BMC cardiovascular disorders · 2026
Development of a risk assessment model for multimorbidity of diabetes, hypertension, and coronary heart disease with XGBoost in primary care in Shanghai, China: a retrospective study.
СтатьяМногоцентровое исследованиеПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод пока не подготовлен
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Chen Ning, Zhang Peipei, Fan Feng, Jin Hua, Chen Yanying, Liu Jing, Chen Lin, Wan Xiaoguang, Guo Jing, Yang Zaijia, Sun Lei, Shan Houqian, Yu Dehua, Wang Zhaoxin, Shi Jianwei
Аннотация
BACKGROUND: Multimorbidity has emerged as a growing global health concern. Within its heterogeneous patterns, the cardiometabolic cluster is notably among the most common. Assessing the risk of such multimorbidity from a general practice perspective has become a priority in primary care. This study aimed to develop a comprehensive risk assessment model for the multimorbidity of diabetes, hypertension, and coronary heart disease among older adults in the community, utilizing large-scale data from Shanghai, China.
METHODS: Retrospective data spanning 2017 to 2019 were collected from 40,261 residents across 47 community health centers. These data comprised residents' health records, health examination results, hospital information system (HIS) records, imaging databases, and lifestyle information. The XGBoost machine learning algorithm was utilized to construct a comprehensive risk assessment model for the multimorbidity of diabetes, hypertension, and coronary heart disease. Model performance was evaluated using the area under the receiver operating characteristic curve, accuracy, precision, recall, and the F1 Score.
RESULTS: The dataset was split into training (80%) and testing (20%) sets. A total of 46 features were incorporated into the final comprehensive risk assessment model for the multimorbidity of diabetes, hypertension, and coronary heart disease. The optimal XGBoost model achieved a micro-average AUC of 0.822, a macro-average AUC of 0.795, and a weighted-average AUC of 0.784. These parameters demonstrate the high superiority of the constructed model.
CONCLUSIONS: The XGBoost-based risk assessment model for the multimorbidity of diabetes, hypertension, and coronary heart disease, integrated clinical and public health data from community residents. It identifies multidimensional predictors across four dimensions, underscoring its practical value in supporting integrated risk assessment and informing targeted health management strategies for individuals with multimorbidity.
CLINICAL TRIAL NUMBER: Not applicable.
METHODS: Retrospective data spanning 2017 to 2019 were collected from 40,261 residents across 47 community health centers. These data comprised residents' health records, health examination results, hospital information system (HIS) records, imaging databases, and lifestyle information. The XGBoost machine learning algorithm was utilized to construct a comprehensive risk assessment model for the multimorbidity of diabetes, hypertension, and coronary heart disease. Model performance was evaluated using the area under the receiver operating characteristic curve, accuracy, precision, recall, and the F1 Score.
RESULTS: The dataset was split into training (80%) and testing (20%) sets. A total of 46 features were incorporated into the final comprehensive risk assessment model for the multimorbidity of diabetes, hypertension, and coronary heart disease. The optimal XGBoost model achieved a micro-average AUC of 0.822, a macro-average AUC of 0.795, and a weighted-average AUC of 0.784. These parameters demonstrate the high superiority of the constructed model.
CONCLUSIONS: The XGBoost-based risk assessment model for the multimorbidity of diabetes, hypertension, and coronary heart disease, integrated clinical and public health data from community residents. It identifies multidimensional predictors across four dimensions, underscoring its practical value in supporting integrated risk assessment and informing targeted health management strategies for individuals with multimorbidity.
CLINICAL TRIAL NUMBER: Not applicable.