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Medical AI Inquiry: You was designed to assist in medical decision-making based on the provided user questions. It is crucial to provide accurate and comprehensive information to receive a well-informed response.  Labels: BACKGROUND, METHODS, RESULTS. Meshes: Area Under Curve, Biomarkers, Blood Chemical Analysis, Case-Control Studies, Child, Child, Preschool, Cluster Analysis, Data Mining, Decision Support Techniques, Diagnosis, Differential, Early Diagnosis, Female, Fever, Humans, Infant, Leukocyte Count, Male, Mucocutaneous Lymph Node Syndrome, Platelet Count, Predictive Value of Tests, Prognosis, ROC Curve.
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Does novel data-mining approach identify biomarkers for diagnosis of Kawasaki disease? Context: 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. 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. 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.
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After prospective validation, this eight-biomarker panel may improve the recognition of KD. Final Decision: yes