Introduction: Atrial fibrillation (AF) is the most common arrhythmia, associated with significant burdens to patients and the healthcare system. The atrioventricular (AV) node plays a vital role in regulating heart rate during AF by filtering electrical impulses from the atria. However, it is often insufficient in regards to maintaining a healthy heart rate, thus the AV node properties are modified using rate-control drugs. Moreover, treatment selection during permanent AF is currently done empirically. Quantifying individual differences in diurnal and short-term variability of AV-nodal function could aid in personalized treatment selection. Methods: This study presents a novel methodology for estimating the refractory period (RP) and conduction delay (CD) trends, and their uncertainty in the two pathways of the AV node during 24 h using non-invasive data. This was achieved by utilizing a network model together with a problem-specific genetic algorithm and an approximate Bayesian computation algorithm. Diurnal variability in the estimated RP and CD was quantified by the difference between the daytime and nighttime estimates, and short-term variability was quantified by the Kolmogorov-Smirnov distance between adjacent 10-min segments in the 24-h trends. Additionally, the predictive value of the derived parameter trends regarding drug outcome was investigated using several machine learning tools. Results: Holter electrocardiograms from 51 patients with permanent AF during baseline were analyzed, and the predictive power of variations in RP and CD on the resulting heart rate reduction after treatment with four rate control drugs was investigated. Diurnal variability yielded no correlation to treatment outcome, and no prediction of drug outcome was possible using the machine learning tools. However, a correlation between the short-term variability for the RP and CD in the fast pathway and resulting heart rate reduction during treatment with metoprolol (ρ = 0.48, p < 0.005 in RP, ρ = 0.35, p < 0.05 in CD) were found. Discussion: The proposed methodology enables non-invasive estimation of the AV node properties during 24 h, which-indicated by the correlation between the short-term variability and heart rate reduction-may have the potential to assist in treatment selection.

The heart rate during atrial fibrillation (AF) is highly dependent on the conduction properties of the atrioventricular (AV) node. These properties can be affected using β-blockers or calcium channel blockers, mainly chosen empirically. Characterization of individual AV-nodal conduction could assist in personalized treatment selection during AF. Individual AV nodal refractory periods and conduction delays were characterized based on 24-hour ambulatory ECGs from 60 patients with permanent AF. This was done by estimating model parameters from a previously created mathematical network model of the AV node using a problem-specific genetic algorithm. Based on the estimated model parameters, the circadian variation and its drug-dependent difference between treatment with two β-blockers and two calcium channel blockers were quantified on a population level by means of cosinor analysis using a linear mixed-effect approach. The mixed-effects analysis indicated increased refractoriness relative to baseline for all drugs. An additional decrease in circadian variation for parameters representing conduction delay was observed for the β-blockers. This indicates that the two drug types have quantifiable differences in their effects on AV-nodal conduction properties. These differences could be important in treatment outcome, and thus quantifying them could assist in treatment selection.

The purpose of this study is to describe the pharmacologic management of rate and rhythm and assess which factors are associated with the prescription of these drugs in patients with nonvalvular atrial fibrillation (AF) from the Effectiveness, Safety, and Costs in Atrial Fibrillation study.
This retrospective, cross-sectional study describes the pharmacologic rate and rhythm control management strategies adopted during 2012 in all patients diagnosed as having nonvalvular AF in 2007 to 2011. The data source is the Information System for the Improvement of Research in Primary Care database, which is based on primary care electronic health records. To answer the study objectives, 3 multivariate regression models to assess the independent factors associated with the prescription of these drugs were conducted for 2012. The rate and rhythm control drugs assessed were β-blockers, nondihydropyridine calcium channel blockers, antiarrhythmic agents, and digoxin.
A total of 21,304 patients were diagnosed as having nonvalvular AF; 11,638 (54.6%) had at least one heart rate measure during 2012. Of them, 7777 (66.8%) received one or more rate and/or rhythm control drugs during 2012. Most patients (5751 [73.9%] of 7777) received only one drug for rate and/or rhythm control. Rate control agents were the most frequently used in 2012, with β-blockers the most prescribed group (4091 patients [52.6%]). A variety of different variables were associated with the prescription of rate and/or rhythm control drugs in the multivariate regression models.
The most used pharmacologic treatment of rate and rhythm control in our AF population is β-blockers, indicating that a rate control strategy is preferred in our setting, as widely recommended.