Prashanthan Sanders1, Helmut Pürerfellner2, Evgeny Pokushalov3, Shantanu Sarkar4, Marco Di Bacco5, Bärbel Maus5, Lukas R C Dekker6. 1. Centre for Heart Rhythm Disorders, South Australian Health and Medical Research Institute, University of Adelaide and Royal Adelaide Hospital, Adelaide, Australia. Electronic address: prash.sanders@adelaide.edu.au. 2. Department of Cardiology, Public Hospital Elisabethinen, Academic Teaching Hospital of the University of Innsbruck, Linz, Austria. 3. State Research Institute of Circulation Pathology, Novosibirsk, Russian Federation. 4. Medtronic PLC, Mounds View, Minnesota. 5. Medtronic Bakken Research Center, Maastricht, The Netherlands. 6. Catharina Hospital, Eindhoven, The Netherlands.
Abstract
BACKGROUND: For clinicians, confidence in atrial fibrillation (AF) episode classification is an important consideration when electing to use insertable cardiac monitors (ICMs). OBJECTIVE: The purpose of this study was to report on the improved AF detection algorithm in the Reveal LINQ ICM. METHODS: The Reveal LINQ Usability Study is a nonrandomized, prospective, multicenter trial. The ICM has been miniaturized, uses wireless telemetry for remote patient monitoring, and its AF algorithm includes a new p-wave filter. At 1 month post-device insertion, Holter monitor data were collected and annotated for true AF episodes ≥2 minutes, and performance metrics were evaluated by comparing Holter annotations with ICM detections. RESULTS: The study enrolled 151 patients (age 56.6 ± 12.1, male 67%). Reasons for monitoring included AF ablation or AF management in 81.5% (n = 123), syncope in 12.6% (n = 19), and other indications in 5.9% (n = 9) of patients. Of the 138 patients with an analyzable Holter recording, a total of 112 true AF episodes were identified in 38 patients (27.5%). The overall accuracy of the ICM to detect durations of AF or non-AF episodes was 99.4%, and the AF burden measured by the ICM was highly correlated with the Holter (Pearson coefficient 0.995). CONCLUSION: The new AF detection algorithm in the Reveal LINQ ICM accurately detects the presence or absence of AF. Additionally, it showed high sensitivity in detecting AF duration in patients with a history of intermittent and symptomatic AF. Crown
BACKGROUND: For clinicians, confidence in atrial fibrillation (AF) episode classification is an important consideration when electing to use insertable cardiac monitors (ICMs). OBJECTIVE: The purpose of this study was to report on the improved AF detection algorithm in the Reveal LINQ ICM. METHODS: The Reveal LINQ Usability Study is a nonrandomized, prospective, multicenter trial. The ICM has been miniaturized, uses wireless telemetry for remote patient monitoring, and its AF algorithm includes a new p-wave filter. At 1 month post-device insertion, Holter monitor data were collected and annotated for true AF episodes ≥2 minutes, and performance metrics were evaluated by comparing Holter annotations with ICM detections. RESULTS: The study enrolled 151 patients (age 56.6 ± 12.1, male 67%). Reasons for monitoring included AF ablation or AF management in 81.5% (n = 123), syncope in 12.6% (n = 19), and other indications in 5.9% (n = 9) of patients. Of the 138 patients with an analyzable Holter recording, a total of 112 true AF episodes were identified in 38 patients (27.5%). The overall accuracy of the ICM to detect durations of AF or non-AF episodes was 99.4%, and the AF burden measured by the ICM was highly correlated with the Holter (Pearson coefficient 0.995). CONCLUSION: The new AF detection algorithm in the Reveal LINQ ICM accurately detects the presence or absence of AF. Additionally, it showed high sensitivity in detecting AF duration in patients with a history of intermittent and symptomatic AF. Crown
Authors: Nicolle S Milstein; Dan L Musat; James Allred; Amber Seiler; Jacqueline Pimienta; Susan Oliveros; Advay G Bhatt; Mark Preminger; Tina Sichrovsky; Richard E Shaw; Suneet Mittal Journal: J Interv Card Electrophysiol Date: 2019-10-14 Impact factor: 1.900
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Authors: Niraj Varma; Iwona Cygankiewicz; Mintu P Turakhia; Hein Heidbuchel; Yu-Feng Hu; Lin Yee Chen; Jean-Philippe Couderc; Edmond M Cronin; Jerry D Estep; Lars Grieten; Deirdre A Lane; Reena Mehra; Alex Page; Rod Passman; Jonathan P Piccini; Ewa Piotrowicz; Ryszard Piotrowicz; Pyotr G Platonov; Antonio Luiz Ribeiro; Robert E Rich; Andrea M Russo; David Slotwiner; Jonathan S Steinberg; Emma Svennberg Journal: Circ Arrhythm Electrophysiol Date: 2021-02-12
Authors: Geoffrey H Tison; José M Sanchez; Brandon Ballinger; Avesh Singh; Jeffrey E Olgin; Mark J Pletcher; Eric Vittinghoff; Emily S Lee; Shannon M Fan; Rachel A Gladstone; Carlos Mikell; Nimit Sohoni; Johnson Hsieh; Gregory M Marcus Journal: JAMA Cardiol Date: 2018-05-01 Impact factor: 14.676
Authors: Michael Gruska; Gerhard Aigner; Johann Altenberger; Dagmar Burkart-Küttner; Lukas Fiedler; Marianne Gwechenberger; Peter Lercher; Martin Martinek; Michael Nürnberg; Gerhard Pölzl; Gerold Porenta; Stefan Sauermann; Christoph Schukro; Daniel Scherr; Clemens Steinwender; Markus Stühlinger; Alexander Teubl Journal: Wien Klin Wochenschr Date: 2020-12-01 Impact factor: 1.704