Atrial Fibrillation Burden Signature and Near-Term Prediction of Stroke: A Machine Learning Analysis.

ABSTRACT:

Background

Atrial fibrillation (AF) increases the risk of stroke 5-fold and there is rising interest to determine if AF severity or burden can further risk stratify these patients, particularly for near-term events. Using continuous remote monitoring data from cardiac implantable electronic devices, we sought to evaluate if machine learned signatures of AF burden could provide prognostic information on near-term risk of stroke when compared to conventional risk scores.

Methods and results

We retrospectively identified Veterans Health Administration serviced patients with cardiac implantable electronic device remote monitoring data and at least one day of device-registered AF. The first 30 days of remote monitoring in nonstroke controls were compared against the past 30 days

SUBMITTER: Han L 

PROVIDER: S-EPMC8284982 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature