Prediction models for major adverse cardiovascular events after percutaneous coronary intervention: a systematic review - Deng, Wenqi; Wang, Dayang; Wan, Yandi; Lai, Sijia; Ding, Yukun; Wang, Xian (Frontiers in Cardiovascular Medicine, 2024)
Background The number of models developed for predicting major adverse cardiovascular events (MACE) in patients undergoing percutaneous coronary intervention (PCI) is increasing, but the performance of these models is unknown. The purpose of this systematic review is to evaluate, describe, and compare existing models and analyze the factors that can predict outcomes. Methods We adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 during the execution of this review. Databases including Embase, PubMed, The Cochrane Library, Web of Science, CNKI, Wanfang Data, VIP, and SINOMED were comprehensively searched for identifying studies published from 1977 to 19 May 2023. Model development studies specifically designed for assessing the occurrence of MACE after PCI with or without external validation were included. Bias and transparency were evaluated by the Prediction Model Risk Of Bias Assessment Tool (PROBAST) and Transparent Reporting of a multivariate Individual Prognosis Or Diagnosis (TRIPOD) statement. The key findings were narratively summarized and presented in tables. Results A total of 5,234 articles were retrieved, and after thorough screening, 23 studies that met the predefined inclusion criteria were ultimately included. The models were mainly constructed using data from individuals diagnosed with ST-segment elevation myocardial infarction (STEMI). The discrimination of the models, as measured by the area under the curve (AUC) or C-index, varied between 0.638 and 0.96. The commonly used predictor variables include LVEF, age, Killip classification, diabetes, and various others. All models were determined to have a high risk of bias, and their adherence to the TRIPOD items was reported to be over 60%. Conclusion The existing models show some predictive ability, but all have a high risk of bias due to methodological shortcomings. This suggests that investigators should follow guidelines to develop high-quality models for better clinical service and dissemination. Systematic Review Registration https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=400835, Identifier CRD42023400835.