Cardiac arrhythmias amongst hospitalised Coronavirus 2019 (COVID-19) patients: Prevalence, characterisation, and
Moshe Rav-Acha1, Amir Orlev1, Itay Itzhaki2
1Cardiology Department, Shaare Zedek Hospital, affiliated to the Hebrew University, Jerusalem, Israel.
Insights
COVID-19 patients with severe disease or heart failure are at higher risk for developing arrhythmias like atrial fibrillation (AF). A new risk classification tree helps identify high-risk patients.
Area of Science:
- Cardiology
- Infectious Diseases
- Critical Care Medicine
Background:
- Cardiac involvement, including arrhythmias, is common in COVID-19 patients.
- Existing data on arrhythmia prevalence and predictors in COVID-19 is conflicting.
- Clinically applicable risk stratification tools for arrhythmias in COVID-19 are needed.
Purpose of the Study:
- To determine the prevalence of arrhythmias in hospitalized COVID-19 patients.
- To identify predictors of new-onset arrhythmias.
- To develop a risk classification algorithm for arrhythmias in COVID-19.
Main Methods:
- Single-center cohort study of 390 hospitalized COVID-19 patients.
- Arrhythmia detection via daily exams, ECG, Holter monitoring, and continuous monitoring.
- Multivariate analysis and classification trees to identify predictors and stratify risk.
Main Results:
- 7.2% of patients developed arrhythmias; atrial fibrillation was most common.
- Arrhythmia prevalence significantly correlated with disease severity (P < .001).
- Independent predictors for overall arrhythmia included heart failure and disease severity; tachyarrhythmias were associated with age, CHF, severity, and symptoms.
Conclusions:
- Atrial fibrillation is the dominant arrhythmia in COVID-19.
- Arrhythmia risk is associated with age, disease severity, heart failure, and troponin levels.
- A novel classification tree effectively stratifies COVID-19 patients into high and low arrhythmic risk groups.
Objectives:
A significant proportion of COVID-19 patients may have cardiac involvement including arrhythmias. Although arrhythmia characterisation and possible predictors were previously reported, there are conflicting data regarding the exact prevalence of arrhythmias. Clinically applicable algorithms to classify COVID patients' arrhythmic risk are still lacking, and are the aim of our study.
Methods:
We describe a single-centre cohort of hospitalised patients with a positive nasopharyngeal swab for COVID-19 during the initial Israeli outbreak between 1/2/2020 and 30/5/2020. The study's outcome was any documented arrhythmia during hospitalisation, based on daily physical examination, routine ECG's, periodic 24-hour Holter, and continuous monitoring. Multivariate analysis was used to find predictors for new arrhythmias and create classification trees for discriminating patients with high and low arrhythmic risk.
Results:
Out of 390 COVID-19 patients included, 28 (7.2%) had documented arrhythmias during hospitalisation, including 23 atrial tachyarrhythmias, combined atrial fibrillation (AF), and ventricular fibrillation, ventricular tachycardia storm, and 3 bradyarrhythmias. Only 7/28 patients had previous arrhythmias. Our study showed a significant correlation between disease severity and arrhythmia prevalence (P < .001) with a low arrhythmic prevalence amongst mild disease patients (2%). Multivariate analysis revealed background heart failure (CHF) and disease severity are independently associated with overall arrhythmia while age, CHF, disease severity, and arrhythmic symptoms are associated with tachyarrhythmias. A novel decision tree using age, disease severity, CHF, and troponin levels was created to stratify patients into high and low risk for developing arrhythmia.
Conclusions:
Dominant arrhythmia amongst COVID-19 patients is AF. Arrhythmia prevalence is associated with age, disease severity, CHF, and troponin levels. A novel simple Classification tree, based on these parameters, can discriminate between high and low arrhythmic risk patients.
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