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Development and multi-cohort validation of a machine learning-based simplified frailty assessment tool for clinical risk prediction

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Title: Development and multi-cohort validation of a machine learning-based simplified frailty assessment tool for clinical risk prediction
Authors: Jiahui Lai, Cailian Cheng, Tiantian Liang, Leile Tang, Xinhua Guo, Xun Liu
Source: Journal of Translational Medicine, Vol 23, Iss 1, Pp 1-13 (2025)
Publisher Information: BMC, 2025.
Publication Year: 2025
Collection: LCC:Medicine
Subject Terms: Frailty assessment, Machine learning, Chronic kidney disease, Cardiovascular risk, Mortality prediction, Risk stratification, Medicine
Description: Abstract Background Frailty significantly impacts health outcomes in aging populations, yet its routine assessment remains challenging due to the complexity and time-consuming nature of existing tools. This study aimed to develop and validate a clinically feasible, machine learning-based frailty assessment tool that balances predictive accuracy with implementation simplicity in real-world clinical settings. Methods We conducted a multi-cohort study leveraging data from the National Health and Nutrition Examination Survey (NHANES, n = 3,480), China Health and Retirement Longitudinal Study (CHARLS, n = 16,792), China Health and Nutrition Survey (CHNS, n = 6,035), and Sun Yat-sen University Third Affiliated Hospital CKD cohort (SYSU3 CKD, n = 2,264). Through systematic application of five complementary feature selection algorithms to 75 potential variables, followed by comparative evaluation of 12 machine learning approaches, we developed a parsimonious assessment tool for predicting frailty diagnosis, chronic kidney disease progression, cardiovascular events, and all-cause mortality. Results Our analysis identified a minimal set of just eight readily available clinical parameters— age, sex, body mass index (BMI), pulse pressure, creatinine, hemoglobin, and preparing meals difficulty and lifting/carrying difficulty—that demonstrated robust predictive power. The extreme gradient boosting (XGBoost) algorithm exhibited superior performance across training (AUC 0.963, 95% CI: 0.951–0.975), internal validation (AUC 0.940, 95% CI: 0.924–0.956), and external validation (AUC 0.850, 95% CI: 0.832–0.868) datasets. This model significantly outperformed traditional frailty indices in predicting CKD progression (AUC 0.916 vs. 0.701, p
Document Type: article
File Description: electronic resource
Language: English
ISSN: 1479-5876
Relation: https://doaj.org/toc/1479-5876
DOI: 10.1186/s12967-025-06728-4
Access URL: https://doaj.org/article/0b944d122a2d4c28ba99ffdc744a9e4e
Accession Number: edsdoj.0b944d122a2d4c28ba99ffdc744a9e4e
Database: Directory of Open Access Journals
Description
Abstract:Abstract Background Frailty significantly impacts health outcomes in aging populations, yet its routine assessment remains challenging due to the complexity and time-consuming nature of existing tools. This study aimed to develop and validate a clinically feasible, machine learning-based frailty assessment tool that balances predictive accuracy with implementation simplicity in real-world clinical settings. Methods We conducted a multi-cohort study leveraging data from the National Health and Nutrition Examination Survey (NHANES, n = 3,480), China Health and Retirement Longitudinal Study (CHARLS, n = 16,792), China Health and Nutrition Survey (CHNS, n = 6,035), and Sun Yat-sen University Third Affiliated Hospital CKD cohort (SYSU3 CKD, n = 2,264). Through systematic application of five complementary feature selection algorithms to 75 potential variables, followed by comparative evaluation of 12 machine learning approaches, we developed a parsimonious assessment tool for predicting frailty diagnosis, chronic kidney disease progression, cardiovascular events, and all-cause mortality. Results Our analysis identified a minimal set of just eight readily available clinical parameters— age, sex, body mass index (BMI), pulse pressure, creatinine, hemoglobin, and preparing meals difficulty and lifting/carrying difficulty—that demonstrated robust predictive power. The extreme gradient boosting (XGBoost) algorithm exhibited superior performance across training (AUC 0.963, 95% CI: 0.951–0.975), internal validation (AUC 0.940, 95% CI: 0.924–0.956), and external validation (AUC 0.850, 95% CI: 0.832–0.868) datasets. This model significantly outperformed traditional frailty indices in predicting CKD progression (AUC 0.916 vs. 0.701, p
ISSN:14795876
DOI:10.1186/s12967-025-06728-4