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Stratification of Pediatric COVID-19 Cases Using Inflammatory Biomarker Profiling and Machine Learning

While pediatric COVID-19 is rarely severe, a small fraction of children infected with SARS-CoV-2 go on to develop multisystem inflammatory syndrome (MIS-C), with substantial morbidity. An objective method with high specificity and high sensitivity to identify current or imminent MIS-C in children in...

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Autores principales: Subramanian, Devika, Vittala, Aadith, Chen, Xinpu, Julien, Christopher, Acosta, Sebastian, Rusin, Craig, Allen, Carl, Rider, Nicholas, Starosolski, Zbigniew, Annapragada, Ananth, Devaraj, Sridevi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10487951/
https://www.ncbi.nlm.nih.gov/pubmed/37685502
http://dx.doi.org/10.3390/jcm12175435
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author Subramanian, Devika
Vittala, Aadith
Chen, Xinpu
Julien, Christopher
Acosta, Sebastian
Rusin, Craig
Allen, Carl
Rider, Nicholas
Starosolski, Zbigniew
Annapragada, Ananth
Devaraj, Sridevi
author_facet Subramanian, Devika
Vittala, Aadith
Chen, Xinpu
Julien, Christopher
Acosta, Sebastian
Rusin, Craig
Allen, Carl
Rider, Nicholas
Starosolski, Zbigniew
Annapragada, Ananth
Devaraj, Sridevi
author_sort Subramanian, Devika
collection PubMed
description While pediatric COVID-19 is rarely severe, a small fraction of children infected with SARS-CoV-2 go on to develop multisystem inflammatory syndrome (MIS-C), with substantial morbidity. An objective method with high specificity and high sensitivity to identify current or imminent MIS-C in children infected with SARS-CoV-2 is highly desirable. The aim was to learn about an interpretable novel cytokine/chemokine assay panel providing such an objective classification. This retrospective study was conducted on four groups of pediatric patients seen at multiple sites of Texas Children’s Hospital, Houston, TX who consented to provide blood samples to our COVID-19 Biorepository. Standard laboratory markers of inflammation and a novel cytokine/chemokine array were measured in blood samples of all patients. Group 1 consisted of 72 COVID-19, 70 MIS-C and 63 uninfected control patients seen between May 2020 and January 2021 and predominantly infected with pre-alpha variants. Group 2 consisted of 29 COVID-19 and 43 MIS-C patients seen between January and May 2021 infected predominantly with the alpha variant. Group 3 consisted of 30 COVID-19 and 32 MIS-C patients seen between August and October 2021 infected with alpha and/or delta variants. Group 4 consisted of 20 COVID-19 and 46 MIS-C patients seen between October 2021 andJanuary 2022 infected with delta and/or omicron variants. Group 1 was used to train an L1-regularized logistic regression model which was tested using five-fold cross validation, and then separately validated against the remaining naïve groups. The area under receiver operating curve (AUROC) and F1-score were used to quantify the performance of the cytokine/chemokine assay-based classifier. Standard laboratory markers predict MIS-C with a five-fold cross-validated AUROC of 0.86 ± 0.05 and an F1 score of 0.78 ± 0.07, while the cytokine/chemokine panel predicted MIS-C with a five-fold cross-validated AUROC of 0.95 ± 0.02 and an F1 score of 0.91 ± 0.04, with only sixteen of the forty-five cytokines/chemokines sufficient to achieve this performance. Tested on Group 2 the cytokine/chemokine panel yielded AUROC = 0.98 and F1 = 0.93, on Group 3 it yielded AUROC = 0.89 and F1 = 0.89, and on Group 4 AUROC = 0.99 and F1 = 0.97. Adding standard laboratory markers to the cytokine/chemokine panel did not improve performance. A top-10 subset of these 16 cytokines achieves equivalent performance on the validation data sets. Our findings demonstrate that a sixteen-cytokine/chemokine panel as well as the top ten subset provides a highly sensitive, and specific method to identify MIS-C in patients infected with SARS-CoV-2 of all the major variants identified to date.
