TY - JOUR
T1 - Bayesian network analyses in atrial fibrillation - A path to better therapies?
AU - Heijman, Jordi
AU - Dobrev, Dobromir
N1 - Funding Information:
The authors' work is supported by the Netherlands Organization for Scientific Research (ZonMW Veni 91616057 to J.H.), the National Institutes of Health ( R01-HL131517 and R01-HL136389 to D.D.), the German Research Foundation (DFG, Do 769/4-1 to D.D.)
Funding Information:
The authors’ work is supported by the Netherlands Organization for Scientific Research (ZonMW Veni 91616057 to J.H.), the National Institutes of Health (R01-HL131517 and R01-HL136389 to D.D.), the German Research Foundation (DFG, Do 769/4-1 to D.D.)
Publisher Copyright:
© 2019 The Author(s)
PY - 2019/3
Y1 - 2019/3
N2 - 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.
AB - 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.
U2 - 10.1016/j.ijcha.2019.02.009
DO - 10.1016/j.ijcha.2019.02.009
M3 - Editorial
C2 - 30963097
SN - 2352-9067
VL - 22
SP - 210
EP - 211
JO - IJC Heart and Vasculature
JF - IJC Heart and Vasculature
ER -