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ORIGINAL RESEARCH article
Front. Artif. Intell.
Sec. Medicine and Public Health
Volume 8 - 2025 | doi: 10.3389/frai.2025.1561079
This article is part of the Research Topic Outbreak Oracles: How AI's Journey through COVID-19 Shapes Future Epidemic Strategy View all 7 articles
Subtle Changes on Electrocardiogram in Severe Patients with COVID-19 may be Predictors of Treatment Outcome
Provisionally accepted
Illya Chaikovsky 1,2
Dmytro Dziuba 2
Olga Kryvova 3 Katerina Marushko 4 Julia Vakulenko 4 Оleg Loskutov 2,4
Kyrylo Serhiiovych Malakhov 1*- 1 V. M. Glushkov Institute of Cybernetics of the NAS of Ukraine, Kyiv, Ukraine
- 2 Shupyk National Medical Academy of Postgraduate Education, Kyiv, Ukraine
- 3 International Research and Training Center of the National Academy of Sciences, Kyiv, Ukraine
- 4 Kyiv City Clinical Hospital № 4, Kyiv, Ukraine
Background: Two years after the COVID-19 pandemic, it became known that one of the complications of this disease is myocardial injury. Electrocardiography (ECG) and cardiac biomarkers play a vital role in the early detection of cardiovascular complications and risk stratification. The study aimed to investigate the value of a new electrocardiographic metric for detecting minor myocardial injury in patients during COVID-19 treatment.Methods: The study was conducted in 2021. A group of 26 patients with verified COVID-19 diagnosis admitted to the intensive care unit for infectious diseases was examined. The severity of a patient's condition was calculated using the NEWS score. The digital ECGs were repeatedly recorded (at the beginning and 2 to 4 times during the treatment). 240 primary and composite ECG parameters were analyzed for each electrocardiogram. Among these patients, 6 patients died during treatment. Cluster analysis was used to identify subgroups of patients that differed significantly in terms of disease severity (NEWS), SрО2and integral ECG index (an indicator of the state of the cardiovascular system).Results: Using analysis of variance (ANOVA repeated measures), a statistical assessment of changes of indicators in subgroups at the end of treatment was given. These subgroup differences persisted at the end of the treatment. To identify potential predictors of mortality, critical clinical and ECG parameters of surviving(S) and non-surviving patients (D) were compared using parametric and non-Deleted: keyword4,
Keywords: electrocardiography1, myocardial injury2, severity3, Mortality4, COVID-195, machine learning6, cloud computing7
Received: 15 Jan 2025; Accepted: 27 Feb 2025.
Copyright: © 2025 Chaikovsky, Dziuba, Kryvova, Marushko, Vakulenko, Loskutov and Malakhov. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Kyrylo Serhiiovych Malakhov, V. M. Glushkov Institute of Cybernetics of the NAS of Ukraine, Kyiv, Ukraine
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