satisfied are grouped with unsatisfied individuals and those who agree are grouped with those who disagree. This was primarily done because more subjects responded at the extreme ends of Likert scales—something that may be related to the fact that research has shown a potential problem with Spanish translated Likert-type response scales (41, 42).

High scores on our NP scale indicate a high level of neighborhood satisfaction, where neighbors are perceived as being willing to help, where the person feels their area of residence is a close-knit neighborhood, and where people can be trusted. From the coding scheme above, we get the following distribution on the NP scale for the full (n=1,547) sample: 0=604, 1=546, 2=136, 3=206, 4=28. The NP distribution from our analytic sample (n=889) is as follows: 0=349, 1=328, 2=88, 3=121, 4=11. Limitations arising  from the ambiguity of the items in the HEPESE questionnaire are addressed in closing.

Frailty Related Covariates

We calculate a respondent’s grip-strength by using a dynamometer. During data collections, respondents were asked to use their strongest hand for their grip strength test.  They placed their strong hand on a table with the palm facing up, grabbed the handles of the dynamometer using an underhand grip and were instructed to squeeze as hard as possible. Two grip strength measures, recorded to the nearest half kilogram, are used to create the average grip strength performance measure. Respondents who had recently had surgery on their arm or hand did not participate in the exercise and were not included.

Gait speed was measured by timing the number of seconds required to walk eight feet of uniform walking surface. Study participants were instructed to walk the “eight foot course” at their usual speed, “just as if [they] were walking down the street to go to the store.” Participants were instructed to walk past the end of the course and not slow down near the end. Respondents were allowed to use assistive devices to complete the exercise while those who could not walk even with assistive devices did not participate in this exercise.

Disability Related Covariates

We do include other health related covariates other than activities of daily living (ADLs). The Center for Epidemiologic Studies-Depression (CES-D) scale is frequently used to determine if depressive symptoms are present (43). We use four CES-D questions from this scale to create a “positive affect” score. To facilitate interpretation, the responses are reverse coded and added such that higher positive affect scores reflect higher positive affect—a lower presence of depression symptomatology.

To create a scale of chronic conditions we constructed a count of the following conditions: (1) pain or discomfort while walking or standing; (2) the presence of doctor diagnosed diabetes, sugar in urine or high blood sugar; (3) ever having cancer diagnosed by a doctor; (4) ever having or suspected having a stroke, blood clot in the brain or brain hemorrhage; (5) ever having a heart attack; (6) ever having a broken or fractured hip; and (7) ever having broken or fractured any other bone. Thus, the chronic health condition scale ranges from 0-7. Please note that during HEPESE Wave-5 data collection, respondents from the original cohort (present in Wave-1) reported on their hip (or other) fractures since “the last time we talked”,  while new recruits during Wave-5 were asked to report if they have “ever” experienced a hip (or other) fractures. Limitations arising from these and the fact that all our aged MAs have at least one chronic health condition are addressed in closing.

Because we believe the effects of NP can be moderated by the functional ability, we account for the presence of limitations with both basic and instrumental ADLs (BADL and IADL). The ability to perform ADLs is the result of complex interactions between physical, social, environmental, and cognitive factors (44) and has in general been found to diminish in older ages (45).  ADLs “constituted the most consistently measured aspects of functional status in” older people [46] and have been used over many decades (47). Our discussion includes interpretations of ADLs on aged MA’s likelihood of being obese versus of normal weight.

Health and Demographic Controls

We include other health and demographic controls. Respondents who reported being in either excellent or good health were split from those who reported fair or poor to form a binary self-reported health variable. To control for the effects of tobacco usage we operationalize this construct as whether or not the respondent reported being a current smoker. We also include demographic covariates. Respondents’ self-reported income was categorized as less than $15,000 or $15,000 or more for the year 2005. Self-reported education was dichotomized as 8 years or less or greater than 8 years of schooling. We also include an exploratory variable of whether or not the respondent reported having no friends or family living in the neighborhood—to account for the fact that this may influence their positive NP score.

Statistical Modeling

As is customary, we provide descriptive statistics for analytic sample. We use three logistic models where the likelihood of being obese is contrasted to that of being of normal weight. In the first model (i.e., Model 1), NP and frailty-related factors are regressed on the likelihood of being obese versus of normal weight. After including disability related factors in Model 2, we add all other health and demographic variables in our final model (i.e., Model 3). Our discussion focuses on the outputs from Model 3. All data management and modeling is done in SAS 9.2 (Copyright, SAS Institute Inc. SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc., Cary, NC, USA).

