Original Article
Electrocardiographic Features and Outcome: Correlations in 124 Hospitalized Patients With COVID-19 and Cardiovascular Events

https://doi.org/10.1053/j.jvca.2022.01.011Get rights and content

Objectives

Electrocardiographic (ECG) changes have been associated with coronavirus disease 2019 (COVID-19) severity. However, the progression of ECG findings in patients with COVID-19 has not been studied. The purpose of this study was to describe ECG features at different stages of COVID-19 cardiovascular (CV) events and to examine the effects of specific ECG parameters and cardiac-related biomarkers on clinical outcomes in COVID-19.

Design

Retrospective, cohort study.

Setting

Major tertiary-care medical centers and community hospitals in Louisville, KY.

Participants

A total of 124 patients with COVID-19 and CV events during hospitalization.

Interventions

None.

Measurements and Main Results

Twelve-lead ECG parameters, biomarkers of cardiac injuries, and clinical outcomes were analyzed with Spearman correlation coefficients and Kruskal-Wallis 1-way analysis of variance. Atrial fibrillation/atrial flutter was more frequent on the ECG obtained at the time of the CV event when compared with admission ECG (9.5% v 26.9%; p = 0.007). Sinus tachycardia was higher in the last available hospital ECG than the CV event ECG (37.5% v 20.4%; p = 0.031). Admission ECG-corrected QT interval was significantly associated with admission troponin levels (R = 0.52; p < 0.001). The last available hospital ECG showed nonsurvivors had longer QRS duration than survivors (114.6 v 91.2 ms; p = 0.026), and higher heart rate was associated with longer intensive care unit length of stay (Spearman ρ = 0.339; p = 0.032).

Conclusions

In hospitalized patients with COVID-19 and CV events, ECGs at various stages of COVID-19 hospitalization showed significantly different features with dissimilar clinical outcome correlations.

Key Words

COVID-19
SARS-CoV-2
cardiovascular events
mortality
electrocardiogram
ECG

Cited by (0)

Research reported in this publication was supported by the National Center for Advancing Translational Sciences of the National Institutes of Health under award number U18TR003787, NIH P30 (P30ES030283) grant, and Gilead Sciences COMMIT COVID-19 RFP Program grant (Gilead IN-US-983-6063). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or Gilead Sciences.

P. Nathala, V. Salunkhe, H. Samanapally, and Q. Xu contributed equally to this article. L. Roser, M. Kong, and J. Huang contributed equally to this article.

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