Assessment of coding-based frailty algorithms for long-term outcome prediction among older people in community settings: a cohort study from the Shizuoka Kokuho Database

Abstract

Abstract Objectives To assess the applicability of Electronic Frailty Index (eFI) and Hospital Frailty Risk Score (HFRS) algorithms to Japanese administrative claims data and to evaluate their association with long-term outcomes. Study Design and Setting A cohort study using a regional government administrative healthcare and long-term care (LTC) claims database in Japan 2014–18. Participants Plan enrollees aged ≥50 years. Methods We applied the two algorithms to the cohort and assessed the scores’ distributions alongside enrollees’ 4-year mortality and initiation of government-supported LTC. Using Cox regression and Fine–Gray models, we evaluated the association between frailty scores and outcomes as well as the models’ discriminatory ability. Results Among 827,744 enrollees, 42.8% were categorised by eFI as fit, 31.2% mild, 17.5% moderate and 8.5% severe. For HFRS, 73.0% were low, 24.3% intermediate and 2.7% high risk; 35 of 36 predictors for eFI, and 92 of 109 codes originally used for HFRS were available in the Japanese system. Relative to the lowest frailty group, the highest frailty group had hazard ratios [95% confidence interval (CI)] of 2.09 (1.98–2.21) for mortality and 2.45 (2.28–2.63) for LTC for eFI; those for HFRS were 3.79 (3.56–4.03) and 3.31 (2.87–3.82), respectively. The area under the receiver operating characteristics curves for the unadjusted model at 48 months was 0.68 for death and 0.68 for LTC for eFI, and 0.73 and 0.70, respectively, for HFRS. Conclusions The frailty algorithms were applicable to the Japanese system and could contribute to the identifications of enrollees at risk of long-term mortality or LTC use.