Findings

We begin with descriptive statistics of the analytic sample—this excludes all those who are not of normal weight or obese. From Table 1, we see that 48% of those in our sample of aged MAs are obese and that the mean NP score is 1. On frailty related performance measure covariates, the average grip strength score is 20 and the average gait speed is 5 seconds. For our disability items, we see that: the average CES-D positive affect score is 2; the average chronic condition score is 5; about 25% of the sample has at least one BADL limitation; and about 70% have at least one IADL limitation. About 39% of the sample reports good or excellent health and only 7% currently smoke. Demographically, the analytic sample is: 63% female; has a mean age of 81; about 40% are married; 56% are born in the US; 79% have less than a high school education; and 76% report a household income lower than $15,000 per-year. More than have (52%) report having no family living in their neighborhood, while 22% report having no friends living in neighborhood.

Table 1 Mean, standard deviation, minimum and maximum values for variables in analytic sample (n=889)

Table 2 makes use of six non-standard BMI cut-points to display in greater detail how NP scores are distributed by BMI groupings. This table compliments our discussion by providing more details on the distribution between dependent and independent variables. In our logistic regressions below, we are predicting the odds of being in the obese categories. Thus, 424 obese study subjects are being compared to 465 subjects of normal weight (as per their BMI). Please note that two BMI groups under the obese category have a mean NP score greater than “1”—indicating that some members within the BMI-group reported very positive views of their neighborhood.

Table 2 Neighborhood Perception (NP) score according to body mass index (BMI)

Overweight study subjects are not included in the analysis but are displayed in the table to mark the BMI gap between those of normal weight and the obese (as per their BMI)

We now turn our attention to the logistic regressions. From Table 2, we see that throughout all three models, NP remains statistically significant. As the level of positivity in NP increases, the odds of being obese increase as well. For example, the odds of being obese increase by 16.1% with every increase on the positive NP scale (net of all frailty-, disability-related, health, and demographic factors in the model). This finding falsifies the hypothesis under investigation. Although NP is related to the odds of being obese, the relationship is opposite of what we had predicted.

Please note that while gait speed was insignificant in our frailty related performance measures, with each kilogram increase in grip strength, there are 3.2% greater odds of being obese than of normal weight. This finding is likely due to the higher amount of muscle mass in persons with high BMIs. Please note that since BMI is a measure of body mass, having a high BMI does not necessarily imply high adiposity. With disability related items, with every increase in the number of chronic conditions, there is a 47% increase in the odds of being obese versus of normal weight—ceteris paribus.  The presence of functional limitation with basic activities of daily living is also related to having greater odds of being obese versus or normal weight. On the demographic variables, greater odds of being obese are found for females and for those with less than some High School education, while age is inversely related with the odds of being obese.

 

Table 3 Logistic model predicting the likelihood of being obese (BMI > 30 Kg/m2) versus normal (BMI 18.5-25.4 Kg/m2) weight

1 Coeff=Coefficient; 2 OR=Odds ratio; 3 PC=Percent change in odds ratio

Conclusion

NP may be associated with obesity in intricate ways because of the mechanisms that affect their assumed bidirectional relationship. In answer to our research question, we find that NP is significantly related to the odds of being obese for aged MAs. However, our hypothesis that our positive NP scale would be indirectly related with the odds of being obese finds no support. It may be the case that having a positive perception of the neighborhood does not reduce the odds of being obese if the person is: amongst the oldest old (i.e., over the age of 74), has a high level of frailty and disability related conditions, and has experienced a lifelong struggle at the lower rungs of the socioeconomic hierarchies in the US—where gender egalitarianism is low and moderate levels of education may be related to poorer diets and limited physical leisure-activity. Our hypothesis was posited with the assumption that a positive perception of the habitat would be related with greater levels of outdoor participation. In light of our functional limitation discussion, we advance that although aged MAs hold a positive perception of their neighborhood; their desire and ability for outdoor participation (or physical activity in general) are mitigated by their physical limitations in old age.
On a more theoretical note, while many difficulties exist in the analysis of neighborhood effects on body weight, one area that may further complicate interpretation of macro- on micro-relationships is how both macro- and micro-units change over time.  If context only affects people as a function of time and depth of exposure, then we must first seek to account for the simpler of the two: time of exposure. We believe that, too frequently, place effect studies disregard the idea that the amount of time in the current social-environment dictates the degree to which said context influences an individual’s behavior. Such an approach treats micro-level units as being equally exposed, with regards to time and depth, to the macro-level measurement of interest. We now discuss two elements within time of exposure.

A person’s history of neighborhoods is not readily captured in most data and measurements of a neighborhood’s demographic and physical characteristics shifts over time are equally illusive. Although theoretical premises are usually implicit rather than explicit in place effect research on obesity, we would argue that in general, environment-obesity causal mechanisms assume the degree and duration of exposure to measured macro-factor matters. For example, current residence may be causally related with current BMI. However, current BMI may be influenced by: (1) within-person (e.g., degree of frailty), and (2) within-neighborhood shifts over time (e.g., accessibility to sidewalks).

