biostudies-literatureZhu JNational Natural Science Foundation of ChinaNational Key Research and Development Program of China10https://www.ebi.ac.uk/biostudies/studies/S-EPMC12816092biostudies-literatureUnknown12(1)Multimorbidity elevates late-life mortality, yet existing tools remain complex. Using two nationally representative Chinese cohorts-the Chinese Longitudinal Healthy Longevity and Happiness Family Study (CLHLS-HF; n = 8675) and the China Health and Retirement Longitudinal Study (CHARLS, n = 4171)-we developed and externally validated a simplified, time-dependent, interpretable survival model. A four-stage feature-selection pipeline (univariate Cox, L1-penalized Cox, multi-model importance with 100 bootstraps, and cumulative performance) identified four routinely available predictors: age, BMI, and cooking and toileting abilities. Among five algorithms, a parsimonious Cox model performed best (C-index 0.7524 internal; 0.7104 external) with a favorable time-Brier Score (0.1417; 0.1157), good calibration, decision-curve net benefit, and subgroup fairness. Time-dependent permutation importance confirmed age as dominant, toileting ability as short-term, and cooking ability as mid- to long-term contributors, while BMI showed modest, stable effects. Implemented as the M-SAGE online tool, this four-item model enables rapid, interpretable mortality risk stratification and supports individualized interventions for older adults with multimorbidity.npj agingDevelopment and validation of a simplified time-dependent interpretable machine learning-based survival model for older adults with multimorbidity.PMC12816092824043732022YFC3603000Fang YZhu JDuan SChen HWu YfalseDevelopment and validation of a simplified time-dependent interpretable machine learning-based survival model for older adults with multimorbidity.Multimorbidity elevates late-life mortality, yet existing tools remain complex. Using two nationally representative Chinese cohorts-the Chinese Longitudinal Healthy Longevity and Happiness Family Study (CLHLS-HF; n = 8675) and the China Health and Retirement Longitudinal Study (CHARLS, n = 4171)-we developed and externally validated a simplified, time-dependent, interpretable survival model. A four-stage feature-selection pipeline (univariate Cox, L1-penalized Cox, multi-model importance with 100 bootstraps, and cumulative performance) identified four routinely available predictors: age, BMI, and cooking and toileting abilities. Among five algorithms, a parsimonious Cox model performed best (C-index 0.7524 internal; 0.7104 external) with a favorable time-Brier Score (0.1417; 0.1157), good calibration, decision-curve net benefit, and subgroup fairness. Time-dependent permutation importance confirmed age as dominant, toileting ability as short-term, and cooking ability as mid- to long-term contributors, while BMI showed modest, stable effects. Implemented as the M-SAGE online tool, this four-item model enables rapid, interpretable mortality risk stratification and supports individualized interventions for older adults with multimorbidity.2025-01-01T00:00:00Z2025 Dec2026-06-12T05:11:45.749Z2026-06-12T03:07:53.645ZS-EPMC128160924139798710.1038/s41514-025-00308-y