Helmut Pürerfellner1, Evgeny Pokushalov2, Shantanu Sarkar3, Jodi Koehler3, Ren Zhou3, Lubos Urban4, Gerhard Hindricks5. 1. Department of Cardiology, Krankenhaus der Elisabethinen, Linz, Austria. Electronic address: helmut.puererfellner@elisabethinen.or.at. 2. State Research Institute of Circulation Pathology, Novosibirsk, Russia. 3. Medtronic Incorporated, Mounds View, Minnesota. 4. The National Institute of Cardiovascular Diseases, Bratislava, Slovakia. 5. Heart Center, University of Leipzig, Leipzig, Germany.
Abstract
BACKGROUND: Frequent premature atrial contractions and sick sinus syndrome are primary causes of inappropriate atrial fibrillation (AF) detection in insertable cardiac monitors (ICMs). OBJECTIVE: The study aimed to validate an algorithm designed to reduce inappropriate AF detection on the basis of the identification of a single P wave during the cardiac cycle. METHODS: The original detection algorithm looks for evidence of AF based on differences in the pattern of R-R intervals over a 2-minute period. The improved algorithm reduces evidence for AF detection if P waves are detected. The algorithm was validated by using Holter data, which collected 2 leads of surface electrocardiogram and continuously uplinked ICM electrocardiogram over a 46-hour period. ICM detections were compared with Holter annotations to compute episode and duration detection performance. RESULTS: Valid Holter recordings (8442 hours) were analyzed from 206 patients. True AF was observed in 76 patients, yielding 482 true AF episodes ≥2 minutes in duration and 1191 hours of AF. The algorithm correctly identified 97.8% of the total AF duration and 99.3% of the total sinus or non-AF rhythm duration. The algorithm detected 85% (90% per-patient average) of all AF episodes ≥2 minutes in duration, and 55% (78% per-patient average) of the detected episodes had AF. AF was found in 95% of the detected episodes >1 hour. The improved algorithm reduced inappropriate episodes and duration by 46% and 55%, respectively, while also reducing appropriate episodes and duration by 2% and 0.1%, respectively. CONCLUSION: An improvement in the ICM algorithm for AF detection incorporating P-wave information substantially reduced inappropriately detected episodes and duration, with minimal reduction in sensitivity for detecting AF.
BACKGROUND: Frequent premature atrial contractions and sick sinus syndrome are primary causes of inappropriate atrial fibrillation (AF) detection in insertable cardiac monitors (ICMs). OBJECTIVE: The study aimed to validate an algorithm designed to reduce inappropriate AF detection on the basis of the identification of a single P wave during the cardiac cycle. METHODS: The original detection algorithm looks for evidence of AF based on differences in the pattern of R-R intervals over a 2-minute period. The improved algorithm reduces evidence for AF detection if P waves are detected. The algorithm was validated by using Holter data, which collected 2 leads of surface electrocardiogram and continuously uplinked ICM electrocardiogram over a 46-hour period. ICM detections were compared with Holter annotations to compute episode and duration detection performance. RESULTS: Valid Holter recordings (8442 hours) were analyzed from 206 patients. True AF was observed in 76 patients, yielding 482 true AF episodes ≥2 minutes in duration and 1191 hours of AF. The algorithm correctly identified 97.8% of the total AF duration and 99.3% of the total sinus or non-AF rhythm duration. The algorithm detected 85% (90% per-patient average) of all AF episodes ≥2 minutes in duration, and 55% (78% per-patient average) of the detected episodes had AF. AF was found in 95% of the detected episodes >1 hour. The improved algorithm reduced inappropriate episodes and duration by 46% and 55%, respectively, while also reducing appropriate episodes and duration by 2% and 0.1%, respectively. CONCLUSION: An improvement in the ICM algorithm for AF detection incorporating P-wave information substantially reduced inappropriately detected episodes and duration, with minimal reduction in sensitivity for detecting AF.
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
Authors: Zhaohan Xiong; Martyn P Nash; Elizabeth Cheng; Vadim V Fedorov; Martin K Stiles; Jichao Zhao Journal: Physiol Meas Date: 2018-09-24 Impact factor: 2.833
Authors: Simon Wechselberger; Mads Kronborg; Yan Huo; Judith Piorkowski; Sebastian Neudeck; Ellen Päßler; Ali El-Armouche; Utz Richter; Julia Mayer; Stefan Ulbrich; Liying Pu; Bettina Kirstein; Thomas Gaspar; Christopher Piorkowski Journal: Europace Date: 2018-11-01 Impact factor: 5.214
Authors: Gregory W Albers; Richard A Bernstein; Johannes Brachmann; John Camm; J Donald Easton; Peter Fromm; Shinya Goto; Christopher B Granger; Stefan H Hohnloser; Elaine Hylek; Amir K Jaffer; Derk W Krieger; Rod Passman; Jesse M Pines; Shelby D Reed; Peter M Rothwell; Peter R Kowey Journal: J Am Heart Assoc Date: 2016-03-15 Impact factor: 5.501