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Longitudinally monitored immune biomarkers predict the timing of COVID-19 outcomes

The clinical outcome of SARS-CoV-2 infection varies widely between individuals. Machine learning models can support decision making in healthcare by assessing fatality risk in patients that do not yet show severe signs of COVID-19. Most predictive models rely on static demographic features and clini...

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Autores principales: Lasso, Gorka, Khan, Saad, Allen, Stephanie A., Mariano, Margarette, Florez, Catalina, Orner, Erika P., Quiroz, Jose A., Quevedo, Gregory, Massimi, Aldo, Hegde, Aditi, Wirchnianski, Ariel S., Bortz, Robert H., Malonis, Ryan J., Georgiev, George I., Tong, Karen, Herrera, Natalia G., Morano, Nicholas C., Garforth, Scott J., Malaviya, Avinash, Khokhar, Ahmed, Laudermilch, Ethan, Dieterle, M. Eugenia, Fels, J. Maximilian, Haslwanter, Denise, Jangra, Rohit K., Barnhill, Jason, Almo, Steven C., Chandran, Kartik, Lai, Jonathan R., Kelly, Libusha, Daily, Johanna P., Vergnolle, Olivia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8812869/
https://www.ncbi.nlm.nih.gov/pubmed/35041647
http://dx.doi.org/10.1371/journal.pcbi.1009778
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author Lasso, Gorka
Khan, Saad
Allen, Stephanie A.
Mariano, Margarette
Florez, Catalina
Orner, Erika P.
Quiroz, Jose A.
Quevedo, Gregory
Massimi, Aldo
Hegde, Aditi
Wirchnianski, Ariel S.
Bortz, Robert H.
Malonis, Ryan J.
Georgiev, George I.
Tong, Karen
Herrera, Natalia G.
Morano, Nicholas C.
Garforth, Scott J.
Malaviya, Avinash
Khokhar, Ahmed
Laudermilch, Ethan
Dieterle, M. Eugenia
Fels, J. Maximilian
Haslwanter, Denise
Jangra, Rohit K.
Barnhill, Jason
Almo, Steven C.
Chandran, Kartik
Lai, Jonathan R.
Kelly, Libusha
Daily, Johanna P.
Vergnolle, Olivia
author_facet Lasso, Gorka
Khan, Saad
Allen, Stephanie A.
Mariano, Margarette
Florez, Catalina
Orner, Erika P.
Quiroz, Jose A.
Quevedo, Gregory
Massimi, Aldo
Hegde, Aditi
Wirchnianski, Ariel S.
Bortz, Robert H.
Malonis, Ryan J.
Georgiev, George I.
Tong, Karen
Herrera, Natalia G.
Morano, Nicholas C.
Garforth, Scott J.
Malaviya, Avinash
Khokhar, Ahmed
Laudermilch, Ethan
Dieterle, M. Eugenia
Fels, J. Maximilian
Haslwanter, Denise
Jangra, Rohit K.
Barnhill, Jason
Almo, Steven C.
Chandran, Kartik
Lai, Jonathan R.
Kelly, Libusha
Daily, Johanna P.
Vergnolle, Olivia
author_sort Lasso, Gorka
collection PubMed
description The clinical outcome of SARS-CoV-2 infection varies widely between individuals. Machine learning models can support decision making in healthcare by assessing fatality risk in patients that do not yet show severe signs of COVID-19. Most predictive models rely on static demographic features and clinical values obtained upon hospitalization. However, time-dependent biomarkers associated with COVID-19 severity, such as antibody titers, can substantially contribute to the development of more accurate outcome models. Here we show that models trained on immune biomarkers, longitudinally monitored throughout hospitalization, predicted mortality and were more accurate than models based on demographic and clinical data upon hospital admission. Our best-performing predictive models were based on the temporal analysis of anti-SARS-CoV-2 Spike IgG titers, white blood cell (WBC), neutrophil and lymphocyte counts. These biomarkers, together with C-reactive protein and blood urea nitrogen levels, were found to correlate with severity of disease and mortality in a time-dependent manner. Shapley additive explanations of our model revealed the higher predictive value of day post-symptom onset (PSO) as hospitalization progresses and showed how immune biomarkers contribute to predict mortality. In sum, we demonstrate that the kinetics of immune biomarkers can inform clinical models to serve as a powerful monitoring tool for predicting fatality risk in hospitalized COVID-19 patients, underscoring the importance of contextualizing clinical parameters according to their time post-symptom onset.
