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Novel data-mining approach identifies biomarkers for diagnosis of Kawasaki disease
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 co...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4628575/ https://www.ncbi.nlm.nih.gov/pubmed/26237629 http://dx.doi.org/10.1038/pr.2015.137 |
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author | Tremoulet, Adriana H. Dutkowski, Janusz Sato, Yuichiro Kanegaye, John T. Ling, Xuefeng B. Burns, Jane C. |
author_facet | Tremoulet, Adriana H. Dutkowski, Janusz Sato, Yuichiro Kanegaye, John T. Ling, Xuefeng B. Burns, Jane C. |
author_sort | Tremoulet, Adriana H. |
collection | PubMed |
description | 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 days 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, gamma glutamyl transferase, concentrations of alpha-1-antitrypsin, C-reactive protein, and fibrinogen, and platelet count) accurately diagnosed 81 to 96% of KD patients in a series of three independent cohorts. CONCLUSIONS: After prospective validation, this 8-biomarker panel may improve the recognition of KD. |
format | Online Article Text |
id | pubmed-4628575 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
record_format | MEDLINE/PubMed |
spelling | pubmed-46285752016-05-01 Novel data-mining approach identifies biomarkers for diagnosis of Kawasaki disease Tremoulet, Adriana H. Dutkowski, Janusz Sato, Yuichiro Kanegaye, John T. Ling, Xuefeng B. Burns, Jane C. Pediatr Res Article 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 days 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, gamma glutamyl transferase, concentrations of alpha-1-antitrypsin, C-reactive protein, and fibrinogen, and platelet count) accurately diagnosed 81 to 96% of KD patients in a series of three independent cohorts. CONCLUSIONS: After prospective validation, this 8-biomarker panel may improve the recognition of KD. 2015-08-03 2015-11 /pmc/articles/PMC4628575/ /pubmed/26237629 http://dx.doi.org/10.1038/pr.2015.137 Text en http://www.nature.com/authors/editorial_policies/license.html#terms Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:http://www.nature.com/authors/editorial_policies/license.html#terms |
spellingShingle | Article Tremoulet, Adriana H. Dutkowski, Janusz Sato, Yuichiro Kanegaye, John T. Ling, Xuefeng B. Burns, Jane C. Novel data-mining approach identifies biomarkers for diagnosis of Kawasaki disease |
title | Novel data-mining approach identifies biomarkers for diagnosis of Kawasaki disease |
title_full | Novel data-mining approach identifies biomarkers for diagnosis of Kawasaki disease |
title_fullStr | Novel data-mining approach identifies biomarkers for diagnosis of Kawasaki disease |
title_full_unstemmed | Novel data-mining approach identifies biomarkers for diagnosis of Kawasaki disease |
title_short | Novel data-mining approach identifies biomarkers for diagnosis of Kawasaki disease |
title_sort | novel data-mining approach identifies biomarkers for diagnosis of kawasaki disease |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4628575/ https://www.ncbi.nlm.nih.gov/pubmed/26237629 http://dx.doi.org/10.1038/pr.2015.137 |
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