4.6 Article

Early diagnosis and better rhythm management to improve outcomes in patients with atrial fibrillation: the 8th AFNET/EHRA consensus conference

Journal

EUROPACE
Volume 25, Issue 1, Pages 6-27

Publisher

OXFORD UNIV PRESS
DOI: 10.1093/europace/euac062

Keywords

Atrial fibrillation; Artificial intelligence; Heart failure; Atrial cardiomyopathy; Cognitive function; Dementia; Outcomes; Quality of care; Cost; Research; Rhythm management; Catheter ablation; Anticoagulation; Bleeding; Research priorities; Technology; Stroke; Integrated care; Screening; AFNET; EHRA; Guidelines; Consensus statement

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This article summarizes the key outcomes of the 8th AFNET/EHRA Consensus Conference, which was held in Hamburg in October 2021 and attended by 83 international experts. The consensus paper highlights new evidence-based approaches to improve care for patients with atrial fibrillation (AF), including population-based screening, evolving management strategies, atrial cardiomyopathy characterization, cognitive function assessment, and the role of artificial intelligence (AI).
Aims Despite marked progress in the management of atrial fibrillation (AF), detecting AF remains difficult and AF-related complications cause unacceptable morbidity and mortality even on optimal current therapy. Methods and results This document summarizes the key outcomes of the 8th AFNET/EHRA Consensus Conference of the Atrial Fibrillation NETwork (AFNET) and the European Heart Rhythm Association (EHRA). Eighty-three international experts met in Hamburg for 2 days in October 2021. Results of the interdisciplinary, hybrid discussions in breakout groups and the plenary based on recently published and unpublished observations are summarized in this consensus paper to support improved care for patients with AF by guiding prevention, individualized management, and research strategies. The main outcomes are (i) new evidence supports a simple, scalable, and pragmatic population-based AF screening pathway; (ii) rhythm management is evolving from therapy aimed at improving symptoms to an integrated domain in the prevention of AF-related outcomes, especially in patients with recently diagnosed AF; (iii) improved characterization of atrial cardiomyopathy may help to identify patients in need for therapy; (iv) standardized assessment of cognitive function in patients with AF could lead to improvement in patient outcomes; and (v) artificial intelligence (AI) can support all of the above aims, but requires advanced interdisciplinary knowledge and collaboration as well as a better medico-legal framework. Conclusions Implementation of new evidence-based approaches to AF screening and rhythm management can improve outcomes in patients with AF. Additional benefits are possible with further efforts to identify and target atrial cardiomyopathy and cognitive impairment, which can be facilitated by AI.

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