ArticlePloS one2021
A novel score system of blood tests for differentiating Kawasaki disease from febrile children.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it, 34 citations in OpenAlex.
- Accuracy of Machine Learning in Discriminating Kawasaki Disease and Other Febrile Illnesses: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2024Pooled it
- Association Between the Persistency and Severity of Mitral Regurgitation and the Hematologic Z-Values of Inflammatory Biomarkers in Kawasaki Disease.Korean circulation journal · 2026Article
- IL-33 blockade attenuates vascular inflammation in a mouse model of Kawasaki disease vasculitis.Clinical and experimental immunology · 2026Article
- Predictive value of blood composite ratios for Kawasaki disease and coronary artery lesions in febrile children.Italian journal of pediatrics · 2025Article
- Interpretable web-based machine learning model for predicting intravenous immunoglobulin resistance in Kawasaki disease.Italian journal of pediatrics · 2025Article
- An interpretable machine learning-assisted diagnostic model for Kawasaki disease in children.Scientific reports · 2025Article
- The association of serum CHI3L1 levels with the presence of Kawasaki disease.Scientific reports · 2025Article
- Impact of different treatment strategies on therapeutic efficacy and biomarkers in children with IVIG-resistant Kawasaki disease: a retrospective cohort study.Frontiers in pharmacology · 2025Article
- Identifying the risk of Kawasaki disease based solely on routine blood test features through novel construction of machine learning models.Computational and structural biotechnology journal · 2025Article
- Exploring the diagnostic value of CLR and CPR in differentiating Kawasaki disease from other infectious diseases based on clinical predictive modeling.Frontiers in pediatrics · 2024Article
- Article
- Diagnosis, Progress, and Treatment Update of Kawasaki Disease.International journal of molecular sciences · 2023Review
- A machine learning model for distinguishing Kawasaki disease from sepsis.Scientific reports · 2023Article
- Use of Machine Learning to Differentiate Children With Kawasaki Disease From Other Febrile Children in a Pediatric Emergency Department.JAMA network open · 2023Article
- Clinical Signs of Kawasaki Disease from the Perspective of Epithelial-to-Mesenchymal Transition Recruiting Erythrocytes: A Literature Review.Reviews in cardiovascular medicine · 2023Review
- Exploring the diagnostic value of eosinophil-to-lymphocyte ratio to differentiate Kawasaki disease from other febrile diseases based on clinical prediction model.Scientific reports · 2023Article
- Article
- Expression of Eosinophilic Subtype Markers in Patients with Kawasaki Disease.International journal of molecular sciences · 2022Article
- Combination of Hemoglobin-for-Age Z-Score and Plasma Hepcidin Identified as a Novel Predictor for Kawasaki Disease.Children (Basel, Switzerland) · 2022Article
- An Artificial Intelligence-guided signature reveals the shared host immune response in MIS-C and Kawasaki disease.Nature communications · 2022Article
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Authors and funding
7 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundKawasaki disease is the most common cause of acquired heart disease among febrile children under the age of 5 years old. It is also a clinically diagnosed disease. In this study, we developed and assessed a novel score system using objective parameters to differentiate Kawasaki disease from febrile children.
methodsWe analyzed 6,310 febrile children and 485 Kawasaki disease subjects in this study. We collected biological parameters of a routine blood test, including complete blood count with differential, C-reactive protein, aspartate aminotransferase, and alanine aminotransferase. Receiver operating characteristic curve, logistic regression, and Youden's index were all used to develop the prediction model. Two other independent cohorts from different hospitals were used for verification.
resultsWe obtained eight independent predictors (platelets, eosinophil, alanine aminotransferase, C-reactive protein, hemoglobin, mean corpuscular hemoglobin, mean corpuscular hemoglobin concentration, and monocyte) and found the top three scores to be eosinophil >1.5% (score: 7), alanine aminotransferase >30 U/L (score: 6), and C-reactive protein>25 mg/L (score: 6). A score of 14 represents the best sensitivity value plus specificity prediction rate for Kawasaki disease. The sensitivity, specificity, and accuracy for our cohort were 0.824, 0.839, and 0.838, respectively. The verification test of two independent cohorts of Kawasaki disease patients (N = 103 and 170) from two different institutes had a sensitivity of 0.780 (213/273).
conclusionOur findings demonstrate a novel score system with good discriminatory ability for differentiating between children with Kawasaki disease and other febrile children, as well as highlight the importance of eosinophil in Kawasaki disease. Using this novel score system can help first-line physicians diagnose and then treat Kawasaki disease early.
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