Transforming acute exacerbations of chronic obstructive pulmonary disease (AECOPD) risk assessment: A multi‐algorithm machine learning approach for precise clinical phenotyping

Abstract Acute exacerbations of chronic obstructive pulmonary disease (AECOPD) significantly impact patient outcomes and quality of life. Accurately predicting AECOPD occurrence remains essential for optimizing disease management, yet existing predictive models have notable limitations. A retrospective analysis was conducted on 878 patients with chronic obstructive pulmonary disease (COPD) at Zhujiang Hospital, encompassing comprehensive clinical data including demographic, biochemical, and pulmonary function parameters. Potential predictors were identified through univariate Cox regression, and the dataset was split into 7:3 training‐test sets. Ninety‐one machine learning algorithms were constructed to predict AECOPD, with performance compared via concordance index (C‐index) metrics. Model performance was evaluated using receiver operating characteristic curves, k ‐fold cross‐validation, and subgroup analyses based on disease severity, age, and gender. Five biochemical indicators (including fibrinogen and prothrombin time), six demographic characteristics (including smoking status and age), and three pulmonary function parameters were significantly associated with AECOPD risk. The integrated machine learning model, which combined stepwise Cox regression and random survival forest algorithms, exhibited superior predictive performance compared to traditional models ( p < .05). Area under the curve, calibration curves, and decision curve analyses consistently confirmed the model's excellent predictive capacity. A high‐performance AECOPD risk score (AECOPD‐RS) prediction model integrating multidimensional clinical features was developed. The findings demonstrate that multi‐algorithm machine learning techniques significantly enhance AECOPD prediction accuracy and stability. Future validation through multicenter prospective studies and incorporation of additional biomarkers could further optimize individualized COPD management strategies.