Development and multi-cohort validation of a machine learning-based simplified frailty assessment tool for clinical risk prediction
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 ass...
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| Format: | Article |
| Language: | English |
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BMC
2025-08-01
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| Series: | Journal of Translational Medicine |
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| Online Access: | https://doi.org/10.1186/s12967-025-06728-4 |
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| author | Jiahui Lai Cailian Cheng Tiantian Liang Leile Tang Xinhua Guo Xun Liu |
| author_facet | Jiahui Lai Cailian Cheng Tiantian Liang Leile Tang Xinhua Guo Xun Liu |
| author_sort | Jiahui Lai |
| collection | DOAJ |
| 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 < 0.001), cardiovascular events (AUC 0.789 vs. 0.708, p < 0.001), and mortality (time-dependent AUC 0.767 − 0.702 vs. 0.690 − 0.627, p < 0.001). SHAP analysis provided transparent insights into model predictions, facilitating clinical interpretation. Conclusion Our simplified frailty assessment tool demonstrates robust performance across multiple health outcomes while minimizing measurement burden. The model’s superior predictive capabilities for CKD progression, cardiovascular events, and mortality underscore its potential utility for risk stratification. |
| format | Article |
| id | doaj-art-0b944d122a2d4c28ba99ffdc744a9e4e |
| institution | Kabale University |
| issn | 1479-5876 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Translational Medicine |
| spelling | doaj-art-0b944d122a2d4c28ba99ffdc744a9e4e2025-08-20T04:03:07ZengBMCJournal of Translational Medicine1479-58762025-08-0123111310.1186/s12967-025-06728-4Development and multi-cohort validation of a machine learning-based simplified frailty assessment tool for clinical risk predictionJiahui Lai0Cailian Cheng1Tiantian Liang2Leile Tang3Xinhua Guo4Xun Liu5Department of Nephrology, Third Affiliated Hospital of Sun Yat-Sen UniversityDepartment of Nephrology, Third Affiliated Hospital of Sun Yat-Sen UniversityDepartment of Nephrology, Third Affiliated Hospital of Sun Yat-Sen UniversityDepartment of Rheumatology, Third Affiliated Hospital of Sun Yat-Sen UniversityDepartment of Cardiology, Third Affiliated Hospital of Sun Yat-Sen UniversityDepartment of Nephrology, Third Affiliated Hospital of Sun Yat-Sen UniversityAbstract 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 < 0.001), cardiovascular events (AUC 0.789 vs. 0.708, p < 0.001), and mortality (time-dependent AUC 0.767 − 0.702 vs. 0.690 − 0.627, p < 0.001). SHAP analysis provided transparent insights into model predictions, facilitating clinical interpretation. Conclusion Our simplified frailty assessment tool demonstrates robust performance across multiple health outcomes while minimizing measurement burden. The model’s superior predictive capabilities for CKD progression, cardiovascular events, and mortality underscore its potential utility for risk stratification.https://doi.org/10.1186/s12967-025-06728-4Frailty assessmentMachine learningChronic kidney diseaseCardiovascular riskMortality predictionRisk stratification |
| spellingShingle | Jiahui Lai Cailian Cheng Tiantian Liang Leile Tang Xinhua Guo Xun Liu Development and multi-cohort validation of a machine learning-based simplified frailty assessment tool for clinical risk prediction Journal of Translational Medicine Frailty assessment Machine learning Chronic kidney disease Cardiovascular risk Mortality prediction Risk stratification |
| title | Development and multi-cohort validation of a machine learning-based simplified frailty assessment tool for clinical risk prediction |
| title_full | Development and multi-cohort validation of a machine learning-based simplified frailty assessment tool for clinical risk prediction |
| title_fullStr | Development and multi-cohort validation of a machine learning-based simplified frailty assessment tool for clinical risk prediction |
| title_full_unstemmed | Development and multi-cohort validation of a machine learning-based simplified frailty assessment tool for clinical risk prediction |
| title_short | Development and multi-cohort validation of a machine learning-based simplified frailty assessment tool for clinical risk prediction |
| title_sort | development and multi cohort validation of a machine learning based simplified frailty assessment tool for clinical risk prediction |
| topic | Frailty assessment Machine learning Chronic kidney disease Cardiovascular risk Mortality prediction Risk stratification |
| url | https://doi.org/10.1186/s12967-025-06728-4 |
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