Reveal LINQ Insertable Cardiac Monitor, LINQ II Insertable Cardiac Monitor, AccuRhythm AI ECG Classification System

The Reveal LINQ Insertable Cardiac Monitor system provides long-term subcutaneous ECG monitoring to detect and record cardiac arrhythmias in symptomatic patients or those at high risk. The integration of the AccuRhythm AI ECG Classification System aims to significantly improve diagnostic accuracy by utilizing artificial intelligence to reduce the incidence of false positive alerts for atrial fibrillation and pause events, thereby enhancing the clinical utility of the monitored data.

The Reveal LINQ Insertable Cardiac Monitor (ICM) Model LNQ11 is a subcutaneously implanted, leadless device designed for long-term cardiac monitoring. It continuously records subcutaneous ECG using two electrodes on its body, automatically detecting and recording various arrhythmias such as atrial tachyarrhythmia/atrial fibrillation (AT/AF), bradyarrhythmia, pause, and ventricular tachyarrhythmia. The system also allows for patient-activated recordings, capturing ECG data during symptomatic episodes like dizziness, palpitation, syncope, or chest pain. This functionality aims to aid in diagnosing cardiac conditions in patients at increased risk of arrhythmias or those experiencing transient, potentially arrhythmia-related symptoms. This submission details the integration of the AccuRhythm AI ECG Classification System (Models ZA400, ZA410, ZA420) into the Reveal LINQ ICM ecosystem. The AccuRhythm AI component is a software-based system that analyzes the ECG data collected by the ICM. Its primary scientific purpose is to improve the accuracy of arrhythmia detection by using artificial intelligence algorithms to reduce false positive alerts, specifically for atrial fibrillation and pause episodes, while ensuring true arrhythmic events are still reliably identified. Design verification and performance validation testing confirmed that the AccuRhythm AI system met its performance criteria, successfully demonstrating its ability to enhance alert specificity for these conditions without compromising sensitivity, using the existing Reveal LINQ ICM Model LNQ11 hardware.

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• Q1. What is the quantified performance (sensitivity, specificity, positive predictive value) of the AccuRhythm AI ECG Classification System in reducing false alerts for atrial fibrillation and pause episodes compared to the Reveal LINQ ICM without this AI enhancement?; How does the AccuRhythm AI algorithm differentiate between true arrhythmic events and noise or artifact that commonly lead to false positives in subcutaneous ECG recordings?; What are the specific patient populations or arrhythmia characteristics where the AccuRhythm AI system demonstrates the most significant improvement in diagnostic accuracy?; Beyond AF and Pause, are there plans to extend the AccuRhythm AI's false alert reduction capabilities to other detected arrhythmias like bradyarrhythmia or ventricular tachyarrhythmia?; What is the computational overhead or impact on device battery life, if any, associated with the integration and operation of the AccuRhythm AI ECG Classification System with the Reveal LINQ ICM?