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1. Department of Physical Therapy, Faculty of Medicine, School of Health Sciences, Kagoshima University, Kagoshima, Japan; 2. Department of Epidemiology, National Center for Geriatrics and Gerontology, Obu, Japan; 3. Department Community Welfare, Niimi University, Niimi, Japan; 4. Section of NILS-LSA, National Center for Geriatrics and Gerontology, Obu, Japan; 5. Department of Preventive Gerontology, National Center for Geriatrics and Gerontology, Obu, Japan; 6. Institute of Gerontology, The University of Tokyo, Bunkyo-ku, Japan; 7. Research Team for Human Care, Tokyo Metropolitan Institute of Gerontology, Itabashi-ku, Japan; 8. Research Team for Promoting Independence and Mental Health, Tokyo Metropolitan Institute of Gerontology, Itabashi-ku, Japan; 9. Research Team for Social Participation and Community Health, Tokyo Metropolitan Institute of Gerontology, Itabashi-ku, Japan; 10. Department of Joint Disease Research, 22nd Century Medical and Research Center, The University of Tokyo, Bunkyo-ku, Japan; 11. Graduate School of Comprehensive Human Sciences, University of Tsukuba, Tsukuba, Japan; 12. Tokyo Metropolitan Institute of Gerontology, Itabashi-ku, Japan; 13. National Center for Geriatrics and Gerontology, Obu, Japan & Institute of Gerontology, J.F. Oberlin University, Machida, Japan.
Corresponding author: Hyuma Makizako, epartment of Physical Therapy, Faculty of Medicine, School of Health Sciences, Kagoshima University, Kagoshima, Japan, makizako@health.nop.kagoshima-u.ac.jp

J Frailty Aging 2020;in press
Published online December 22, 2020, http://dx.doi.org/10.14283/jfa.2020.68



Objective: To examine whether age-specific prevalence of frailty in Japan changed between 2012 and 2017. Design: This study performed meta-analyses of data collected from 2012 to 2017 using the Integrated Longitudinal Studies on Aging in Japan (ILSA-J), a collection of representative Japanese cohort studies. Setting: The ILSA-J studies were conducted on community-living older adults. Participants: ILSA-J studies were considered eligible for analysis if they assessed physical frailty status and presence of frailty in the sample. Seven studies were analyzed for 2012 (±1 year; n = 10312) and eight studies were analyzed for 2017 (±1 year; n = 7010). Five studies were analyzed for both 2012 and 2017. Measurements: The study assessed the prevalence of frailty and frailty status according to 5 criteria: slowness, weakness, low activity, exhaustion, and weight loss.Results: The overall prevalence of physical frailty was 7.0% in 2012 and 5.3% in 2017. The prevalence of frailty, especially in people 70 years and older, tended to decrease in 2017 compared to 2012. Slight decreases were found in the prevalence of frailty subitems including weight loss, slowness, exhaustion, and low activity between 2012 and 2017, but change in the prevalence of weakness was weaker than other components. Conclusions: The prevalence of physical frailty decreased from 2012 to 2017. There are age- and gender-related variations in the decrease of each component of frailty.

Key words: Frailty, aging, cohort study, older.



Frailty is defined as a clinically recognizable state of increased vulnerability in older adults resulting from age-associated declines in physiologic reserves and function across multiple organ systems (1). Although it is recognized as a multidimensional construct, comprising psychological and social conditions and symptoms in addition to physical, the physical frailty phenotype is well defined and its impact on adverse health outcomes such as disability, hospitalization, and death has been examined in many prior studies (2-5). Clinical practice guidelines based on the current evidence base provide recommendations for identifying and managing frailty in older adults (6). Reducing the risk and prevalence of frailty may play an important role in extending healthy life expectancy in the aged population.
The most common components used to assess physical frailty are the frailty phenotype proposed by Fried et al. using data from the Cardiovascular Health Study (CHS) (2). Based on the Fried criteria, a wide prevalence of frailty has been reported among community-dwelling people aged 65 years and older, ranging from 4% to 27% (7, 8). In Japan, with a rapidly increasing aging population, the overall prevalence of frailty was 7.4%, with a similar prevalence in men (7.6%) and women (8.1%) (9). These prevalence rates increased with advancing age (1.9%, 3.8%, 10.0%, 20.4%, and 35.1% for people aged 65 to 69, 70 to 74, 75 to 79, 80 to 84, and 85 or older, respectively) (9).
In the past several decades, both life and health expectancy have increased in many countries. In Japan, the average life expectancy was 81.3 years for men and 87.3 years for women in 2018, according to data from the Ministry of Health. There may be improvement in physical health status among older adults based on increased life and health expectancy. Although previous studies indicated the prevalence of frailty in a large cohort or meta-analysis, no studies focused on trends in the prevalence of frailty and assessment years.
This study performed meta-analyses using data from the National Center for Geriatrics and Gerontology’s Integrated Longitudinal Studies on Aging in Japan (ILSA-J), a collection of 13 longitudinal cohort studies on aging in Japan involving community-dwelling older adults, to test whether the age-specific prevalence of frailty changed in Japan between 2012 and 2017.



