title: A diagnostic algorithm combining clinical and molecular data distinguishes Kawasaki disease from other febrile illnesses.
abstract: BACKGROUND
  Kawasaki disease is an acute vasculitis of infants and young children that is recognized through a constellation of clinical signs that can mimic other benign conditions of childhood. The etiology remains unknown and there is no specific laboratory-based test to identify patients with Kawasaki disease. Treatment to prevent the complication of coronary artery aneurysms is most effective if administered early in the course of the illness. We sought to develop a diagnostic algorithm to help clinicians distinguish Kawasaki disease patients from febrile controls to allow timely initiation of treatment.
  
  
  METHODS
  Urine peptidome profiling and whole blood cell type-specific gene expression analyses were integrated with clinical multivariate analysis to improve differentiation of Kawasaki disease subjects from febrile controls.
  
  
  RESULTS
  Comparative analyses of multidimensional protein identification using 23 pooled Kawasaki disease and 23 pooled febrile control urine peptide samples revealed 139 candidate markers, of which 13 were confirmed (area under the receiver operating characteristic curve (ROC AUC 0.919)) in an independent cohort of 30 Kawasaki disease and 30 febrile control urine peptidomes. Cell type-specific analysis of microarrays (csSAM) on 26 Kawasaki disease and 13 febrile control whole blood samples revealed a 32-lymphocyte-specific-gene panel (ROC AUC 0.969). The integration of the urine/blood based biomarker panels and a multivariate analysis of 7 clinical parameters (ROC AUC 0.803) effectively stratified 441 Kawasaki disease and 342 febrile control subjects to diagnose Kawasaki disease.
  
  
  CONCLUSIONS
  A hybrid approach using a multi-step diagnostic algorithm integrating both clinical and molecular findings was successful in differentiating children with acute Kawasaki disease from febrile controls.
authors.affiliation: Department of Pediatrics, Stanford University, Stanford, CA 94305, USA.
authors.forename: Xuefeng B
authors.initials: XB
authors.lastname: Ling
authors.forename: Kenneth
authors.initials: K
authors.lastname: Lau
authors.forename: John T
authors.initials: JT
authors.lastname: Kanegaye
authors.forename: Zheng
authors.initials: Z
authors.lastname: Pan
authors.forename: Sihua
authors.initials: S
authors.lastname: Peng
authors.forename: Jun
authors.initials: J
authors.lastname: Ji
authors.forename: Gigi
authors.initials: G
authors.lastname: Liu
authors.forename: Yuichiro
authors.initials: Y
authors.lastname: Sato
authors.forename: Tom T S
authors.initials: TT
authors.lastname: Yu
authors.forename: John C
authors.initials: JC
authors.lastname: Whitin
authors.forename: James
authors.initials: J
authors.lastname: Schilling
authors.forename: Jane C
authors.initials: JC
authors.lastname: Burns
authors.forename: Harvey J
authors.initials: HJ
authors.lastname: Cohen
mesh_terms: D000328:Adult; D000465:Algorithms; D019540:Area Under Curve; D015415:Biomarkers / Q000097:blood / Q000378:metabolism / Q000652:urine; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D003937:Diagnosis, Differential; D005260:Female; D005334:Fever / Q000097:blood / Q000175:diagnosis / Q000235:genetics / Q000652:urine; D020869:Gene Expression Profiling / Q000379:methods; D006801:Humans; D008297:Male; D008875:Middle Aged; D009080:Mucocutaneous Lymph Node Syndrome / Q000097:blood / Q000175:diagnosis* / Q000235:genetics / Q000652:urine; D015999:Multivariate Analysis; D011507:Proteinuria / Q000097:blood / Q000175:diagnosis / Q000652:urine; D040901:Proteomics / Q000379:methods; D012372:ROC Curve; D059467:Transcriptome
chemical_list: D015415:Biomarkers