Readmission Risk Prediction Model for Patients with Chronic Heart Failure: A Systematic Review and Meta-Analysis

BACKGROUND: The present systematic review and meta-analysis aimed to systematically evaluate a risk prediction model for the readmission of patients with CHF. METHODS: The search was carried out in databases including PubMed, Embase, EBSCO, Web of Science, Cochrane Library and also domestic databases including Chinese Biomedical Literature Database, Chinese Academic Journal Full Text Database, Wanfang Database, and Vipu Chinese Journal Service Platform. All the original studies published by July 2021. Two researchers identified previous studies involving readmission risk prediction models that met our selection criteria. The quality of the included studies was evaluated based on the CHARMS checklist, and the prediction models were systematically evaluated. RESULTS: Of the overall 4787 studies retrieved, nine studies—two prospective, seven retrospective—met our selection criteria. The area under the receiver operating characteristic curve exceeded 0.63 (0.63–0.80) for all the studies. The most common predictors in the model were B-type natriuretic peptide (BNP) or N-terminal pro-brain BNP (Odds Ratio 4.35; 95% confidence interval (CI) 2.53–7.49; P<0.001), renal insufficiency (Odds Ratio 1.60; 95%CI 1.24–2.08; P<0.001), comorbidities, and a history of hospitalization. CONCLUSION: The use of non-parametric statistical methods and assessment of large samples of electronic data improve the predictive abilities of the risk assessment models. It is necessary to calibrate and verify such models and promote the combined use of parametric and non-parametric methods to establish precise predictive models for clinical use.