Abstract 17815: Effectiveness of Artificial Intelligence in Triaging Transmissions From Cardiac Implantable Electronic Devices

Abstract

Background: Monitoring of cardiac implantable electronic devices (CIED) may cause tremendous burden on device clinics. Multiple transmissions for device alerts pose challenges in terms of triaging and clinical action. In this study we compared the effectiveness of artificial Intelligence (AI) as compared to Device technicians (DTs) in triaging the data from CIED. Methods: The study included remote transmissions from CIEDs being monitored at 78 clinics across USA from Jan to April 2023. Transmissions were initially processed by AI and were either dismissed, forwarded to the DT for review or forwarded directly to the device clinic. The data forwarded to the DT was analyzed and was either dismissed or forwarded to the device clinic with and without alerts. Transmissions were further classified based on urgency of response needed. About 15% of all transmissions processed by AI and DT were randomly examined by EP for accuracy. Results: A total of 42,534 patients with CIEDs generated 273,435 transmissions. Of these, 21% (57,345) transmissions were dismissed by AI - 17.8% (38,530) without alerts and 33.1% (18,815) with alerts. Of the transmissions dismissed, alerts for AT/AF represented the largest proportion in DT group, whereas alerts for pacing percentage was most seen with AI (table 1). Similar trends were seen with transmissions that were forwarded to clinics with pacing percentage dominating in AI group, and AT/AF detection in the DT group (table 2). Conclusion: In our study, compared to DT, AI algorithm was found to be more accurate in overall triage of remote transmission data and has the potential to improve the clinical efficiency of device clinics and care of patients with CIEDs.