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spelling pubmed-88128692022-02-04 Longitudinally monitored immune biomarkers predict the timing of COVID-19 outcomes Lasso, Gorka Khan, Saad Allen, Stephanie A. Mariano, Margarette Florez, Catalina Orner, Erika P. Quiroz, Jose A. Quevedo, Gregory Massimi, Aldo Hegde, Aditi Wirchnianski, Ariel S. Bortz, Robert H. Malonis, Ryan J. Georgiev, George I. Tong, Karen Herrera, Natalia G. Morano, Nicholas C. Garforth, Scott J. Malaviya, Avinash Khokhar, Ahmed Laudermilch, Ethan Dieterle, M. Eugenia Fels, J. Maximilian Haslwanter, Denise Jangra, Rohit K. Barnhill, Jason Almo, Steven C. Chandran, Kartik Lai, Jonathan R. Kelly, Libusha Daily, Johanna P. Vergnolle, Olivia PLoS Comput Biol Research Article The clinical outcome of SARS-CoV-2 infection varies widely between individuals. Machine learning models can support decision making in healthcare by assessing fatality risk in patients that do not yet show severe signs of COVID-19. Most predictive models rely on static demographic features and clinical values obtained upon hospitalization. However, time-dependent biomarkers associated with COVID-19 severity, such as antibody titers, can substantially contribute to the development of more accurate outcome models. Here we show that models trained on immune biomarkers, longitudinally monitored throughout hospitalization, predicted mortality and were more accurate than models based on demographic and clinical data upon hospital admission. Our best-performing predictive models were based on the temporal analysis of anti-SARS-CoV-2 Spike IgG titers, white blood cell (WBC), neutrophil and lymphocyte counts. These biomarkers, together with C-reactive protein and blood urea nitrogen levels, were found to correlate with severity of disease and mortality in a time-dependent manner. Shapley additive explanations of our model revealed the higher predictive value of day post-symptom onset (PSO) as hospitalization progresses and showed how immune biomarkers contribute to predict mortality. In sum, we demonstrate that the kinetics of immune biomarkers can inform clinical models to serve as a powerful monitoring tool for predicting fatality risk in hospitalized COVID-19 patients, underscoring the importance of contextualizing clinical parameters according to their time post-symptom onset. Public Library of Science 2022-01-18 /pmc/articles/PMC8812869/ /pubmed/35041647 http://dx.doi.org/10.1371/journal.pcbi.1009778 Text en https://creativecommons.org/publicdomain/zero/1.0/This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 (https://creativecommons.org/publicdomain/zero/1.0/) public domain dedication.
spellingShingle Research Article
Lasso, Gorka
Khan, Saad
Allen, Stephanie A.
Mariano, Margarette
Florez, Catalina
Orner, Erika P.
Quiroz, Jose A.
Quevedo, Gregory
Massimi, Aldo
Hegde, Aditi
Wirchnianski, Ariel S.
Bortz, Robert H.
Malonis, Ryan J.
Georgiev, George I.
Tong, Karen
Herrera, Natalia G.
Morano, Nicholas C.
Garforth, Scott J.
Malaviya, Avinash
Khokhar, Ahmed
Laudermilch, Ethan
Dieterle, M. Eugenia
Fels, J. Maximilian
Haslwanter, Denise
Jangra, Rohit K.
Barnhill, Jason
Almo, Steven C.
Chandran, Kartik
Lai, Jonathan R.
Kelly, Libusha
Daily, Johanna P.
Vergnolle, Olivia
Longitudinally monitored immune biomarkers predict the timing of COVID-19 outcomes
title Longitudinally monitored immune biomarkers predict the timing of COVID-19 outcomes
title_full Longitudinally monitored immune biomarkers predict the timing of COVID-19 outcomes
title_fullStr Longitudinally monitored immune biomarkers predict the timing of COVID-19 outcomes
title_full_unstemmed Longitudinally monitored immune biomarkers predict the timing of COVID-19 outcomes
title_short Longitudinally monitored immune biomarkers predict the timing of COVID-19 outcomes
title_sort longitudinally monitored immune biomarkers predict the timing of covid-19 outcomes
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8812869/
https://www.ncbi.nlm.nih.gov/pubmed/35041647
http://dx.doi.org/10.1371/journal.pcbi.1009778
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