A Pattern Combining the Cognitive and Physical Risks Predicts Frailty Reversal in Community-Dwelling Older...

This study aimed to identify predictive risk patterns for older individuals who are likely or unlikely to reverse from frailty 3 years later using the Kihon Checklist (KCL) and a machine learning model. Data were collected from community-dwelling individuals aged 65 and over with frailty but without long-term care at baseline. A decision tree analysis revealed that the cognitive function domain of the KCL was the primary determinant of frailty reversal. Among those with low cognitive function, low physical activity predicted remaining frailty status 3 years later. The derived model achieved a specificity of 81.0% and a precision of 82.6% in predicting individuals with remaining persistent frailty. These findings suggest that the pattern combining cognitive and physical risk factors plays a key role in predicting frailty outcomes, and that the proposed model may be useful for screening individuals who require targeted interventions.