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

应用机器学习模型识别 COVID-19 严重程度和结果的血清学预测因子

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作者:Sabra Klein, Santosh Dhakal, Anna Yin, Marta Escarra-Senmarti, Zoe Demko, Nora Pisanic, Trevor Johnston, Maria Trejo-Zambrano, Kate Kruczynski, John Lee, Justin Hardick, Patrick Shea, Janna Shapiro, Han-Sol Park, Maclaine Parish, Christopher Caputo, Abhinaya Ganesan, Sarika Mullapudi, Stephen Gould,

Abstract

Critically ill people 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. In 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 >20 serological antibody measures with intubation or death due to COVID-19 was determined using machine learning algorithms. Predictive models revealed that IgG binding and ACE2 binding inhibition responses at 1 MPE were positively and C1q complement activity at enrollment was negatively associated with an increased probability of intubation or death from COVID-19 within 3 MPE. Serological antibody measures were more predictive than demographic variables of intubation or death among COVID-19 patients.

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