Modelling passive cardiac conductivity during ischaemia

Accurate diagnosis of severe coronary stenosis based on resting magnetocardiography: a prospective, single-center, cross-sectional analysis

OBJECTIVE

To evaluate the role of resting magnetocardiography in identifying severe coronary artery stenosis in patients with suspected coronary artery disease.

METHODS

A total of 513 patients with angina symptoms were included and divided into two groups based on the extent of coronary artery disease determined by angiography: the non-severe coronary stenosis group (< 70% stenosis) and the severe coronary stenosis group (≥ 70% stenosis). The diagnostic model was constructed using magnetic field map (MFM) parameters, either individually or in combination with clinical indicators. The performance of the models was evaluated using receiver operating characteristic curves, accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). Calibration plots and decision curve analysis were performed to investigate the clinical utility and performance of the models, respectively.

RESULTS

In the severe coronary stenosis group, QR_MCTDd, S_MDp, and TT_MAC50 were significantly higher than those in the non-severe coronary stenosis group (10.46 ± 10.66 vs. 5.11 ± 6.07, P < 0.001; 7.2 ± 8.64 vs. 4.68 ± 6.95, P = 0.003; 0.32 ± 57.29 vs. 0.26 ± 57.29, P < 0.001). While, QR_MVamp, R_MA, and T_MA in the severe coronary stenosis group were lower (0.23 ± 0.16 vs. 0.28 ± 0.16, P < 0.001; 55.06 ± 48.68 vs. 59.24 ± 53.01, P < 0.001; 51.67 ± 39.32 vs. 60.45 ± 51.33, P < 0.001). Seven MFM parameters were integrated into the model, resulting in an area under the curve of 0.810 (95% CI: 0.765-0.855). The sensitivity, specificity, PPV, NPV, and accuracy were 71.7%, 80.4%, 93.3%, 42.8%, and 73.5%; respectively. The combined model exhibited an area under the curve of 0.845 (95% CI: 0.798-0.892). The sensitivity, specificity, PPV, NPV, and accuracy were 84.3%, 73.8%, 92.6%, 54.6%, and 82.1%; respectively. Calibration curves demonstrated excellent agreement between the nomogram prediction and actual observation. The decision curve analysis showed that the combined model provided greater net benefit compared to the magnetocardiography model.

CONCLUSIONS

The novel quantitative MFM parameters, whether used individually or in combination with clinical indicators, have been shown to effectively predict the risk of severe coronary stenosis in patients presenting with angina-like symptoms. Magnetocardiography, an emerging non-invasive diagnostic tool, warrants further exploration for its potential in diagnosing coronary heart disease.

Confocal microscopy-based estimation of intracellular conductivities in myocardium for modeling of the normal and infarcted heart

Ventricular arrhythmias are the leading cause of mortality in patients with ischemic heart diseases, such as myocardial infarction (MI). Computational simulation of cardiac electrophysiology provides insights into these arrhythmias and their treatment. However, only sparse information is available on crucial model parameters, for instance, the anisotropic intracellular electrical conductivities. Here, we introduced an approach to estimate these conductivities in normal and MI hearts. We processed and analyzed images from confocal microscopy of left ventricular tissue of a rabbit MI model to generate 3D reconstructions. We derived tissue features including the volume fraction of myocytes (V_myo), gap junctions-containing voxels (V_gj), and fibrosis (V_fibrosis). We generated models of the intracellular space and intercellular coupling. Applying numerical methods for solving Poisson's equation for stationary electrical currents, we calculated normal (σ_myo,n), longitudinal (σ_myo,l), and transverse (σ_myo,t) intracellular conductivities. Using linear regression analysis, we assessed relationships of conductivities to tissue features. V_gj and V_myo were reduced in MI vs. control, but V_fibrosis was increased. Both σ_myo,l and σ_myo,n were lower in MI than in control. Differences of σ_myo,t between control and MI were not significant. We found strong positive relationships of σ_myo,l with V_myo and V_gj, and a strong negative relationship with V_fibrosis. The relationships of σ_myo,n with these tissue features were similar but less pronounced. Our study provides quantitative insights into the intracellular conductivities in the normal and MI heart. We suggest that our study establishes a framework for the estimation of intracellular electrical conductivities of myocardium with various pathologies.

