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

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, two BPMs (HEM-7371T1-AZ and HEM-7372T1-AZAZ, OMRON HEALTHCARE Co., Ltd.) which share a novel AF screening feature have been developed. Their AF screening feature utilizes an algorithm which incorporates machine learning, with the potential to improve diagnostic accuracy. Objective To evaluate the performance of this AF screening feature we performed a multicenter, prospective clinical study at five sites in the United States. Methods A total of 559 subjects were enrolled for this study – 267 in AF (AF cohort) and 292 in the Non-AF cohort. AF screening was carried out on all subjects by the two Omron BPMs and by one Microlife BPM (BP 3MX1-3, WatchBP Home A, Microlife Corp), and a simultaneous 12-lead ECG was recorded for comparison. All 12-lead ECGs were interpreted by a board-certified cardiologist who was blind to the BPM results. Sensitivity, specificity, and accuracy for the diagnosis of AF were calculated. Results Omron HEM-7371T1-AZ BPM had a sensitivity of 95.1% 95% CI 91.8-97.4, specificity of 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, and 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.