Abstract

BackgroundCapturing frailty within administrative claims data may help to identify high-risk patients and inform population health management strategies. Although it is common to ascertain frailty status utilizing claims-based surrogates (e.g. diagnosis and health service codes) selected according to clinical knowledge, the accuracy of this approach has not yet been examined. We evaluated the accuracy of claims-based surrogates against two clinical definitions of frailty.MethodsThis cross-sectional study was conducted in a Health and Retirement Study subsample of 3097 participants, aged 65 years or older and with at least 12-months of continuous fee-for-service Medicare enrollment. We defined 18 previously utilized claims-based surrogates of frailty from Medicare data and evaluated each against clinical reference standards, ascertained from a direct examination: a deficit accumulation frailty index (FI) (range: 0–1) and frailty phenotype. We also compared the accuracy of the total count of 18 claims-based surrogates with that of a validated claims-based FI model, comprised of 93 claims-based variables.Results19% of participants met clinical criteria for the clinical frailty phenotype. The mean clinical FI for our sample was 0.20 (standard deviation 0.13). Hospital Beds and associated supplies was the claims-based surrogate associated with the highest clinical FI (mean FI 0.49). Claims-based surrogates had low sensitivity ranging from 0.01 (cachexia, adult failure to thrive, anorexia) to 0.38 (malaise and fatigue) and high specificity ranging from 0.79 (malaise and fatigue) to 0.99 (cachexia, adult failure to thrive, anorexia) in discriminating the clinical frailty phenotype. Compared with a validated claims-based FI, the total count of claims-based surrogates demonstrated lower Spearman correlation with the clinical FI (0.41 [95% CI 0.38–0.44] versus 0.59 [95% CI, 0.56–0.61]) and poorer discrimination of the frailty phenotype (C-statistics 0.68 [95% CI, 0.66–0.70] versus 0.75 [95% CI, 0.73–0.77]).ConclusionsClaims-based surrogates, selected according to clinical knowledge, do not accurately capture frailty in Medicare claims data. A simple count of claims-based surrogates improves accuracy but remains inferior to a claims-based FI model.

Highlights

  • Capturing frailty within administrative claims data may help to identify high-risk patients and inform population health management strategies

  • This study evaluates the accuracy of diagnosis and health service codes in identifying frailty, as defined by the frailty phenotype and a deficit-accumulation frailty index (FI) from a clinical examination, using Medicare data linked to the Health and Retirement Study (HRS)

  • At the imputed prevalence of phenotypic frailty (19%) within our sample, the highest positive predictive value (PPV) was observed for cachexia (PPV 0.75), hospital beds and associated supplies (PPV 0.73), and wheelchairs, components, and accessories (PPV 0.63)

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Summary

Introduction

Capturing frailty within administrative claims data may help to identify high-risk patients and inform population health management strategies. It is common to ascertain frailty status utilizing claims-based surrogates (e.g. diagnosis and health service codes) selected according to clinical knowledge, the accuracy of this approach has not yet been examined. We evaluated the accuracy of claims-based surrogates against two clinical definitions of frailty. Frailty is a major risk factor for adverse health outcomes among older adults, including falls, hospitalization, disability, institutionalization, and death [1,2,3]. The societal impact of frailty is projected to increase as its associated adverse outcomes accrue with population aging [4]. As prevalent frailty increases with population aging, a standardized approach to its measurement is a precondition to informed health policy. In the absence of routine clinical assessment of frailty, administratively-derived measures that are well calibrated to clinical screening instruments will underpin its reliable identification and measurement

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