Risk prediction models for unplanned readmission in patients with chronic obstructive pulmonary disease:a systematic review
YAN Ting
XIE Xiangmei
MAO Min
ZHENG Wanting
LIU Liping
Abstract：Objective:To systematically review the performance of unplanned readmission risk prediction models for patients with chronic obstructive pulmonary disease(COPD),and to provide reference for the construction and application of related prediction models.Methods:Relevant studies on readmission risk prediction models for COPD patients were searched in the CNKI,WanFang Database,CBM,VIP,EMbase,PubMed,Web of Science,CINAHL and the Cochrane Library.The time limit for searching was from the establishment of the database to 30 June 2023.Totally 2 investigators independently screened the literature,extracted data,and evaluated the risk of bias and applicability of the included studies.Results:A total of 21 articles were included,including 29 prediction models.The area under the curve of 22 models was over 0.7.Except for one study,the risk of bias for all models was evaluated as high.The most common predictors of the included models were age,number of acute exacerbations of COPD in the past 1 year,smoking history,comorbidities,FEV1/FVC,and CAT score.Conclusions:The unplanned readmission risk prediction models for COPD have good predictive performance,but lacks internal and external validation.In the future,the study design and report should be improved,and a prospective multicentre large-sample cohort study should be conducted to explore a prediction model with a wider applicability population.
Keywords：chronic obstructive pulmonary diseaseunplanned readmissionrisk predictionmodelsystematic reviewevidence-based nursing
Publication Date：2025-06-25
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：11( 2356-2366 )