Atrial fibrillation detection by the subcutaneous defibrillator: real-world clinical performances and implications from a multicentre study. Issue 11 (23rd August 2020) - Ollitrault, Pierre; Jacon, Peggy; Auquier, Nathanaël; Champ-Rigot, Laure; Ben Kilani, Mouna; Vandevelde, Florence; Pellissier, Arnaud; Ferchaud, Virginie; Legallois, Damien; Defaye, Pascal; Anselme, Frédéric; Milliez, Paul
Abstract: Aims: No data exist concerning the clinical performances of the subcutaneous implantable cardioverter-defibrillator (S-ICD) atrial fibrillation (AF) detection algorithm. We aimed to study the performances and implications of the latter in a 'real-world' setting. Methods and results: Between July 2017 and August 2019, 155 consecutive S-ICD recipients were included. Endpoint of the study was the incidence of de novo or recurrent AF using a combined on-site and remote-monitoring follow-up approach. After a mean follow-up of 13 ± 8 months, 2531 AF alerts were generated for 55 patients. A blinded analysis of the 1950 subcutaneous electrocardiograms available was performed. Among them 47% were true AF, 23% were premature atrial contractions or non-sustained AF, 29% were premature ventricular contractions or non-sustained ventricular tachycardia, and 1% were misdetection. Fourteen percent (21/155) patients had at least one correct diagnosis of AF by the S-ICD algorithm. One patient presented symptomatic paroxysmal AF not diagnosed by the S-ICD algorithm (false negative patient). Patient-based sensitivity, specificity, positive, and negative predictive values were respectively 95%, 74%, 38%, and 99%. Among patients with at least one correct diagnosis of AF, 38% (8/21) had subsequent clinical implications (anticoagulation initiation or rhythm control therapies). Conclusion: The S-ICD AF detection algorithm yields a high sensitivity for AF diagnosis. Low specificity and positive predictive value contribute to a high remote monitoring-notification workload and underline the necessity of a manual analysis. Atrial fibrillation diagnosis by the S-ICD AF detection algorithm might lead to significant therapeutic adjustments.