Reveal LINQ Insertable Cardiac Monitor Research Articles

Abstract WP231: Risk Factors for Paroxysmal Atrial Fibrillation and Atrial Flutter Detection Following Cryptogenic Ischemic Stroke

Introduction: Long-term post-stroke insertable cardiac monitor (ICM) implantation has resulted in increasing frequencies of paroxysmal atrial fibrillation and atrial flutter (PAF) detection in a significant proportion of cryptogenic ischemic stroke (CIS) patients. Determining risk factors for PAF detection specific to the CIS population could allow for better selection of patients for ICM implantation. Methods: A retrospective study of CIS patients (n=95; mean age 65.9 years; 56.8% female) implanted with the Reveal LINQ ICM between September 2013 to July 2015. Device implantation was performed during, or soon after, index stroke admission. The study cardiac electrophysiologist confirmed PAF diagnosis. Univariate and multivariate logistic regression analyses compared clinical, laboratory, ECG and echocardiographic variables between patients with and without PAF. Results: PAF was detected in 22/95 (23.2%) patients. Factors associated with PAF detection include older age (mean (yr) 74.0 vs. 63.4; p=0.003), prolonged PR-interval (PR>175 ms; OR 3.88, 95% CI 1.28-11.7), mitral regurgitation (MR; OR 3.36, 95% CI 1.24-9.08), left atrial diameter (LA diameter mean (cm) 3.80 vs. 3.51; p=0.039), and left atrial volume index (LAVI mean (cc/m2); 32.8 vs. 25.0; p=0.037). Controlling for age in the multivariate logistic regression model, obesity (BMI>30; OR 4.80, 95% CI 3.50-9.60) and MR (OR 4.47, 95% CI 3.15-5.79) are independently associated with PAF detection. Conclusion: Long-term cardiac monitoring identified PAF in a substantial number of CIS patients. Older age, prolonged PR, MR, and larger LA diameter and LAVI are significantly associated with PAF diagnosis. Controlling for age, significant independent risk factors include obesity and MR. A larger prospective study is warranted to confirm these findings and to identify other possible risk factors. Ultimately, a risk factor based algorithm could enable better selection of CIS patients for ICM implantation.

Miniaturized Reveal LINQ insertable cardiac monitoring system: First-in-human experience.

The Reveal LINQ is a miniaturized insertable cardiac monitor (ICM) with wireless telemetry for remote monitoring of patients with suspected arrhythmias. The primary objective of this study was to evaluate the functionality of the Reveal LINQ system by measuring R-wave sensing and data transmission. The Reveal LINQ Usability Study was a nonrandomized, prospective, multicenter trial. The study enrolled 30 patients with any indication for an ICM. Data were collected at baseline, implantation, and 1-month follow-up visits and through daily wireless transmissions. Thirty patients were enrolled and had a Reveal LINQ device implanted. The mean age was 55 ± 15 years. All patients had successful implantation of the ICM in one of the recommended locations. Ease of implantation procedure was rated as easy or very easy for 90% of implantations. R-wave amplitudes were 0.584 ± 0.325 mV at implantation and 0.596 ± 0.336 mV at 1 month (P = .8). Automatic transmissions were successful 79.5% (69.5%-86.9%) of the time. Transmission failures that caused a delay in data transfer occurred because of incomplete data reception or patients being out of range in 45% and 42% of instances, respectively. For all patients, transmission failures were followed by successful automated or manual transmission of information on a subsequent day. The devices stored 217 arrhythmic episodes during 30 days of follow-up, identified as atrial fibrillation (n = 111), asystole (n = 95), bradycardia (n = 4), fast ventricular tachycardia (n = 1), and ventricular tachycardia ( n = 6). No serious procedure- or system-related adverse events occurred during the 1-month follow-up period. The miniaturized Reveal LINQ ICM supports arrhythmia detection and monitoring, achieving adequate sensing performance without safety issues. URL: http://www.clinicaltrials.gov. Unique identifier: NCT01965899.