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Comparison of Claims Based Frailty Indices in US Veterans 65 and Older for Prediction of Long-Term Institutionalization and Mortality.
Orkaby AR, Huan T, Intrator O, Cai S, Schwartz AW, Wieland D, Hall DE, Figueroa JF, Strom JB, Kim DH, Driver JA, Kinosian B. Comparison of Claims Based Frailty Indices in US Veterans 65 and Older for Prediction of Long-Term Institutionalization and Mortality. The journals of gerontology. Series A, Biological sciences and medical sciences. 2023 Jul 3.
Frailty is increasingly recognized as a useful measure of vulnerability in older adults. Multiple claims-based frailty indices (CFIs) can readily identify individuals with frailty, but whether one CFI improves prediction over another is unknown. We sought to assess the ability of five distinct CFIs to predict long-term institutionalization (LTI) and mortality in older Veterans.
Retrospective study conducted in US Veterans 65 years without prior LTI or hospice use in 2014. Five CFIs were compared: Kim, Orkaby (VAFI), Segal, Figueroa, and the JEN-FI, grounded in different theories of frailty: Rockwood cumulative deficit (Kim and VAFI), Fried physical phenotype (Segal), or expert opinion (Figueroa and JFI). The prevalence of frailty according to each CFI was compared. CFI performance for the co-primary outcomes of any LTI or mortality from 2015-2017 was examined. Because Segal and Kim include age, sex, or prior utilization, these variables were added to regression models to compare all 5 CFIs. Logistic regression was used to calculate model discrimination and calibration for both outcomes.
2.6 million Veterans were included (mean age 75, 98% male, 80% White, 9% Black). Frailty was identified for between 6.8%-25.7% of the cohort with 2.6% identified as frail by all 5 CFIs. There was no meaningful difference between CFIs in the area under the receiver operating characteristic curve for LTI (0.78-0.80) or mortality (0.77-0.79).
Based on different frailty constructs, and identifying different subsets of population, all five CFIs similarly predicted LTI or death, suggesting each could be used for prediction or analytics.