Cardiovascular and renal comorbidities included into neural networks predict the outcome in COVID-19 patients admitted to an intensive care unit: three-center, cross-validation, age- and sex-matched study
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- dc.contributor.author Ovcharenko, Evgeny A.
- dc.contributor.author Kutikhin, Anton G.
- dc.contributor.author Gruzdeva, Olga
- dc.contributor.author Kuzmina, Anastasia
- dc.contributor.author Slesareva, Tamara
- dc.contributor.author Brusina, Elena
- dc.contributor.author Kudasheva, Svetlana
- dc.contributor.author Bondarenko, Tatiana
- dc.contributor.author Kuzmenko, Svetlana
- dc.contributor.author Osyaev, Nikolay
- dc.date.accessioned 2025-11-04T06:43:28Z
- dc.date.available 2025-11-04T06:43:28Z
- dc.date.issued 2023
- dc.description.abstract Here, we performed a multicenter, age- and sex-matched study to compare the efficiency of various machine learning algorithms in the prediction of COVID-19 fatal outcomes and to develop sensitive, specific, and robust artificial intelligence tools for the prompt triage of patients with severe COVID-19 in the intensive care unit setting. In a challenge against other established machine learning algorithms (decision trees, random forests, extra trees, neural networks, k-nearest neighbors, and gradient boosting: XGBoost, LightGBM, and CatBoost) and multivariate logistic regression as a reference, neural networks demonstrated the highest sensitivity, sufficient specificity, and excellent robustness. Further, neural networks based on coronary artery disease/chronic heart failure, stage 3–5 chronic kidney disease, blood urea nitrogen, and C-reactive protein as the predictors exceeded 90% sensitivity and 80% specificity, reaching AUROC of 0.866 at primary cross-validation and 0.849 at secondary cross-validation on virtual samples generated by the bootstrapping procedure. These results underscore the impact of cardiovascular and renal comorbidities in the context of thrombotic complications characteristic of severe COVID-19. As aforementioned predictors can be obtained from the case histories or are inexpensive to be measured at admission to the intensive care unit, we suggest this predictor composition is useful for the triage of critically ill COVID-19 patients.en
- dc.format.mimetype application/pdf
- dc.identifier.citation Ovcharenko E, Kutikhin A, Gruzdeva O, Kuzmina A, Slesareva T, Brusina E, et al. Cardiovascular and renal comorbidities included into neural networks predict the outcome in COVID-19 patients admitted to an intensive care unit: three-center, cross-validation, age- and sex-matched study. J Cardiovasc Dev Dis. 2023 Jan 23;10(2):39. DOI: 10.3390/jcdd10020039
- dc.identifier.doi http://dx.doi.org/10.3390/jcdd10020039
- dc.identifier.issn 2308-3425
- dc.identifier.uri http://hdl.handle.net/10230/71741
- dc.language.iso eng
- dc.publisher MDPI
- dc.relation.ispartof Journal of Cardiovascular Development and Disease. 2023 Jan 23;10(2):39
- dc.rights © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
- dc.rights.accessRights info:eu-repo/semantics/openAccess
- dc.rights.uri http://creativecommons.org/licenses/by/4.0/
- dc.subject.keyword COVID-19en
- dc.subject.keyword Machine learningen
- dc.subject.keyword Neural networksen
- dc.subject.keyword Prognosticationen
- dc.subject.keyword Coronary artery diseaseen
- dc.subject.keyword Chronic kidney diseaseen
- dc.subject.keyword Blood urea nitrogenen
- dc.subject.keyword C-reactive proteinen
- dc.subject.keyword Lymphocyte counten
- dc.subject.keyword Neutrophil-to-lymphocyte ratioen
- dc.title Cardiovascular and renal comorbidities included into neural networks predict the outcome in COVID-19 patients admitted to an intensive care unit: three-center, cross-validation, age- and sex-matched studyen
- dc.type info:eu-repo/semantics/article
- dc.type.version info:eu-repo/semantics/publishedVersion