ArticlePediatric research2023
Risk-prediction models for intravenous immunoglobulin resistance in Kawasaki disease: Risk-of-Bias Assessment using PROBAST.
Article in Pediatric research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 3 of them syntheses that pooled it.
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Who cites it
10 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Risk prediction models for dental caries in children and adolescents: a systematic review and meta-analysis.BMJ open · 2025Pooled it
- Establishment and validation of risk prediction model to predict intravenous immunoglobulin-resistance in Kawasaki disease based on meta-analysis of 15 cohorts.Italian journal of pediatrics · 2025Pooled it
- Pooled it
- External Validation of Brief Resolved Unexplained Events Prediction Rules for Serious Underlying Diagnosis.JAMA pediatrics · 2025Article
- Kawasaki disease: insights into the roles of T cells.Frontiers in immunology · 2025Review
- An Update on Kawasaki Disease.Current rheumatology reports · 2024Review
- Article
- A Systematic Review of Recent Studies on Hospital Readmissions of Patients With Diabetes.Cureus · 2024Review
- Utilizing T-Lymphocyte Activation-Related Cytokines to Predict Non-Responsiveness to Treatment in Pediatric Kawasaki Disease.Pediatric health, medicine and therapeutics · 2024Article
- A novel model for predicting intravenous immunoglobulin-resistance in Kawasaki disease: a large cohort study.Frontiers in cardiovascular medicine · 2023Article
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe 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.
methodsStudies 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).
resultsSeventeen 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.
conclusionIVIG 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.
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