Risk-prediction models for intravenous immunoglobulin resistance in Kawasaki disease: Risk-of-Bias Assessment using PROBAST.

BACKGROUND: The prediction model of intravenous immunoglobulin (IVIG) resistance in Kawasaki disease can calculate the probability of IVIG resistance and provide a basis for clinical decision-making. We aim to assess the quality of these models developed in the children with Kawasaki disease. METHODS: Studies of prediction models for IVIG-resistant Kawasaki disease were identified through searches in the PubMed, Web of Science, and Embase databases. Two investigators independently performed literature screening, data extraction, quality evaluation, and discrepancies were settled by a statistician. The checklist for critical appraisal and data extraction for systematic reviews of prediction modeling studies (CHARMS) was used for data extraction, and the prediction models were evaluated using the Prediction Model Risk of Bias Assessment Tool (PROBAST). RESULTS: Seventeen studies meeting the selection criteria were included in the qualitative analysis. The top three predictors were neutrophil measurements (peripheral neutrophil count and neutrophil %), serum albumin level, and C-reactive protein (CRP) level. The reported area under the curve (AUC) values for the developed models ranged from 0.672 (95% confidence interval [CI]: 0.631-0.712) to 0.891 (95% CI: 0.837-0.945); The studies showed a high risk of bias (ROB) for modeling techniques, yielding a high overall ROB. CONCLUSION: IVIG resistance models for Kawasaki disease showed high ROB. An emphasis on improving their quality can provide high-quality evidence for clinical practice. IMPACT STATEMENT: This study systematically evaluated the risk of bias (ROB) of existing prediction models for intravenous immunoglobulin (IVIG) resistance in Kawasaki disease to provide guidance for future model development meeting clinical expectations. This is the first study to systematically evaluate the ROB of IVIG resistance in Kawasaki disease by using PROBAST. ROB may reduce model performance in different populations. Future prediction models should account for this problem, and PROBAST can help improve the methodological quality and applicability of prediction model development.

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

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.