Medtronic’s Insertable Cardiac Monitor Detects Post-Stroke Atrial Fibrillation
The median time from randomization to atrial fibrillation detection was 99 days in the cardiac monitor group compared to 181 days for the control group.
Richard Bernstein, MD, PhD
Results from the STROKE AF trial (NCT02700945) using Medtronic’s Reveal LINQ Insertable Cardiac Monitor (ICM) found the system to significantly predict atrial fibrillation (AF) over 12 months in patients with stroke attributed to large- or small-vessel disease compared with usual care.1,2
Patients were randomized 1:1 to either the intervention group (n = 242), where they received ICM insertion within 10 days of the index, or the control group (n = 250), where they received site-specific usual care consisting of external cardiac monitoring, such as 12-lead electrocardiograms, Holter monitoring, telemetry, or event recorders.
At 12 months, the incidence of AF was 12.1% (n = 27) in the ICM group compared to 1.8% in the control group (hazard risk [HR], 7.4 [95% CI, 2.6-21.3]; P <.001). Additionally, the median time from randomization to AF detection was 99 (interquartile range [IQR], 36-235) days for the ICM group and 181 (IQR, 86-231) days for the control group.
"STROKE AF showed that many patients with large or small vessel stroke actually have AF, which is challenging to find using standard cardiac monitoring,” Richard Bernstein, MD, PhD, Northwestern Medicine Distinguished Physician in Vascular Neurology, medical director of Telehealth, and professor of neurology, Northwestern University Feinberg School of Medicine, said in a statement.1 “Our job as neurologists includes identifying potential causes of future strokes to adequately protect our patients. AF is a risk that we can’t afford to miss, and without long-term monitoring, we are missing it.”
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A subgroup analysis was performed to compare the detection incidence of AF between patients with an index stroke classified as large-vessel disease (n = 284; 57.3%) and small-vessel disease (n = 208; 42.3%). Among patients in the ICM group, investigators found no significant difference in AF detection in participants with large- vs small-vessel stroke (15 [11.7%] vs 12 [12.6%]; HR, 0.9 [95% CI, 0.4-1.9]; P = .74).
Post-hoc analysis showed that the incidence of AF was 7.9% (n = 18) in the ICM group at 6 months compared to 0.8% (n = 2) in the control group (HR, 9.9 [95% CI, 2.3-43.5]; P = .002). Furthermore, fewer AF episodes were detected in the ICM group during the first 30 days of follow-up (6; 2.6%), suggesting that 30 days of continuous monitoring would have missed 78% (21 of 27) of the patients detected by the ICM at 12 months. Notably, in the ICM group, 26 of 27 patients (96.3%) had asymptomatic first AF episode detected.
"Preventing recurrent stroke is challenging. I believe these findings from the STROKE AF study strongly suggest the need to re-examine the role of ICMs in secondary prevention, go beyond just the cryptogenic stroke patient, and embrace a broader conceptual framework that shifts the emphasis away from the cause of the index stroke and onto future stroke prevention,” Lee H. Schwamm, MD, vice president of Virtual Care and Digital Health, Mass General Brigham and C. Miller Fisher chair in Vascular Neurology, Massachusetts General Hospital, said in a statement.1
Among patients with AF in the ICM group, the median duration for the longest single episode of AF detected was 88 (IQR, 10-526) minutes, and was longer in those with small-vessel vs large-vessel stroke (267 [IQR, 49-438] vs 44 [IQR, 8-526] minutes). Episodes lasting longer than 1 hour occurred in 15 patients (55.5%) with AF in the ICM group.
Safety analysis showed that only 4 of 221 (1.8%) patients in the ICM group had insertion procedure-related adverse events (AEs). They included site infection (1, 0.5%), incision site hemorrhages (2, 0.9%), and implant site pain (1, 0.5%).
The FDA approved the Reveal LINQ ICM with TruRhythm Detection in March 2017 as an advanced cardiac monitor to better identify abnormal heartbeats. The device features a self-learning AF algorithm, which learns and adapts to a patient’s heart rhythm over time.3
