Real-world experience with implantable loop recorder monitoring to detect subclinical atrial fibrillation in patients with cryptogenic stroke: The value of p wave dispersion in predicting arrhythmia occurrence. (15th March 2021) - Marks, Daniel; Ho, Rady; Then, Ryna; Weinstock, Joshua L.; Teklemariam, Ephrem; Kakadia, Bhavika; Collins, Jared; Andriulli, John; Hunter, Krystal; Ortman, Matthew; Russo, Andrea M.
Abstract: Purpose: We hypothesized patients implanted with ILRs for cryptogenic stroke in "real life" clinical practice will show an AF detection rate comparable to prior clinical studies, and that clinical or imaging features may help to identify those at higher risk of AF detection. Methods: A retrospective chart review was conducted of all patients who presented with cryptogenic stroke and received an ILR at an academic medical center from 2015 to 2017 with an active inpatient stroke service. The electronic health record and remote monitoring were used to identify occurrence of AF. Results: A total of 178 patients who received ILRs for cryptogenic stroke were included. Overall, after a thorough evaluation for other etiologies of stroke, 35 (19.6%) were found to have AF detected. Mean follow-up was 365 days with a median time to detection of 131 days. Advanced age ( p = 0.001), diastolic dysfunction on echo ( p = 0.03), as well as ECG findings of premature atrial contractions (PACs) and p wave dispersion (PWD) > 40 ms were found to be predictive of AF detection ( p = 0.04, p < 0.001, respectively). On multiple regression analysis, the only independent predictor of AF detection was PWD > 40 ms. Conclusion: After a thorough evaluation to exclude other etiologies for stroke, approximately 20% of patients of our cryptogenic stroke population were found to have AF with ILR surveillance. Advanced age, diastolic dysfunction, as well as ECG findings of PACs and increased PWD may help to predict those at higher risk of AF detection, while PWD was the only independent predictor. This has important clinical implications, as better prediction of AF may help identify those at highest risk and might subsequently aid in guiding therapy.