Bridging Claims-Based and Clinical Frailty Assessment: Translation of the mFI-v10 to the Clinical Frailty Scale.

BACKGROUND: While the Clinical Frailty Scale (CFS) is intuitive for clinical use, its reliance on clinician interviews limits its feasibility for large-scale population monitoring. The claims-based multimorbidity frailty index-version 10 (mFI-v10) is highly scalable but has not been directly linked to the CFS standard. This study aimed to develop a model to translate mFI-v10 scores into clinically interpretable CFS categories. METHODS: In this study, 1038 individuals aged ≥ 65 years were recruited from a tertiary medical center between January 2020 and December 2021. They underwent assessments including the CFS, cognitive function, mood, physical health, and quality of life. The mFI-v10 score was calculated from electronic medical records. Logistic regression with 5-fold cross-validation was used to develop a prediction model for moderate-to-severe frailty, adjusting for age and sex. Model performance was evaluated using accuracy, sensitivity, specificity, precision, the F1-score, and the area under the receiver operating characteristic curve (AUC-ROC). RESULTS: Among the 1038 participants, 331 (32%) had moderate-to-severe frailty. The mean age of the individuals with moderate-to-severe frailty was 83.31 years (SD, 7.32), and 40% were male. The mFI-v10 showed moderate positive correlations with the CFS (ρ = 0.42) and Charlson Comorbidity Index (ρ = 0.57), and a negative correlation with ADL functional independence (ρ = -0.46). The prediction model incorporating mFI-v10 score, age, and sex demonstrated good discriminative ability with a mean AUC-ROC of 76.5% (range: 70.7%-82.6%). CONCLUSIONS: The mFI-v10 is a valid, scalable surrogate for clinical frailty assessment. This translation framework allows efficient population-level case-finding, prioritizing high-risk older adults for targeted clinical intervention.