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spelling pubmed-104879512023-09-09 Stratification of Pediatric COVID-19 Cases Using Inflammatory Biomarker Profiling and Machine Learning Subramanian, Devika Vittala, Aadith Chen, Xinpu Julien, Christopher Acosta, Sebastian Rusin, Craig Allen, Carl Rider, Nicholas Starosolski, Zbigniew Annapragada, Ananth Devaraj, Sridevi J Clin Med Article While pediatric COVID-19 is rarely severe, a small fraction of children infected with SARS-CoV-2 go on to develop multisystem inflammatory syndrome (MIS-C), with substantial morbidity. An objective method with high specificity and high sensitivity to identify current or imminent MIS-C in children infected with SARS-CoV-2 is highly desirable. The aim was to learn about an interpretable novel cytokine/chemokine assay panel providing such an objective classification. This retrospective study was conducted on four groups of pediatric patients seen at multiple sites of Texas Children’s Hospital, Houston, TX who consented to provide blood samples to our COVID-19 Biorepository. Standard laboratory markers of inflammation and a novel cytokine/chemokine array were measured in blood samples of all patients. Group 1 consisted of 72 COVID-19, 70 MIS-C and 63 uninfected control patients seen between May 2020 and January 2021 and predominantly infected with pre-alpha variants. Group 2 consisted of 29 COVID-19 and 43 MIS-C patients seen between January and May 2021 infected predominantly with the alpha variant. Group 3 consisted of 30 COVID-19 and 32 MIS-C patients seen between August and October 2021 infected with alpha and/or delta variants. Group 4 consisted of 20 COVID-19 and 46 MIS-C patients seen between October 2021 andJanuary 2022 infected with delta and/or omicron variants. Group 1 was used to train an L1-regularized logistic regression model which was tested using five-fold cross validation, and then separately validated against the remaining naïve groups. The area under receiver operating curve (AUROC) and F1-score were used to quantify the performance of the cytokine/chemokine assay-based classifier. Standard laboratory markers predict MIS-C with a five-fold cross-validated AUROC of 0.86 ± 0.05 and an F1 score of 0.78 ± 0.07, while the cytokine/chemokine panel predicted MIS-C with a five-fold cross-validated AUROC of 0.95 ± 0.02 and an F1 score of 0.91 ± 0.04, with only sixteen of the forty-five cytokines/chemokines sufficient to achieve this performance. Tested on Group 2 the cytokine/chemokine panel yielded AUROC = 0.98 and F1 = 0.93, on Group 3 it yielded AUROC = 0.89 and F1 = 0.89, and on Group 4 AUROC = 0.99 and F1 = 0.97. Adding standard laboratory markers to the cytokine/chemokine panel did not improve performance. A top-10 subset of these 16 cytokines achieves equivalent performance on the validation data sets. Our findings demonstrate that a sixteen-cytokine/chemokine panel as well as the top ten subset provides a highly sensitive, and specific method to identify MIS-C in patients infected with SARS-CoV-2 of all the major variants identified to date. MDPI 2023-08-22 /pmc/articles/PMC10487951/ /pubmed/37685502 http://dx.doi.org/10.3390/jcm12175435 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Subramanian, Devika
Vittala, Aadith
Chen, Xinpu
Julien, Christopher
Acosta, Sebastian
Rusin, Craig
Allen, Carl
Rider, Nicholas
Starosolski, Zbigniew
Annapragada, Ananth
Devaraj, Sridevi
Stratification of Pediatric COVID-19 Cases Using Inflammatory Biomarker Profiling and Machine Learning
title Stratification of Pediatric COVID-19 Cases Using Inflammatory Biomarker Profiling and Machine Learning
title_full Stratification of Pediatric COVID-19 Cases Using Inflammatory Biomarker Profiling and Machine Learning
title_fullStr Stratification of Pediatric COVID-19 Cases Using Inflammatory Biomarker Profiling and Machine Learning
title_full_unstemmed Stratification of Pediatric COVID-19 Cases Using Inflammatory Biomarker Profiling and Machine Learning
title_short Stratification of Pediatric COVID-19 Cases Using Inflammatory Biomarker Profiling and Machine Learning
title_sort stratification of pediatric covid-19 cases using inflammatory biomarker profiling and machine learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10487951/
https://www.ncbi.nlm.nih.gov/pubmed/37685502
http://dx.doi.org/10.3390/jcm12175435
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