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Index or illusion: The case of frailty indices in the Health and Retirement Study.

ABSTRACT: INTRODUCTION:Frailty is a geriatric syndrome that has been defined differently with various indices. Without a uniform definition, it remains unclear how to interpret and compare different frailty indices (FIs). With the advances in index mining, we find it necessary to review the implicit assumptions about the creation of FIs. We are concerned the processing of frailty data may introduce measurement error and bias. We aim to review the assumptions, interpretability and predictive power of FIs regarding mortality. METHODS:Three FIs, the Functional Domains Model proposed by Strawbridge et al. (1998), the Burden Model by Rockwood et al. (2007) and the Biologic Syndrome Model by Fried et al. (2004), were directly compared using the data from the Health and Retirement Study (HRS), a longitudinal study since 1996 mainly following up Americans aged 50 years and over. The FIs were reproduced according to Cigolle et al. (2009) and interpreted with their input variables through forward-stepwise regression. Biases were the residuals of the FIs that could not be explained by own input variables. Any four of the input variables were used to create alternative indices. Discrete-time survival analysis was conducted to compare the predictive power of FIs, input variables and alternative indices on mortality. RESULTS:We found frailty a syndrome not unique to the elderly. The FIs were produced with different degrees of bias. The FIs could not be fully interpreted with the theory-based input variables. The bias induced by the Biological Syndrome Model better predicted mortality than frailty status. A complicated FI, the Burden Model, could be simplified. The input variables better predicted mortality than the FIs. The continuous FIs predicted mortality better than the frailty statuses. At least 6865 alternative indices better predicted mortality than the FIs. CONCLUSION:FIs have been used as outcome in clinical trials and need to be reviewed for adequacy based on our findings. The three FIs are not closely linked to the theories because of bias introduced by data manipulation and excessive numbers of input variables. We are developing new algorithms to develop and validate innovative indices.

SUBMITTER: Chao YS

PROVIDER: S-EPMC6051600 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Index or illusion: The case of frailty indices in the Health and Retirement Study.

Chao Yi-Sheng YS Wu Hsing-Chien HC Wu Chao-Jung CJ Chen Wei-Chih WC

PloS one 20180718 7

<h4>Introduction</h4>Frailty is a geriatric syndrome that has been defined differently with various indices. Without a uniform definition, it remains unclear how to interpret and compare different frailty indices (FIs). With the advances in index mining, we find it necessary to review the implicit assumptions about the creation of FIs. We are concerned the processing of frailty data may introduce measurement error and bias. We aim to review the assumptions, interpretability and predictive power ...[more]

PMID: 30020923

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Project description:ObjectivesThe development of frailty tools from electronically recorded healthcare data allows frailty assessments to be routinely generated, potentially beneficial for individuals and healthcare providers. We wished to assess the predictive validity of a frailty index (FI) derived from interRAI Community Health Assessment (CHA) for outcomes in older adults residing in retirement villages (RVs), elsewhere called continuing care retirement communities.DesignProspective cohort study.Setting and participants34 RVs across two district health boards in Auckland, Aotearoa New Zealand (NZ). 577 participants, mean age 81 years; 419 (73%) female; 410 (71%) NZ European, 147 (25%) other European, 8 Asian (1%), 7 Māori (1%), 1 Pasifika (<1%), 4 other (<1%).MethodsinterRAI-CHA FI tool was used to stratify participants into fit (0-0.12), mild (>0.12-0.24), moderate (>0.24-0.36) and severe (>0.36) frail groups at baseline (the latter two grouped due to low numbers of severely frail). Primary outcome was acute hospitalization; secondary outcomes included long-term care (LTC) entry and mortality. The relationship between frailty and outcomes were explored with multivariable Cox regression, estimating hazard ratios (HRs) and 95% confidence intervals (95%CIs).ResultsOver mean follow-up of 2.5 years, 33% (69/209) of fit, 58% (152/260) mildly frail and 79% (85/108) moderate-severely frail participants at baseline had at least one acute hospitalization. Compared to the fit group, significantly increased risk of acute hospitalization were identified in mildly frail (adjusted HR = 1.88, 95%CI = 1.41-2.51, p<0.001) and moderate-severely frail (adjusted HR = 3.52, 95%CI = 2.53-4.90, p<0.001) groups. Similar increased risk in moderate-severely frail participants was seen in LTC entry (adjusted HR = 5.60 95%CI = 2.47-12.72, p<0.001) and mortality (adjusted HR = 5.06, 95%CI = 1.71-15.02, p = 0.003).Conclusions and implicationsThe FI derived from interRAI-CHA has robust predictive validity for acute hospitalization, LTC entry and mortality. This adds to the growing literature of use of interRAI tools in this way and may assist healthcare providers with rapid identification of frailty.

Project description:BackgroundMortality prediction models are useful to guide clinical decision-making based on prognosis. The frailty index, which allows prognostication and personalized care planning, has not been directly compared with validated prognostic models.ObjectiveTo compare the discrimination of mortality, disability, falls, and hospitalization between a frailty index and validated prognostic indices.DesignSecondary Analysis of the National Health and Aging Trends Study.ParticipantsSeven thousand thirty-three Medicare beneficiaries 65 years or older.MeasurementsWe measured a deficit-accumulation frailty index, Schonberg index, and Lee index at the 2011 baseline assessment. Primary outcome was mortality at 5 years. Secondary outcomes were decline in activities of daily living (ADL), decline in instrumental activities of daily living (IADL), fall, and hospitalization at 1 year. We used C-statistics to compare discrimination between indices, adjusting for age and sex.ResultsThe study population included 4146 (44.8%) with age ≥ 75 years, with a median frailty index of 0.15 (interquartile range 0.09-0.25). A total of 1385 participants died (14.7%) and 2386 (35.2%) were lost to follow-up. Frailty, Schonberg, and Lee indices predicted mortality similarly: C-statistics (95% confidence interval) were 0.78 (0.77-0.80) for frailty index; 0.79 (0.78-0.81) for Schonberg index; and 0.78 (0.77-0.80) for Lee index. The frailty index had higher C-statistics for decline in ADL function (frailty index, 0.80 [0.78-0.83]; Schonberg, 0.74 [0.72-0.76]; Lee, 0.74 [0.71-0.77]) and falls (frailty index, 0.66 [0.65-0.68]; Schonberg, 0.61 [0.58-0.63]; Lee, 0.61 [0.59-0.63]). C-statistics were similar for decline in IADL function (frailty index, 0.61 [0.59-0.63]; Schonberg, 0.60 [0.59-0.62]; Lee, 0.60 [0.58-0.62]) and hospitalizations (frailty index, 0.68 [0.66-0.70]; Schonberg, 0.68 [0.66-0.69]; Lee, 0.65 [0.63-0.67]).ConclusionsA deficit-accumulation frailty index performs as well as prognostic indices for mortality prediction, and better predicts ADL disability and falls in community-dwelling older adults. Frailty assessment offers a unifying approach to risk stratification for key health outcomes relevant to older adults.

Dataset Information

Index or illusion: The case of frailty indices in the Health and Retirement Study.

Publications

Index or illusion: The case of frailty indices in the Health and Retirement Study.

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