Abstract
There is an unmet need of models for early prediction of morbidity and mortality of Coronavirus disease-19 (COVID-19). We aimed to a) identify complement-related genetic variants associated with the clinical outcomes of ICU hospitalization and death, b) develop an artificial neural network (ANN) predicting these outcomes and c) validate whether complement-related variants are associated with an impaired complement phenotype. We prospectively recruited consecutive adult patients of Caucasian origin, hospitalized due to COVID-19. Through targeted next-generation sequencing, we identified variants in complement factor H/CFH, CFB, CFH-related, CFD, CD55, C3, C5, CFI, CD46, thrombomodulin/THBD, and A Disintegrin and Metalloproteinase with Thrombospondin motifs (ADAMTS13). Among 381 variants in 133 patients, we identified 5 critical variants associated with severe COVID-19: rs2547438 (C3), rs2250656 (C3), rs1042580 (THBD), rs800292 (CFH) and rs414628 (CFHR1). Using age, gender and presence or absence of each variant, we developed an ANN predicting morbidity and mortality in 89.47% of the examined population. Furthermore, THBD and C3a levels were significantly increased in severe COVID-19 patients and those harbouring relevant variants. Thus, we reveal for the first time an ANN accurately predicting ICU hospitalization and death in COVID-19 patients, based on genetic variants in complement genes, age and gender. Importantly, we confirm that genetic dysregulation is associated with impaired complement phenotype.
Keywords: COVID-19; SARS-CoV2; artificial intelligence; complement; complement inhibition; genetic susceptibility.
【저자키워드】 COVID-19, SARS-CoV2, artificial intelligence, complement, complement inhibition, genetic susceptibility., 【초록키워드】 coronavirus disease, Hospitalized, severe COVID-19, thrombomodulin, Hospitalization, artificial intelligence, Genetic, variant, Gender, complement, outcome, variants, clinical outcomes, ICU, Clinical outcome, Artificial neural network, ADAMTS13, Next-generation sequencing, morbidity, Patient, death, phenotype, genetic susceptibility, age, morbidity and mortality, genetic variants, Genetic variant, Critical, patients, COVID-19 patients, ANN, dysregulation, presence or absence, unmet need, severe COVID-19 patients, motif, CFHR1, metalloproteinase, CD46, Thrombospondin, CD55, Genes, disintegrin, THBD, significantly increased, identify, develop, examined, recruited, absence, CFH, age and gender, C3a, CFB, CFD, CFI, death in COVID-19, identified variant, severe COVID-19 patient, 【제목키워드】 Mortality, Hospitalization, ICU, COVID-19 patient,