Atrial fibrillation (AF) is the most common type of sustained cardiac arrhythmia encountered in clinical practice, and its burden is expected to increase in most developed countries over the next few decades. Because AF can be silent, it is often not diagnosed until an AF-related complication occurs, such as stroke. AF is also associated with increased risk of heart failure, lower quality of life, and death. Anticoagulation has been shown to dramatically decrease embolic risk in the setting of atrial fibrillation, resulting in growing interest in early detection of previously undiagnosed AF. Newly developed monitoring devices have improved the detection of AF and have been recommended in guidelines for screening of AF in individuals aged 65 years and over. While screening is currently targeted to these older individuals, younger patients with obstructive sleep apnoea (OSA) are at higher risk of AF and stroke than the general population, indicating a need for targeted early detection of AF in this group. Compared to individuals without OSA, those with OSA are four times more likely to develop AF, and the risk of AF is strongly associated with OSA severity. The overall prevalence of AF among individuals with OSA remains unknown because of limitations related to study design and to the conventional methods previously used for AF detection. Recent and emerging technological advances may improve the detection of undiagnosed AF in high-risk population groups, such as those with OSA. In this clinical review, we discuss the methods of screening for AF and the applications of newer technologies for AF detection in patients with OSA. We conclude the review with a brief description of our research agenda in this area.
Keywords: Artificial intelligence; Atrial fibrillation; Detection; Device; Obstructive sleep apnoea; Stroke.
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