title: Novel data-mining approach identifies biomarkers for diagnosis of Kawasaki disease.
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.
  
  
  CONCLUSIONS
  After prospective validation, this eight-biomarker panel may improve the recognition of KD.
authors.affiliation: Department of Pediatrics, University of California San Diego, La Jolla, California.|Rady Children's Hospital San Diego, San Diego, California.
authors.forename: Adriana H
authors.initials: AH
authors.lastname: Tremoulet
authors.affiliation: Data4Cure, Inc., La Jolla, California.|Department of Medicine, University of California San Diego, La Jolla, California.
authors.forename: Janusz
authors.initials: J
authors.lastname: Dutkowski
authors.affiliation: Department of Pediatrics, University of California San Diego, La Jolla, California.|Rady Children's Hospital San Diego, San Diego, California.
authors.forename: Yuichiro
authors.initials: Y
authors.lastname: Sato
authors.affiliation: Department of Pediatrics, University of California San Diego, La Jolla, California.|Rady Children's Hospital San Diego, San Diego, California.
authors.forename: John T
authors.initials: JT
authors.lastname: Kanegaye
authors.affiliation: Department of Surgery, Stanford University, Palo Alto, California.
authors.forename: Xuefeng B
authors.initials: XB
authors.lastname: Ling
authors.affiliation: Department of Pediatrics, University of California San Diego, La Jolla, California.|Rady Children's Hospital San Diego, San Diego, California.
authors.forename: Jane C
authors.initials: JC
authors.lastname: Burns
mesh_terms: D019540:Area Under Curve; D015415:Biomarkers / Q000097:blood*; D001774:Blood Chemical Analysis; D016022:Case-Control Studies; D002648:Child; D002675:Child, Preschool; D016000:Cluster Analysis; D057225:Data Mining / Q000379:methods*; D003661:Decision Support Techniques; D003937:Diagnosis, Differential; D042241:Early Diagnosis; D005260:Female; D005334:Fever / Q000209:etiology; D006801:Humans; D007223:Infant; D007958:Leukocyte Count; D008297:Male; D009080:Mucocutaneous Lymph Node Syndrome / Q000097:blood* / Q000150:complications / Q000175:diagnosis*; D010976:Platelet Count; D011237:Predictive Value of Tests; D011379:Prognosis; D012372:ROC Curve
chemical_list: D015415:Biomarkers