Bayesian network analyses in atrial fibrillation - A path to better therapies?

Jordi Heijman*, Dobromir Dobrev

*Corresponding author for this work

Research output: Contribution to journalEditorialAcademicpeer-review

1 Citation (Web of Science)

Abstract

Despite several major innovations in atrial fibrillation (AF) management, including the improved detection of AF and advances in catheter-ablation-based rhythm control, AF remains a major health-care burden. Recent advances have enabled curation of increasingly large data sets, which, togetherwith improvements in AF detection through screening and continuous rhythm monitoring, enable novel 'big data' approaches to better predict and classify AF. In this issue of the International Journal of Cardiology Heart & Vasculature, Drs. Ebana and Furakawa describe an approach to shed light on potential causal links between several risk factors and atrial arrhythmias from the superior vena cava using a Bayesian network analysis. This approach may be a relevant step from statistical association towards identification of causative mechanisms and together with experimental work and mechanistic computer models may help to establish tailored mechanism-based therapies for AF. (c) 2019 The Author(s). Published by Elsevier B.V.

Original languageEnglish
Pages (from-to)210-211
Number of pages2
JournalIJC Heart and Vasculature
Volume22
DOIs
Publication statusPublished - Mar 2019

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