SARS-CoV-2 severity prediction in young adults using artificial intelligence.

ABSTRACT

Aim: to build, a predictive model for severe COVID-19 prediction in young adults using deep learning methods. Material(s) and Method(s) data from 906 medical records of patients aged. 18 to 44 years with laboratory-confirmed SARS- CoV-2 infection during 2020-2021 period, was analyzed. Evaluation of laboratory and. instrumental data was carried out using the Mann-Whitney U-test. The level of statistical significance was p<0,05. The neural network was trained, using the Pytorch. framework. Result(s) in patients with mild to moderate SARS-CoV-2 infection, peripheral oxygen saturation, erythrocytes, hemoglobin, total protein, albumin, hematocrit, serum, iron, transferrin, and. absolute peripheral blood, eosinophil and. lymphocyte counts were significantly higher than in patients with severe SOVID-19 (p< 0,001). The values of the absolute number of neutrophils, ESR, glucose, ALT, AST, CPK, urea, LDH, ferritin, CRP, fibrinogen, D-dimer, respiration rate, heart rate, blood, pressure in the group of patients with mild and. moderate severity were statistically significantly lower than in the group of severe patients (p < 0.001). Eleven indicators were identified as predictors of severe COVID-19 (peripheral oxygen level, peripheral blood erythrocyte count, hemoglobin level, absolute eosinophil count, absolute lymphocyte count, absolute neutrophil count, LDH, ferritin, C-reactive protein, D-dimer levels) and. their threshold, values. A model intended, to predict COVID-19 severity in young adults was built. Conclusion. The values of laboratory and instrumental indicators obtained in patients with SARS-CoV-2 infection upon admission significantly differ. Among them, eleven indicators were significantly associated with the development of a severe COVID-19. A predictive model based, on artificial intelligence method, with high, accuracy predicts the likelihood, of severe SARS-CoV-2 course development in young adults.Copyright © 2022 Interregional public organization Association of infectious disease specialists of Saint-Petersburg and Leningrad region (IPO AIDSSPbR). All rights reserved.