Smart-device ECGs and inconclusive results: impact of ECG anomalies

Abstract Background Evermore smart-devices are capable of recording single-lead (SL) electrocardiograms (ECGs). Each recording comes with an automated rhythm interpretation usually consisting of sinus rhythm (SR), atrial fibrillation (AF) or inconclusive. The impact of common ECG anomalies possibly leading to an inconclusive interpretation by the devices algorithm is largely unknown. Purpose to assess the impact of baseline ECG anomalies on automated smart-device rhythm classification. Methods In this prospective study, consecutive hospitalized patients undergoing electrophysiological procedures in a tertiary referral center were included. Each participant obtained a 12-lead ECG followed by SL-ECGs with 5 different smart-devices (AliveCor KardiaMobile, Apple Watch 6, Fitbit Sense, Samsung Galaxy Watch3 and Withings ScanWatch). The smart-device generated rhythm determination was compared to manual interpretation. ECG anomalies were assessed by 2 independent cardiologists and included atrial Flutter (AFlut), pacing and conduction delay consisting of AV-block or left/right bundle branch block, artifacts and ectopy. Inconclusive tracings were labelled false-positive if the goldstandard was SR in the 12-lead ECG and false-negative if the goldstandard was AF in the 12-lead ECG. Relative Risk (RR) for anomalies to result as inconclusive diagnosis by the smart-device was calculated. Results 245 participants were included (34% female, mean age 64±13 years) resulting in a total of 1’165 recorded SL-ECGs. Among these, 247 SL-ECGs (21%) were labeled as inconclusive by at least one smart-device (Figure 1). Overall, 834 ECG anomalies were present. 169 inconclusive SL-ECGs were labelled false-positive with 192 anomalies present. Among these, 34 (18%) had ventricular pacing, RR 2.9 (CI 2.2-3.8), 52 (27%) showed conduction delay, RR 1.5 (CI 1.1-2), 44 (23 %) had ectopy, RR 2.3 (CI 1.7-3), and 62 (32%) some sort of artifacts, RR 1.9 (CI 1.5-2.5). Among 78 inconclusive SL-ECGs with false-negative results, 84 anomalies were present. 18 (21%) were in AFlut, RR 1.5 (CI 0.94-2.3), 12 (14%) had ventricular pacing RR, 4.2 (CI 3.1-5.6), 14 (17%) showed conduction delay, RR 1.3 (CI 0.8-2.1), 9 (11%) had ectopy, RR 0.99 (CI 0.54-1.8), and 31 (37%) some sort of artifacts, RR 1.7 (CI 1.2-2.5). There were no differences between assessed smart-devices. Conclusion Underlying ECG anomalies are common in inconclusive SL-ECGs and impact automated rhythm classification.Figure 1