Prediction Model Risk-of-Bias Assessment Tool for coronary artery lesions in Kawasaki disease

• DOI: 10.3389/fcvm.2022.1014067
• Publication Date: 2022-10-13T04:34:05Z
• Authors: Hongbiao Huang; Jinfeng Dong; Shuhui Wang; Yueping Shen; Yiming Zheng; Jiaqi Jiang; Bihe Zeng; Xuan Li; Fang Yang; Shurong Ma; Ying He; Fan Lin; Chunqiang Chen; Qiaobin Chen; Haitao Lv
• Keywords: prediction model; 03 medical and health sciences; 0302 clinical medicine; Kawasaki disease; diagnosis; RC666-701; Diseases of the circulatory (Cardiovascular) system; coronary artery lesions; Prediction Model Risk-of-Bias Assessment Tool; Cardiovascular Medicine; 3. Good health

ObjectiveTo 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 methodsStudy 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.ResultsThe 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.ConclusionThe 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.