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Published on: July 24, 2013
Performance of a Claims-Based Frailty Proxy Using Varying Frailty Ascertainment Lookback Windows
Emilie D Duchesneau1, Til Stürmer2,3, Dae Hyun Kim4,5
1Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem, NC.
Background:
Frailty is an aging-related syndrome of reduced physiological reserve to maintain homeostasis. The Faurot frailty index has been validated as a Medicare claims-based proxy for predicting frailty using billing information from a user-specified ascertainment window.
Objectives:
We assessed the validity of the Faurot frailty index as a predictor of the frailty phenotype and 1-year mortality using varying frailty ascertainment windows.
Research Design:
We identified older adults (66+ y) in Round 5 (2015) of the National Health and Aging Trends Study with Medicare claims linkage. Gold standard frailty was assessed using the frailty phenotype. We calculated the Faurot frailty index using 3, 6, 8, and 12 months of claims prior to the survey or all-available lookback. Model performance for each window in predicting the frailty phenotype was assessed by quantifying calibration and discrimination. Predictive performance for 1-year mortality was assessed by estimating risk differences across claims-based frailty strata.
Results:
Among 4253 older adults, the 6 and 8-month windows had the best frailty phenotype calibration (calibration slopes: 0.88 and 0.87). All-available lookback had the best discrimination (C-statistic=0.780), but poor calibration. Mortality associations were strongest using a 3-month window and monotonically decreased with longer windows. Subgroup analyses revealed worse performance in Black and Hispanic individuals than counterparts.
Conclusions:
The optimal ascertainment window for the Faurot frailty index may depend on the clinical context, and researchers should consider tradeoffs between discrimination, calibration, and mortality. Sensitivity analyses using different durations can enhance the robustness of inferences. Research is needed to improve prediction across racial and ethnic groups.
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