Deriving the Bidomain Model of Cardiac Electrophysiology From a Cell-Based Model; Properties and Comparisons

The bidomain model is considered to be the gold standard for numerical simulation of the electrophysiology of cardiac tissue. The model provides important insights into the conduction properties of the electrochemical wave traversing the cardiac muscle in every heartbeat. However, in normal resolution, the model represents the average over a large number of cardiomyocytes, and more accurate models based on representations of all individual cells have therefore been introduced in order to gain insight into the conduction properties close to the myocytes. The more accurate model considered here is referred to as the EMI model since both the extracellular space (E), the cell membrane (M) and the intracellular space (I) are explicitly represented in the model. Here, we show that the bidomain model can be derived from the cell-based EMI model and we thus reveal the close relation between the two models, and obtain an indication of the error introduced in the approximation. Also, we present numerical simulations comparing the results of the two models and thereby highlight both similarities and differences between the models. We observe that the deviations between the solutions of the models become larger for larger cell sizes. Furthermore, we observe that the bidomain model provides solutions that are very similar to the EMI model when conductive properties of the tissue are in the normal range, but large deviations are present when the resistance between cardiomyocytes is increased.

Which bidomain conductivity is the most important for modelling heart and torso surface potentials during ischaemia?

Mathematical simulations using the bidomain model, which represents cardiac tissue as consisting of an intracellular and an extracellular space, are a key approach that can be used to improve understanding of heart conditions such as ischaemia. However, key inputs to these models, such as the bidomain conductivity values, are not known with any certainty. Since efforts are underway to measure these values, it would be useful to be able to quantify the effect on model outputs of uncertainty in these inputs, and also to determine, if possible, which are the most important values to focus on in experimental studies. Our previous work has systematically studied the sensitivity of heart surface potentials to the bidomain conductivity values, and this was performed using a half-ellipsoidal model of the left ventricle. This study uses a bi-ventricular heart in a torso model and this time looks at the sensitivity of the torso surface potentials, as well as the heart surface potentials, to various conductivity values (blood, torso and the six bidomain conductivities). We found that both epicardial and torso potentials are the most sensitive to the intracellular longitudinal (along the cardiac fibres) conductivity (g_il) with more minor sensitivity to the torso conductivity, and that changes in g_il have a significant effect on the surface potential distributions on both the torso and the heart.

Uncertainty Quantification in Simulations of Myocardial Ischemia

Computational models of myocardial ischemia are parameterized using assumptions of tissue properties and physiological values such as conductivity ratios in cardiac tissue and conductivity changes between healthy and ischemic tissues. Understanding the effect of uncertainty in these parameter selections would provide useful insight into the performance and variability of the modeling outputs. Recently developed uncertainty quantification tools allow for the application of polynomial chaos expansion uncertainty quantification to such bioelectric models in order to parsimoniously examine model response to input uncertainty. We applied uncertainty quantification to examine reconstructed extracellular potentials from the cardiac passive bidomain based on variation in the conductivity values for the ischemic tissue. We investigated the model response in both a synthetic dataset with simulated ischemic regions and a dataset with ischemic regions derived from experimental recordings. We found that extracellular longitudinal and intracellular longitudinal conductivities predominately affected simulation output, with the highest standard deviations in regions of extracellular potential elevations. We found that transverse conductivity had almost no effect on model output.

Double-peak signal features in microfluidic impedance flow cytometry enable sensitive measurement of cell membrane capacitance

The probing of individual cells at specific frequency regimes in a microfluidic impedance flow cytometer led to the observation of unusual "double peak" features in the reactive component of the resulting signal. The phenomenon was restricted to the lower frequencies (400-800 kHz) of the β-dispersion regime and its occurrence was facilitated by the co-planar microelectrode geometry in the device. To understand the reasons for this anomalous behaviour, the system was modelled using COMSOL. The simulated model agreed well with experimental observations and provided insight into the origins of this signal profile and the effect of various parameters on its behaviour. One of the most significant observations of this study was the high sensitivity of the features in the "double peak" profile to changes in cell membrane capacitance (CMC), compared to conventional "single peaks" of reactive impedance. This was consequently exploited to accurately distinguish populations of normal and glutaraldehyde treated erythrocytes based on variations in their CMC, indicating a drastic decrease in the CMC of treated cells. Additionally, we demonstrate the applicability of using this double peak effect to identify cell populations within a mixture of PBMCs. This study is an improvement over conventional approaches of measuring CMC via impedance flow cytometry by enabling the measurement of both cell size and cell membrane properties at a single frequency rather than using multiple frequencies. Using a single frequency significantly simplifies the system and reduces the associated costs. Additionally, this technique enables the measurement of CMC at relatively low frequencies.

