Key result
XGBoost model predicts concurrent CVD risk in chronic lung disease with ~0.70 AUC.
Why the study?
Patients with chronic lung disease are at increased risk of cardiovascular disease, but specific risk assessment tools tailored for this population are lacking.
Can a machine learning model accurately predict the risk of concurrent cardiovascular disease in patients with chronic lung disease?
Observational (n=3,942)
Yes
Can a machine learning model accurately predict the risk of concurrent cardiovascular disease in patients with chronic lung disease?
Effect estimate: AUC 0.695
An XGBoost machine learning model utilizing 8 routine clinical predictors can robustly identify cardiovascular disease risk in patients with chronic lung disease, offering a practical tool for primary care risk stratification.
May support risk stratification in chronic lung disease; leaves open prospective validation before clinical use.
Background Patients with chronic lung disease (CLD) are at a significantly increased risk of developing cardiovascular disease (CVD); however, specific risk assessment tools tailored for this high-risk population are currently lacking. This study aimed to develop, validate, and interpret a machine learning model specifically designed to predict the risk of concurrent CVD in patients with CLD. Methods Based on the China Health and Retirement Longitudinal Study (CHARLS) cohort, 2,639 patients with CLD were included. Core features were selected using univariate and multivariate logistic regression. Seven machine learning algorithms were systematically compared. After identifying the optimal model, external validation was conducted using the English Longitudinal Study of Ageing (ELSA) cohort (n = 1,303). The SHapley Additive exPlanations (SHAP) framework was employed to interpret the model’s predictive mechanisms, and an interactive web application was developed based on the optimal model. Results The study ultimately identified 8 core predictors: age, body mass index (BMI), depression score, hypertension, dyslipidemia, impaired instrumental activities of daily living (IADL), and medication history for lung diseases and lipid-lowering drugs. The XGBoost model demonstrated the best performance, achieving Area Under the Curve (AUC) values of 0.838, 0.797, and 0.695 in the training, testing, and external validation sets, respectively, while exhibiting excellent calibration and clinical net benefit. SHAP analysis revealed that hypertension, depression score, and age were the primary contributing variables, and confirmed a significant synergistic amplification effect between lipid metabolism and psychophysical functional indicators. Conclusion The model constructed based on the XGBoost algorithm can accurately and robustly predict CVD risk in patients with CLD. Coupled with SHAP interpretability analysis and the online prediction tool, this study provides reliable digital decision support for CVD risk stratification, early identification, and personalized intervention among patients with CLD in primary care settings.
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Zhu et al. (2026) conducted an observational in Chronic lung disease (n=3,942). XGBoost machine learning model was evaluated on Concurrent cardiovascular disease risk prediction (AUC 0.695). An XGBoost machine learning model accurately predicted concurrent cardiovascular disease risk in patients with chronic lung disease, achieving an AUC of 0.695 in external validation.
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