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Identification of Electrocardiographic Patterns Related to Mortality with COVID-19

Authors :
Agnese Sbrollini
Chiara Leoni
Micaela Morettini
Massimo W. Rivolta
Cees A. Swenne
Luca Mainardi
Laura Burattini
Roberto Sassi
Source :
Applied Sciences, Vol 14, Iss 2, p 817 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

COVID-19 is an infectious disease that has greatly affected worldwide healthcare systems, due to the high number of cases and deaths. As COVID-19 patients may develop cardiac comorbidities that can be potentially fatal, electrocardiographic monitoring can be crucial. This work aims to identify electrocardiographic and vectorcardiographic patterns that may be related to mortality in COVID-19, with the application of the Advanced Repeated Structuring and Learning Procedure (AdvRS&LP). The procedure was applied to data from the “automatic computation of cardiovascular arrhythmic risk from electrocardiographic data of COVID-19 patients” (COVIDSQUARED) project to obtain neural networks (NNs) that, through 254 electrocardiographic and vectorcardiographic features, could discriminate between COVID-19 survivors and deaths. The NNs were validated by a five-fold cross-validation procedure and assessed in terms of the area under the curve (AUC) of the receiver operating characteristic. The features’ contribution to the classification was evaluated through the Local-Interpretable Model-Agnostic Explanations (LIME) algorithm. The obtained NNs properly discriminated between COVID-19 survivors and deaths (AUC = 84.31 ± 2.58% on hold-out testing datasets); the classification was mainly affected by the electrocardiographic-interval-related features, thus suggesting that changes in the duration of cardiac electrical activity might be related to mortality in COVID-19 cases.

Details

Language :
English
ISSN :
20763417
Volume :
14
Issue :
2
Database :
Directory of Open Access Journals
Journal :
Applied Sciences
Publication Type :
Academic Journal
Accession number :
edsdoj.6fd74079ad8f4f508744536a66637250
Document Type :
article
Full Text :
https://doi.org/10.3390/app14020817