Opinion
Video
Unmet Needs in the Management of Atrial Fibrillation
Experts discuss the persistent clinical and systemic challenges in diagnosing and treating atrial fibrillation, emphasizing the need for earlier identification—especially in high-risk, comorbid populations—while highlighting the limitations of current risk models, the complexity of managing atrial fibrillation burden, and the importance of integrating wearable data into precision-based care without overburdening clinicians or compromising clinical relevance.
Unmet needs in the diagnosis and treatment of atrial fibrillation—and arrhythmias more broadly—span both clinical and system-level challenges. A major gap lies in preventing the first cardiovascular event, such as stroke, in individuals who have undiagnosed or subclinical atrial fibrillation (AFib). While current risk scoring systems offer some predictive value, they weren’t originally designed for early identification of AFib before complications occur. Populations with complex comorbidities, such as those with cancer, kidney disease, or heart failure, face unique treatment challenges, including limited anticoagulation options and increased bleeding risks. Improving patient education and engagement is also essential, as misunderstanding or fear often hinders effective care.
From a broader health care perspective, a major challenge is defining and acting on the concept of “AFib burden.” While studies show that screening high-risk populations can detect subclinical atrial fibrillation in up to one-third of patients, data indicate that initiating anticoagulation in these groups may offer only marginal stroke prevention benefits and may increase bleeding risk. This underscores the need for more nuanced risk stratification—potentially identifying subgroups where screening and early treatment are truly beneficial. So far, general screening in asymptomatic populations lacks strong support from major medical bodies due to these uncertainties.
Emerging technology, particularly consumer-grade wearables, has opened the door to unprecedented volumes of rhythm-related data. However, integrating this influx of information into clinical decision-making remains a challenge. There’s growing recognition that care delivery needs to evolve toward more precision-based, individualized strategies that incorporate both traditional risk factors and digital health inputs. That said, with more data comes more responsibility—health systems must build workflows that can intelligently process and respond to this data without overwhelming clinicians or leading to unnecessary interventions. It’s a balancing act between innovation, evidence, and clinical practicality.
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