Background: Early detection of atrial fibrillation (AF) is key for preventing strokes. Blood pressure monitors (BPMs) with built-in AF screening features have the potential for early detection at home. Recently, 2 BPMs (HEM-7371T1-AZ and HEM-7372T1-AZAZ, Omron Healthcare Co., Ltd.) that share a novel AF screening feature have been developed. Their AF screening feature utilizes an algorithm that incorporates machine learning, with the potential to improve diagnostic accuracy.
Objective: The purpose of this study was to evaluate the performance of this AF screening feature in a multicenter, prospective clinical study at 5 sites in the United States.
Methods: A total of 559 subjects were enrolled for this study: 267 in AF cohort and 292 in the non-AF cohort. AF screening was performed in all subjects by the 2 Omron BPMs and by 1 Microlife BPM (BP 3MX1-3, WatchBP Home A, Microlife Corp.), and a simultaneous 12-lead electrocardiogram (ECG) was recorded for comparison. All 12-lead ECGs were interpreted by a board-certified cardiologist who was blinded to the BPM results. Sensitivity, specificity, and accuracy for the diagnosis of AF were calculated.
Results: Omron HEM-7371T1-AZ BPM had sensitivity of 95.1% (95% confidence interval [CI] 91.8%-97.4%), specificity 98.6% (95% CI 96.6%-99.7%), and accuracy of 97.0% (95% CI 95.2%-98.2%). Equivalent results were obtained with the Omron HEM-7371T1-AZAZ BPM. This compared favorably to the Microlife BPM (sensitivity 78.5%, 95% CI 73.1%-83.3%; specificity 97.6%, 95% CI 95.1%-99.0%; accuracy 88.4%, 95% CI 85.5%-91.0%).
Conclusion: These data support both home and professional use of these novel Omron BPMs for the detection of AF.
Keywords: Arrhythmia; Atrial fibrillation; Blood pressure monitor; Machine learning; Screening.
Copyright © 2024 Heart Rhythm Society. Published by Elsevier Inc. All rights reserved.