Scholarlink

Background <p>Chronic Obstructive Pulmonary Disease (COPD) and Heart Failure with preserved Ejection Fraction (HFpEF) frequently coexist, leading to increased hospitalization, mortality, and healthcare burden. Early identification of HFpEF risk in COPD patients is critical for timely intervention.</p> Aim <p>To develop and validate an interpretable machine learning (ML) model for predicting HFpEF risk in COPD patients and to identify key predictors using explainable artificial intelligence techniques.</p> Methods <p>This retrospective study analyzed 1,550 COPD patients, divided into COPD-only and COPD-HFpEF groups. Feature selection was performed using LASSO regression, logistic regression, and Boruta random forest. Ten ML models were developed and evaluated on an internal test set, with the best model further validated on an external cohort (<i>n</i> = 69). Model interpretability was assessed using SHapley Additive exPlanations (SHAP).</p> Results <p>Nine predictors were consistently selected: NT-proBNP, red blood cell count, fibrinogen, cholesterol, arterial PaO₂, inspiratory capacity (IC), IC% predicted, late diastolic mitral inflow velocity (A wave), and CAT score. The XGBoost model achieved the best performance, with an AUC of 0.898 (95% CI: 0.867–0.929) on the internal test set and 0.819 (95%CI: 0.713 − 0.924) on external validation. SHAP analysis identified NT-proBNP as the most influential predictor.</p> Conclusion <p>The developed XGBoost model accurately predicts HFpEF risk in COPD patients and offers clinically interpretable insights into key predictive markers, supporting early identification and stratified management.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Development and external validation of an interpretable machine-learning model for HFpEF comorbidity risk in COPD patients

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月8日

查看全文
  • Jing Cao,
  • Boyu Kang,
  • Shuangshuang Li,
  • Yan Lei,
  • Dan Liu,
  • Chunmei Li,
  • Wei Guo,
  • Binghua Zhang,
  • Xiaoyan Xie

摘要

Background

Chronic Obstructive Pulmonary Disease (COPD) and Heart Failure with preserved Ejection Fraction (HFpEF) frequently coexist, leading to increased hospitalization, mortality, and healthcare burden. Early identification of HFpEF risk in COPD patients is critical for timely intervention.

Aim

To develop and validate an interpretable machine learning (ML) model for predicting HFpEF risk in COPD patients and to identify key predictors using explainable artificial intelligence techniques.

Methods

This retrospective study analyzed 1,550 COPD patients, divided into COPD-only and COPD-HFpEF groups. Feature selection was performed using LASSO regression, logistic regression, and Boruta random forest. Ten ML models were developed and evaluated on an internal test set, with the best model further validated on an external cohort (n = 69). Model interpretability was assessed using SHapley Additive exPlanations (SHAP).

Results

Nine predictors were consistently selected: NT-proBNP, red blood cell count, fibrinogen, cholesterol, arterial PaO₂, inspiratory capacity (IC), IC% predicted, late diastolic mitral inflow velocity (A wave), and CAT score. The XGBoost model achieved the best performance, with an AUC of 0.898 (95% CI: 0.867–0.929) on the internal test set and 0.819 (95%CI: 0.713 − 0.924) on external validation. SHAP analysis identified NT-proBNP as the most influential predictor.

Conclusion

The developed XGBoost model accurately predicts HFpEF risk in COPD patients and offers clinically interpretable insights into key predictive markers, supporting early identification and stratified management.