Application of machine learning algorithms to identify serological predictors of COVID-19 severity and outcomes - 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; et al. (Communications Medicine)
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.