Data Sources

This study performed meta-analyses using ILSA-J data on frailty. The ILSA-J included a total of 13 longitudinal cohort studies conducted throughout Japan (Table 1). Studies were considered eligible for inclusion in the present analysis if they assessed physical frailty status and prevalence of frailty in the sample using the Fried criteria (2) (e.g., slowness, weakness, exhaustion, low activity, and weight loss). Of the 13 cohort studies, 7 (total n = 10312; 4611 men and 5701 women) were analyzed for 2012 (±1 year), and 8 (total n = 7010; 2662 men and 4348 women) were analyzed for 2017 (±1 year). Finally, only 10 of the 13 cohort studies in the ILSA-J project were included in this meta-analysis, because 3 cohort studies did not provide data on frailty status in 2012 and 2017.

Characteristics of the cohort studies included in the meta-analysis


Main Outcome Measures and Operational Definition of Frailty

The main outcome measures in this study were the prevalence of frailty status and the five frailty sub-items (%). This study determined physical frailty status according to the 5 criteria of physical frailty suggested by the Japanese version of the CHS (J-CHS) (10, 11) and a slightly revised criterion: weight loss, slowness, weakness, exhaustion, and low activity. Participants whose responses did not correspond to any of these target criteria were considered to be robust; those who responded positively for 1 or 2 criteria were considered pre-frail; and those with 3 or more positive criteria responses were considered frail.
Although all the cohort studies included in the current meta-analysis used the same 5 criteria to assess frailty status, there were differences in the subcriteria (Appendix table 1). The 5 criteria defining physical frailty were assessed as follows. Weight loss was identified by a response of “yes” to the question (12), “Have you lost 2 kg or more in the past 6 months?” Slowness was identified by a normal walking speed of <1.0 m/s (10). Weakness was identified according to grip strength of the subject’s dominant hand: <26 kg for men and <18 kg for women (13). Exhaustion was identified by a response of “yes” to the question (12), “In the last 2 weeks, have you felt tired for no reason?” Low activity was identified by a response of “no” to both the following questions (10): “Do you engage in moderate levels of physical exercise or sports aimed at health?” and “Do you engage in low levels of physical exercise aimed at health?”

Data Collection

All ILSA-J cohort studies were approved by the ethics committee of the relevant university or institute. Among the 13 total cohort studies, those that assessed frailty provided data on the prevalence of frailty status (frailty, pre-frailty, and robust) and the 5 frailty subitems for meta-analyses. Thus, no author of the present study could access participants’ individual data.

Statistical Analysis

A two-step approach was used in the current study. First, we obtained the frailty prevalence in each cohort study separately, then, we calculated the combined prevalence using meta-analysis. The prevalence rates of frailty and pre-frailty for the years 2012 and 2017 were calculated by age group and gender. The 5 frailty items were also included to calculate prevalence. The present meta-analysis used a two-step approach. First, Cochran’s Q test was used to assess the presence of heterogeneity across cohorts, indicated by p<0.05, and I2 statistic values of 25%, 50%, and 75% indicated low, moderate, and high degrees of heterogeneity, respectively (14). Then, prevalence and 95% confidence intervals (CIs) were calculated for frailty and pre-frailty using a random-effects model if heterogeneity was present (p<0.05) and a fixed-effects model if heterogeneity was absent based on Cochran’s Q test (9). In addition, we performed a sensitivity analysis restricting the meta-analysis to surveys performed at both time-points, 2012 and 2017. Statistical analyses were completed using Comprehensive Meta-Analysis software (Version 3; Biostat, Englewood, NJ, USA).



Table 2 presents the data on the presence of heterogeneity across cohorts and the prevalence of physical frailty among each age group in 2012 and 2017. There was a slight decrease (1.7%) in overall prevalence of physical frailty between 2012 and 2017. The overall prevalence of physical frailty was 7.0% (95% CI 5.4-9.0%) in 2012 and 5.3% (95% CI 4.3-6.6%) in 2017. The sensitivity analysis restricted to surveys with data at both time-points (2012 and 2017) provided similar results to the main analysis (Appendix table 2). Greater decreases in the prevalence of frailty were found in adults aged 75 years and older. Specifically, in 2012, the prevalence of frailty was 7.4% in the 75-79 age group, 12.6% in the 80-84 group, and 23.2% in the 85-89 group. In 2017, a 3.0% decrease was found in the 75-79 age group, a 4.2% decrease in the 80-84 group, and a 6.2% decrease in the 85-89 group.

