Asymptomatic Atrial Fibrillation

Atrial fibrillation is a highly prevalent and incidental arrhythmia, often asymptomatic, and frequently detected incidentally or in association with a stroke. Subclinical atrial fibrillation increases cardioembolic risk, highlighting the need for timely diagnosis. New wireless devices capable of recording heart rhythm, combined with innovative artificial intelligence tools, could be useful in the prediction and detection of this arrhythmia. Objective: to determine the usefulness of home blood pressure and heart rhythm monitoring strategy in the detection of subclinical atrial fibrillation. Methods: observational, cohort, prospective, multicenter study involving 25 researchers from six Latin American countries. Home blood pressure monitoring and single-lead electrocardiogram recording were performed in a population at moderate to high risk of developing atrial fibrillation. A minimum of twenty 30-second electrocardiographic and blood pressure recordings over 7 days using an Omron Complete Hem-7530 T ECG device will be uploaded from a mobile phone app and then sent to a database for analysis. Conclusions: the results of this study can provide a simple and accessible home monitoring system for detecting subclinical atrial fibrillation and for optimizing the predictive capacity of arrhythmia risk scores through deep learning.

Postoperative atrial fibrillation (POAF) occurs in approximately 20% of patients following thoracic surgery. Early diagnosis is essential to prevent complications such as heart failure, stroke, myocardial infarction, and increased mortality. Smartwatches equipped with single-lead ECG capabilities and algorithms to detect asymptomatic atrial fibrillation (AF) offer a potential solution. This study aims to evaluate the effectiveness of smartwatches in detecting POAF compared to standard care.