Implantation of cardiac electronic devices in active COVID-19 patients: Results from an international survey.

BACKGROUND: Cardiac implantable electronic device (CIED) implantation rates as well as the clinical and procedural characteristics and outcomes in patients with known active coronavirus disease 2019 (COVID-19) are unknown. OBJECTIVE: The purpose of this study was to gather information regarding CIED procedures during active COVID-19, performed with personal protective equipment, based on an international survey. METHODS: Fifty-three centers from 13 countries across 4 continents provided information on 166 patients with known active COVID-19 who underwent a CIED procedure. RESULTS: The CIED procedure rate in 133,655 hospitalized COVID-19 patients ranged from 0 to 16.2 per 1000 patients (P <.001). Most devices were implanted due to high-degree/complete atrioventricular block (112 [67.5%]) or sick sinus syndrome (31 [18.7%]). Of the 166 patients in the study survey, the 30-day complication rate was 13.9% and the 180-day mortality rate was 9.6%. One patient had a fatal outcome as a direct result of the procedure. Differences in patient and procedural characteristics and outcomes were found between Europe and North America. An older population (76.6 vs 66 years; P <.001) with a nonsignificant higher complication rate (16.5% vs 7.7%; P = .2) was observed in Europe vs North America, whereas higher rates of critically ill patients (33.3% vs 3.3%; P <.001) and mortality (26.9% vs 5%; P = .002) were observed in North America vs Europe. CONCLUSION: CIED procedure rates during known active COVID-19 disease varied greatly, from 0 to 16.2 per 1000 hospitalized COVID-19 patients worldwide. Patients with active COVID-19 infection who underwent CIED implantation had high complication and mortality rates. Operators should take these risks into consideration before proceeding with CIED implantation in active COVID-19 patients.

Cardiac arrhythmias amongst hospitalised Coronavirus 2019 (COVID-19) patients: Prevalence, characterisation, and clinical algorithm to classify arrhythmic risk.

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.