The journals of gerontology. Series A, Biological sciences and medical sciences · 2026
Performance of Claims-based Frailty Indices in Medicare Advantage and Fee-for-Service Populations.
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Luo Lan, Pritchard Kevin, Mi Park Chan, Chen Xiecheng, Kim Eunji G, McCarthy Ellen P, Kim Dae Hyun, Shi Sandra M
Аннотация
BACKGROUND: Despite growing enrollment in Medicare Advantage (MA) plans, the performance of claims-based frailty algorithms, which were developed for Medicare fee-for-service (FFS), remains unclear in MA.
METHODS: We calculated the Kim claims-based frailty index (CFI), Veterans Affairs-Frailty Index (VA-FI), and Hospital Frailty Risk Score (HFRS) using the National Health and Aging Trends Study linked to Medicare fee-for-service (FFS) and MA data. We evaluated their performance against the comprehensive geriatric assessment-based frailty index (CGA-FI) and the frailty phenotype (FP) in Round 11 and Round 9.
RESULTS: Among 1,527 FFS and 1,457 MA beneficiaries in Round 11, frailty prevalence was 34.2% and 35.5%, respectively, by CGA-FI ≥0.25, and 13.3% and 16.2%, respectively, by FP. Across FFS and MA populations, the CFI, which prioritizes specificity and positive predictive value, classified 10-12% of the overall population and 30-36% of those with prior hospitalization as frail, whereas the VA-FI and HFRS, which emphasize sensitivity, classified 53-71% of the overall population and 56-87% of those with prior hospitalization as frail. Although claims-based frailty measures performed similarly across FFS and MA populations, their discriminatory ability was higher for CGA-FI ≥0.25 (C-statistics: CFI, 0.79-0.82; VA-FI, 0.77-0.82; HFRS, 0.63-0.77) than for FP (CFI, 0.71-0.77; VA-FI, 0.67-0.75; HFRS, 0.60-0.74). Performance was consistent by race and survey rounds, with modestly better discrimination in women than men.
CONCLUSIONS: Claims-based frailty measures are scalable tools for contemporary FFS and MA populations. However applying published thresholds led to differences in frailty prevalence and model performances. Thresholds should therefore be tailored to the intended.
METHODS: We calculated the Kim claims-based frailty index (CFI), Veterans Affairs-Frailty Index (VA-FI), and Hospital Frailty Risk Score (HFRS) using the National Health and Aging Trends Study linked to Medicare fee-for-service (FFS) and MA data. We evaluated their performance against the comprehensive geriatric assessment-based frailty index (CGA-FI) and the frailty phenotype (FP) in Round 11 and Round 9.
RESULTS: Among 1,527 FFS and 1,457 MA beneficiaries in Round 11, frailty prevalence was 34.2% and 35.5%, respectively, by CGA-FI ≥0.25, and 13.3% and 16.2%, respectively, by FP. Across FFS and MA populations, the CFI, which prioritizes specificity and positive predictive value, classified 10-12% of the overall population and 30-36% of those with prior hospitalization as frail, whereas the VA-FI and HFRS, which emphasize sensitivity, classified 53-71% of the overall population and 56-87% of those with prior hospitalization as frail. Although claims-based frailty measures performed similarly across FFS and MA populations, their discriminatory ability was higher for CGA-FI ≥0.25 (C-statistics: CFI, 0.79-0.82; VA-FI, 0.77-0.82; HFRS, 0.63-0.77) than for FP (CFI, 0.71-0.77; VA-FI, 0.67-0.75; HFRS, 0.60-0.74). Performance was consistent by race and survey rounds, with modestly better discrimination in women than men.
CONCLUSIONS: Claims-based frailty measures are scalable tools for contemporary FFS and MA populations. However applying published thresholds led to differences in frailty prevalence and model performances. Thresholds should therefore be tailored to the intended.