Hidden in Plain Sight: Earlier Detection of HFpEF and Cardiac Amyloidosis to Improve AF Ablation and LAAC Outcomes - Powered by Ultromics-Hidden in Plain Sight: Earlier Detection of HFpEF and Cardiac Amyloidosis to Improve AF Ablation and LAAC Outcomes

Hidden in Plain Sight: Earlier Detection of HFpEF and Cardiac Amyloidosis to Improve AF Ablation and LAAC Outcomes - Powered by Ultromics

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The panel discussed how atrial fibrillation and heart failure, especially HFpEF and cardiac amyloid, are often linked but underdiagnosed. Speakers agreed that electrophysiologists frequently treat AF without fully considering the broader disease context, which can delay diagnosis and reduce treatment effectiveness.<br /><br />A major theme was the potential of AI-based tools to improve early detection of HFpEF and amyloidosis using data from ECGs and echocardiograms. The group emphasized that better phenotyping is needed so patients can be matched to the right therapy earlier, including anticoagulation, amyloid workup, and possibly tailored ablation strategies. They noted that amyloid patients have higher stroke risk and may need more aggressive anticoagulation and imaging.<br /><br />FDA perspectives highlighted the need to define the next step after a positive AI screening result and to balance sensitivity, specificity, safety, and intended use. The panel also discussed workflow integration, reimbursement, cybersecurity, and the importance of keeping AI models locked and clinically validated.<br /><br />Overall, the consensus was that the biggest need is not just better detection, but seamless integration into clinical workflows and stronger collaboration between EP, heart failure, imaging, IT, and regulatory teams.

Keywords

atrial fibrillation

heart failure

HFpEF

cardiac amyloidosis

artificial intelligence

ECG screening

echocardiography

clinical workflow