Application of machine learning algorithms to identify serological predictors of COVID-19 severity and outcomes

Autor: Santosh Dhakal, Anna Yin, Marta Escarra-Senmarti, Zoe O. Demko, Nora Pisanic, Trevor S. Johnston, Maria Isabel Trejo-Zambrano, Kate Kruczynski, John S. Lee, Justin P. Hardick, Patrick Shea, Janna R. Shapiro, Han-Sol Park, Maclaine A. Parish, Christopher Caputo, Abhinaya Ganesan, Sarika K. Mullapudi, Stephen J. Gould, Michael J. Betenbaugh, Andrew Pekosz, Christopher D. Heaney, Annukka A. R. Antar, Yukari C. Manabe, Andrea L. Cox, Andrew H. Karaba, Felipe Andrade, Scott L. Zeger, Sabra L. Klein
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Communications Medicine, Vol 4, Iss 1, Pp 1-15 (2024)
Druh dokumentu: article
ISSN: 2730-664X
DOI: 10.1038/s43856-024-00658-w
Popis: Abstract Background Critically ill hospitalized patients with COVID-19 have greater antibody titers than those with mild to moderate illness, but their association with recovery or death from COVID-19 has not been characterized. Methods In a cohort study of 178 COVID-19 patients, 73 non-hospitalized and 105 hospitalized patients, mucosal swabs and plasma samples were collected at hospital enrollment and up to 3 months post-enrollment (MPE) to measure virus RNA, cytokines/chemokines, binding antibodies, ACE2 binding inhibition, and Fc effector antibody responses against SARS-CoV-2. The association of demographic variables and more than 20 serological antibody measures with intubation or death due to COVID-19 was determined using machine learning algorithms. Results Predictive models reveal that IgG binding and ACE2 binding inhibition responses at 1 MPE are positively and anti-Spike antibody-mediated complement activation at enrollment is negatively associated with an increased probability of intubation or death from COVID-19 within 3 MPE. Conclusions At enrollment, serological antibody measures are more predictive than demographic variables of subsequent intubation or death among hospitalized COVID-19 patients.
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