Implicity Data
The Implicity cardiac remote monitoring platform is associated with reduced mortality by 22% and reduced hospitalization by 4% vs. conventional remote monitoring.
Varma N, Marijon E, Abraham A, Ibnouhsein I, Bonnet J-L, Rosier A, Singh
Heart Rhythm Journal, LB-456088-2, in press, May 16, 2023
The original abstract results can be found here. Differences in results with HRS presentation are due to methodological improvements.
Remote monitoring delivers a 28% reconnection rate within two days post SMS alert in CIED study.
An algorithm that detects clinically relevant events leads to a >85% decrease in atrial fibrillation (AF) burden–related alerts compared to alerts transmitted by a cardiac implantable electronic device (CIED).
Arnaud Lazarus, MD, Marika Gentils, Stefan Klaes, PhD, Issam Ibnouhsein, PhD, Arnaud Rosier, MD, PhD, Ghassan Moubarak, MD, Jean-Luc Bonnet, PhD, Jagmeet P. Singh, MD, PhD, Pascal Defaye, MD, PhD Published: September 09, 2023 DOI: https://doi.org/10.1016/j.cvdhj.2023.08.019
In this real-world study, the AI algorithm re-diagnosed 43.1% of LINQ-detected episodes as “Normal Rhythm”. In addition, the AI algorithm showed good agreement rates with the HCPs reviewing the same episodes, with a 99.0% NPV and a 78.2% PPV.
A Rosier , Real-world; performance and agreement rates with healthcare professionals of a novel AI algorithm reclassifying ILR episodes, Heart Rhythm, Volume 20, Issue 5, Supplement, S496-S497, May 2023 https://doi.org/10.1016/j.hrthm.2023.03.1075
Artificial intelligence algorithm: ILR ECG Analyzer reduces the number of false positives by 79% when analyzing ECG recordings from patients implanted with Medtronic ILRs while maintaining a sensitivity of 99%.
A Rosier , A novel machine learning algorithm has the potential to reduce by 1/3 the quantity of ILR episodes needing review, European Heart Journal, Volume 42, Issue Supplement_1, October 2021, ehab724.0316, https://doi.org/10.1093/eurheartj/ehab724.0316
A pilot study tests filtering the importance of atrial fibrillation (AF) alerts, using AI algorithms resulting in an 84% reduction in notification workload while preserving patient safety.
Rosier A, Mabo P, Temal L, Van Hille P, Dameron O, Deléger L, Grouin C, Zweigenbaum P, Jacques J, Chazard E, Laporte L, Henry C, Burgun A. Personalized and automated remote monitoring of atrial fibrillation. Europace. 2016 Mar;18(3):347-52. doi: 10.1093/europace/euv234. Epub 2015 Oct 20. PMID: 26487670.