ArticleBMC pediatrics2022
A decision tree model for predicting intravenous immunoglobulin resistance and coronary artery involvement in Kawasaki disease.
Article in BMC pediatrics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 2 of them syntheses that pooled it.
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Who cites it
7 citing papers in PubMed, 2 syntheses or guidelines pooled it, 9 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
- Association Between Levels of N-Terminal Pro-brain Natriuretic Peptide and Coronary Artery Lesion in Patients with Kawasaki Disease: A Systematic Review and Meta-analysis.Archives of rheumatology · 2025Article
- An interpretable machine learning-assisted diagnostic model for Kawasaki disease in children.Scientific reports · 2025Article
- Applications of Artificial Intelligence in Vasculitides: A Systematic Review.ACR open rheumatology · 2025Review
- Establishment and validation of a nomogram for predicting intravenous immunoglobulin resistance and coronary artery lesion involvement in Kawasaki disease: a retrospective study.Clinical rheumatology · 2025Article
- Clinical Significance of B-Type Natriuretic Peptide and N-Terminal Pro-B-Type Natriuretic Peptide in Pediatric Patients: Insights into Their Utility in the Presence or Absence of Pre-Existing Heart Conditions.International journal of molecular sciences · 2024Review
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThis study aims to develop a new algorithm for predicting intravenous immunoglobulin (IVIG) resistance and coronary artery involvement in Kawasaki disease (KD) through decision tree models.
methodsMedical records of children hospitalized for KD were analysed retrospectively. We compared the clinical characteristics, and the laboratory data in the groups with IVIG resistance and coronary artery dilatations (CADs) in KD patients. The decision tree models were developed to predict IVIG resistance and CADs.
resultsA total 896 patients (511 males and 385 females; 1 month-12 years) were eligible. IVIG resistance was identified in 111 (12.3%) patients, and CADs were found in 156 (17.4%). Total bilirubin and nitrogen terminal- pro-brain natriuretic peptide (NT-proBNP) were significantly higher in IVIG resistant group than in IVIG responsive group (0.62 ± 0.8 mg/dL vs 1.38 ± 1.4 mg/dL and 1231 ± 2136 pg/mL vs 2425 ± 4459 mL, respectively, P < 0.01). Also, CADs were more developed in the resistant group (39/111; 14.9% vs. 117/785; 35.1%, P < 0.01). The decision tree for predicting IVIG resistance was classified based on total bilirubin (0.7 mg/mL, 1.46 mg/dL) and NT-proBNP (1561 pg/mL), consisting of two layers and four nodes, with 86.2% training accuracy and 90.5% evaluation accuracy. The Receiver Operating Characteristic (ROC) evaluated the predictive ability of the decision tree, and the area under the curve (AUC) (0.834; 95% confidence interval, 0.675-0.973; P < 0.05) showed relatively higher accuracy. The group with CADs had significantly higher total bilirubin and NT-proBNP levels than the control group (0.64 ± 0.82 mg/dL vs 1.04 ± 1.14 mg/dL and 1192 ± 2049 pg/mL vs 2268 ± 4136 pg/mL, respectively, P < 0.01). The decision trees for predicting CADs were classified into two nodes based on NT-proBNP (789 pg/mL) alone, with 83.5% training accuracy and 90.3% evaluation accuracy.
conclusionA new algorithm decision tree model presents for predicting IVIG resistance and CADs in KD, confirming the usefulness of NT-proBNP as a predictor of KD.
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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.