Surface electrocardiographic characteristics in coronavirus disease 2019: repolarization abnormalities associated
Liang Chen1,2,3, Yi Feng4, Jia Tang1
1Department of Emergency, Huashan Hospital, Fudan University, 12 Middle Urumqi Road, Shanghai, 200040, China.
Insights
Electrocardiogram (ECG) abnormalities, including abnormal T waves and prolonged QTc intervals, are common in COVID-19 patients with cardiac injury. These ECG findings help predict poor outcomes and mortality in patients with coronavirus disease 2019.
Area of Science:
- Cardiology
- Infectious Diseases
- Medical Diagnostics
Background:
- The global spread of coronavirus disease 2019 (COVID-19) has led to significant morbidity and mortality.
- Cardiac involvement is a recognized complication of COVID-19, impacting patient outcomes.
- Electrocardiography (ECG) is a non-invasive tool for assessing cardiac health.
Purpose of the Study:
- To characterize the electrocardiographic (ECG) findings in hospitalized COVID-19 patients.
- To identify specific ECG parameters associated with cardiac involvement in COVID-19.
- To evaluate the predictive value of ECG parameters for clinical outcomes and mortality.
Main Methods:
- Retrospective analysis of 63 hospitalized COVID-19 patients with cardiac biomarker assessment and 12-lead ECGs.
- Comparison of ECG characteristics between patients with and without cardiac injury.
- Logistic and Cox regression models to identify independent predictors of cardiac injury and mortality.
Main Results:
- Patients with cardiac injury were older, had more comorbidities, and experienced higher mortality.
- Prolonged QTc intervals and abnormal T waves were more frequent in patients with cardiac injury.
- Abnormal T waves (OR 2.36) and QTc interval (OR 1.31) were independent indicators of cardiac injury.
- A model combining T wave changes, QTc interval, and age effectively discriminated cardiac injury (AUC 0.881).
- T wave changes independently predicted mortality (HR 3.57).
Conclusions:
- Cardiac injury in COVID-19 patients at admission is linked to adverse clinical outcomes.
- Repolarization abnormalities, specifically abnormal T waves and prolonged QTc intervals, are more prevalent in COVID-19 patients with cardiac involvement.
- These ECG findings can aid in risk stratification for COVID-19 patients, guiding clinical management.
Aims:
The coronavirus disease 2019 (COVID-19) has spread rapidly around the globe, causing significant morbidity and mortality. This study aims to describe electrocardiographic (ECG) characteristics of COVID-19 patients and to identify ECG parameters that are associated with cardiac involvement.
Methods And Results:
The study included patients who were hospitalized with COVID-19 diagnosis and had cardiac biomarker assessments and simultaneous 12-lead surface ECGs. Sixty-three hospitalized patients (median 53 [inter-quartile range, 43-65] years, 76.2% male) were enrolled, including patients with (n = 23) and without (n = 40) cardiac injury. Patients with cardiac injury were older, had more pre-existing co-morbidities, and had higher mortality than those without cardiac injury. They also had prolonged QTc intervals and more T wave changes. Logistic regression model identified that the number of abnormal T waves (odds ratio (OR), 2.36 [95% confidence interval (CI), 1.38-4.04], P = 0.002) and QTc interval (OR, 1.31 [95% CI, 1.03-1.66], P = 0.027) were independent indicators for cardiac injury. The combination model of these two parameters along with age could well discriminate cardiac injury (area the under curve 0.881, P < 0.001) by receiver operating characteristic analysis. Cox regression model identified that the presence of T wave changes was an independent predictor of mortality (hazard ratio, 3.57 [1.40, 9.11], P = 0.008) after adjustment for age.
Conclusions:
In COVID-19 patients, presence of cardiac injury at admission is associated with poor clinical outcomes. Repolarization abnormalities on surface ECG such as abnormal T waves and prolonged QTc intervals are more common in patients with cardiac involvement and can help in further risk stratification.
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