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Pediatr Res. 2015 Nov;78(5):547-53. doi: 10.1038/pr.2015.137. Epub 2015 Aug 3.

Novel data-mining approach identifies biomarkers for diagnosis of Kawasaki disease.

Author information

1
Department of Pediatrics, University of California San Diego, La Jolla, California.
2
Rady Children's Hospital San Diego, San Diego, California.
3
Data4Cure, Inc., La Jolla, California.
4
Department of Medicine, University of California San Diego, La Jolla, California.
5
Department of Surgery, Stanford University, Palo Alto, California.

Abstract

BACKGROUND:

As 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.

METHODS:

Plasma 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.

RESULTS:

A 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.

CONCLUSION:

After prospective validation, this eight-biomarker panel may improve the recognition of KD.

PMID:
26237629
PMCID:
PMC4628575
DOI:
10.1038/pr.2015.137
[Indexed for MEDLINE]
Free PMC Article

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