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Balkissa Tonde^1,2,3,4, Metogara Mohamed Traore^2,4,5, Paolo Landa^1,3,4

¹Department of Operations and Decision Systems, Université Laval, Quebec, Quebec, Canada.

|April 27, 2025

Abstract

Introduction:

Chronic 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.

Keywords:

Hospitalization Machine Learning Prognosis Pulmonary Disease, Chronic Obstructive Risk Factors

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