Table 2
Prevalence of physical frailty by age group


Among men, frailty prevalence increased with advancing age in both 2012 and 2017. In 2012, prevalence was 6.3% in the 75-79 age group, 9.9% in the 80-84 group, and 24.6% in the 85-89 group ; in 2017, a decrease of 3.1% was found in the 75-79 age group (prevalence of 3.2%), 3.1% in the 80-84 group (prevalence of 6.8%), and 8.2% in the 85-89 group (prevalence of 16.4%).
Similar trends were observed in women. The prevalence of frailty in 2012 was 8.1% in the 75-79 age group, 14.8% in the 80-84 group, and 26.3% in the 85-89 group. In 2017, a decrease of 3.1% was found in the 75-79 age group (prevalence of 5.0%), 5.4% in the 80-84 group (prevalence of 9.4%), and 8.7% in the 85-89 group (prevalence of 17.6%).
The gender-stratified prevalence of physical frailty subitems is shown in Tables 3 and 4. Regardless of gender, slight decreases (less than 5%) in the subitems were found between 2012 and 2017 among young old groups (ages 65-69 and 70-74), with the exception of low activity in men aged 65-69 and women aged 70-74. Differing trends between men and women were found among old groups (ages 75-79, 80-84, and 85-89). In men, subitems with greater decreases (more than 5%) included exhaustion, which decreased 6.0% in the 75-79 age group, 5.2% in the 80-84 group, and 8.9% in the 85-89 group; slowness, which decreased 7.7% in the 85-59 group; and low activity, which decreased 7.2% in the 85-89 group).

Table 3
Prevalence of physical frailty components (Men)

Note. Sample sizes for 2012 age groups were as follows: 65-69, n=1540 (6 studies); 70-74, n=1434 (6 studies); 75-79, n=942 (6 studies); 80-84, n=519 (6 studies); 85-89, n=176 (6 studies). Sample sizes for 2017 age groups were as follows: 65-69, n=357 (5 studies); 70-74, n=629 (6 studies); 75-79, n=882 (7 studies); 80-84, n=565 (7 studies); 85-89, n=229 (7 studies).

Table 4
Prevalence of physical frailty components (Women)

Note. Sample sizes for 2012 age groups were as follows: 65-69, n=1808 (6 studies); 70-74, n=1518 (6 studies); 75-79, n=1205 (7 studies); 80-84, n=892 (7 studies); 85-89, n=278 (7 studies). Sample sizes for 2017 age groups were as follows: 65-69, n=835 (6 studies); 70-74, n=1115 (7 studies); 75-79, n=1400 (8 studies); 80-84, n=756 (8 studies); 85-89, n=242 (7 studies).


Compared with men, women were found to have decreased prevalence in many components. In the 75-79 age group, all components expect for weakness decreased (weight loss, 9.7%; slowness, 5.8%; exhaustion, 7.3%; low activity, 6.4%). All components decreased in the 80-84 and 85-89 groups (weight loss, 7.5% and 8.1%, respectively; slowness, 12.1% and 16.6%; weakness, 6.1% and 5.5%; exhaustion, 9.3% and 5.8%; low activity, 5.4% and 5.9%).



This study performed meta-analyses using data from ILSA-J cohort studies and showed that the prevalence of frailty tended to decrease in 2017 compared to 2012, especially in adults 75 years and older. The sensitivity analysis confirmed the main findings and indicates that this increases the robustness of the findings.
A recent systematic review of articles published in 28 countries estimated the global incidence of frailty among community-dwelling adults (15). Among robust individuals who survived a median follow-up of 3.0 years, 13.6% became frail, with a pooled incidence rate of 43.4 cases per 1000 person-years (15); incidence rates varied by diagnostic criteria and country income level. Previous systematic review and meta-analysis studies have also suggested variation in the prevalence of frailty based on diagnostic criteria (16), country income level (17), and residential environment (18, 9). Additionally, the prevalence of frailty among community-dwelling older adults has been reported to differ based on race (9, 19). Therefore, the influences of those characteristics should be considered when discussing the prevalence of frailty and prevention strategies.
Most systematic review and meta-analysis studies that examine the prevalence of frailty include articles published after 2000. Worldwide, there were 901 million people aged 60 years or over in 2015, an increase of 48% over the global total of 607 million older people in 2000 (20). The global number of people aged 60 years or over increased by 68% in urban areas, compared to 25% in rural areas, from 2000 to 2015 (20). In Japan, approximately 12% of the population was 65 years or older in 1990, about the same as the total in the USA in 1990 (21). By 2010, the 65 and older population in Japan doubled, rising from 15 million to 29 million and comprising 23% of the total population, the highest proportion in the world (21). The percentage rose to over 28% in 2019. Although the number of older people in Japan is increasing rapidly, their latent capabilities and background factors can be changed. Health-related measures among Japanese community-dwelling older adults from 2007 to 2017 indicate that a phenomenon of “rejuvenation” is occurring among the new generation of older Japanese adults (22). In the United States, dementia declined significantly between 2000 and 2012, and one associated factor was an increase in educational attainment (23). Thus, better change in older adults’ latent capabilities and background factors may lead to a decrease in the prevalence of frailty.

