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-19patients 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 COVIDpatients' 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-19patients 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-19patients 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.
Authors: Szymon Urban; Michał Fułek; Mikołaj Błaziak; Gracjan Iwanek; Maksym Jura; Katarzyna Fułek; Mateusz Guzik; Mateusz Garus; Piotr Gajewski; Łukasz Lewandowski; Jan Biegus; Piotr Ponikowski; Przemysław Trzeciak; Agnieszka Tycińska; Robert Zymliński Journal: J Clin Med Date: 2022-09-21 Impact factor: 4.964