Abstract 18672: Impact of Enhanced Artificial Intelligence on Clinic Burden From False Alerts of Insertable Cardiac Monitors

Abstract

Background: AccuRhythm AI is a suite of cloud-based classification algorithms that filter and suppress false pause alerts and atrial fibrillation (AF) episodes transmitted by insertable cardiac monitors (ICMs). An enhancement to the AF algorithm (AI v2) reduces false AF alerts by 50% relative to the original AI (AI v1) but the impact of this improvement on clinic experience is unknown. Aim: Determine the projected annual impact of AI v2 on Pause and AF alerts in a real-world patient population. Methods: Real-world data were obtained from de-identified LINQ II ICM patients with ≥3 months of follow-up. Previously published performance of each AI algorithm broken out by patient reason for monitor were projected onto the data. Results were extrapolated to the average 200-ICM clinic. The study endpoints were true alert retention, false alert suppression, and clinic review burden reduction. Results: The dataset consisted of 16,301 ICMs with mean follow-up of 7 months. Reasons for monitor were Known AF (25%), Suspected AF / Cryptogenic Stroke (40%), Syncope (23%), and Other (12%). The mean number of alerts per patient-year was 17.9 AF and 4.3 Pause. Projection of AI v2 resulted in minimal loss (0.9%) of true AF and Pause alerts, annually, with no loss in Pause and 1.0% in AF. AI v2 reduced annualized false AF and Pause alerts by 91% (97.4% false Pause and 88.1% false AF reduction) relative to the ICM and 35% relative to AI v1. This translates to 401 and 22 annual review hours saved for a 200-ICM clinic, respectively. Conclusion: The AF enhancement provides a meaningful reduction to the average ICM clinic’s review burden by eliminating most false Pause and AF alerts while maintaining nearly all true alerts.