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.
Objectives: A significant proportion of COVID-19 patients may have cardiac involvement including arrhythmias. Although arrhythmia characterization 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 center cohort of hospitalized patients with a positive nasopharyngeal swab for COVID-19 during the initial Israeli outbreak between 1/2/2020-30/5/2020. The study's outcome was any documented arrhythmia during hospitalization, 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 hospitalization, 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 significant correlation between disease severity and arrhythmia prevalence (p<0.001) with a low arrhythmic prevalence among 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 among COVID-19 patients is AF. Arrhythmia prevalence is dependent on 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. WHAT'S KNOWN? • A significant proportion of COVID-19 patients may have cardiac involvement including arrhythmias. • There is a correlation between disease severity in general and cardiac involvement specifically to occurrence of cardiac arrhythmias. • Arrhythmia characterization and possible predictors. WHAT'S NEW? • Using a 24-hour Holter monitoring among hospitalized COVID-19 patients, for better arrythmias detection. • Among of all hospitalized COVID-19 patients, 7.2% had new arrhythmias during hospitalization. • Classification tree which discriminate between high and low arrhythmic risk patients
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.