Heijman Jordi, Dobrev Dobromir
Department of Cardiology, CARIM School for Cardiovascular Diseases, Faculty of Health, Medicine, and Life Sciences, Maastricht University, Maastricht, the Netherlands.
Institute of Pharmacology, West German Heart and Vascular Center, University Duisburg-Essen, Essen, Germany.
Int J Cardiol Heart Vasc. 2019 Mar 4;22:210-211. doi: 10.1016/j.ijcha.2019.02.009. eCollection 2019 Mar.
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 , 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.
尽管在心房颤动（AF）管理方面有多项重大创新，包括AF检测的改善以及基于导管消融的节律控制方面的进展，但AF仍然是一个主要的医疗负担。最近的进展使得能够整理越来越大的数据集，再加上通过筛查和连续节律监测在AF检测方面的改进，使得新颖的“大数据”方法能够更好地预测和分类AF。在本期杂志中，江波博士和古川博士描述了一种方法，通过贝叶斯网络分析来揭示上腔静脉的几个风险因素与房性心律失常之间潜在的因果联系。这种方法可能是从统计关联迈向确定致病机制的一个相关步骤，并且与实验工作和机制计算机模型一起，可能有助于建立针对AF的基于机制的个性化治疗方法。
