Prediction model for cardiovascular mortality risk in asthma patients based on machine learning

Clinical Snapshot

Key Findings

• P Value: Not reported in abstract

• Effect Size: C-index 0.863 (training set), 0.832 (validation set)

• Primary Outcome: Cardiovascular mortality prediction in asthma patients

• Nnt Or Sensitivity: Superior discrimination compared to ASCVD and Framingham risk scores

• Confidence Interval: Not reported in abstract

Clinical Application

Requires routine clinical data and pulmonary function tests, web calculator available Relevant to Australian practice given similar asthma prevalence and CVD risk factors, though validation in Australian cohort needed. Could inform PBS preventive medication decisions and RACGP risk assessment guidelines Adults with asthma requiring cardiovascular risk stratification

Abstract

BACKGROUND: Asthma 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. METHODS: Data 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. RESULTS: Among 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. CONCLUSION: A 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 personalized preventive strategies.

References

1. 1.Wang, Y., Fang, L., Liu, Q., Wu, T., Su, Z., Deng, W., & Zhang, Q. (2026). Prediction model for cardiovascular mortality risk in asthma patients based on machine learning. The Journal of Asthma, 1-12. https://doi.org/10.1080/02770903.2026.2618108