Shifts in these elements may influence the causal mechanism between obesity and NP. For example, using a hypothetical case of the within-person fluctuation, we could say that subject-a (sa) changes marital status from time-1 (t1) to time-2 (t2) and becomes ambulatory-challenged between the same time period. Measuring the shift between sat1 and sat2 may be necessary to outline causal mechanisms between BMI and NP. Even more complex, assume sa moves to different neighborhoods (e.g., across different states) every 20 years. If sa is 80 years of age, then he has four different neighborhoods (n1-4) that may be causally related with his current BMI. Consequently, measurements of his current neighborhood perception would only capture n4t1 and ignore n1-3t1-3 (which may be causally related to his current BMI). Measuring the shift between san1 through san4 may be necessary to decipher the causal mechanisms between BMI and NP.

On the within-neighborhood fluctuation element, assume sa initially moved into his current residence because there was an 80% co-ethnic concentration (c1). Years later, his neighborhood’s co-ethnic concentration drops to 20% (c2) where now his area is largely occupied by non-co-ethnics. Since not all people have the same ability to re-locate or to select their ideal place of residence, within-neighborhood fluctuations between sac1 and sac2 may influence the causal mechanisms between his BMI and NP.  Consequently, accounting for within-subject and –neighborhood shifts may be critical if we are to understand the BMI-NP causal mechanism. Future research should seek to establish if these shifts matter.

If our proposition on the importance of within-person and –neighborhood shifts seem logical and important, then a series of challenging questions arise which should be addressed in future work. (1) How does an individual’s history of contexts influence how he/she interacts with their current social and physical environment? (2) Are individuals equally equipped to benefit from beneficial environments and/or resist harsh social habitats? (3) What biological, child-age factors, family attributes, and other elements influence an individual’s disposition as she/he interacts with their multiple environments?

Assessing depth of exposure may prove an extremely challenging task—for its scientific operationalization must first make sense of the various elements involved in its construction. At the forefront of the challenge stands the need to first delineate what a neighborhood is. However, if such geographical boundarization is possible, new challenges will arise. For example, how should we treat micro-level units that reside near neighborhood borders?  If we decide that a group of 10 city-blocks making up a semi-even rectangular geographical-polygon constitutes a neighborhood, should we assume that individuals at the center of the “neighborhood” are related in the same way to their neighborhood’s measure as those in the edges of the polygon? More research on these important topics is needed.

There are technical limitations with our study, the NP questions in HEPESE may not provide a complete count of how an individual feels about their residential environment and in particular how these views affect BMI related behaviors like exercise and food consumption. Another limitation is that we do not account for an objective measure of context (e.g., availability of sidewalks and parks)—which may erase the NP-obesity statistically significant relationship we found. As noted earlier, study participants received different hip-fracture related questions. Our chronic condition scale ignores the fact that 20 cases reported having hip fractures two or more years ago and not in the last two years. This is only present with 2% of our analytic sample. Although we found no major issues with the fracture questions, longitudinal surveys should strive towards maintaining similar questions in all waves. A more complex limitation is the fact that we do not have study participants with zero chronic health conditions. This means we are unable to observe how NP is related to obesity in aged MAs who are not chronically ill.

Notwithstanding the limitations, we believe our investigation substantively contributes to the literature by showing that within aged MAs, NP is significantly related to obesity. Our exploratory project provides some validity to the argument that at the very least; discussions of place effects on obesity should be age-specific. Discourse on the effects of place on obesity should also explicitly account for how frailty and disability may play a role in the relationship between NP and BMI. Future research should aim at exploring the same topic with different populations, measurements, and modeling techniques.

Appendix 1
HEPESE Neighborhood Perception Related Questions

All things considered, would you say you are very satisfied, satisfied, neither satisfied nor dissatisfied, dissatisfied, or very dissatisfied with your neighborhood as a place to live?
(1) Very Satisfied
(2)  Satisfied
(3)  Neither Sat or Dissatisfied
(4)  Dissatisfied
(5)  Very Dissatisfied
(8)  Don’t know
(9)  Refused
(.)   Missing

About how many adults do you recognize or know by sight in this neighborhood? – Would you say you recognize no adults, a few, many or most?
(1)  No adults
(2)  A few adults
(3)  Many adults
(4)  Most or all adults
(8)  Don’t know
(9)  Refuse
(.)   Missing

Now I am going to read you some statements, which may or may not be true about your neighborhood.  Please look at this card. For each statement tell me whether you strongly agree, agree, disagree, or strongly disagree.   (If interviewee is unsure, mark neutral)

A.    This is a close-knit neighborhood
B.    People around here are willing to help their neighbors
C.    People in this neighborhood generally don’t get along with each other
D.    People in this neighborhood do not share the same values
E.    People in this neighborhood can be trusted

(1)  Strongly agree
(2)  Agree
(3)  Neutral
(4)  Disagree
(5)  Strongly disagree
(8)  Don’t know
(9)  Refused
(.)   Missing

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