Porcine hearts are widely used for preclinical cardiac evaluation. Computational models, by effectively integrating comprehensive experimental data, often reinforce this preclinical assessment. Using extensive multi-modal data, we developed swine ventricular digital twins for healthy and chronic myocardial infarction (MI) conditions to investigate the roles of the cardiac conduction system (CS), spatial repolarization heterogeneities, cardiomyocyte orientation, cell-to-cell coupling, and MI characteristics on ventricular function. We analyzed cardiac magnetic resonance (CMR) images, electrocardiograms (ECGs), and optical (OM) and electroanatomical mapping from 5 healthy and 10 MI pigs. CS architectures were built from OM and ECG recordings. Myocardial fiber orientation, action potential characteristics, and cell-to-cell conductivity in MI tissue were defined from OM and CMR data. Simulated ECGs for healthy and MI models of left anterior descending and left circumflex occlusions were compared to experimental ECGs and were used to assess MI-induced changes. Subject-specific fiber orientation calibration minimally affected electrophysiology, with conduction velocity (CV) and action potential duration (APD) changing by less than 3.6% with respect to standard orientation. Accurate CS and repolarization heterogeneities reproduced depolarization (Pearson correlation 0.76 for QRS) and repolarization (Pearson correlation 0.74 for T-wave) patterns. Incorporating experimentally guided MI-induced alterations enabled the replication of MI depolarization and repolarization features (relative errors: 0.5% CV, 2.9% APD), yielded realistic T-wave morphologies (0.63 Pearson correlation), and revealed ECG patterns specific to vessel-dependent occlusions. Thus, by integrating extensive multi-modal data, we advance porcine cardiac digital twins and demonstrate the influence of key structural and electrophysiological parameters on healthy and MI heart function, providing a robust computational framework for mechanistic and translational applications.

OBJECTIVE: The pathophysiological role of the small conductance calcium-activated potassium (SK) channels in human ventricular myocytes remains unclear. Experimental studies have reported upregulation of SK channels in pathological states, potentially contributing to ventricular repolarization. In heart failure (HF) patients, this upregulation could be an adaptive physiological response to shorten the action potential duration (APD) under conditions of reduced repolarization reserve. This work aimed to uncover the contribution of SK channels to ventricular repolarization in failing myocytes. METHODS: We extended an in silico electrophysiological model of human ventricular failing myocytes by including SK channel activity. To calibrate the maximal SK current conductance (G$_{{SK}}$), we simulated action potentials (APs) at different pacing frequencies and matched the AP duration changes induced by SK channel inhibition or activation to different available experimental data from human failing ventricles for adjustment. RESULTS: The optimal value obtained for G$_{{SK}}$ was 4.288 $\mu$S/$\mu$F in mid-myocardial cells, and 6.4 $\mu$S/$\mu$F for endocardial and epicardial cells. The output of the models was compared with independent experimental data for validation. 1-D simulations of a transmural ventricular fiber indicated that SK channel block may prolong the QT interval and increase the transmural dispersion of repolarization, potentially increasing the risk of arrhythmia in HF. CONCLUSION: Our results highlight the importance of considering the SK channels to improve the characterization of HF-induced ventricular remodeling. Simulations across various single-cell and 1-D scenarios suggest that pharmacological SK channel inhibition could lead to adverse effects in failing ventricles.

Changes induced by intrauterine growth restriction (IUGR) in cardiovascular anatomy and function that persist throughout life have been associated with a higher predisposition to heart disease in adulthood. Together with cardiac morphological remodelling, evaluated through the ventricular sphericity index, alterations in cardiac electrical function have been reported by characterization of the depolarization and repolarization loops, and their angular relationship, measured from the vectorcardiogram. The underlying relationship between the morphological remodelling and the angular variation of QRS and T-wave dominant vectors, if any, has not been explored. The aim of this study was to evaluate this relationship using computational models based on realistic heart and torso in which IUGR-induced morphological changes were incorporated by reducing the ventricular sphericity index. Specifically, we departed from a control model and we built eight different globular heart models by reducing the base-to-apex length and enlarging the basal ventricular diameter. We computed QRS and T-wave dominant vectors and angles from simulated pseudo-electrocardiograms and we compared them with clinical measurements. Results for the QRS to T angles follow a change trend congruent with that reported in clinical data, supporting the hypothesis that the IUGR-induced morphological remodelling could contribute to explain the observed angle changes in IUGR patients. By additionally varying the position of the ventricles with respect to the torso and the electrodes, we found that electrode displacement can impact the quantified angles and should be considered when interpreting the results.

