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Data-driven clustering identifies features distinguishing multisystem inflammatory syndrome from acute COVID-19 in children and adolescents
BACKGROUND: Multisystem inflammatory syndrome in children (MIS-C) consensus criteria were designed for maximal sensitivity and therefore capture patients with acute COVID-19 pneumonia. METHODS: We performed unsupervised clustering on data from 1,526 patients (684 labeled MIS-C by clinicians) <21...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8405351/ https://www.ncbi.nlm.nih.gov/pubmed/34485878 http://dx.doi.org/10.1016/j.eclinm.2021.101112 |
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author | Geva, Alon Patel, Manish M. Newhams, Margaret M. Young, Cameron C. Son, Mary Beth F. Kong, Michele Maddux, Aline B. Hall, Mark W. Riggs, Becky J. Singh, Aalok R. Giuliano, John S. Hobbs, Charlotte V. Loftis, Laura L. McLaughlin, Gwenn E. Schwartz, Stephanie P. Schuster, Jennifer E. Babbitt, Christopher J. Halasa, Natasha B. Gertz, Shira J. Doymaz, Sule Hume, Janet R. Bradford, Tamara T. Irby, Katherine Carroll, Christopher L. McGuire, John K. Tarquinio, Keiko M. Rowan, Courtney M. Mack, Elizabeth H. Cvijanovich, Natalie Z. Fitzgerald, Julie C. Spinella, Philip C. Staat, Mary A. Clouser, Katharine N. Soma, Vijaya L. Dapul, Heda Maamari, Mia Bowens, Cindy Havlin, Kevin M. Mourani, Peter M. Heidemann, Sabrina M. Horwitz, Steven M. Feldstein, Leora R. Tenforde, Mark W. Newburger, Jane W. Mandl, Kenneth D. Randolph, Adrienne G. |
author_facet | Geva, Alon Patel, Manish M. Newhams, Margaret M. Young, Cameron C. Son, Mary Beth F. Kong, Michele Maddux, Aline B. Hall, Mark W. Riggs, Becky J. Singh, Aalok R. Giuliano, John S. Hobbs, Charlotte V. Loftis, Laura L. McLaughlin, Gwenn E. Schwartz, Stephanie P. Schuster, Jennifer E. Babbitt, Christopher J. Halasa, Natasha B. Gertz, Shira J. Doymaz, Sule Hume, Janet R. Bradford, Tamara T. Irby, Katherine Carroll, Christopher L. McGuire, John K. Tarquinio, Keiko M. Rowan, Courtney M. Mack, Elizabeth H. Cvijanovich, Natalie Z. Fitzgerald, Julie C. Spinella, Philip C. Staat, Mary A. Clouser, Katharine N. Soma, Vijaya L. Dapul, Heda Maamari, Mia Bowens, Cindy Havlin, Kevin M. Mourani, Peter M. Heidemann, Sabrina M. Horwitz, Steven M. Feldstein, Leora R. Tenforde, Mark W. Newburger, Jane W. Mandl, Kenneth D. Randolph, Adrienne G. |
author_sort | Geva, Alon |
collection | PubMed |
description | BACKGROUND: Multisystem inflammatory syndrome in children (MIS-C) consensus criteria were designed for maximal sensitivity and therefore capture patients with acute COVID-19 pneumonia. METHODS: We performed unsupervised clustering on data from 1,526 patients (684 labeled MIS-C by clinicians) <21 years old hospitalized with COVID-19-related illness admitted between 15 March 2020 and 31 December 2020. We compared prevalence of assigned MIS-C labels and clinical features among clusters, followed by recursive feature elimination to identify characteristics of potentially misclassified MIS-C-labeled patients. FINDINGS: Of 94 clinical features tested, 46 were retained for clustering. Cluster 1 patients (N = 498; 92% labeled MIS-C) were mostly previously healthy (71%), with mean age 7·2 ± 0·4 years, predominant cardiovascular (77%) and/or mucocutaneous (82%) involvement, high inflammatory biomarkers, and mostly SARS-CoV-2 PCR negative (60%). Cluster 2 patients (N = 445; 27% labeled MIS-C) frequently had pre-existing conditions (79%, with 39% respiratory), were similarly 7·4 ± 2·1 years old, and commonly had chest radiograph infiltrates (79%) and positive PCR testing (90%). Cluster 3 patients (N = 583; 19% labeled MIS-C) were younger (2·8 ± 2·0 y), PCR positive (86%), with less inflammation. Radiographic findings of pulmonary infiltrates and positive SARS-CoV-2 PCR accurately distinguished cluster 2 MIS-C labeled patients from cluster 1 patients. INTERPRETATION: Using a data driven, unsupervised approach, we identified features that cluster patients into a group with high likelihood of having MIS-C. Other features identified a cluster of patients more likely to have acute severe COVID-19 pulmonary disease, and patients in this cluster labeled by clinicians as MIS-C may be misclassified. These data driven phenotypes may help refine the diagnosis of MIS-C. FUNDING: This work was funded by the US Centers for Disease Control and Prevention (75D30120C07725) and National Institutes of Health (K12HD047349 and R21HD095228). |
format | Online Article Text |
