Diagnostic accuracy for detecting atrial fibrillation using a novel machine learning algorithm in a blood pressure monitor

Abstract

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.