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

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dc.citation.articleNumber5435en_US
dc.citation.issueNumber17en_US
dc.citation.journalTitleJournal of Clinical Medicineen_US
dc.citation.volumeNumber12en_US
dc.contributor.authorSubramanian, Devikaen_US
dc.contributor.authorVittala, Aadithen_US
dc.contributor.authorChen, Xinpuen_US
dc.contributor.authorJulien, Christopheren_US
dc.contributor.authorAcosta, Sebastianen_US
dc.contributor.authorRusin, Craigen_US
dc.contributor.authorAllen, Carlen_US
dc.contributor.authorRider, Nicholasen_US
dc.contributor.authorStarosolski, Zbigniewen_US
dc.contributor.authorAnnapragada, Ananthen_US
dc.contributor.authorDevaraj, Sridevien_US
dc.date.accessioned2024-05-03T15:51:11Zen_US
dc.date.available2024-05-03T15:51:11Zen_US
dc.date.issued2023en_US
dc.description.abstractWhile 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.en_US
dc.identifier.citationSubramanian, D., Vittala, A., Chen, X., Julien, C., Acosta, S., Rusin, C., Allen, C., Rider, N., Starosolski, Z., Annapragada, A., & Devaraj, S. (2023). Stratification of Pediatric COVID-19 Cases Using Inflammatory Biomarker Profiling and Machine Learning. Journal of Clinical Medicine, 12(17), Article 17. https://doi.org/10.3390/jcm12175435en_US
dc.identifier.digitaljcm-12-05435en_US
dc.identifier.doihttps://doi.org/10.3390/jcm12175435en_US
dc.identifier.urihttps://hdl.handle.net/1911/115557en_US
dc.language.isoengen_US
dc.publisherMDPIen_US
dc.rightsExcept where otherwise noted, this work is licensed under a Creative Commons Attribution (CC BY) license. Permission to reuse, publish, or reproduce the work beyond the terms of the license or beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.titleStratification of Pediatric COVID-19 Cases Using Inflammatory Biomarker Profiling and Machine Learningen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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