ArticlePediatric research2015
Novel data-mining approach identifies biomarkers for diagnosis of Kawasaki disease.
Article in Pediatric research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Machine learning for early diagnosis of Kawasaki disease in acute febrile children: retrospective cross-sectional study in China.Scientific reports · 2025Article
- From molecular mechanisms of prostate cancer to translational applications: based on multi-omics fusion analysis and intelligent medicine.Health information science and systems · 2024Review
- A machine learning model for distinguishing Kawasaki disease from sepsis.Scientific reports · 2023Article
- Phenomapping approach to interpreting coronary dimensions in febrile children.Pediatric investigation · 2022Article
- Single center blind testing of a US multi-center validated diagnostic algorithm for Kawasaki disease in Taiwan.Frontiers in immunology · 2022Article
- Future Biomarkers for Infection and Inflammation in Febrile Children.Frontiers in immunology · 2021Review
- Biomarkers for the Discrimination of Acute Kawasaki Disease From Infections in Childhood.Frontiers in pediatrics · 2020Article
- A Random Forests Quantile Classifier for Class Imbalanced Data.Pattern recognition · 2019Article
- Risk factors and implications of progressive coronary dilatation in children with Kawasaki disease.BMC pediatrics · 2017Article
- Genome-Wide Expression Profiling Reveals S100B as Biomarker for Invasive Aspergillosis.Frontiers in microbiology · 2016Article
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Authors and funding
6 authors.
Funding
Abstract
backgroundAs Kawasaki disease (KD) shares many clinical features with other more common febrile illnesses and misdiagnosis, leading to a delay in treatment, increases the risk of coronary artery damage, a diagnostic test for KD is urgently needed. We sought to develop a panel of biomarkers that could distinguish between acute KD patients and febrile controls (FC) with sufficient accuracy to be clinically useful.
methodsPlasma samples were collected from three independent cohorts of FC and acute KD patients who met the American Heart Association definition for KD and presented within the first 10 d of fever. The levels of 88 biomarkers associated with inflammation were assessed by Luminex bead technology. Unsupervised clustering followed by supervised clustering using a Random Forest model was used to find a panel of candidate biomarkers.
resultsA panel of biomarkers commonly available in the hospital laboratory (absolute neutrophil count, erythrocyte sedimentation rate, alanine aminotransferase, γ-glutamyl transferase, concentrations of α-1-antitrypsin, C-reactive protein, and fibrinogen, and platelet count) accurately diagnosed 81-96% of KD patients in a series of three independent cohorts.
conclusionAfter prospective validation, this eight-biomarker panel may improve the recognition of KD.
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