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.

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.

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.

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.

审查和批判性评价 1980 年 1 月 1 日至 2021 年 12 月 23 日 PubMed、Embase 和 Web of Science 数据库中有关川崎病冠状动脉病变 (CAL) 预测模型的文章。

研究筛选、数据提取和质量评估由两名独立评审员进行，统计专家解决差异。包括开发或验证川崎病 CAL 预测模型的文章。预测建模研究系统评价的关键评估和数据提取清单用于从不同文章中提取数据，预测模型偏差风险评估工具 (PROBAST) 用于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 项研究。

这些研究包括 73-5,151 名患者。在大多数研究中，单变量逻辑回归用于开发预测模型。在两项研究中，外部数据用于验证开发模型。最常见的预测因子是 C 反应蛋白 (CRP) 水平、男性和发热持续时间。所有研究都存在较高的偏倚风险，主要是因为样本量小、缺失数据处理不当以及对模型性能和评估模型的描述不当。

预测模型适用于研究中的受试者，但在其他人群中效果不佳。这种现象可能部分是由于预测模型中的偏差风险。未来的模型应该解决这些问题，并且应该使用 PROBAST 来指导研究设计。