Predictive Models for Mortality Readmission Events & Cardiovascular Complications in Patients with COPD: A Systematic Review and Meta-Analysis

Background COPD is a major global health burden, associated with high rates of mortality, readmissions, and CVD complications. Predictive models, including statistical and 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 synthesized via meta-analysis. Pooled area under the curve (AUC) values were calculated for each model type. Risk of bias was assessed using PROBAST. Results Of 3 488 records screened, 37 studies met inclusion criteria: 20 focused on mortality, 14 on readmissions, and 6 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 generalizability challenges due to overfitting. Future work should emphasize real-world validation and hybrid approaches to enhance interpretability and clinical applicability in COPD care.