ArticleBMJ open2025
Predictive modelling methods of hospital readmission risks for patients with chronic obstructive pulmonary disease (COPD): a systematic review protocol.
Article in BMJ open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Tackling Imbalanced Data in Chronic Obstructive Pulmonary Disease Diagnosis: An Ensemble Learning Approach with Synthetic Data Generation.Bioengineering (Basel, Switzerland) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionChronic obstructive pulmonary disease (COPD) is a significant chronic respiratory condition characterised by persistent airway obstruction, leading to substantial morbidity and mortality worldwide. Patients with COPD frequently experience hospital readmissions shortly after discharge, mainly due to acute exacerbations. This review aims to identify and synthesise the reported performance metrics and methodological limitations of different predictive modelling methods for hospital readmissions in COPD patients. METHOD AND ANALYSIS: This protocol adheres to the Preferred Reporting Items for Systematic reviews and Meta-Analysis Protocols (PRISMA-P) guidelines. The review will include studies that develop or validate predictive models for hospital readmissions in COPD patients. A comprehensive search will be conducted across PubMed, Embase, Cochrane Library, IEEE Xplore, Web of Science and Google Scholar using predefined keywords. Eligible studies will include those utilising any predictive modelling method, focusing on unplanned readmissions within specified timeframes (30, 60 or 90 days). Two independent reviewers will screen titles, abstracts and full texts, selecting studies based on predefined inclusion criteria.Data extraction will be conducted based on the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS), and the methodological quality and risk of bias will be assessed using the Prediction Model Risk Of Bias Assessment Tool (PROBAST).The results will be synthesised narratively. A meta-analysis using a random-effects model will be conducted if at least five external validation studies are available for the same prediction model. ETHICS AND DISSEMINATION: This research is based exclusively on published studies and does not involve the collection of primary data collection from patients. Therefore, ethical approval is not required. Findings will be disseminated through publications in peer-reviewed journals and presentations at national and international conferences. PROSPERO REGISTRATION NUMBER: CRD42024579524.
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Registered trials
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