Several important factors, such as comorbidities, low socioeconomic position, poor diet, and sedentary lifestyle, increase the risks of frailty (24). Some of these are modifiable. Therefore, it may be possible to reduce the prevalence of frailty by controlling or improving risk factors. Although this study’s meta-analyses had a relatively short observational term of 5 years, decreasing trends in the prevalence of frailty may become clearer based on long-term observation.
Among 5 components of frailty, weakness and slowness may have greater impacts on increased risk of disability (11, 25). In this study, there was a decreasing trend in the prevalence of almost all items, however there was less change in the prevalence of weakness compared with other items. No change or a slight increase in the prevalence of weakness was observed in men in all age groups, whereas for women, only a decrease in the 80 years and older group was observed.
This study found significant differences in frailty prevalence between men and women. Older women, especially in the old-old population (aged 75 years and over), were found to have decreased prevalence in almost all frailty items. Recently, the ILSA-J reported differences between the years 2007 (± 2 years) and 2017 (± 2 years) in several indices (e.g., body composition, walking speed, and grip strength) that are related to the health and functioning of older adults (22). Better health status and a slower decline in most of the health-related measures were observed in 2017 compared with a decade ago. Japanese older adults living in the community have been consistently increasing their walking speed over the past 25 years, and the improvement in walking speed is especially striking in women (26, 27). In a previous study that analyzed IADL performance in 17,680 older adults with dependency in basic ADL, the men were found to have 3 times higher prevalence of poor performance of IADL compared with the women (28). Older adult women may reduce age-related decline in functional level by increasing or maintaining the multidimensional aspects of their lives, such as social and leisure time activities. In addition, our data showed higher study participation rates in women than in men for both 2012 and 2017. These findings may indicate that women have more interest in their health compared with men. Increased interest in personal health may prevent or delay the progression of frailty.
Consistent “female disadvantage” in physical performance among older adults has been demonstrated (29). One previous study with 4683 Japanese nondisabled community-dwelling older adults demonstrated increasing significant gender differences in one-legged stance performance and gait speed with age. In contrast, gender differences significantly decreased in hand-grip strength with increasing age (30). In other words, strength may be more affected by advancing age in older adult men than in older adult women. Thus, preventing or delaying the progression of weakness with age may be difficult in men. Weakness was determined according to grip strength of the subject’s dominant hand, with cutoff values of 26 kg for men and 18 kg for women. Although the average values of grip strength may decrease slightly in new generation of Japanese older adults (22), the changes may not reach sufficient levels, indicating that this component is less susceptible to generation changes than the others.
Several limitations of the present study should be noted. First, the meta-analyses in the present study used cross-sectional data from 7 cohort studies in 2012 (±1 year) and 8 cohort studies in 2017 (±1 year). Therefore, the study design was not longitudinal, following the same individuals and cohorts. Second, the current study used data from 2012 and 2017, analyzing the trends in prevalence over a period of 5 years. This may be too short to fully examine trends of change. Third, the number of participants varied widely by age group, especially participants in the 85 and older group, which had a relatively small sample size (fewer than 200 men in 2012). Finally, assessment protocols were dependent on each cohort study, not unified across all cohorts. We believe that the cohort studies included in the current meta-analysis had high data quality, but not all of the studies were designed using probabilistic samples. For instance, recruitment methods (e.g., random sampling, direct mail to all citizen, and volunteers) varied. In addition, more knowledge on the prevalence of the risk factors for frailty and those components, such as comorbidities, nutritional status, and cognitive function will support the phenomenon of decreasing frailty in the new generation of Japanese older adults.
Although this study examines a relatively short period of time (5 years), it has several strengths. First, it is, to our knowledge, the first study to describe trends in the prevalence of frailty. Second, the prevalence of frailty and subitems were assessed through a meta-analysis of 10 Japanese cohort studies, which provided data from 287 to 4779 older adults. Third, frailty status was assessed not only by questionnaires but also by objective measures such as grip strength and walking speed; therefore, are results may reflect functional status.
In conclusion, the current meta-analyses suggested that the prevalence of frailty has shown a decreasing trend in the new generation of Japanese older adults, especially in adults aged 75 years and older. This finding may indicate physical rejuvenation in older adults. Progression of this trend may improve health expectancy and shorten the gap between life expectancy and health expectancy. Future studies with more long-term follow-up period and a larger sample will be needed to clarify the trends in the prevalence of frailty among community-dwelling older adults.


Acknowledgement: This work was funded by the National Center for Geriatrics and Gerontology (Choujyu 29-42). We are grateful to all the participants for their valuable contribution to this study. The authors thank Dr. Katsunori Kondo of the National Center for Geriatrics and Gerontology & Chiba University, Dr. Yoshinori Fujiwara of the Tokyo Metropolitan Institute of Gerontology, and Dr. Shuichiro Watanabe of J.F. Oberlin University who are members of the ILSA-J project for their contributions in study progression. We also thank to Dr. Takehiko Doi of the National Center for Geriatrics and Gerontology, Dr. Tomoki Tanaka of the Institute of Gerontology, The University of Tokyo, Dr. Hisashi Kawai, Dr. Yu Nofuji, Dr. Takumi Abe and Dr. Susumu Ogawa of the Tokyo Metropolitan Institute of Gerontology, Dr. Yu Taniguchi of National Institute for Environmental Studies, and Dr. Yutaka Watanabe of the Faculty of Dental Medicine, Hokkaido University for their helpful supporting data sharing process and Ms. Shiho Fujii of the National Center for Geriatrics and Gerontology for her help in preparing the tables.
Conflicts of Interest: None declared.
Ethics Statement: This study was conducted in compliance with the current laws of Japan.
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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H. Mian1, M. Brouwers1,2, C.T. Kouroukis1, T.M. Wildes3


