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

Asteris, Panagiotis G. and Gavriilaki, Eleni and Touloumenidou, Tasoula and Koravou, Evaggelia-Evdoxia and Koutra, Maria and Papayanni, Penelope Georgia and Pouleres, Alexandros and Karali, Vassiliki and Lemonis, Minas E. and Mamou, Anna and Skentou, Athanasia D. and Papalexandri, Apostolia and Varelas, Christos and Chatzopoulou, Fani and Chatzidimitriou, Maria and Chatzidimitriou, Dimitrios and Veleni, Anastasia and Rapti, Evdoxia and Kioumis, Ioannis and Kaimakamis, Evaggelos and Bitzani, Milly and Boumpas, Dimitrios and Tsantes, Argyris and Sotiropoulos, Damianos and Papadopoulou, Anastasia and Kalantzis, Ioannis G. and Vallianatou, Lydia A. and Armaghani, Danial J. and Cavaleri, Liborio and Gandomi, Amir H. and Hajihassani, Mohsen and Hasanipanah, Mahdi and Koopialipoor, Mohammadreza and Lourenco, Paulo B. and Samui, Pijush and Zhou, Jian and Sakellari, Ioanna and Valsami, Serena and Politou, Marianna and Kokoris, Styliani and Anagnostopoulos, Achilles (2022) Genetic prediction of ICU hospitalization and mortality in COVID-19 patients using artificial neural networks. Journal of Cellular and Molecular Medicine, 26 (5). pp. 1445-1455. ISSN 1582-1838, DOI

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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.

Item Type: Article
Funders: None
Uncontrolled Keywords: Artificial intelligence; Complement; Complement inhibition; COVID-19; Genetic susceptibility; SARS-CoV2
Subjects: Q Science > QH Natural history > QH301 Biology
Divisions: Faculty of Engineering > Department of Civil Engineering
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 01 Aug 2022 06:44
Last Modified: 01 Aug 2022 06:44

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