Mechanisms linking electrical alternans and clinical ventricular arrhythmia in human heart failure.

BACKGROUND: Mechanisms of ventricular tachycardia (VT) and ventricular fibrillation (VF) in patients with heart failure (HF) are undefined. OBJECTIVE: The purpose of this study was to elucidate VT/VF mechanisms in HF by using a computational-clinical approach. METHODS: In 53 patients with HF and 18 control patients, we established the relationship between low-amplitude action potential voltage alternans (APV-ALT) during ventricular pacing at near-resting heart rates and VT/VF on long-term follow-up. Mechanisms underlying the transition of APV-ALT to VT/VF, which cannot be ascertained in patients, were dissected with multiscale human ventricular models based on human electrophysiological and magnetic resonance imaging data (control and HF). RESULTS: For patients with APV-ALT k-score >1.7, complex action potential duration (APD) oscillations (≥2.3% of mean APD), rather than APD alternans, most accurately predicted VT/VF during long-term follow-up (+82%; -90% predictive values). In the failing human ventricular models, abnormal sarcoplasmic reticulum (SR) calcium handling caused APV-ALT (>1 mV) during pacing with a cycle length of 550 ms, which transitioned into large magnitude (>100 ms) discordant repolarization time alternans (RT-ALT) at faster rates. This initiated VT/VF (cycle length <400 ms) by steepening apicobasal repolarization (189 ms/mm) until unidirectional conduction block and reentry. Complex APD oscillations resulted from nonstationary discordant RT-ALT. Restoring SR calcium to control levels was antiarrhythmic by terminating electrical alternans. CONCLUSION: APV-ALT and complex APD oscillations at near-resting heart rates in patients with HF are linked to arrhythmogenic discordant RT-ALT. This may enable novel physiologically tailored, bioengineered indices to improve VT/VF risk stratification, where SR calcium handling and spatial apicobasal repolarization are potential therapeutic targets.

A novel rule-based algorithm for assigning myocardial fiber orientation to computational heart models.

Electrical waves traveling throughout the myocardium elicit muscle contractions responsible for pumping blood throughout the body. The shape and direction of these waves depend on the spatial arrangement of ventricular myocytes, termed fiber orientation. In computational studies simulating electrical wave propagation or mechanical contraction in the heart, accurately representing fiber orientation is critical so that model predictions corroborate with experimental data. Typically, fiber orientation is assigned to heart models based on Diffusion Tensor Imaging (DTI) data, yet few alternative methodologies exist if DTI data is noisy or absent. Here we present a novel Laplace-Dirichlet Rule-Based (LDRB) algorithm to perform this task with speed, precision, and high usability. We demonstrate the application of the LDRB algorithm in an image-based computational model of the canine ventricles. Simulations of electrical activation in this model are compared to those in the same geometrical model but with DTI-derived fiber orientation. The results demonstrate that activation patterns from simulations with LDRB and DTI-derived fiber orientations are nearly indistinguishable, with relative differences ≤6%, absolute mean differences in activation times ≤3.15 ms, and positive correlations ≥0.99. These results convincingly show that the LDRB algorithm is a robust alternative to DTI for assigning fiber orientation to computational heart models.