Objective: New electronic devices offer an opportunity within routine primary care settings for improving the detection of atrial fibrillation (AF), which is a common cardiac arrhythmia and a modifiable risk factor for stroke. We aimed to assess the performance of a modified blood pressure (BP) monitor and two single-lead ECG devices, as diagnostic triage tests for the detection of AF.
Setting: 6 General Practices in the UK.
Participants: 1000 ambulatory patients aged 75 years and over.
Primary And Secondary Outcome Measures: Comparative diagnostic accuracy of modified BP monitor and single-lead ECG devices, compared to reference standard of 12-lead ECG, independently interpreted by cardiologists.
Results: A total of 79 participants (7.9%) had AF diagnosed by 12-lead ECG. All three devices had a high sensitivity (93.9-98.7%) and are useful for ruling out AF. WatchBP is a better triage test than Omron autoanalysis because it is more specific-89.7% (95% CI 87.5% to 91.6%) compared to 78.3% (95% CI 73.0% to 82.9%), respectively. This would translate into a lower follow-on ECG rate of 17% to rule in/rule out AF compared to 29.7% with the Omron text message in the study population. The overall specificity of single-lead ECGs analysed by a cardiologist was 94.6% for Omron and 90.1% for Merlin.
Conclusions: WatchBP performs better as a triage test for identifying AF in primary care than the single-lead ECG monitors as it does not require expertise for interpretation and its diagnostic performance is comparable to single-lead ECG analysis by cardiologists. It could be used opportunistically to screen elderly patients for undiagnosed AF at regular intervals and/or during BP measurement.
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http://dx.doi.org/10.1136/bmjopen-2013-004565 | DOI Listing |
Digit Health
January 2025
Department of Cardiology, Peking University First Hospital, Beijing, China.
Background: Wearables satisfactorily detect atrial fibrillation (AF) longer than 1 hour. Our study aims to evaluate smartwatch performances for long-term AF monitoring, including AF with short durations.
Methods: This prospective study enrolled AF patients from 2020 to 2023.
Bioengineering (Basel)
January 2025
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China.
Atrial fibrillation (AF) is the most common persistent arrhythmia, and it is crucial to develop generalizable automatic AF detection methods. However, supervised AF detection is often limited in performance due to the difficulty in obtaining labeled data. To address the gap between limited labeled data and the requirements for model robustness and generalization in single-lead ECG AF detection, we proposed a semi-supervised contrastive learning method named MLMCL for AF detection.
View Article and Find Full Text PDFSci Rep
January 2025
National Clinical Research Center for Geriatrics, West China Hospital of Sichuan University, Chengdu, Sichuan Province, China.
This study aimed to develop a real-time, noninvasive hyperkalemia monitoring system for dialysis patients with chronic kidney disease. Hyperkalemia, common in dialysis patients, can lead to life-threatening arrhythmias or sudden death if untreated. Therefore, real-time monitoring of hyperkalemia in this population is crucial.
View Article and Find Full Text PDFHeart Rhythm
January 2025
IDOVEN Research, Madrid, Spain; Centro Nacional de Investigaciones Cardiovasculares (CNIC), Myocardial Pathophysiology Area, Madrid, Spain; Centro de Investigación Biomédica en Red. Enfermedades Cardiovasculares (CIBERCV), Madrid, Spain. Electronic address:
Background: Although smartphone-based devices have been developed to record 1-lead ECG, existing solutions for automatic atrial fibrillation (AF) detection often has poor positive predictive value.
Objective: This study aimed to validate a cloud-based deep learning platform for automatic AF detection in a large cohort of patients using 1-lead ECG records.
Methods: We analyzed 8,528 patients with 30-second ECG records from a single-lead handheld ECG device.
JMIR Form Res
January 2025
Limburg Clinical Research Center/Mobile Health Unit, Faculty of Medicine and Life Sciences, Hasselt University, Hasselt, Belgium.
Background: Consumer-oriented wearable devices (CWDs) such as smartphones and smartwatches have gained prominence for their ability to detect atrial fibrillation (AF) through proprietary algorithms using electrocardiography or photoplethysmography (PPG)-based digital recordings. Despite numerous individual validation studies, a direct comparison of interdevice performance is lacking.
Objective: This study aimed to evaluate and compare the ability of CWDs to distinguish between sinus rhythm and AF.
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