Implicity Data
Impact of a universal monitoring system (“third party”) on outcomes of ICD patients: A nationwide study.
N. Varma et al., “Impact of a universal monitoring system (‘third party’) on outcomes of ICD patients: A nationwide study,” Heart Rhythm, 2024.
Organizational impact study of Implicity RM platform for the remote monitoring of patients with cardiac implantable electronic devices: results from a French multi-center user-centric survey.
C. Dorra, M. Cassiau, G. Faedda, A. Vinas, C. Henry, A. Rosier, December 2023.
Heart failure events prediction algorithm for patients implanted with multi-brand CIED.
N. Varma, S. Boveda, I. Ibnouhsein, G. Faedda, A. Rosier, and J. Singh, “Heart failure events prediction algorithm for patients implanted with multi-brand CIED,” Europace, vol. 26, no. Supplement_1, 2024.
The Implicity cardiac remote monitoring platform is associated with reduced mortality by 22% and reduced hospitalization by 4% vs. conventional remote monitoring.
N. Varma et al., “Lb-456088-2 Clinical impact of a universal remote monitoring platform for ICD and CRT-D follow-up from a large real-world registry,” Heart Rhythm, vol. 20, no. 7, p. 1095, 2023.
The original abstract results can be found here. Differences in results with HRS presentation are due to methodological improvements.
New AI-based ILR-ECG-A significantly lowered the rate of FP ICM diagnoses while retaining a > 98% sensitivity.
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).
A. Lazarus et al., “Filtering of remote monitoring alerts transmitted by cardiac implantable electronic devices and reclassification of atrial fibrillation events by a new algorithm,” Cardiovasc. Digit. Health J., vol. 4, no. 5, pp. 149–154, 2023.
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.
E. Crespin et al., “Po-03-083 Real-world performance and agreement rates with healthcare professionals of a novel AI algorithm reclassifying ILR episodes,” Heart Rhythm, vol. 20, no. 5, pp. S496–S497, 2023.
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 et al., “A novel machine learning algorithm has the potential to reduce by 1/3 the quantity of ILR episodes needing review,” Eur. Heart J., vol. 42, no. Supplement_1, 2021.
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.
A. Rosier et al., “Personalized and automated remote monitoring of atrial fibrillation,” Europace, vol. 18, no. 3, pp. 347–352, 2015.