BackgroundAsthma is a common chronic respiratory disease, cardiovascular disease (CVD) mortality constitutes a major public health concern. At present, no personalized tools are available to accurately estimate CVD mortality risk in this population. Accordingly, a machine learning model was developed to predict incident CVD mortality of asthma.MethodsData were extracted from NHANES (2007-2012). Predictors were selected by LASSO and multivariable Cox regression. Discrimination and calibration were quantified with ROC curves and calibration curves, nomogram and web calculator were subsequently deployed. Incremental predictive value relative to ASCVD risk and Framingham risk scores were assessed with the integrated discrimination improvement and net reclassification improvement. Six machine learning algorithms were trained, and feature importance of the optimal model was interpreted with SHAP values.ResultsAmong 2,033 adults participants with asthma, with mean age 44 years, and they comprised 834 males (41.02%) and 1199 females (58.98%). Twelve variables (age, sex, smoking, hypertension, diabetes, CKD, SBP, LYM, UA, K, FENO, and PEF) were retained as predictors of CVD mortality. Calibration was stable across training sets (C-index = 0.863) and validation sets (C-index = 0.832). The pulmonary function test augmented NHANES model outperformed both ASCVD risk and Framingham risk scores. RF and XGB model achieved the highest discrimination.ConclusionA machine learning model integrating routine clinical and pulmonary function test indices was developed. The model enables accurate estimation of long term CVD mortality risk with asthma, facilitating early identification of high risk individuals and informing personalised preventive strategies.
Wang et al. (2026) studied this question.