Prognostic risk prediction model for patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD): a systematic review and meta-analysis.

Abstract

Background: Chronic obstructive pulmonary disease (COPD) is a prevalent respiratory condition and a leading cause of mortality, with acute exacerbations (AECOPD) significantly complicating its management and prognosis. Despite the development of various prognostic prediction models for patients with AECOPD, their performance and clinical applicability remain unclear, necessitating a systematic review to evaluate these models and provide guidance for their future improvement and clinical use.

Method: PubMed, Web of Science, CINAHL, Scopus, EMBASE, and Medline were searched for studies published from their inception until February 5, 2024. Data extraction and evaluation were conducted using the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS). The Prediction model Risk Of Bias Assessment Tool (PROBAST) was employed to assess the risk of bias and applicability of the models.

Results: After deduplication and screening 5942 retrieved articles, 46 studies comprising 53 models were included. Of these, 17 (37.0%) studies developed from studies conducted in China. All models were based on cohort studies. Mortality was the predicted outcome in 27 (50.9%) models. Logistic regression was used in 41 (77.4%) models, while machine learning methods were employed in 9 (17.0%) models. The median (minimum, maximum) sample size for model development was 672 (106, 150,035). The median (minimum, maximum) number of predictors per model was 5 (2, 42). Frequently used predictors included age (n = 28), dyspnea severity scores (n = 12), and PaCO2 (n = 11). The pooled AUC was 0.80 for mortality prediction models and 0.84 for hospitalization-related outcomes. 52 models have a high overall risk of bias, and all models were judged to have low concern regarding applicability. Major sources of bias included insufficient sample sizes (83.0%), reliance on univariate analysis for predictor selection (73.6%), inappropriate internal and external validation methods (54.7%), inappropriate inclusion and exclusion criteria for study subjects (50.9%) and so on. The only model with low bias was the PEARL score.

Conclusion: Current prognostic risk prediction models for patients with AECOPD generally exhibit high bias. Future efforts should standardize model development and validation methods, and develop widely usable clinical models.