Key result
Bayesian network analysis may help identify causative mechanisms between risk factors and atrial arrhythmias, potentially guiding tailored therapies for atrial fibrillation.
Bayesian network analysis may help identify causative mechanisms for atrial arrhythmias, potentially leading to tailored therapies for atrial fibrillation.
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, together with 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.
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Heijman et al. (2019) conducted an editorial in Atrial fibrillation. Bayesian network analysis was evaluated. Bayesian network analysis may help identify causative mechanisms between risk factors and atrial arrhythmias, potentially guiding tailored therapies for atrial fibrillation.
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