ArticleItalian journal of pediatrics2025
Interpretable web-based machine learning model for predicting intravenous immunoglobulin resistance in Kawasaki disease.
Article in Italian journal of pediatrics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 2 of them syntheses that pooled it.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Quality and performance of machine learning versus logistic regression for predicting IVIG resistance in Kawasaki disease: a PROBAST+AI systematic comparison.BMC medical research methodology · 2026Pooled it
- Machine learning prediction models for intravenous immunoglobulin resistance in Kawasaki disease: a meta-analysis.BMC medical informatics and decision making · 2026Pooled it
- An interpretable machine learning model for early risk stratification of medium-to-giant coronary artery aneurysm in Kawasaki disease: development and external validation.Pediatric research · 2026Article
- Development and validation of an interpretable machine learning model for predicting medium-to-giant coronary aneurysms in Kawasaki disease.Frontiers in immunology · 2026Article
- Machine learning models predict risk of lower extremity deep vein thrombosis in hospitalized patients with spontaneous intracerebral hemorrhage.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
backgroundKawasaki disease (KD) is a leading cause of acquired heart disease in children that is treated with intravenous immunoglobulin (IVIG). However, 10-20% of cases exhibit IVIG resistance, which increases the risk of coronary complications. Existing predictive models do not integrate multiple machine learning (ML) algorithms or facilitate real-time clinical use. This study presents a region-specific, interpretable ML model for early IVIG resistance prediction in KD.
methodsA retrospective cohort of 463 children diagnosed with KD at Fuzhou University Affiliated Provincial Hospital (2012-2024) was analyzed. Thirteen ML algorithms were evaluated via cross-validation, with performance assessed by AUC and other metrics. Feature importance was determined using SHapley Additive exPlanations (SHAP), and risk of bias was evaluated using the Prediction Model Risk of Bias Assessment Tool.
resultsThe random forest (RF) model demonstrated the highest predictive performance (AUC = 0.78). After feature selection based on SHAP values, a final interpretable RF model incorporating 10 key features was developed, and a web-based tool integrating the Youden index (16.9%) was deployed for real-time risk estimation.
conclusionThis region-specific, interpretable ML model ( https://milailai.shinyapps.io/data1/ ) is a practical tool for early risk stratification and personalized treatment of IVIG resistance in KD.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.