ArticleClinical rheumatology2023
A simple scoring model based on machine learning predicts intravenous immunoglobulin resistance in Kawasaki disease.
Article in Clinical rheumatology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 18 papers, 2 of them syntheses that pooled it.
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Who cites it
18 citing papers in PubMed, 2 syntheses or guidelines pooled it, 21 citations in OpenAlex.
- 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
- Machine learning model for intravenous immunoglobulin resistance in Kawasaki disease: model development and validation study.World journal of pediatrics : WJP · 2026Article
- Infliximab as an Effective Second-Line Treatment for IVIG-Resistant Kawasaki Disease Accompanied by Severe Dyslipidemia.Pediatric cardiology · 2026Article
- Development and validation of a novel interpretable machine learning model integrating immune-inflammatory indicators for intravenous immunoglobulin resistance in Kawasaki disease.Translational pediatrics · 2026Article
- Predicting the Need for Infliximab in Kawasaki Disease Using N-Terminal Pro-Brain Natriuretic Peptide at Diagnosis.Pediatric cardiology · 2026Article
- The future of Kawasaki disease management: data-driven innovations from bedside to bench and back again.Pediatric research · 2025Article
- Heterogeneity in Kawasaki disease patients with coronary artery abnormalities investigated by data-driven cluster analysis.Pediatric research · 2025Article
- An integrated and interpretable machine learning framework for Kawasaki disease diagnosis and risk prediction.Translational pediatrics · 2025Article
- Learning-Based Models for Predicting IVIG Resistance and Coronary Artery Lesions in Kawasaki Disease: A Review of Technical Aspects and Study Features.Paediatric drugs · 2025Review
- A machine learning-based model to predict intravenous immunoglobulin resistance in Kawasaki disease.iScience · 2025Article
- Applications of Artificial Intelligence in Vasculitides: A Systematic Review.ACR open rheumatology · 2025Review
- Development and validation of an explainable machine learning-based prediction model for primary Kawasaki disease complicated with coronary artery aneurysms.Translational pediatrics · 2025Article
- Routine Laboratory Markers-Based Machine Learning Model for Predicting Severe Kawasaki Disease in Pediatric Patients.Journal of inflammation research · 2025Article
- A machine learning algorithm to predict treatment effectiveness for Kawasaki disease in China: a retrospective model development and validation study.Frontiers in immunology · 2025Article
- Construction and validation of predictive models for intravenous immunoglobulin-resistant Kawasaki disease using an interpretable machine learning approach.Clinical and experimental pediatrics · 2024Article
- Knowledge framework of intravenous immunoglobulin resistance in the field of Kawasaki disease: A bibliometric analysis (1997-2023).Immunity, inflammation and disease · 2024Article
- Article
Corrections and comments
- Erratum issued
Authors and funding
11 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionIn Kawasaki disease (KD), accurate prediction of intravenous immunoglobulin (IVIG) resistance is crucial to reduce a risk for developing coronary artery lesions.
objectiveTo establish a simple scoring model predicting IVIG resistance in KD patients based on the machine learning model.
methodsA retrospective cohort study of 1002 KD patients diagnosed at 12 facilities for 10 years, in which 22.7% were resistant to initial IVIG treatment. We performed machine learning with diverse models using 30 clinical variables at diagnosis in 801 and 201 cases for training and test datasets, respectively. SHAP was applied to identify the variables that influenced the prediction model. A scoring model was designed using the influential clinical variables based on the Shapley additive explanation results.
resultsLight gradient boosting machine model accurately predicted IVIG resistance (area under the receiver operating characteristic curve (AUC), 0.78; sensitivity, 0.50; specificity, 0.88). Next, using top three influential features (days of illness at initial therapy, serum levels of C-reactive protein, and total cholesterol), we designed a simple scoring system. In spite of its simplicity, it predicted IVIG resistance (AUC, 0.72; sensitivity, 0.49; specificity, 0.82) as accurately as machine learning models. Moreover, accuracy of our scoring system with three clinical features was almost identical to that of Gunma score with seven clinical features (AUC, 0.73; sensitivity, 0.53; specificity, 0.83), a well-known logistic regression scoring model.
conclusionA simple scoring system based on the findings in machine learning seems to be a useful tool to accurately predict IVIG resistance in KD patients.
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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 OpenQuestion graph.