Diagnostic Performance of an Automated Blood Pressure Monitor With an Irregular Heartbeat Algorithm Designed to Detect Atrial Fibrillation.

Abstract

Background: Early detection of atrial fibrillation (AF) remains an unsolved challenge and because the greatest risk factor for AF is hypertension, blood pressure (BP) monitors with AF detectors have been developed. We evaluated the clinical performance of an irregular heartbeat (IHB) algorithm built into an A&D automated BP monitor for AF diagnosis. Methods and Results: Each of the 239 enrolled patients underwent BP measurement 3 times using the A&D UM-212 with the IHB algorithm. Real-time 3-lead ECG was recorded using automated ECG analysis software. Independent of the ECG analysis software results, 2 cardiologists interpreted the ECG and made the final diagnosis. Of the 239 patients, 135 were in sinus rhythm, 31 had AF, and 73 had non-AF arrhythmias. The respective sensitivity, specificity, and accuracy of the IHB algorithm for AF diagnosis were 98.9%, 91.2%, and 92.2% for the per-measurement evaluation, and 96.8%, 95.7%, and 95.8% for the per-patient evaluation (3/3 positive measurements). The respective sensitivity, specificity, and accuracy of the ECG analysis software for AF diagnosis were 91.4%, 97.9%, and 97.1% for the per-measurement evaluation, and 77.4%, 99.5%, and 96.7% for the per-patient evaluation (3/3 positive measurements). Conclusions: The IHB algorithm built into an A&D automated BP monitor had high diagnostic performance for AF in general cardiology patients, especially when multiple measurements were obtained.