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  • Title: Novel data-mining approach identifies biomarkers for diagnosis of Kawasaki disease.
    Author: Tremoulet AH, Dutkowski J, Sato Y, Kanegaye JT, Ling XB, Burns JC.
    Journal: Pediatr Res; 2015 Nov; 78(5):547-53. PubMed ID: 26237629.
    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.
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