Risk Prediction Models for Hospital Readmission After Percutaneous Coronary Intervention: A Systematic Review and Meta-Analysis.

Background: To rigorously evaluate the methodological quality and predictive performance of risk models for hospital readmission following percutaneous coronary intervention (PCI), as well as identify key predictive factors, and evaluate potential biases along with the clinical suitability of these models. Method: An extensive search was performed across multiple databases, including PubMed, Web of Science, The Cochrane Library, Embase, Cumulative Index to Nursing and Allied Health Literature (CINAHL), China National Knowledge Infrastructure (CNKI), Wanfang Database, China Science and Technology Journal Database (VIP), and SinoMed, to identify studies on risk prediction models for hospital readmission following PCI. This search encompassed all available records from the establishment of these databases up to November 1, 2024. The screening procedure was conducted by two independent researchers, who also gathered the relevant data. Results: A total of 10 studies were incorporated, encompassing 18 models designed to predict readmission. The sample sizes across these models ranged significantly, from those containing as few as 247 participants to samples with as many as 388,078 participants. The reported incidence of readmission varied between 0.70% and 31.44%. Frequently identified predictor variables (occurring in at least four studies) included age, concurrent heart failure, diabetes, chronic lung disease, three-vessel disease, and gender. Nine models provided the area under the receiver operating characteristic (AUROC) curve, with values ranging from 0.660 to 0.899, while calibration metrics were provided in six studies. Internal validation was performed in eight studies, while one study incorporated both an internal and external validation. Eight studies were assessed and found to possess a high risk of bias, largely related to deficiencies in data analysis. The combined AUROC curve for the nine validated models was 0.80 (95% confidence interval (CI): 0.74-0.85), suggesting moderate discrimination ability. Conclusion: Although existing risk prediction models for hospital readmission following PCI demonstrate a moderate level of predictive discrimination, most of the included studies were found to have a high risk of bias according to the Prediction model Risk Of Bias ASsessment Tool (PROBAST). Therefore, future studies should aim to develop more robust models using larger sample sizes, rigorous methodologies, and multicenter external validation. The PROSPERO Registration: CRD42024616342. https://www.crd.york.ac.uk/PROSPERO/view/CRD42024616342.