OBJECTIVE: Elevated spatio-temporal variability of human ventricular repolarization has been related to increased risk for ventricular arrhythmias and sudden cardiac death, particularly under β-adrenergic stimulation ( β-AS). This work presents a methodology for theoretical characterization of temporal and spatial repolarization variability at baseline conditions and in response to β-AS. For any measured voltage trace, the proposed methodology estimates the parameters and state variables of an underlying human ventricular action potential (AP) model by combining Double Greedy Dimension Reduction (DGDR) with automatic selection of biomarkers and the Unscented Kalman Filter (UKF). Such theoretical characterization can facilitate subsequent characterization of underlying variability mechanisms. MATERIAL AND METHODS: Given an AP trace, initial estimates for the ionic conductances in a stochastic version of the baseline human ventricular O'Hara et al. model were obtained by DGDR. Those estimates served to initialize and update model parameter estimates by the UKF method based on formulation of an associated nonlinear state-space representation and joint estimation of model parameters and state variables. Similarly, β-AS-induced phosphorylation levels of cellular substrates were estimated by the DGDR-UKF methodology. Performance was tested by building an experimentally-calibrated population of virtual cells, from which synthetic AP traces were generated for baseline and β-AS conditions. RESULTS: The combined DGDR-UKF methodology led to 25% reduction in the error associated with estimation of ionic current conductances at baseline conditions and phosphorylation levels under β-AS with respect to individual DGDR and UKF methods. This improvement was not at the expense of higher computational load, which was diminished by 90% with respect to the individual UKF method. Both temporal and spatial AP variability of repolarization were accurately characterized by the DGDR-UKF methodology. CONCLUSIONS: A combined DGDR-UKF methodology is proposed for parameter and state variable estimation of human ventricular cell models from available AP traces at baseline and under β-AS. This methodology improves the estimation performance and reduces the convergence time with respect to individual DGDR and UKF methods and renders a suitable approach for computational characterization of spatio-temporal repolarization variability to be used for ascertainment of variability mechanisms and its relation to arrhythmogenesis.

OBJECTIVE: Enhanced spatiotemporal ventricular repolarization variability has been associated with ventricular arrhythmias and sudden cardiac death, but the involved mechanisms remain elusive. In this paper, a methodology for estimation of parameters and state variables of stochastic human ventricular cell models from input voltage data is proposed for investigation of repolarization variability. METHODS: The proposed methodology formulates state-space representations based on developed stochastic cell models and uses the unscented Kalman filter to perform joint parameter and state estimation. Evaluation over synthetic and experimental data is presented. RESULTS: Results on synthetically generated data show the ability of the methodology to: first, filter out measurement noise from action potential (AP) traces; second, identify model parameters and state variables from each of those individual AP traces, thus allowing robust characterization of cell-to-cell variability; and, third, replicate statistical population's distributions of input AP-based markers, including dynamic markers quantifying beat-to-beat variability. Application onto experimental data demonstrates the ability of the methodology to match input AP traces while concomitantly inferring the characteristics of underlying stochastic cell models. CONCLUSION: A novel methodology is presented for estimation of parameters and hidden variables of stochastic cardiac computational models, with the advantage of providing a one-to-one match between each individual AP trace and a corresponding set of model characteristics. SIGNIFICANCE: The proposed methodology can greatly help in the characterization of temporal (beat-to-beat) and spatial (cell-to-cell) variability in human ventricular repolarization and in ascertaining the corresponding underlying mechanisms, particularly in scenarios with limited available experimental data.

Instabilities in ventricular repolarization have been documented to be tightly linked to arrhythmia vulnerability. Translation of the information contained in the repolarization phase of the electrocardiogram (ECG) into valuable clinical decision-making tools remains challenging. This work aims at providing an overview of the last advances in the proposal and quantification of ECG-derived indices that describe repolarization properties and whose alterations are related with threatening arrhythmogenic conditions. A review of the state of the art is provided, spanning from the electrophysiological basis of ventricular repolarization to its characterization on the surface ECG through a set of temporal and spatial risk markers.