经皮冠状动脉介入治疗后主要不良心血管事件的预测模型：一项系统评价
Prediction models for major adverse cardiovascular events after percutaneous coronary intervention: a systematic review.
作者信息
Deng Wenqi, Wang Dayang, Wan Yandi, Lai Sijia, Ding Yukun, Wang Xian
机构信息
Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, China.
Institute of Cardiovascular Diseases, Beijing University of Chinese Medicine, Beijing, China.
出版信息
Front Cardiovasc Med. 2024 Jan 8;10:1287434. doi: 10.3389/fcvm.2023.1287434. eCollection 2023.
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.
背景
为预测接受经皮冠状动脉介入治疗（PCI）患者的主要不良心血管事件（MACE）而开发的模型数量不断增加，但这些模型的性能尚不清楚。本系统评价的目的是评估、描述和比较现有模型，并分析可预测结局的因素。
方法
在本评价执行过程中，我们遵循了系统评价与Meta分析的首选报告项目（PRISMA）2020。全面检索了包括Embase、PubMed、Cochrane图书馆、Web of Science、中国知网、万方数据、维普资讯和中国生物医学文献数据库在内的数据库，以识别1977年至2023年5月19日发表的研究。纳入了专门设计用于评估PCI术后MACE发生情况且有或无外部验证的模型开发研究。通过预测模型偏倚风险评估工具（PROBAST）和多变量个体预后或诊断的透明报告（TRIPOD）声明评估偏倚和透明度。对关键发现进行了叙述性总结并列表呈现。
结果
共检索到5234篇文章，经过全面筛选，最终纳入了23项符合预定义纳入标准的研究。这些模型主要使用被诊断为ST段抬高型心肌梗死（STEMI）个体的数据构建。以曲线下面积（AUC）或C指数衡量，模型的辨别力在0.638至0.96之间。常用的预测变量包括左心室射血分数（LVEF）、年龄、Killip分级、糖尿病及其他各种因素。所有模型均被判定存在高偏倚风险，据报告它们对TRIPOD项目的遵循率超过60%。
结论
现有模型显示出一定的预测能力，但由于方法学缺陷均存在高偏倚风险。这表明研究者应遵循指南开发高质量模型，以提供更好的临床服务和推广。
系统评价注册
https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=400835，标识符CRD42023400835。
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