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  • Title: Prediction Model Risk-of-Bias Assessment Tool for coronary artery lesions in Kawasaki disease.
    Author: Huang H, Dong J, Wang S, Shen Y, Zheng Y, Jiang J, Zeng B, Li X, Yang F, Ma S, He Y, Lin F, Chen C, Chen Q, Lv H.
    Journal: Front Cardiovasc Med; 2022; 9():1014067. PubMed ID: 36312287.
    Abstract:
    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.
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