Treatment of Cryptogenic Stroke Patients with Atrial Fibrillation Detected by Insertable Cardiac Monitors Reduces Recurrent Stroke Risk to Background Levels

Atrial Fibrillation Detection and Ischemic Stroke Recurrence in Cryptogenic Stroke: A Retrospective, Multicenter, Observational Study

BACKGROUND

Atrial fibrillation (AF) is known to be a strong risk factor for stroke. However, the risk of stroke recurrence in patients with cryptogenic stroke with AF detected after stroke by an insertable cardiac monitor (ICM) is not well known. We sought to evaluate the risk of ischemic stroke recurrence in patients with cryptogenic stroke with and without ICM-detected AF.

METHODS AND RESULTS

We retrospectively reviewed patients with cryptogenic stroke who underwent ICM implantation at 8 stroke centers in Japan. Cox regression models were developed using landmark analysis and time-dependent analysis. We set the target sample size at 300 patients based on our estimate of the annualized incidence of ischemic stroke recurrence to be 3% in patients without AF detection and 9% in patients with AF detection. Of the 370 patients, 121 were found to have AF, and 110 received anticoagulation therapy after AF detection. The incidence of ischemic stroke recurrence was 4.0% in 249 patients without AF detection and 5.8% in 121 patients with AF detection (P=0.45). In a landmark analysis, the risk of ischemic stroke recurrence was not higher in patients with AF detected ≤90 days than in those without (hazard ratio, 1.47 [95% CI, 0.41-5.28]). In a time-dependent analysis, the risk of ischemic stroke recurrence did not increase after AF detection (hazard ratio, 1.77 [95% CI, 0.70-4.47]).

CONCLUSIONS

The risk of ischemic stroke recurrence in patients with cryptogenic stroke with ICM-detected AF, 90% of whom were subsequently anticoagulated, was not higher than in those without ICM-detected AF.

Towards a new classification of atrial fibrillation detected after a stroke or a transient ischaemic attack

Globally, up to 1·5 million individuals with ischaemic stroke or transient ischaemic attack can be newly diagnosed with atrial fibrillation per year. In the past decade, evidence has accumulated supporting the notion that atrial fibrillation first detected after a stroke or transient ischaemic attack differs from atrial fibrillation known before the occurrence of as stroke. Atrial fibrillation detected after stroke is associated with a lower prevalence of risk factors, cardiovascular comorbidities, and atrial cardiomyopathy than atrial fibrillation known before stroke occurrence. These differences might explain why it is associated with a lower risk of recurrence of ischaemic stroke than known atrial fibrillation. Patients with ischaemic stroke or transient ischaemic attack can be classified in three categories: no atrial fibrillation, known atrial fibrillation before stroke occurrence, and atrial fibrillation detected after stroke. This classification could harmonise future research in the field and help to understand the role of prolonged cardiac monitoring for secondary stroke prevention with application of a personalised risk-based approach to the selection of patients for anticoagulation.

The Potential and Limitations of Mobile Health and Insertable Cardiac Monitors in the Detection of Atrial Fibrillation in Cryptogenic Stroke Patients: Preliminary Results From the REMOTE Trial

Aim

This paper presents the preliminary results from the ongoing REMOTE trial. It aims to explore the opportunities and hurdles of using insertable cardiac monitors (ICMs) and photoplethysmography-based mobile health (PPG-based mHealth) using a smartphone or smartwatch to detect atrial fibrillation (AF) in cryptogenic stroke and transient ischemic attack (TIA) patients.

Methods and Results

Cryptogenic stroke or TIA patients (n = 39) received an ICM to search for AF and were asked to use a blinded PPG-based mHealth application for 6 months simultaneously. They were randomized to smartphone or smartwatch monitoring. In total, 68,748 1-min recordings were performed using PPG-based mHealth. The number of mHealth recordings decreased significantly over time in both smartphone and smartwatch groups (p < 0.001 and p = 0.002, respectively). Insufficient signal quality was more frequently observed in smartwatch (43.3%) compared to smartphone recordings (17.8%, p < 0.001). However, when looking at the labeling of the mHealth recordings on a patient level, there was no significant difference in signal quality between both groups. Moreover, the use of a smartwatch resulted in significantly more 12-h periods (91.4%) that were clinically useful compared to smartphone users (84.8%) as they had at least one recording of sufficient signal quality. Simultaneously, continuous data was collected from the ICMs, resulting in approximately 6,660,000 min of data (i.e., almost a 100-fold increase compared to mHealth). The ICM algorithm detected AF and other cardiac arrhythmias in 10 and 19 patients, respectively. However, these were only confirmed after adjudication by the remote monitoring team in 1 (10%) and 5 (26.3%) patients, respectively. The confirmed AF was also detected by PPG-based mHealth.

Conclusion

Based on the preliminary observations, our paper illustrates the potential as well as the limitations of PPG-based mHealth and ICMs to detect AF in cryptogenic stroke and TIA patients in four elements: (i) mHealth was able to detect AF in a patient in which AF was confirmed on the ICM; (ii) Even state-of-the-art ICMs yielded many false-positive AF registrations; (iii) Both mHealth and ICM still require physician revision; and (iv) Blinding of the mHealth results impairs compliance and motivation.