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Common Comorbidities and Underdiagnosis Associated With Atrial Fibrillation
Experts discuss the multifaceted management of atrial fibrillation, stressing the importance of addressing comorbid conditions alongside the arrhythmia itself, while highlighting the challenges of underdiagnosis, the rise of subclinical cases detected through wearable technology, and the need to align emerging diagnostic tools with evidence-based care to avoid unnecessary interventions and optimize outcomes.
Managing atrial fibrillation (AFib) involves more than just addressing rhythm disturbances and preventing stroke—it also requires a comprehensive approach to comorbidities. Common coexisting conditions include heart failure, obesity, diabetes, and thyroid dysfunction. These issues not only worsen existing AFib but may also contribute to its development. As these cardiometabolic conditions rise in prevalence, so too does the burden of atrial fibrillation. Addressing both the arrhythmia and its underlying contributors is essential for long-term disease control and better patient outcomes.
A significant challenge in atrial fibrillation care is the high rate of underdiagnosis. Newer technologies, such as implantable loop recorders and wearable devices, have revealed that a substantial portion of AFib cases—potentially up to one-third—are subclinical and would otherwise go undetected. Contributing factors include limited monitoring duration, patients being asymptomatic in early stages, and a lack of standardized screening protocols even in high-risk populations. Without persistent or overt symptoms, AFib may only be identified when complications arise, such as stroke or heart failure, underscoring the need for better diagnostic strategies.
The rise of consumer health technology further complicates this landscape. Devices like smartwatches can detect irregular heart rhythms, but these readings often lead to increased downstream testing without clear diagnostic value. As access to wearable tech grows, questions remain about the clinical implications of data from non-diagnostic tools and how best to interpret and act on this information. While technology holds promise for earlier detection, it also introduces uncertainty and potential overutilization of resources. Ultimately, there's a pressing need to align diagnostic advancements with evidence-based treatment pathways, ensuring that technological progress translates into meaningful and efficient care.
