Medtronic announced that its LINQ family of insertable cardiac monitors (ICM), together with AI-powered algorithms, showed promise in detecting atrial fibrillation (AF).
The company said that its ICMs with AI algorithms could detect Afib episodes and differentiate high-risk patients before an AF-related healthcare utilisation 80% of the time.
Medtronic’s announcement is based on primary results from the DEFINE AFib clinical study.
The DEFINE AFib study enrolled 973 patients using an app-based enrolment feature and characterised the impact of AF burden on patient outcomes and quality of life.
The study used AI and machine-learning techniques to analyse changes in AF burden, a measure of time a person spends in Afib during a monitored period.
Medtronic’s ICM-based model separated individuals into high- vs low-risk groups.
Medtronic cardiac rhythm management business chief medical officer Alan Cheng said: “Wearables allow patients to capture more real-time heart health data than ever before, but medical grade technology, like the LINQ family of ICMs, is necessary to provide clinicians with an accurate and reliable way to detect and manage cardiac conditions like AF.
“These findings also indicate that, while consumer-grade devices such as smartwatches and monitors can provide some insights into overall heart health, they are limited in their ability to screen for and help manage chronic conditions like AF.”
In the DEFINE AFib study, the company’s Reveal LINQ and LINQ II ICMs quantified AF burden to inform treatment decisions and help anticipate future healthcare needs.
Using the data, researchers built an algorithm that can predict patients who may need AF-related healthcare in the next 30-day period and predict their reduction in quality of life.
In the study, 22% of participants beyond the high-risk threshold experienced an AF-related healthcare utilisation (AFHCU), compared to 9% of patients in the low-risk group.
AFHCUs included ablation, cardioversion, initiation or intensification of rate or rhythm control medication, or progression to a pacemaker or implantable cardioverter-defibrillator.
The study data shows that the ICMs with AI algorithms will benefit those at high risk of an AF-related hospitalisation, clinic visit, or therapeutic intervention.
Also, a sub-analysis from the DEFINE AFib study showed key performance differences between the LINQ ICMs and the Apple Watch for AF detection.
DEFINE AFib clinical study steering committee chair Jonathan Piccini said: “The first-of-its-kind DEFINE AFib study leveraged a unique design that engaged patients from the very beginning.
“We know that how much AF a patient experiences matters, but we don’t know how different durations or patterns impact the risk of future health events.
“Combining continuous rhythm monitoring with traditional risk factors has helped clarify how AF burden and patterns can inform risk, prioritisation, and treatment decisions.
“Using upgraded AI-based algorithms and ICM data, physicians are better equipped to understand variance in patients’ AF patterns, offering the opportunity to provide the right patient with the right therapy at the right time.”
