Identification of Electrocardiographic Patterns Related to Mortality with COVID-19

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 elect...

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Veröffentlicht in:Applied sciences Jg. 14; H. 2; S. 817
Hauptverfasser: Sbrollini, Agnese, Leoni, Chiara, Morettini, Micaela, Rivolta, Massimo W., Swenne, Cees A., Mainardi, Luca, Burattini, Laura, Sassi, Roberto
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.01.2024
Schlagworte:
Advanced Repeated Structuring and Learning Procedure
Algorithms
Cardiac patients
Cardiology
Clinical medicine
COVID-19
Deep learning
Disease transmission
Electric properties
Electrocardiography
Health aspects
Heart failure
Hospitalization
Infections
Ischemia
local-interpretable model-agnostic explanations
Machine learning
Morphology
Mortality
neural network
Neural networks
Neurons
Pandemics
Respiratory system
ISSN:2076-3417, 2076-3417
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2076-3417
2076-3417
DOI:10.3390/app14020817