ArticleThe Journal of international medical research2023
A nomogram for predicting immunoglobulin-resistant Kawasaki disease in children.
Article in The Journal of international medical research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 2 of them syntheses that pooled it.
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Who cites it
6 citing papers in PubMed, 2 syntheses or guidelines pooled it, 3 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
- Electrocardiographic Alterations Combined with Hematological, Biochemical, and Metabolic Profiles Predict Prognosis in Kawasaki Disease.Journal of cardiovascular development and disease · 2026Article
- Explainable deep learning algorithm for distinguishing IVIG-Resistant Kawasaki disease in Shandong peninsula, China.BMC 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 visualized nomogram to predict intravenous immunoglobulin resistance in Kawasaki disease: a study based on the population in Southern China.Italian journal of pediatrics · 2025Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis case-control study focused on the establishment and internal validation of a risk nomogram for intravenous immunoglobulin (IVIG)-resistant Kawasaki disease (KD) using the Kawasaki Disease Database.
methodsThe Kawasaki Disease Database is the first public database for KD researchers. A prediction nomogram for IVIG-resistant KD was constructed using multivariable logistic regression. Then, the C-index was used to assess the discriminating ability of the proposed prediction model, a calibration plot was drawn to evaluate its calibration, and a decision curve analysis was adopted to assess its clinical usefulness. Bootstrapping validation was performed for interval validation.
resultsThe median ages of IVIG-resistant and -sensitive KD groups were 3.3 and 2.9 years, respectively. Predicting factors incorporated into the nomogram were coronary artery lesions, C-reactive protein, percentage of neutrophils, platelets, aspartate aminotransferase, and alanine transaminase. Our constructed nomogram exhibited favorable discriminating ability (C-index: 0.742; 95% confidence interval: 0.673-0.812) and excellent calibration. Moreover, interval validation achieved a high C-index of 0.722.
conclusionsThe as-constructed new IVIG-resistant KD nomogram that incorporated C-reactive protein, coronary artery lesions, platelets, percentage of neutrophils, alanine transaminase, and aspartate aminotransferase may be adopted for predicting the risk of IVIG-resistant KD.
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