Genetic prediction of ICU hospitalization and mortality in COVID-19
patients using artificial neural networks
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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