1. Juravinski Cancer Center, Department of Oncology, McMaster University, Hamilton, Canada; 2. School of Epidemiology and Public Health, University of Ottawa, Ottawa, Canada; 3. Division of Oncology, Department of Medicine, Washington University School of Medicine, St. Louis MO, USA.
Corresponding author: Hira Mian, 699 Concession St, Hamilton, ON, L8V, 5C2, Phone: 905-387-9495, Fax: 905-575-6340, Email: mianh@hhsc.ca

J Frailty Aging 2019;8(4)215-221
Published online July 11, 2019, http://dx.doi.org/10.14283/jfa.2019.25



Multiple myeloma is a malignant plasma cell disease, which typically affects older patients, with a median age at diagnosis of 70 years. The challenge in treating older patients is to accurately identify ‘fit’ patients that can tolerate more intensive treatment to maximize disease control, while simultaneously identifying vulnerable or ‘frail’ patients who may develop toxicity with significant morbidity and mortality, requiring different treatment options or dose modification. Multiple frailty scores have been devised for multiple myeloma over the years in newly-diagnosed patients. This paper gives an overview of the three common frailty measurements: the International Myeloma Working Group Frailty Score, Mayo Clinic Frailty Score and the Revised Myeloma Co-Morbidity Index. We will summarize the derivation, validation, usability and applicability of these scores in different clinical settings, emphasizing the main strengths and limitations for each index score. We will also highlight future directions in the operationalization of frailty in multiple myeloma.

Key words: Frailty, older, multiple myeloma.



Multiple myeloma is a neoplasm characterized by the clonal proliferation of malignant plasma cells within the bone marrow (1). It accounts for approximately 13% of all hematological malignancies, and 20% of hematological malignancy-related deaths (2, 3). In Western countries, the median age at diagnosis is 70 years, making myeloma a disease burden for older adults, with 35 – 40% of patients being older than 75 years (4).
The introduction of novel therapeutic agents (immunomodulatory drugs, proteasome inhibitors, and monoclonal antibodies) and better supportive care have improved outcomes in patients with myeloma, with a substantial benefit in both progression-free survival (PFS) and overall survival (OS) (5). Although there has been improvement in outcomes for older adults, several clinical trials and population-based registries demonstrate that this benefit has been less pronounced in the ‘oldest’ subgroup of patients age 75 or older (2, 6, 7), though that gap may be closing (8). The challenge in treating older patients is to accurately identify ‘fit’ patients who can tolerate more intensive regimens and potentially a stem cell transplant, while simultaneously identifying ‘frail’ patients who are transplant ineligible and require substantial modification of the regimen and dose of individual drugs to decrease morbidity and mortality.
A geriatric assessment is one tool which has been utilized in the oncologic setting to risk stratify the ‘frailty’ of patients. It includes assessment of comorbidities, functional status, medications (including polypharmacy and inappropriate medication), cognition, psychological status, and social support [9]. Over the past two decades, a large body of literature has developed, demonstrating that geriatric assessment provides a better picture of the health of older adults than traditional oncologic assessments of performance status (10, 11). A geriatric assessment is also known to impact treatment decision in older patients with cancer in up to 2/3 of the patients, with most recommendations being attenuated therapy in more frail patients (12).
There is no clear consensus of frailty in the setting of multiple myeloma; however, multiple tools have been developed to help identify ‘frail’ patients and stratify older patients with myeloma. The International Myeloma Working Group (IMWG) Frailty Score is based on chronological age plus 3 tools: Katz Activity of Daily Living, the Lawton Instrumental activity of Daily Living and the Charlson Comorbidity Index. Patients categorized as frail are more likely to experience grades 3–4 non-hematologic toxicity of therapy, early discontinuation of treatment and a shorter OS (13). The Mayo Clinic Frailty Score is a tool based upon age, Eastern Cooperative Oncology Performance Status (ECOG PS), and the serum biomarker N-terminal fragment of the type-B natriuretic peptide (NT-proBNP) level (14). Another tool, the Revised Myeloma Comorbidity Index (R-MCI) is also associated with OS and accounts for several prognostic factors including chronological age, impaired lung and kidney function, the karnofsky performance status, frailty, and unfavourable cytogenetics (15). While the IMWG and R-MCI scores rely more on the ‘fitness’ level of a patient as judged by their ability to function in different domains of daily living, the Mayo Clinic Frailty Score incorporates biomarkers in defining frailty as well.
In this review, we will examine and critically appraise three commonly utilized frailty scores in myeloma (IMWG Frailty Score, Mayo Clinic Frailty Score, R-MCI Index). Recent reviews have summarized these scores (16, 17); our aim was to extend these reviews and explore in greater depth the derivations of these scores focusing on the patient population, methods and validations of these tools in clinical setting.


