Predictive models for mortality readmission events and cardiovascular complications in patients with COPD: a systematic review and meta-analysis

Abstract

Background COPD is a major global health burden, associated with high rates of mortality, readmissions and cardiovascular disease (CVD) complications. Predictive models, including statistical and machine learning (ML) approaches, have been developed to support risk stratification and clinical decision-making. This review assesses their performance and generalisability. Methods A systematic search of EMBASE, MEDLINE and PubMed identified studies published since 2015 evaluating predictive models for COPD-related outcomes. Studies were screened using predefined criteria, and model performance was synthesised via meta-analysis. Pooled area under the curve (AUC) values were calculated for each model type. Risk of bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). Results Of 3 488 records screened, 37 studies met inclusion criteria: 20 focused on mortality, 14 on readmissions and six on CVD complications. Statistical models had a pooled AUC of 0.787 (95% CI 0.755–0.816). For mortality, statistical models outperformed or matched ML models (AUC 0.801 versus 0.760; p=0.1195), while ML models outperformed statistical approaches for readmissions (AUC 0.812 versus 0.758; p=0.4423). CVD outcomes showed a pooled AUC of 0.810 (95% CI 0.749–0.859). External validation often reduced ML model performance, raising concerns about overfitting. Conclusions ML models improve readmission prediction but offer no consistent advantage for mortality, where statistical models perform similarly. ML models face generalisability challenges due to overfitting. Future work should emphasise real-world validation and hybrid approaches to enhance interpretability and clinical applicability in COPD care.