Incidence of atrial fibrillation detected by an insertable cardiac monitor in a large real-world cohort of heart failure patients with reduced and preserved ejection fraction

Abstract Background Subcutaneous implantable cardiac monitors (ICM) have the capability to detect atrial fibrillation (AF) episodes with high degree of accuracy [1]. Objective We investigated the incidence of AF in patients implanted with an ICM and a clinical history of heart failure (HF) with reduced or preserved left ventricular ejection fraction (LVEF). Methods Patients with history of HF admissions who were implanted with an ICM were identified from the aggregated and de-identified electronic health record (EHR) database during 2007-2021. The device collected data were merged with the EHR data to create a de-identified database of real-world patients. Patients were included if they had ≥180 days of device follow-up. ICMs detect AF based on incoherence of RR intervals and absence of single p-wave between two R-waves [1]. All ICM detected AF episodes stored with at least 30 seconds of ECG at onset were adjudicated using an artificial intelligence (AI) model. The AI model was previously trained and validated using over 60K manually adjudicated episodes and was shown to have an accuracy of over 97% [2]. ICM detected AF episodes with AI model based true probability of AF ≥ 0.9 was analyzed. The Kaplan-Meier incidence curves for AF incidence as a function of episode duration, clinical history of AF, and LVEF are reported. Results A total of 1020 LINQ ICM patients with history of HF admission prior to implant were identified from the deidentified real-world dataset. LVEF was available in 889 (87%) patients, of whom 394 (44%) had EF