Derivation of the Frailty Scores

Patient characteristics of the prognostic scores

In order to understand the applicability of the frailty prognostic scores in myeloma, it is essential to understand the population base that was used to develop these scores. The patient characteristics used to develop each prognostic score are detailed below and a summary is provided in Table 1.

Table 1 Patient characteristics of the derivation cohorts for frailty measures in myeloma

Table 1
Patient characteristics of the derivation cohorts for frailty measures in myeloma

*incorporated into SCT or non-SCT regimen; ‡minimum percentage of patients that received either a proteasome inhibitor or an immunomodulatory drug; ECOG, Eastern Cooperative Oncology Group; ISS, International Staging System; SCT, stem cell transplant; IMWG, International Myeloma Working Group; KPS, Karnofsky Peformance Scale


IMWG Frailty Score

Palumbo et al. created a score using data from patients with newly-diagnosed multiple myeloma that were enrolled in three prospective multicenter clinical trials (13). The population inclusion criteria included all ages; however, patients had to be ineligible for transplant. Most of the patients were recruited from Italy and the Czech Republic. A total of 869 patients were included in the analysis with a median age of 74 years. Given that the patients were recruited from clinical trials, despite their ‘older’ age, they were relatively fit, with only 21% of the patients with an ECOG PS status of 2 or greater. Most of the patients in the cohort received novel agents and about one-third had higher stage disease (International Staging System [ISS] Stage III).

Mayo Clinic Frailty Score

Milani et al. created a score using data from 351 patients with newly-diagnosed multiple myeloma that were enrolled in a prospective registry at a single institution from 2007-2011 (14). As both transplant eligible and ineligible patients were included in the analysis, the median age was lower than that of the IMWG, at 65 years. Comparable to IMWG, approximately one-fifth of the patients had an ECOG PS of 2 or greater, and one-third has an ISS stage of III. Although greater than 63% of the patients received novel agents as part of the treatment, only 39% underwent transplantation.

Revised Myeloma Co-Morbidity Index

Engelhardt et al. created a prognostic score prospectively consisting of all newly-diagnosed myeloma patients that presented consecutively to their institution (15). Their population was younger, with a median age of 63; however, as all patients were included, there was a wide age distribution from 21 to 93 years. Overall, there were slightly more patients that had higher stage disease (41% with ISS Stage III) compared to the other two scores. Approximately half of the patients were treated with transplant and at least 63% had novel agents incorporated into their therapy.

Methodology in development of the prognostic score

Rigorous development of a prognostic model is essential in its ability to provide useful information regarding patient prognosis and potentially for informing patient treatment. The development of each frailty index is outlined below and the key characteristics are summarized in Table 2.

Table 2 Development of the prognostic scores

Table 2
Development of the prognostic scores

ADL, Activities of daily living; ASCT autologous stem cell transplant; CCI, Charlson Comorbidity Index; ECOG PS, Eastern Cooperative Oncology Group Performance Status; eGFR estimated glomerular filtration rate; IADL, Independent Activities of daily living; ISS, International Staging System; KPS, Karnofsky Performance Status; NT-proBNP N-terminal fragment of the type-B natriuretic peptide;  R-ISS, Revised International Staging System;


IMWG Frailty Score

For the development of this frailty index, patients from three clinical trials were pooled. Patients were analyzed on an intention-to-treat basis in the original clinical trials used as a dataset for this score. Multiple confounding factors were considered that are clinically relevant, including the staging (ISS). Although the newer revised international staging system (18), which is currently in use, was not accounted for given the time frame of the study, other high-risk features such as cytogenetics were included. The final model developed using an additive frailty score based upon the integer part of the hazard ratio for each prognostic factor that was significant in the multivariate analysis. The score system (range 0-5), based on age, comorbidities (Charlson Comorbidity Index), and physical function (Katz Activity of Daily Living and Lawton Instrumental Activity of Daily Living) identified 3 groups of patients: fit (score=0), intermediate-fitness (score=1), and frail (score≥2).
In their final model, the 3-year overall survival was 84% in fit patients, 76% in intermediate-fitness patients (Hazard Ratio [HR] 1.61, 95% Confidence Interval [CI] 1.02-2.56, p=0.042) and 57% in frail patients (HR 3.57 CI 95% 2.37-5.39, p<0.001). The authors also used their model to predict additional outcomes such as the cumulative incidence of grade ≥3 non-hematologic adverse events and treatment discontinuation at 12 months in each risk group. Mayo Clinic Frailty Score The cornerstone of this model was the measurement of the biomarker NT-proBNP that was conducted using one specific assay on frozen sera sample. While the authors do acknowledge the relationship NT-proBNP with age, they do not show account for this in their model and have rather created a stringed dichotomized variable. NT-proBNP is also affected by decreased renal function; however, it is not entirely clear how the prognostic ability of this model changes for those subset of myeloma patients presenting with renal dysfunction. The final proposed model is the ‘simplest’ model consisting of age ≥70, ECOG PS ≥2, and the serum biomarker NT-proBNP level of ≥300 ng/L which a score of 1 for each of these variables. This final model allowed patients to be categorized into four risk groups with scores of 0-3 point respectively and is associated with a median OS not reached with 0 risk factors, 58 months with one risk factor, 28 months with two risk factors and 18 months with all of the three risk factors. Revised Myeloma Co-Morbidity Index For the development of the prognostic score, 552 patients were included. Based upon the authors’ previous studies and analyses, which led to the initial additive Myeloma Co-morbidity Index, impaired renal function, lung function and karnofsky performance status were known to be relevant risk factors for OS and PFS (19-21). A multivariate analysis with backward selection for OS to evaluate the significance of the additional variables was conducted. The model identified a total of 5 variables that were associated with OS including age, renal status, lung dysfunction, Karnofsky Performance Status and frailty status (includes Karnofsky Performance Status > 70%, timed up and go test > 10 sec, Instrumental Activity of Daily Living score < 4, and subjective fitness). Cytogenetics could also be added if available; however, were not essential in the calculation of the score. A score was assigned to each of the five predicted variables based upon the log hazard ratio leading to a cumulative score of 9 points. The patients were characterized into ‘fit’, ‘intermediate-fit’ and ‘frail’ with median OS of 10.1, 4.4 and 1.2 years.

