Application of machine learning models to identify serological predictors of COVID-19 severity and outcomes.
Sabra L KleinSantosh DhakalAnna YinMarta Escarra-SenmartiZoe DemkoNora PisanicTrevor S JohnstonMaria Trejo-ZambranoKate KruczynskiJohn LeeJustin HardickPatrick SheaJanna ShapiroHan-Sol ParkMaclaine ParishChristopher CaputoAbhinaya GanesanSarika MullapudiStephen GouldMichael BetenbaughAndrew S PekoszChristopher D HeaneyAnnukka AntarYukari C ManabeAndrea L CoxAndrew H KarabaFelipe AndradeScott ZegerPublished in: Research square (2023)
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.