Development and validation of the machine learning model for acute exacerbation of chronic obstructive pulmonary disease prediction based on inflammatory biomarkers.

Objective: Acute exacerbation of chronic obstructive pulmonary disease (AECOPD) is a major cause of hospitalization and mortality in COPD patients. Current prediction methods rely primarily on clinical symptoms and physician experience, lacking objective and precise tools. This study aimed to integrate multiple inflammatory biomarkers to develop and compare machine learning models for predicting AECOPD, providing evidence for early intervention.

Methods: This retrospective study included 763 COPD patients (443 AECOPD, 320 stable COPD), randomly divided into training ( = 534) and validation ( = 229) cohorts at a 7:3 ratio. Demographic characteristics, comorbidities, and inflammatory indices were collected, including neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio, monocyte-to-lymphocyte ratio (MLR), eosinophil-to-lymphocyte ratio (ELR), and basophil-to-lymphocyte ratio. After variable selection using least absolute shrinkage and selection operator (LASSO) regression, traditional logistic regression (LR) and three machine learning models-random forest, gradient boosting machine (GBM), and support vector machine-were constructed. Model performance was evaluated using receiver operating characteristic curves, calibration curves, and decision curve analysis, with SHapley Additive exPlanations (SHAP) analysis for feature importance interpretation.

Results: The GBM model demonstrated superior performance with an area under the curve (AUC) of 0.900 (95%CI: 0.858-0.942), accuracy of 0.948, specificity of 0.952, and sensitivity of 0.944 in the validation cohort, significantly outperforming the traditional LR model (AUC = 0.870). SHAP analysis identified MLR (mean SHAP value = 0.5), NLR (0.35), and pulmonary heart disease (0.32) as the three most important predictive factors. AECOPD risk increased significantly with rising MLR and NLR values, while ELR showed a negative correlation with AECOPD risk. Decision curve analysis confirmed that the GBM model provided the highest net benefit within clinically relevant threshold ranges (0.2-0.8).

Conclusion: The GBM model integrating multiple inflammatory indices effectively predicts AECOPD. Based on routine blood test indicators without requiring expensive additional tests, this model is particularly suitable for resource-limited primary healthcare settings, providing a precise tool for early identification and individualized treatment of AECOPD, potentially improving prognosis and quality of life for COPD patients.