Predictors of Acute Exacerbations in COPD: A Systematic Review.

Background COPD is characterized by acute exacerbations (AECOPD), which drive its progression and burden. AECOPD can be caused by various factors. Better prediction of AECOPD provides opportunity for initiating preventive treatment.

Methods This systematic review provides an evidence-based overview of clinical predictors of moderate, severe AECOPD and relapse of moderate and severe AECOPD (≤28 days of a previous AECOPD). Cohort studies, case-control studies, or (cluster) randomized controlled trials published in English between January 2011-December 2023 in PubMed, CINAHL, Embase, Web of Science and the Cochrane library were assessed. Eligible studies included patients diagnosed with COPD, aged ≥40 years, and who were current or ex-smokers. Predictors of AECOPD were categorized into: patient characteristics, symptoms, biomarkers, lung function test results, and composite scores. Critical appraisal was performed with the QUIPS-tool.

Results Out of 1261 screened articles, 30 were included. Sixty-one distinct possible predictors of AECOPD were assessed, of which 37 were statistically significant (P < 0.05). Age, AECOPD history, fibrinogen, FEV1, bronchodilator response, health status measured with the COPD specific St. George's Respiratory Questionnaire (SGRQ-C) score, and COPD GOLD classification (2-4, B-D) were significant predictors of severe and/or moderate AECOPD in more than one article. Only 11 out of the 30 included studies had a low risk of bias.

Discussion Based on the highest relative statistical significance, combined with the best overall risk of bias, the most promising predictors of AECOPD are history of ≥1 AECOPD, higher SGRQ-C scores, elevated fibrinogen levels, and worse COPD GOLD (2-4, B-D). Future research should focus on standardization of AECOPD and predictor definitions, including the use of clearly defined cut-off values. Given the complexity and heterogeneity of COPD, combining diverse predictor domains into composite measures may enhance predictive accuracy of AECOPD, and the integration of such composites is promising for advancing COPD management in clinical practice.