SynthesisBMJ open respiratory research2026
Community-based prediction models of cardiovascular events, acute exacerbations and all-cause mortality in individuals with chronic obstructive pulmonary disease: a systematic review and meta-analysis on behalf of the International Cardiovascular and Respiratory Alliance.
Synthesis in BMJ open respiratory research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Cardiovascular Risk During the 90-Day Vulnerable Window After COPD Exacerbations: A Narrative Review.Life (Basel, Switzerland) · 2026Review
- Development and External Validation of a Machine Learning Model for 90-Day Readmission in Hospitalized Older Patients with AECOPD: A Two-Center Study.International journal of chronic obstructive pulmonary disease · 2026Article
Corrections and comments
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Authors and funding
12 authors.
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No grant is acknowledged in the PubMed record.
Abstract
introductionPreventable morbidity and mortality from chronic obstructive pulmonary disease (COPD) accrue from major adverse cardiovascular events (MACEs) and acute exacerbations of COPD (AECOPD). The study aims to summarise models for the prediction of these cardiopulmonary events in community-based settings.
methodsWe searched for studies of multivariable models derived, validated or augmented for the prediction of cardiopulmonary events in COPD and used community-based data sources using MEDLINE and Embase from inception through 10 April 2025. Discrimination measures for the model with C-statistic data from ≥3 cohorts were pooled by Bayesian meta-analysis, and heterogeneity and risk of bias assessments were undertaken.
resultsNo models were identified that predicted cardiopulmonary events in COPD using community-based data. Of the 71 models included, 5 predicted cardiovascular events, 32 predicted AECOPD and 30 predicted all-cause mortality. None were eligible for meta-analysis for the prediction of cardiovascular events or AECOPD. For all-cause mortality, age, dyspnoea and airflow obstruction-surprise question (ADO-SQ) (0.763, 95% CI 0.533 to 0.942) and body mass index, airflow obstruction, dyspnoea score and exercise capacity (BODE) (0.753, 95% CI 0.583 to 0.907) demonstrated good prediction performance, while ADO (0.638, 95% CI 0.443 to 0.827) demonstrated adequate prediction performance. The risk of bias was high for 57.9% of studies, and none had clinical utility evaluated.
conclusionsDespite the high burden of MACE and AECOPD, there is an absence of community-based models that predict this composite outcome. Models to identify individuals with COPD at high risk of cardiopulmonary events could enable targeted clinical intervention. PROSPERO REGISTRATION NUMBER: CRD420251026275.
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