Networking analysis on superior vena cava arrhythmogenicity in atrial fibrillation

Abstract

Atrial fibrillation (AF) can be initiated from arrhythmogenic foci within the muscular sleeves that extend not only into the pulmonary veins but also into both vena cavae. Patients with SVC-derived AF have the common clinical and genetic risk factors. Bayesian network analysis is a probabilistic model in which a qualitative dependency relationship among random variables is represented by a graph structure and a quantitative relationship between individual variables is expressed by a conditional probability.We used data of meta-analysis of 2170 AF patients with and without SVC arrhythmogenicity in the previous article. Bayesian Networking analysis was performed using the software “bnlearn”. Using the clinical and genetic factors associated with SVC arrhythmogenicity in the previous article, we investigated a Bayesian networking structure to determine the probabilitic causation of variants to clinical parameters and found that the rate of recurrence depended on SVC arrhythmogenicity and LA diameter, and that SVC arrhythmogenicity was conditionally dependent on gender, body mass index, and genetic risk score. We found the possibility of prediction model generated from three factors. Receiver-operation characteristic analysis showed the area under the curve was 0.84.Using the clinical/genetic factors associated with SVC arrhythmogenicity through the previous meta-analysis of over 2000 patients, Bayesian networking analysis indicated the probabilistic causation of SVC arrhythmogenicity and associated clinical/genetic factors. Keywords: Superior vena cava arrhythmogenicity, Atrial fibrillation, Bayesian networking structure