Related Experiment Videos
Multi‑feature Prediction Model for Coronary Heart Disease Comorbidity in Middle‑aged and Older Adults with COPD Based
Rui Li1, Qiushi Wang1, Xiao Zhang2
1Department of Respiratory Medicine, Affiliated Banan Hospital of Chongqing Medical University, Chongqing, 401320, People's Republic of China.
Insights
Predicting coronary heart disease (CHD) in patients with chronic obstructive pulmonary disease (COPD) is crucial. An XGBoost model identified key predictors like age and hypertension, showing moderate accuracy for CHD comorbidity in older adults.
Area of Science:
- Cardiology
- Pulmonology
- Medical Informatics
Background:
- Chronic obstructive pulmonary disease (COPD) frequently coexists with coronary heart disease (CHD), worsening prognosis in older patients.
- Early identification of CHD comorbidity in COPD patients is clinically imperative.
Purpose of the Study:
- To develop and evaluate machine learning models for predicting CHD comorbidity in middle-aged and older COPD patients.
- To identify key predictors of CHD in this patient population.
Main Methods:
- A cross-sectional study of 17,862 COPD patients aged 45+ years.
- Data imputation using random forest and feature selection via LASSO regression.
- Nine machine learning models were built and evaluated using AUC, calibration curves, and decision curve analysis. SHapley Additive exPlanations and restricted cubic splines were used for interpretation.
Main Results:
- 40.37% of patients had coexisting CHD. Sixteen predictors were identified.
- The XGBoost model showed moderate predictive performance (training AUC 0.871, validation AUC 0.743).
- Top predictors included age, hypertension, total cholesterol, chronic gastritis, and uric acid. Nonlinear dose-response patterns were observed for several factors.
Conclusions:
- The XGBoost model demonstrated moderate discriminative ability for predicting CHD in COPD patients.
- External validation is necessary before clinical application.
- Findings should be interpreted cautiously due to the single-center, cross-sectional design.
Background:
Chronic obstructive pulmonary disease (COPD) frequently coexists with coronary heart disease (CHD), markedly worsening prognosis in middle-aged and older patients. Early identification of CHD comorbidity in this population remains clinically imperative.
Methods:
This single-center, cross-sectional study included COPD patients aged 45 years or older admitted between 2020 and 2025. Missing data were imputed using random forest, and least absolute shrinkage and selection operator regression was applied for feature selection. Nine machine learning models were constructed and evaluated by the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis. SHapley Additive exPlanations and restricted cubic splines (RCS) were employed for model interpretation and dose-response exploration.
Results:
Of 17,862 eligible patients, 7,211 (40.37%) had coexisting CHD. Sixteen predictors were identified. The XGBoost model demonstrated moderate predictive performance (training AUC 0.871, 95% CI: 0.864-0.877; validation AUC 0.743, 95% CI: 0.730-0.756), significantly outperforming all other models in the training set and showing comparable performance to GBDT in the validation set. Age, hypertension, total cholesterol (TC), chronic gastritis, and uric acid (UA) were the top five predictors. RCS identified various dose-response patterns, including nonlinear associations for pulse rate, diastolic blood pressure, TC, and platelet count, and linear positive associations for prothrombin time and UA.
Conclusion:
The XGBoost model showed moderate discriminative ability for predicting CHD comorbidity in middle-aged and older COPD patients. However, further external validation is required before clinical application, and the findings should be interpreted with caution given the single-center, cross-sectional design.