id | pubmed-8405351 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-84053512021-08-31 Data-driven clustering identifies features distinguishing multisystem inflammatory syndrome from acute COVID-19 in children and adolescents Geva, Alon Patel, Manish M. Newhams, Margaret M. Young, Cameron C. Son, Mary Beth F. Kong, Michele Maddux, Aline B. Hall, Mark W. Riggs, Becky J. Singh, Aalok R. Giuliano, John S. Hobbs, Charlotte V. Loftis, Laura L. McLaughlin, Gwenn E. Schwartz, Stephanie P. Schuster, Jennifer E. Babbitt, Christopher J. Halasa, Natasha B. Gertz, Shira J. Doymaz, Sule Hume, Janet R. Bradford, Tamara T. Irby, Katherine Carroll, Christopher L. McGuire, John K. Tarquinio, Keiko M. Rowan, Courtney M. Mack, Elizabeth H. Cvijanovich, Natalie Z. Fitzgerald, Julie C. Spinella, Philip C. Staat, Mary A. Clouser, Katharine N. Soma, Vijaya L. Dapul, Heda Maamari, Mia Bowens, Cindy Havlin, Kevin M. Mourani, Peter M. Heidemann, Sabrina M. Horwitz, Steven M. Feldstein, Leora R. Tenforde, Mark W. Newburger, Jane W. Mandl, Kenneth D. Randolph, Adrienne G. EClinicalMedicine Research Paper BACKGROUND: Multisystem inflammatory syndrome in children (MIS-C) consensus criteria were designed for maximal sensitivity and therefore capture patients with acute COVID-19 pneumonia. METHODS: We performed unsupervised clustering on data from 1,526 patients (684 labeled MIS-C by clinicians) <21 years old hospitalized with COVID-19-related illness admitted between 15 March 2020 and 31 December 2020. We compared prevalence of assigned MIS-C labels and clinical features among clusters, followed by recursive feature elimination to identify characteristics of potentially misclassified MIS-C-labeled patients. FINDINGS: Of 94 clinical features tested, 46 were retained for clustering. Cluster 1 patients (N = 498; 92% labeled MIS-C) were mostly previously healthy (71%), with mean age 7·2 ± 0·4 years, predominant cardiovascular (77%) and/or mucocutaneous (82%) involvement, high inflammatory biomarkers, and mostly SARS-CoV-2 PCR negative (60%). Cluster 2 patients (N = 445; 27% labeled MIS-C) frequently had pre-existing conditions (79%, with 39% respiratory), were similarly 7·4 ± 2·1 years old, and commonly had chest radiograph infiltrates (79%) and positive PCR testing (90%). Cluster 3 patients (N = 583; 19% labeled MIS-C) were younger (2·8 ± 2·0 y), PCR positive (86%), with less inflammation. Radiographic findings of pulmonary infiltrates and positive SARS-CoV-2 PCR accurately distinguished cluster 2 MIS-C labeled patients from cluster 1 patients. INTERPRETATION: Using a data driven, unsupervised approach, we identified features that cluster patients into a group with high likelihood of having MIS-C. Other features identified a cluster of patients more likely to have acute severe COVID-19 pulmonary disease, and patients in this cluster labeled by clinicians as MIS-C may be misclassified. These data driven phenotypes may help refine the diagnosis of MIS-C. FUNDING: This work was funded by the US Centers for Disease Control and Prevention (75D30120C07725) and National Institutes of Health (K12HD047349 and R21HD095228). Elsevier 2021-08-31 /pmc/articles/PMC8405351/ /pubmed/34485878 http://dx.doi.org/10.1016/j.eclinm.2021.101112 Text en © 2021 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Research Paper Geva, Alon Patel, Manish M. Newhams, Margaret M. Young, Cameron C. Son, Mary Beth F. Kong, Michele Maddux, Aline B. Hall, Mark W. Riggs, Becky J. Singh, Aalok R. Giuliano, John S. Hobbs, Charlotte V. Loftis, Laura L. McLaughlin, Gwenn E. Schwartz, Stephanie P. Schuster, Jennifer E. Babbitt, Christopher J. Halasa, Natasha B. Gertz, Shira J. Doymaz, Sule Hume, Janet R. Bradford, Tamara T. Irby, Katherine Carroll, Christopher L. McGuire, John K. Tarquinio, Keiko M. Rowan, Courtney M. Mack, Elizabeth H. Cvijanovich, Natalie Z. Fitzgerald, Julie C. Spinella, Philip C. Staat, Mary A. Clouser, Katharine N. Soma, Vijaya L. Dapul, Heda Maamari, Mia Bowens, Cindy Havlin, Kevin M. Mourani, Peter M. Heidemann, Sabrina M. Horwitz, Steven M. Feldstein, Leora R. Tenforde, Mark W. Newburger, Jane W. Mandl, Kenneth D. Randolph, Adrienne G. Data-driven clustering identifies features distinguishing multisystem inflammatory syndrome from acute COVID-19 in children and adolescents |
title | Data-driven clustering identifies features distinguishing multisystem inflammatory syndrome from acute COVID-19 in children and adolescents |
title_full | Data-driven clustering identifies features distinguishing multisystem inflammatory syndrome from acute COVID-19 in children and adolescents |
title_fullStr | Data-driven clustering identifies features distinguishing multisystem inflammatory syndrome from acute COVID-19 in children and adolescents |
title_full_unstemmed | Data-driven clustering identifies features distinguishing multisystem inflammatory syndrome from acute COVID-19 in children and adolescents |
title_short | Data-driven clustering identifies features distinguishing multisystem inflammatory syndrome from acute COVID-19 in children and adolescents |
title_sort | data-driven clustering identifies features distinguishing multisystem inflammatory syndrome from acute covid-19 in children and adolescents |
topic | Research Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8405351/ https://www.ncbi.nlm.nih.gov/pubmed/34485878 http://dx.doi.org/10.1016/j.eclinm.2021.101112 |
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