Computational Prediction of the Combined Effect of CRT and LVAD on Cardiac Electromechanical Delay in LBBB and RBBB

Sensitivity Analysis of Ion Channel Conductance on Myocardial Electromechanical Delay: Computational Study

It is well known that cardiac electromechanical delay (EMD) can cause dyssynchronous heart failure (DHF), a prominent cardiovascular disease (CVD). This work computationally assesses the conductance variation of every ion channel on the cardiac cell to give rise to EMD prolongation. The electrical and mechanical models of human ventricular tissue were simulated, using a population approach with four conductance reductions for each ion channel. Then, EMD was calculated by determining the difference between the onset of action potential and the start of cell shortening. Finally, EMD data were put into the optimized conductance dimensional stacking to show which ion channel has the most influence in elongating the EMD. We found that major ion channels, such as L-type calcium (CaL), slow-delayed rectifier potassium (Ks), rapid-delayed rectifier potassium (Kr), and inward rectifier potassium (K1), can significantly extend the action potential duration (APD) up to 580 ms. Additionally, the maximum intracellular calcium (Cai) concentration is greatly affected by the reduction in channel CaL, Ks, background calcium, and Kr. However, among the aforementioned major ion channels, only the CaL channel can play a superior role in prolonging the EMD up to 83 ms. Furthermore, ventricular cells with long EMD have been shown to inherit insignificant mechanical response (in terms of how strong the tension can grow and how far length shortening can go) compared with that in normal cells. In conclusion, despite all variations in every ion channel conductance, only the CaL channel can play a significant role in extending EMD. In addition, cardiac cells with long EMD tend to have inferior mechanical responses due to a lack of Cai compared with normal conditions, which are highly likely to result in a compromised pump function of the heart.

Prediction of Cardiac Mechanical Performance From Electrical Features During Ventricular Tachyarrhythmia Simulation Using Machine Learning Algorithms

In ventricular tachyarrhythmia, electrical instability features including action potential duration, dominant frequency, phase singularity, and filaments are associated with mechanical contractility. However, there are insufficient studies on estimated mechanical contractility based on electrical features during ventricular tachyarrhythmia using a stochastic model. In this study, we predicted cardiac mechanical performance from features of electrical instability during ventricular tachyarrhythmia simulation using machine learning algorithms, including support vector regression (SVR) and artificial neural network (ANN) models. We performed an electromechanical tachyarrhythmia simulation and extracted 12 electrical instability features and two mechanical properties, including stroke volume and the amplitude of myocardial tension (ampTens). We compared predictive performance according to kernel types of the SVR model and the number of hidden layers of the ANN model. In the SVR model, the prediction accuracies of stroke volume and ampTens were the highest when using the polynomial kernel and linear kernel, respectively. The predictive performance of the ANN model was better than that of the SVR model. The prediction accuracies were the highest when the ANN model consisted of three hidden layers. Accordingly, we propose the ANN model with three hidden layers as an optimal model for predicting cardiac mechanical contractility in ventricular tachyarrhythmia. The results of this study are expected to be used to indirectly estimate the hemodynamic response from the electrical cardiac map measured by the optical mapping system during cardiac surgery, as well as cardiac contractility under normal sinus rhythm conditions.

Computational Study to Identify the Effects of the KCNJ2 E299V Mutation in Cardiac Pumping Capacity

The KCNJ2 gene mutations induce short QT syndrome (SQT3) by directly increasing the I_K1 current. There have been many studies on the electrophysiological effects of mutations such as the KCNJ2 D172N that cause the SQT3. However, the KCNJ2 E299V mutation is distinguished from other representative gene mutations that can induce the short QT syndrome (SQT3) in that it increased I_K1 current by impairing the inward rectification of K⁺ channels. The studies of the electromechanical effects on myocardial cells and mechanisms of E299V mutations are limited. Therefore, we investigated the electrophysiological changes and the concomitant mechanical responses according to the expression levels of the KCNJ2 E299V mutation during sinus rhythm and ventricular fibrillation. We performed excitation-contraction coupling simulations using a human ventricular model with both electrophysiological and mechanical properties. In order to observe the electromechanical changes due to the expression of KCNJ2 E299V mutation, the simulations were performed under normal condition (WT), heterogeneous mutation condition (WT/E299V), and pure mutation condition (E299V). First, a single-cell simulation was performed in three types of ventricular cells (endocardial cell, midmyocardial cell, and epicardial cell) to confirm the electrophysiological changes and arrhythmogenesis caused by the KCNJ2 E299V mutation. In three-dimensional sinus rhythm simulations, we compared electrical changes and the corresponding changes in mechanical performance caused by the expression level of E299V mutation. Then, we observed the electromechanical properties of the E299V mutation during ventricular fibrillation using the three-dimensional reentry simulation. The KCNJ2 E299V mutation accelerated the opening of the I_K1 channel and increased I_K1 current, resulting in a decrease in action potential duration. Accordingly, the QT interval was reduced by 48% and 60% compared to the WT condition, for the WT/E299V and E299V conditions, respectively. During sustained reentry, the wavelength was reduced due to the KCNJ2 E299V mutation. Furthermore, there was almost no ventricular contraction in both WT/E299V and E299V conditions. We concluded that in both sinus rhythm and fibrillation, the KCNJ2 E299V mutation results in very low contractility regardless of the expression level of mutation and increases the risk of cardiac arrest and cardiac death.