Electrocardiographic signs of cardiovascular system dysfunction in the context of various courses of COVID-19 in young and middle-aged individuals

Abstract

BACKGROUD: Intensive monitoring of patients with the coronavirus disease should include functional monitoring of the cardiovascular system. Physiologic methods can detect dysfunction of the cardiovascular system, however, it is not always possible to identify initial or hidden dysfunction. Therefore, clinical data should be complemented with results from instrumental methods, particularly electrocardiography.

AIM: To identify preclinical electrocardiography signs of cardiovascular system dysfunction in the context of various courses of coronavirus disease in young and middle-aged individuals.

MATERIALS AND METHODS: A retrospective analysis of 337 medical records of young and middle-aged individuals (40.0 ± 13.3 years old) was conducted. The analysis of the medical records included electrocardiograms in 12 leads with the assessment of standard parameters, results of laboratory and instrumental tests. In order to identify the main factors predicting an unfavorable course of the coronavirus disease, a factor analysis was conducted using electrocardiography parameters. Logistic regression analysis was used to develop a statistical model for predicting the likelihood of a severe course and fatal outcome of COVID-19, followed by assessing the diagnostic value of the prognostic model using the ROC curve and determining the area under the curve.

RESULTS: The main factors predicting an unfavorable course of the new coronavirus disease in young and middle-aged individuals based on electrocardiography data include the α angle factor, the ventricular repolarization factor and the heart rate factor. In addition, as electrocardiography predictors, the elongation of the P wave and PQ interval, the amplitude of the R wave in lead one, the decrease in the T wave amplitude in the precordial leads, and the amplitude of the R wave in leads two and three can be considered. Based on the identified electrocardiography predictors, a mathematical model for predicting a severe course and fatal outcome of the new coronavirus disease was developed.

CONCLUSIONS: Thus, electrocardiography is an indicative method that can be influenced by infectious process which is performed for all individuals upon admission to the hospital, allowing this examination to be considered the most accessible for detecting preclinical signs of an unfavorable course of COVID-19.

Keywords

coronavirus disease / T wave morphology changes / alpha angle changes / cardiovascular complications / electrocardiography in COVID-19

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