Prognostic Models for Disease Progression and Outcomes in Chronic Obstructive Pulmonary Disease: A Systematic Review and Meta-Analysis.

ABSTRACT: Background/Objectives: Prevalence and burden of chronic obstructive pulmonary disease (COPD) are projected to increase in the coming decades. Although prognostic models for disease progression and exacerbation risk have proliferated, especially with the advent of machine learning (ML), their methodological rigor, generalizability, and predictive performance remain inconsistent. This study aimed to systematically review prognostic models for disease progression in adults with COPD, including traditional regression-based methods and ML techniques, evaluating model performance, sources of heterogeneity and methodological issues. Methods: PubMed and Embase were searched for all studies that developed and/or validated prognostic models for mortality (overall and cause-specific), e

SUBMITTER: Testa D 

PROVIDER: S-EPMC12734152 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature