Prediction Model Risk-of-Bias Assessment Tool for coronary artery lesions in Kawasaki disease.
Prediction Model Risk-of-Bias Assessment Tool for coronary artery lesions in Kawasaki disease.
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川崎病冠状动脉病变预测模型偏倚风险评估工具
DOI：
10.3389/fcvm.2022.1014067
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发表时间：
2022
影响因子：
3.6
通讯作者：
中科院分区：
文献类型：
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目的对PubMed、Embase和Web of Science数据库1980年1月1日至2021年12月23日收录的川崎病冠状动脉病变预测模型的文献进行综述和评价。材料与方法研究筛选、数据提取和质量评估由两名独立的评价者进行，由一名统计学专家解决差异。纳入了开发或验证川崎病CALS预测模型的文章。使用《预测建模研究系统综述关键评价和数据提取》检查表从不同的文章中提取数据，并使用预测模型偏差风险评估工具(PROBAST)来评估不同预测模型中的偏差风险。我们从881篇文章中筛选出19篇研究。结果研究对象为73-5151例患者。在大多数研究中，使用单变量Logistic回归来建立预测模型。在两项研究中，使用外部数据来验证开发模型。最常见的预测因素是C-反应蛋白(CRP)水平、男性和发热时间。所有研究都有很高的偏差风险，主要是因为样本量小，对缺失数据的处理不当，以及对模型性能和评估模型的描述不当。结论该预测模型适用于研究对象，但对其他人群的预测效果较差。这一现象可能部分归因于预测模型中的偏差风险。未来的模型应该解决这些问题，PROBAST应该被用来指导研究设计。
Objective To review and critically appraise articles on prediction models for coronary artery lesions (CALs) in Kawasaki disease included in PubMed, Embase, and Web of Science databases from January 1, 1980, to December 23, 2021. Materials and methods Study screening, data extraction, and quality assessment were performed by two independent reviewers, with a statistics expert resolving discrepancies. Articles that developed or validated a prediction model for CALs in Kawasaki disease were included. The Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies checklist was used to extract data from different articles, and Prediction Model Risk-of-Bias Assessment Tool (PROBAST) was used to assess the bias risk in different prediction models. We screened 19 studies from a pool of 881 articles. Results The studies included 73–5,151 patients. In most studies, univariable logistic regression was used to develop prediction models. In two studies, external data were used to validate the developing model. The most commonly included predictors were C-reactive protein (CRP) level, male sex, and fever duration. All studies had a high bias risk, mostly because of small sample size, improper handling of missing data, and inappropriate descriptions of model performance and the evaluation model. Conclusion The prediction models were suitable for the subjects included in the studies, but were poorly effective in other populations. The phenomenon may partly be due to the bias risk in prediction models. Future models should address these problems and PROBAST should be used to guide study design.