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.

Atrial Fibrillation: Pathogenesis, Predisposing Factors, and Genetics

“…Various studies attempt to analyze the frequencies of single-nucleotide polymorphisms (SNPs) in genes whose protein products are involved in the pathogenesis of AF. Genome-wide association (GWAS) in the Japanese population identified that rs2200733, rs10033464 (located in the PITX2), and rs6584555 (located in the NURL1) were associated with AF [ 94 ]. In previous studies in Japan, six more loci were associated with AF: at 1q24 in PRRX1 (rs593479), 4q25 near PITX2 (rs2634073), 7q31 in CAV1 (rs1177384), 10q25 in NURL1 (rs6584555), 12q24 in CUX2 (rs649002), and 16q22 in ZFHX3 (rs12932445) [ 95 ].…”

Bayesian network modelling study to identify factors influencing the risk of cardiovascular disease in Canadian adults with hepatitis C virus infection

“…28 Over the past few years, BNs have been extensively used to model clinical problems in CVD for the purposes of diagnosis, risk assessment and disease prediction. [29][30][31][32][33] In the present study, we introduced a BN analysis to evaulate the aetiological role of HCV infection in CVD risk. Our objective is to characterise the multivariable probabilistic connection between the two diseases and identify factors that mediate and influence this relationship in a population of Canadian adults.…”

Using electronic health records to develop and validate a machine-learning tool to predict type 2 diabetes outcomes: a study protocol

“… 16 17 These relationships are represented by a graphical structure, whereas the quantitative dependencies between individual variables are expressed as a conditional probability. 18 Over the last years, BNs have been extensively used to model diagnosis, risk assessment and disease prediction in the context of cardiovascular diseases, 19–21 but have not yet been much explored in the context of T2DM. Recently, a few studies developed promising approaches applying deep learning neural networks and comparative machine-learning approaches for predicting T2DM.…”