Objectives Chronic obstructive pulmonary disease (COPD) is a common respiratory disorder. Acute exacerbation of COPD (AECOPD) severely affects patients’ quality of life and prognosis. This study aimed to identify novel risk factors and develop an effective predictive model for AECOPD using machine learning (ML) models. Methods In this retrospective single-center study, clinical data and biomarkers from 565 participants were analyzed using ML algorithms. Feature selection employed least absolute shrinkage and selection operator regression. Eight ML models were trained and evaluated using receiver operating characteristic (ROC) and clinical decision curve analysis. The Shapley Additive explanations (SHAP) framework assessed feature contributions. An online personalized risk calculator was developed based on the optimal model and individual SHAP values. Results The XGBoost model demonstrated excellent discriminative performance, with areas under the ROC curve of 0.818 and 0.838 for the training and test sets, respectively. Key predictors identified by SHAP analysis included age, current smoking status, frequency of exacerbations in the previous year, albumin levels, sarcopenia index, and COPD Assessment Test score. These variables were integrated into an online calculator for research to illustrate individualized AECOPD risk estimation. However, external validation is still required before its clinical application. Conclusions We developed a preliminary ML model for predicting AECOPD, which provides a valuable tool for clinical risk assessment. The results also highlighted the correlation between sarcopenia and AECOPD risk.
Zhang et al. (Fri,) studied this question.
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