Validation of the prognostic scores

Validation of a prognostic model is essential in establishing whether the model is generalizable to other similar populations than the ones used in the original dataset (22). A model can be validated either internally or externally. External validation is considered more stringent as it tests the generalizability of the model in an entirely different cohort and is considered essential prior to the broad usage of a prognostic score in daily clinical practice (23). The validation for each frailty index is outlined below.

IMWG Frailty Score

No internal validation cohort was conducted as part of the original study by Palumbo et al (13). As the authors acknowledge themselves, this is a major limitation of their study. They chose not to split their sample into a derivation and validation cohort as they felt their sample size was inadequate. External validation of the IMWG score was done by prospectively analyzing 125 patients at a single institution in Germany (24). While this prospective cohort and the original IMWG cohort were similarly matched for most baseline characteristics, the validation cohort had a younger population, with over 59% under age 65, compared to 2% in the IMWG cohort. As this was a real-world population, there was a higher percentage of patients identified as frail (48% versus 30% in the IMWG cohort) despite the younger age range of patients in the validation cohort. The IMWG was prognostic for OS and PFS in this validation cohort; however, both the OS and PFS were shorter in the validation cohort than the derivation cohort for the intermediate-fit and frail patients. This external validation of the IMWG score adds to the validity of the score and furthermore suggests its prognostic value, even in transplant eligible patients, which were originally not included in the derivation sample. To our knowledge, there has been no further updating or adjustment of the IMWG frailty index following this external validation.

Mayo Clinic Frailty Score

No validation cohort data was presented as part of the original derivation and to date, there is no published external validation cohort.

Revised Myeloma Co-Morbidity Index

The R-MCI was internally validated; the cohort of 810 patients was randomly split into 2/3 and 1/3, training (n=552) and validation set (n=249). Since that time, there have been additional studies demonstrating the treatment responsiveness of R-MCI in different clinical setting. Not only are the proportion of patients categorized as fit or frail found to be comparable in real world setting as those on clinical trials (25), additional studies show that R-MCI along with the IMWG as a longitudinal measure is responsive to change and the score may improve in patients responding to different types of myeloma therapy (26, 27).

Usability and applicability of the prognostic score

Studies have shown that although a large number of prognostic scores are published, very few models are actually used in clinic practice (28). Although this is likely due to a number of factors, the ease of utility and the practicality of the score is essential for it to enter into routine clinical practice. There needs to be sufficient reporting that is presented in an easily accessible way regarding how to compute a risk score and also a link to the corresponding survival probabilities. Currently, there is no data published on the interrater reliability on these three scores. Further information regarding the usability of each score is outline below:

IMWG Frailty Score

The score is currently available as an online tool at http://www.myelomafrailtyscorecalculator.net/. There are 31 categories that need to be filled out; however, as all the variables are self-reported by the patient, no additional investigations are required improving its usability in a busy clinic schedule. The online tool only requires us to fill out categories with deficits. The average time to calculate this score is estimated to be between 5 to 7 minutes (13).

Mayo Clinic Frailty Score

This prognostic score is easy to use given that it only consists of three variables. Age and ECOG-PS are routinely measured by oncologists. The NT pro-BNP level is a blood test which is available in most laboratories; however, the sensitivity and specificity of the assay differs across different labs and may be variable. Additionally, this test is not routinely recommended as part of the evaluation of myeloma (29, 30), has cost associated, depending upon resource constraints, it may not be available to all demographics. When the NT-proBNP level is available, the score can likely be calculated in 1-2 minutes.

Revised Myeloma Co-Morbidity Index

In the derivation of the score, the assessment of each variable in this index was performed by staff members with specialized training in oncogeriatrics. This score is currently available online http://www.myelomacomorbidityindex.org/en_calc.html. It has an easy drop-down menu format with the definition of each variable listed below. The variables included in this score include additional investigations such as pulmonary function tests. Additionally, the variables used to calculate frailty consist of multiple objective and subjective tools such as timed up and go test and fitness respectively which oncologists do not routinely utilize; however are overall simple tests to conduct. The score can be calculated on the website and the output is an easily interpretable Kaplan-Meier survival curve (24).



