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Prediction model for cardiovascular mortality risk in asthma patients based on machine learning
Yuxi Wang1, Linxia Fang1, Quanfang Liu1
1Department of Pneumology, Hangzhou Lin'an Traditional Chinese Medicine Hospital, Hangzhou, China.
Summary
A new machine learning model accurately predicts cardiovascular disease mortality in asthma patients using clinical and pulmonary function data. This tool aids in early risk identification and personalized prevention strategies for asthma individuals.
Area of Science:
- Cardiology
- Pulmonology
- Data Science
Background:
- Asthma is a prevalent chronic respiratory condition.
- Cardiovascular disease (CVD) mortality is a significant public health issue.
- Personalized CVD risk prediction tools are lacking for asthma patients.
Purpose of the Study:
- Develop and validate a machine learning model to predict incident CVD mortality in individuals with asthma.
- Compare the model's performance against existing risk scores (ASCVD, Framingham).
Main Methods:
- Utilized NHANES data (2007-2012) from 2,033 adult asthma participants.
- Selected predictors using LASSO and Cox regression; trained six machine learning algorithms.
- Assessed model discrimination and calibration; interpreted feature importance with SHAP values.
Main Results:
- Twelve key variables including clinical and pulmonary function indices (FENO, PEF) were identified as predictors.
- The developed model demonstrated strong calibration and discrimination (C-index: 0.863 training, 0.832 validation).
- The pulmonary function-augmented model outperformed ASCVD and Framingham risk scores.
Conclusions:
- A novel machine learning model integrating clinical and pulmonary function data accurately predicts CVD mortality in asthma patients.
- This tool facilitates early identification of high-risk individuals.
- Enables personalized preventive strategies for cardiovascular health in asthma management.
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