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

Asteris, PG; Gavriilaki, E; Touloumenidou, T; Koravou, E-E; Koutra, M; Papayanni, PG; Pouleres, A; Karali, V; Lemonis, ME; Mamou, A; Skentou, AD; Papalexandri, A; Varelas, C; Chatzopoulou, F; Chatzidimitriou, M; Chatzidimitriou, D; Veleni, A; Rapti, E; Kioumis, I; Kaimakamis, E; Bitzani, M; Boumpas, D; Tsantes, A; Sotiropoulos, D; Papadopoulou, A; Kalantzis, IG; Vallianatou, LA; Armaghani, DJ; Cavaleri, L; Gandomi, AH; Hajihassani, M; Hasanipanah, M; Koopialipoor, M; Lourenço, PB; Samui, P; Zhou, J; Sakellari, I; Valsami, S; Politou, M; Kokoris, S; Anagnostopoulos, A

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

Asteris, PG Gavriilaki, E Touloumenidou, T Koravou, E-E Koutra, M Papayanni, PG Pouleres, A Karali, V Lemonis, ME Mamou, A Skentou, AD Papalexandri, A Varelas, C Chatzopoulou, F Chatzidimitriou, M Chatzidimitriou, D Veleni, A Rapti, E Kioumis, I Kaimakamis, E Bitzani, M Boumpas, D Tsantes, A Sotiropoulos, D Papadopoulou, A Kalantzis, IG Vallianatou, LA Armaghani, DJ Cavaleri, L Gandomi, AH Hajihassani, M Hasanipanah, M Koopialipoor, M Lourenço, PB Samui, P Zhou, J Sakellari, I Valsami, S Politou, M Kokoris, S Anagnostopoulos, A

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Publisher:: WILEY
Publication Type:: Journal Article
Citation:: J Cell Mol Med, 2022, 26, (5), pp. 1445-1455
Issue Date:: 2022-03

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Field	Value	Language
dc.contributor.author	Asteris, PG
dc.contributor.author	Gavriilaki, E
dc.contributor.author	Touloumenidou, T
dc.contributor.author	Koravou, E-E
dc.contributor.author	Koutra, M
dc.contributor.author	Papayanni, PG
dc.contributor.author	Pouleres, A
dc.contributor.author	Karali, V
dc.contributor.author	Lemonis, ME
dc.contributor.author	Mamou, A
dc.contributor.author	Skentou, AD
dc.contributor.author	Papalexandri, A
dc.contributor.author	Varelas, C
dc.contributor.author	Chatzopoulou, F
dc.contributor.author	Chatzidimitriou, M
dc.contributor.author	Chatzidimitriou, D
dc.contributor.author	Veleni, A
dc.contributor.author	Rapti, E
dc.contributor.author	Kioumis, I
dc.contributor.author	Kaimakamis, E
dc.contributor.author	Bitzani, M
dc.contributor.author	Boumpas, D
dc.contributor.author	Tsantes, A
dc.contributor.author	Sotiropoulos, D
dc.contributor.author	Papadopoulou, A
dc.contributor.author	Kalantzis, IG
dc.contributor.author	Vallianatou, LA
dc.contributor.author	Armaghani, DJ
dc.contributor.author	Cavaleri, L
dc.contributor.author	Gandomi, AH
dc.contributor.author	Hajihassani, M
dc.contributor.author	Hasanipanah, M
dc.contributor.author	Koopialipoor, M
dc.contributor.author	Lourenço, PB
dc.contributor.author	Samui, P
dc.contributor.author	Zhou, J
dc.contributor.author	Sakellari, I
dc.contributor.author	Valsami, S
dc.contributor.author	Politou, M
dc.contributor.author	Kokoris, S
dc.contributor.author	Anagnostopoulos, A
dc.date.accessioned	2023-03-13T22:48:15Z
dc.date.available	2021-11-23
dc.date.available	2023-03-13T22:48:15Z
dc.date.issued	2022-03
dc.identifier.citation	J Cell Mol Med, 2022, 26, (5), pp. 1445-1455
dc.identifier.issn	1582-1838
dc.identifier.issn	1582-4934
dc.identifier.uri	http://hdl.handle.net/10453/167183
dc.description.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.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	WILEY
dc.relation.ispartof	J Cell Mol Med
dc.relation.isbasedon	10.1111/jcmm.17098
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0304 Medicinal and Biomolecular Chemistry, 0601 Biochemistry and Cell Biology, 1103 Clinical Sciences
dc.subject.classification	Biochemistry & Molecular Biology
dc.subject.mesh	COVID-19
dc.subject.mesh	Complement Activation
dc.subject.mesh	Complement Factor H
dc.subject.mesh	Complement System Proteins
dc.subject.mesh	Female
dc.subject.mesh	Greece
dc.subject.mesh	Hospitalization
dc.subject.mesh	Humans
dc.subject.mesh	Intensive Care Units
dc.subject.mesh	Male
dc.subject.mesh	Middle Aged
dc.subject.mesh	Models, Genetic
dc.subject.mesh	Morbidity
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Polymorphism, Single Nucleotide
dc.subject.mesh	Thrombomodulin
dc.subject.mesh	Humans
dc.subject.mesh	Complement Factor H
dc.subject.mesh	Thrombomodulin
dc.subject.mesh	Hospitalization
dc.subject.mesh	Morbidity
dc.subject.mesh	Complement Activation
dc.subject.mesh	Polymorphism, Single Nucleotide
dc.subject.mesh	Models, Genetic
dc.subject.mesh	Middle Aged
dc.subject.mesh	Intensive Care Units
dc.subject.mesh	Greece
dc.subject.mesh	Complement System Proteins
dc.subject.mesh	Female
dc.subject.mesh	Male
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	COVID-19
dc.subject.mesh	COVID-19
dc.subject.mesh	Complement Activation
dc.subject.mesh	Complement Factor H
dc.subject.mesh	Complement System Proteins
dc.subject.mesh	Female
dc.subject.mesh	Greece
dc.subject.mesh	Hospitalization
dc.subject.mesh	Humans
dc.subject.mesh	Intensive Care Units
dc.subject.mesh	Male
dc.subject.mesh	Middle Aged
dc.subject.mesh	Models, Genetic
dc.subject.mesh	Morbidity
dc.subject.mesh	Neural Networks, Computer
dc.subject.mesh	Polymorphism, Single Nucleotide
dc.subject.mesh	Thrombomodulin
dc.title	Genetic prediction of ICU hospitalization and mortality in COVID-19 patients using artificial neural networks.
dc.type	Journal Article
utslib.citation.volume	26
utslib.location.activity	England
utslib.for	0304 Medicinal and Biomolecular Chemistry
utslib.for	0601 Biochemistry and Cell Biology
utslib.for	1103 Clinical Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	open_access	*
dc.date.updated	2023-03-13T22:48:11Z
pubs.issue	5
pubs.publication-status	Published
pubs.volume	26
utslib.citation.issue	5

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.

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http://hdl.handle.net/10453/167183