{"title":"Risk Prediction Models for Contrast-associated Acute Kidney Injury After Percutaneous Coronary Intervention: A Systematic Review.","authors":"Hui Zhang, Tongtong Chen, Ning Chen, Lixia Liu","doi":"10.1177/00033197251326394","DOIUrl":null,"url":null,"abstract":"<p><p>The aim of this review was to systematically review published studies on risk prediction models for contrast-associated acute kidney injury (CA-AKI) in patients with ST-segment elevation myocardial infarction (STEMI) after percutaneous coronary intervention (PCI). We searched PubMed, Embase, Web of Science, Scopus, Medline, Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Chinese databases from inception to July 1, 2024. The Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) was used to extract data and The Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess the risk of bias and applicability. A total of 2784 publications were retrieved; 16 studies were included. The models' area under the curve (AUC) or C-index ranged from 0.719 to 0.877. Commonly used predictors included age, diabetes, Killip class, and use of intra-aortic balloon pump (IABP). Thirteen studies were determined to be at high risk of bias, while three were unclear, but their applicability was satisfactory. The models' clinical utility was still up for debate. Future development or validation of models should focus on methodology and combine machine learning and natural language processing to analyze data to improve the predictive ability and clinical applicability of models.</p>","PeriodicalId":8264,"journal":{"name":"Angiology","volume":" ","pages":"33197251326394"},"PeriodicalIF":2.2000,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Angiology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1177/00033197251326394","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PERIPHERAL VASCULAR DISEASE","Score":null,"Total":0}
引用次数: 0
Abstract
The aim of this review was to systematically review published studies on risk prediction models for contrast-associated acute kidney injury (CA-AKI) in patients with ST-segment elevation myocardial infarction (STEMI) after percutaneous coronary intervention (PCI). We searched PubMed, Embase, Web of Science, Scopus, Medline, Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Chinese databases from inception to July 1, 2024. The Checklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) was used to extract data and The Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess the risk of bias and applicability. A total of 2784 publications were retrieved; 16 studies were included. The models' area under the curve (AUC) or C-index ranged from 0.719 to 0.877. Commonly used predictors included age, diabetes, Killip class, and use of intra-aortic balloon pump (IABP). Thirteen studies were determined to be at high risk of bias, while three were unclear, but their applicability was satisfactory. The models' clinical utility was still up for debate. Future development or validation of models should focus on methodology and combine machine learning and natural language processing to analyze data to improve the predictive ability and clinical applicability of models.
本综述的目的是系统回顾已发表的关于st段抬高型心肌梗死(STEMI)患者经皮冠状动脉介入治疗(PCI)后对比剂相关急性肾损伤(CA-AKI)风险预测模型的研究。我们检索了PubMed, Embase, Web of Science, Scopus, Medline, Cumulative Index to Nursing and Allied Health Literature (CINAHL),以及从成立到2024年7月1日的中文数据库。预测模型研究系统评价关键评价和数据提取清单(CHARMS)用于提取数据,预测模型偏倚风险评估工具(PROBAST)用于评估偏倚风险和适用性。共检索了2784份出版物;共纳入16项研究。模型的曲线下面积(AUC)或c指数范围为0.719 ~ 0.877。常用的预测因素包括年龄、糖尿病、Killip分级和使用主动脉内球囊泵(IABP)。13项研究被确定为高偏倚风险,3项研究尚不清楚,但其适用性令人满意。这些模型的临床效用仍有待商榷。未来模型的开发或验证应注重方法学,结合机器学习和自然语言处理对数据进行分析,以提高模型的预测能力和临床适用性。
期刊介绍:
A presentation of original, peer-reviewed original articles, review and case reports relative to all phases of all vascular diseases, Angiology (ANG) offers more than a typical cardiology journal. With approximately 1000 pages per year covering diagnostic methods, therapeutic approaches, and clinical and laboratory research, ANG is among the most informative publications in the field of peripheral vascular and cardiovascular diseases. This journal is a member of the Committee on Publication Ethics (COPE). Average time from submission to first decision: 13 days