The three frailty scores created for multiple myeloma have provided clinicians with additional information regarding prognostication of their patients. There was a wide range in the applicability of these frailty measurements: from short, fast to more time-consuming and sophisticated measurements of frailty. Clinicians will have to be aware that each of these scores was tested in a specific patient population and derived during a specific time period. Furthermore, as new treatment modalities and new drugs are introduced into the treatment algorithm, the natural history of myeloma may change and therefore these prognostic scores will need to be updated and validated continuously.
Although most clinicians believe that routine frailty assessment is time-consuming and costly, research has shown that, in fact, not only is frailty assessment feasible, it is also likely less time-consuming and costly than many other prognostic tests done in oncology (31). Targeting the most promising tools and building the evidence-base in prospective studies to support and establish their psychometric and clinical-metric features has been an important research priority for the past decade and will require ongoing collaboration between groups. Specific tools, for example, may be better for population-level frailty screening for patients with newly-diagnosed myeloma; others may be best suited for a comprehensive assessment to predict toxicity of specific treatment regimens. The role of biomarkers, such as the senescence marker p16INK4a and sarcopenia, in addition to objectively measured functional geriatric assessments, such as gait speed and handgrip strength, may provide further opportunities for refinement of frailty indices (17, 32).
Although none of these tools has been derived in patients with relapsed or refractory myeloma, nor evaluated longitudinally, they may be applicable in this patient cohort as well and may represent an additional tool to understand treatment outcomes in patients on clinical trials or undergoing different treatment modalities (26, 27, 33). This represents an important area for further research, as the concept of ‘frailty’ may be even more important in relapsed patients who have more aggressive disease, accumulated more co-morbidities (34) and who have residual toxicities from previous myeloma treatments. The role of these scores in understanding the experiences of patients undergoing transplantation and in other plasma cell dyscrasias will also need to be further explored.
Based upon the multiple tools, the IMWG frailty score and the R-MCI index appears to be the most comprehensive tools for risk stratification. A limitation of all those scores, including the IMWG, is the inclusion of chronological age, which automatically increases ‘frailty’ without taking biological or functional age (35) into account and may not accurately represent outcomes for functionally vigorous older individuals (36, 37).
After defining a specific frailty measurement, the next step in improving the usability and the clinical utility of these scores will be to conduct studies aimed at certain interventions for each risk groups, which can lead to a potential change in health outcomes. At a public health level, for example, if a patient is identified as frail, perhaps additional home resources or early initiation of palliative care may be justified. At a physician level, understanding the overall prognosis of a patient may lead to specific interventions such as initial dose reduction or alternative chemotherapy regimens. Several groups have proposed dose modifications for ‘frail’ vs ‘fit’ patients based upon the IMWG and R-MCI frailty scores (16, 17, 38). While they represent an important step in the operationalization of frailty in routine clinical practise, the prospective demonstration of the impact of frailty-based treatment arms in clinical trials is now increasingly being recognized (39, 40). The results of the phase III clinical trial designed for intermediate fit (as defined by the IMWG Frailty Score) in older patients with newly diagnosed multiple myeloma were recently presented highlighting similar efficacy and improved tolerability of a dose reduced regimen of Lenalidomide and Dexamethasone as compared to standard continuous therapy (39). Additionally, a randomized controlled trial is currently being conducted with over 700 older patients with newly diagnosed myeloma being assigned to either a standard arm or a frailty adapted regimen based upon the IMWG Frailty Score (Myeloma XIV: Frailty-adjusted Therapy in Transplant Non-Eligible Patients with Newly Diagnosed Multiple Myeloma, NCT03720041). These recent studies highlight the exciting advancement that is occurring in understanding the prognostic implication of frailty in multiple myeloma.
Ongoing areas of research include the associations between frailty and patient-centered outcomes as they may in fact be most relevant to patients. The IMWG Frailty Scores is associated with toxicity and treatment discontinuation, which are more patient-centered, though direct patient-reported outcomes such as quality-of-life have not been examined. Other geriatric outcomes such as future risk of falls and the effect of quality of life measurements will also need to be incorporated into further development and validation of any myeloma specific prognostic scores.
Lastly, from a methodological perspective, whereas in the past, the credibility of prognostic scores was mostly derived from individual studies, recently there has been research and development into using systematic reviews for understanding the comprehensive body of evidence and a growing number of systematic reviews in this field are being published (41). Furthermore, Grades of Recommendation, Assessment, Development and Evaluation (GRADE) approach is increasingly being optimized order to understand the strength of inferences from the combined summarization of these individual prognostic studies (42). Currently, there exists no formal systematic review of the common frailty scores in myeloma; however, that may represent an important future direction in trying to understand the overall assessment of the prognostic estimates from these individual studies.


Funding: 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.
Conflicts of Interest: The authors have no conflict of interest pertaining to the subject matter in the manuscript.



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