Abstract 11930: Ascension Health System Experience With Extended Ambulatory Electrocardiographic Monitoring: Implementation of a Novel Integrated Approach to Better Characterize Disparities in Healthcare

Abstract

Introduction: Atrial fibrillation (AF) is an established risk factor for stroke, hospitalization and mortality. Whether AF differs by race has not been well characterized in the real world setting due to inherent challenges in linking large datasets across multiple databases. Methods: Extended ambulatory electrocardiographic (AECG) monitoring with Zio XT allows for continuous recording of cardiac rhythm data for up to 14 days in indicated patients. A preliminary dataset from the Ascension Health System electronic health records (EHR) archive on patients prescribed the Zio XT patch was retrospectively analyzed. The study population consisted of 6,293 patients treated at 34 Ascension centers in 6 states between January 2020 and April 2022. Patient EHR data were linked to proprietary device-specific rhythm reports for analyses. Results: Among 6,293 patients in whom EHR data were matched to Zio XT findings, the median (IQR) age was 63 (47, 73) years, 3,816 (60.6%) were female, and 1,038 (16.5%) were non-Caucasian. Median device wear time was 7.2 (5.0, 13.9) days. AF was detected in 749 (11.9%) patients, including 326 (5.2%) with 100% AF. Median AF burden was 37.0% (3.4%, 100%). Older white males had the highest risk of AF events, whereas younger non-white females had the lowest risk (Figure 1). Non-white subgroups demonstrated greater freedom from AF compared to whites. Conclusions: In these preliminary findings, the use of Zio XT for extended AECG monitoring in the Ascension Health System is in predominantly older patients. Patients were racially and geographically diverse, and females were well represented. More than 10% of patients had AF detected during the monitoring period. Despite small sample sizes, differences in AF detection by subgroups demonstrate that a tailored approach to managing AF remains a critically important research question. Linkage of patient EHR data with device-specific outcomes can be a useful approach to address this important unmet need.