AUTHOR=Deng Wenqi , Wang Dayang , Wan Yandi , Lai Sijia , Ding Yukun , Wang Xian 

TITLE=Prediction models for major adverse cardiovascular events after percutaneous coronary intervention: a systematic review

JOURNAL=Frontiers in Cardiovascular Medicine

VOLUME=Volume 10 - 2023

YEAR=2024

URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2023.1287434

DOI=10.3389/fcvm.2023.1287434

ISSN=2297-055X

ABSTRACT=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. We conduct this systematic review to evaluate, describe, and compare existing models and analyze the predictors. 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 May 19, 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. Key findings were narratively summarized and presented in tables. Results: A total of 5234 articles were retrieved, and after thorough screening, 23 studies that met the predefined inclusion criteria were ultimately included. Models were mainly constructed based on ST-segment Elevation Myocardial Infarction (STEMI) patients. Models discrimination, as measured by the area under the curve (AUC) or C-index, varied between 0.638 and 0.96. Commonly used predictor variables encompass LVEF, age, Killip classification, diabetes, and others. All models were determined to have a high risk of bias and reporting adherence to the TRIPOD items was above 60%.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.