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Longer and better lives for patients with atrial fibrillation: the 9th AFNET/EHRA consensus conference

Dominik Linz1,2, Jason G Andrade3,4, Elena Arbelo5,6,7,8

  • 1Department of Cardiology, Maastricht University Medical Center, Cardiovascular Research Institute Maastricht, Maastricht, The Netherlands.

Europace : European Pacing, Arrhythmias, and Cardiac Electrophysiology : Journal of the Working Groups on Cardiac Pacing, Arrhythmias, and Cardiac Cellular Electrophysiology of the European Society of Cardiology
|April 9, 2024
PubMed

Insights

Active rhythm management is recommended for atrial fibrillation (AF) patients. Lower arrhythmia burden in AF reduces stroke risk, but more research is needed for accurate prediction in these cases.

Area of Science:

  • Cardiology
  • Electrophysiology
  • Digital Health

Background:

  • Recent trials highlight benefits of active rhythm management in atrial fibrillation (AF).
  • A low arrhythmia burden correlates with reduced AF-related complications.
  • The 9th AFNET/EHRA Consensus Conference convened experts to discuss AF management.

Purpose of the Study:

  • To summarize key outcomes from the 9th AFNET/EHRA Consensus Conference.
  • To provide guidance on active rhythm management and stroke risk in AF patients.
  • To highlight the role of comprehensive care and emerging technologies in AF treatment.

Main Methods:

  • Consensus conference involving 83 international experts.
  • Discussion and synthesis of recent trial data and clinical findings.
  • Focus on device-detected AF, stroke risk prediction, and concomitant conditions.

Main Results:

  • Active rhythm management should be a default treatment for suitable AF patients.
  • Device-detected AF with low burden indicates low stroke risk; anticoagulation benefits and risks require careful consideration.
  • Enhanced stroke risk prediction needs further research using biomarkers, genetics, and imaging.
  • AF diagnosis necessitates systematic evaluation and treatment of co-existing cardiovascular conditions.
  • Machine learning shows promise for AF detection and prediction, requiring clinical-data science collaboration.

Conclusions:

  • Patients with lower AF burden face reduced risks of stroke and cardiovascular events.
  • Integrated management combining rhythm control, anticoagulation, rate control, and treatment of comorbidities improves patient outcomes.
  • Further research and technological integration are crucial for optimizing AF care.
Abstract

Keywords:
AFNETAnticoagulationArtificial intelligenceAtrial cardiomyopathyAtrial fibrillationBiomarkersBleedingCatheter ablationCognitive functionConsensus statementCostDementiaEHRAGuidelinesHeart failureIntegrated careOutcomesQuality of careResearchResearch prioritiesRhythm managementScreeningStrokeTechnology

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