Novel data-mining approach identifies biomarkers for diagnosis of Kawasaki disease.
Pediatr Res
; 78(5): 547-53, 2015 Nov.
Article
en En
| MEDLINE
| ID: mdl-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.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Biomarcadores
/
Minería de Datos
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Síndrome Mucocutáneo Linfonodular
Tipo de estudio:
Diagnostic_studies
/
Etiology_studies
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
/
Screening_studies
Límite:
Child
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Child, preschool
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Female
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Humans
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Infant
/
Male
Idioma:
En
Revista:
Pediatr Res
Año:
2015
Tipo del documento:
Article