Smartwatch Algorithm for Automated Detection of Atrial Fibrillation

Abstract

Background: The Kardia Band (KB) is a novel technology that enables patients to record a rhythm strip using an Apple Watch (Apple, Cupertino, California). The band is paired with an app providing automated detection of atrial fibrillation (AF).

Objectives: The purpose of this study was to examine whether the KB could accurately differentiate sinus rhythm (SR) from AF compared with physician-interpreted 12-lead electrocardiograms (ECGs) and KB recordings.

Methods: Consecutive patients with AF presenting for cardioversion (CV) were enrolled. Patients underwent pre-CV ECG along with a KB recording. If CV was performed, a post-CV ECG was obtained along with a KB recording. The KB interpretations were compared to physician-reviewed ECGs. The KB recordings were reviewed by blinded electrophysiologists and compared to ECG interpretations. Sensitivity, specificity, and K coefficient were measured.

Results: A total of 100 patients were enrolled (age 68 ± 11 years). Eight patients did not undergo CV as they were found to be in SR. There were 169 simultaneous ECG and KB recordings. Fifty-seven were noninterpretable by the KB. Compared with ECG, the KB interpreted AF with 93% sensitivity, 84% specificity, and a K coefficient of 0.77. Physician interpretation of KB recordings demonstrated 99% sensitivity, 83% specificity, and a K coefficient of 0.83. Of the 57 noninterpretable KB recordings, interpreting electrophysiologists diagnosed AF with 100% sensitivity, 80% specificity, and a K coefficient of 0.74. Among 113 cases where KB and physician readings of the same recording were interpretable, agreement was excellent (K coefficient = 0.88).

Conclusions: The KB algorithm for AF detection supported by physician review can accurately differentiate AF from SR. This technology can help screen patients prior to elective CV and avoid unnecessary procedures.

Keywords: ECG monitoring; atrial fibrillation; cardioversion; digital health; smartwatch.