Discordant Alternans Mechanism for Initiation of Ventricular Fibrillation In Vitro

Background: Ventricular tachyarrhythmias are often preceded by short sequences of premature ventricular complexes. In a previous study, a restitution-based computational model predicted which sequences of stimulated premature complexes were most likely to induce ventricular fibrillation in canines in vivo. However, the underlying mechanism, based on discordant-alternans dynamics, could not be verified in that study. The current study seeks to elucidate the mechanism by determining whether the spatiotemporal evolution of action potentials and initiation of ventricular fibrillation in in vitro experiments are consistent with model predictions. Methods and Results: Optical mapping voltage signals from canine right-ventricular tissue (n=9) were obtained simultaneously from the entire epicardium and endocardium during and after premature stimulus sequences. Model predictions of action potential propagation along a 1-dimensional cable were developed using action potential duration versus diastolic interval data. The model predicted sign-change patterns in action potential duration and diastolic interval spatial gradients with posterior probabilities of 91.1%, and 82.1%, respectively. The model predicted conduction block with 64% sensitivity and 100% specificity. A generalized estimating equation logistic-regression approach showed that model-prediction effects were significant for both conduction block (P \u3c 1x10E-15, coefficient 44.36) and sustained ventricular fibrillation (P=0.0046, coefficient, 1.63) events. Conclusions: The observed sign-change patterns favored discordant alternans, and the model successfully identified sequences of premature stimuli that induced conduction block. This suggests that the relatively simple discordant-alternans-based process that led to block in the model may often be responsible for ventricular fibrillation onset when preceded by premature beats. These observations may aid in developing improved methods for anticipating block and ventricular fibrillation

Transmural Ultrasound-based Visualization of Patterns of Action Potential Wave Propagation in Cardiac Tissue

The pattern of action potential propagation during various tachyarrhythmias is strongly suspected to be composed of multiple re-entrant waves, but has never been imaged in detail deep within myocardial tissue. An understanding of the nature and dynamics of these waves is important in the development of appropriate electrical or pharmacological treatments for these pathological conditions. We propose a new imaging modality that uses ultrasound to visualize the patterns of propagation of these waves through the mechanical deformations they induce. The new method would have the distinct advantage of being able to visualize these waves deep within cardiac tissue. In this article, we describe one step that would be necessary in this imaging process—the conversion of these deformations into the action potential induced active stresses that produced them. We demonstrate that, because the active stress induced by an action potential is, to a good approximation, only nonzero along the local fiber direction, the problem in our case is actually overdetermined, allowing us to obtain a complete solution. Use of two- rather than three-dimensional displacement data, noise in these displacements, and/or errors in the measurements of the fiber orientations all produce substantial but acceptable errors in the solution. We conclude that the reconstruction of action potential-induced active stress from the deformation it causes appears possible, and that, therefore, the path is open to the development of the new imaging modality

A New Defibrillation Mechanism: Termination of Reentrant Waves by Propagating Action Potentials Induced by Nearby Heterogeneities

Introduction. Recently, there has been a major effort to develop new, low-energy defibrillation methods that would be less damaging and less traumatic for the patient, and would save battery energy. However, these methods have not been entirely successful, due in part to an incomplete understanding of all the mechanisms present that may help or hinder the process of terminating the rotating waves present during fibrillation. Here we describe new mechanisms whereby a far-field electric field pulse terminates unpinned waves that are rotating in the vicinity of a blood vessel, plaque deposit or other heterogeneity in the gap junction conductivity. Methods. We ran a series of two-dimensional computer simulations of a spiral wave rotating in the vicinity of a non-conducting obstacle. Application of a low-energy electric field pulse caused a semicircular action potential wave to be launched from the heterogeneity which then interacted with the rotating wave. Results and Conclusions: We found, that, when this interaction is combined with, importantly, the presence of nearby non-conductive boundaries, termination of the rotating waves can occur via a number of new mechanisms, overa wide range of timings of the electric field pulse, and for a number of different initial locations of the rotating wave. The mechanisms only require the rotating wave to be nearby, but not necessarily pinned to the heterogeneity, and thus extends the effectiveness of the electric field pulses used in low-energy defibrillation. Consideration of these mechanisms together with those already discovered could result in the development of improved, low-energy defibrillation protocols

Use of machine learning and Poincaré density grid in the diagnosis of sinus node dysfunction caused by sinoatrial conduction block in dogs

Abstract Background Sinus node dysfunction because of abnormal impulse generation or sinoatrial conduction block causes bradycardia that can be difficult to differentiate from high parasympathetic/low sympathetic modulation (HP/LSM). Hypothesis Beat‐to‐beat relationships of sinus node dysfunction are quantifiably distinguishable by Poincaré plots, machine learning, and 3‐dimensional density grid analysis. Moreover, computer modeling establishes sinoatrial conduction block as a mechanism. Animals Three groups of dogs were studied with a diagnosis of: (1) balanced autonomic modulation (n = 26), (2) HP/LSM (n = 26), and (3) sinus node dysfunction (n = 21). Methods Heart rate parameters and Poincaré plot data were determined [median (25%‐75%)]. Recordings were randomly assigned to training or testing. Supervised machine learning of the training data was evaluated with the testing data. The computer model included impulse rate, exit block probability, and HP/LSM. Results Confusion matrices illustrated the effectiveness in diagnosing by both machine learning and Poincaré density grid. Sinus pauses >2 s differentiated (P < .0001) HP/LSM (2340; 583‐3947 s) from sinus node dysfunction (8503; 7078‐10 050 s), but average heart rate did not. The shortest linear intervals were longer with sinus node dysfunction (315; 278‐323 ms) vs HP/LSM (260; 251‐292 ms; P = .008), but the longest linear intervals were shorter with sinus node dysfunction (620; 565‐698 ms) vs HP/LSM (843; 799‐888 ms; P < .0001). Conclusions Number and duration of pauses, not heart rate, differentiated sinus node dysfunction from HP/LSM. Machine learning and Poincaré density grid can accurately identify sinus node dysfunction. Computer modeling supports sinoatrial conduction block as a mechanism of sinus node dysfunction