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.

Rapid Exclusion of COVID Infection With the Artificial Intelligence Electrocardiogram.

OBJECTIVE: To rapidly exclude severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection using artificial intelligence applied to the electrocardiogram (ECG). METHODS: A global, volunteer consortium from 4 continents identified patients with ECGs obtained around the time of polymerase chain reaction-confirmed COVID-19 diagnosis and age- and sex-matched controls from the same sites. Clinical characteristics, polymerase chain reaction results, and raw electrocardiographic data were collected. A convolutional neural network was trained using 26,153 ECGs (33.2% COVID positive), validated with 3826 ECGs (33.3% positive), and tested on 7870 ECGs not included in other sets (32.7% positive). Performance under different prevalence values was tested by adding control ECGs from a single high-volume site. RESULTS: The area under the curve for detection of acute COVID-19 infection in the test group was 0.767 (95% CI, 0.756 to 0.778; sensitivity, 98%; specificity, 10%; positive predictive value, 37%; negative predictive value, 91%). To more accurately reflect a real-world population, 50,905 normal controls were added to adjust the COVID prevalence to approximately 5% (2657/58,555), resulting in an area under the curve of 0.780 (95% CI, 0.771 to 0.790) with a specificity of 12.1% and a negative predictive value of 99.2%. CONCLUSION: Infection with SARS-CoV-2 results in electrocardiographic changes that permit the artificial intelligence-enhanced ECG to be used as a rapid screening test with a high negative predictive value (99.2%). This may permit the development of electrocardiography-based tools to rapidly screen individuals for pandemic control.