Mechanisms of ventricular arrhythmias: a dynamical systems-based perspective

Abstract

Defining the cellular electrophysiological mechanisms for ventricular tachyarrhythmias is difficult, given the wide array of potential mechanisms, ranging from abnormal automaticity to various types of reentry and kk activity. The degree of difficulty is increased further by the fact that any particular mechanism may be influenced by the evolving ionic and anatomic environments associated with many forms of heart disease. Consequently, static measures of a single electrophysiological characteristic are unlikely to be useful in establishing mechanisms. Rather, the dynamics of the electrophysiological triggers and substrates that predispose to arrhythmia development need to be considered. Moreover, the dynamics need to be considered in the context of a system, one that displays certain predictable behaviors, but also one that may contain seemingly stochastic elements. It also is essential to recognize that even the predictable behaviors of this complex nonlinear system are subject to small changes in the state of the system at any given time. Here we briefly review some of the short-, medium-, and long-term alterations of the electrophysiological substrate that accompany myocardial disease and their potential impact on the initiation and maintenance of ventricular arrhythmias. We also provide examples of cases in which small changes in the electrophysiological substrate can result in rather large differences in arrhythmia outcome. These results suggest that an interrogation of cardiac electrical dynamics is required to provide a meaningful assessment of the immediate risk for arrhythmia development and for evaluating the effects of putative antiarrhythmic interventions.

Fig. 1.

Nonsustained ventricular fibrillation in rabbit ventricles initiated by rapid pacing at a period of 80 ms. A: anterior and posterior views. B–E: optical signal during nonsustained fibrillation; color represents voltage. Frames show anterior and posterior views at 4 different times (600, 1,200, 2,000, and 2,400 ms). F: spatial frequency domain showing a maximum frequency of about 10 Hz. Color bar indicates frequency in hertz. Hearts were Langendorff perfused with oxygenated Tyrode solution at 37°C with 10 μM blebbistatin.

Fig. 2.

Sustained ventricular fibrillation in rabbit ventricles initiated by rapid pacing at a period of 80 ms preceded by a conditioning period (15 min) of fast pacing at 250 ms in the same heart as shown in Fig. 1. A–E: optical signal during sustained fibrillation; color represents voltage. Frames show anterior and posterior views at 5 different times (600, 1,200, 1,800, 2,400, and 3,000 ms). F: spatial frequency domain showing a maximum frequency of about 10 Hz. When compared with the nonsustained VF episode shown in Fig. 1, a larger fraction of the tissue exhibits the maximum frequency. Color bar indicates frequency in hertz.

Fig. 3.

Stable and unstable spiral waves in a computer simulation of the FMG mathematical model. A: initiation of a spiral wave with transient breakup that results in a stable spiral wave. The spiral ultimately follows a complex hypermeandering trajectory. B: sustained spiral wave breakup initiated following tissue preconditioning by pacing at 300 ms for 30 s. C: sustained spiral wave breakup initiated following tissue preconditioning at 220 ms for 30 s, resulting in more waves with shorter wavelengths. The domain in all cases is 30 cm × 30 cm, and the spatial resolution is 0.0125 cm.