B-po02-079 performance of atrial fibrillation burden patterns detected via insertable cardiac monitor for stroke risk stratification

Atrial fibrillation (AF) is associated with increased stroke risk, yet limitations in conventional monitoring restrict our understanding of AF burden risk thresholds. Predictive algorithms incorporating continuous monitoring data may be useful for stroke prediction. To evaluate the relative importance of temporal AF burden patterns as a predictor of stroke from a large retrospective cohort with insertable cardiac monitors (ICMs). Using the Optum© claims de-identified database (01/2007-6/2019) linked with the Medtronic CareLink ICM database, we identified patients indicated for AF management (AFM; N = 1,209), suspected AF (SAF; N = 1,629), and cryptogenic stroke (CS; N = 2,263) with at least 12 months continuous enrollment pre- and post-ICM implantation. AF burden, defined as daily (24-hr) AF/AT via the ICM, was transformed into a set of simple moving averages (SMAs) of different periods for each follow-up. Temporal pattern of AF burden was defined as the comparison of unique SMA pairs. The same definition was applied to the other diagnostic parameters. The occurrence of ischemic stroke was treated as a binary classification problem using baseline characteristics (including anticoagulant use), CHA2DS2-VASc score, and diagnostic parameters & patterns as predictors. Variable importance was calculated as the goodness-of-fit for each predictor in a decision tree model, and its bootstrapped mean was used to quantify significance of AF burden. A total of 5,101 patients (age 69.2±11.7 yrs, 50% male, CHA2DS2-VASc 3.9±1.9) met the inclusion criteria. Overall, 879 had an ischemic stroke over 2,445,925 days follow-up (151±166 days with a stroke outcome and 548±200 days without a stroke outcome). Other than prior stroke, AF burden patterns were the most important predictors of stroke in follow-up. The optimal moving average period and AF burden thresholds differed by device indication (AFM & SAF <8 days, CS 8-21 days). A combination of baseline characteristics and diagnostic patterns performed optimally on measures of AUC (0.69), specificity (0.60), and reliability. In this real-world dataset of patients with an ICM, AF burden patterns may provide incremental prognostic value to conventional risk stratification.