Effectiveness of magnetocardiography to identify patients in need of coronary artery revascularization: a cross-sectional study

BACKGROUND

Patients with angina-like symptoms need invasive or non-invasive angiography to determine whether revascularization is necessary. For patients in need of revascularization, undergoing coronary computed tomography angiography (CCTA) may delay the treatment of revascularization and increase exposure to contrast agents and radiation. The aim of this cross-sectional study was to accessed the effectiveness of magnetocardiography (MCG) to identify patients who should undergo coronary revascularization.

METHODS

A total of 203 patients who were suffering from angina-like symptoms and underwent percutaneous coronary angiography (PCA) between July 27, 2015 and April 10, 2017 at the 8th Medical Center of Chinese PLA General Hospital, were enrolled in this cross-sectional study. In all patients, 12-lead electrocardiography (ECG) and MCG test were performed before PCA. For each subject. The value at every single sampling point was extracted from T wave of each MCG channel in time sequence. Pearson's correlation coefficients were calculated for each two T-waves. A binary logistic regression diagnosis model of these coefficients was established to identify patients in need of revascularization.

RESULTS

Ten pairings of coefficients were entered into diagnostic regression model as covariates. The area under the receiver operating characteristic (ROC) curve (AUC) was 0.747 (95% CI: 0.680-0.815), and the asymptotic P value was less than 0.001. At the cut-off value of 0.55, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were 72.9%, 65.9%, 74.8%, 63.6% and 69.9%, and the positive and negative post-test probabilities were 65.9% and 25.7%. The accuracy, sensitivity, specificity, PPV and NPV for 12-lead ECG were 67.0%, 62.7%, 63.5%, 70.5% and 55.1%, respectively. However, when those acute myocardial infarction (AMI) patients were ruled out from both groups, the MCG model had an accuracy of 68.2%, a sensitivity of 70.1%, a specificity of 66.3%, a PPV of 68.5% and an NPV of 67.9%. But, the accuracy, sensitivity, specificity, PPV and NPV for 12-lead ECG were 60.0%, 55.2%, 65.1%, 62.3% and 58.1%, respectively.

CONCLUSIONS

Patients suffering from angina-like symptoms, with a logistic regression model value over 0.55, should be recommended for PCA.

Differences between models of partial thickness and subendocardial ischaemia in terms of sensitivity analyses of ST-segment epicardial potential distributions

Mathematical modelling is a useful technique to help elucidate the connection between non-transmural ischaemia and ST elevation and depression of the ECG. Generally, models represent non-transmural ischaemia using an ischaemic zone that extends from the endocardium partway to the epicardium. However, recent experimental work has suggested that ischaemia typically arises within the heart wall. This work examines the effect of modelling cardiac ischaemia in the left ventricle using two different models: subendocardial ischaemia and partial thickness ischaemia, representing the first and second scenarios, respectively. We found that it is possible, only in the model of subendocardial ischaemia, to see a single minimum on the epicardial surface above the ischaemic region, and this only occurs for low ischaemic thicknesses. This may help to explain the rarity of ST depression that is located over the ischaemic region. It was also found that, in both models, the epicardial potential distribution is most sensitive to the proximity of the ischaemic region to the epicardium, rather than to the thickness of the ischaemic region. Since proximity does not indicate the thickness of the ischaemic region, this suggests a reason why it may be difficult to determine the degree of ischaemia using the ST segment of the ECG.

Image-based modeling of acute myocardial ischemia using experimentally derived ischemic zone source representations

BACKGROUND

Computational models of myocardial ischemia often use oversimplified ischemic source representations to simulate epicardial potentials. The purpose of this study was to explore the influence of biophysically justified, subject-specific ischemic zone representations on epicardial potentials.

METHODS

We developed and implemented an image-based simulation pipeline, using intramural recordings from a canine experimental model to define subject-specific ischemic regions within the heart. Static epicardial potential distributions, reflective of ST segment deviations, were simulated and validated against measured epicardial recordings.

RESULTS

Simulated epicardial potential distributions showed strong statistical correlation and visual agreement with measured epicardial potentials. Additionally, we identified and described in what way border zone parameters influence epicardial potential distributions during the ST segment.

CONCLUSION

From image-based simulations of myocardial ischemia, we generated subject-specific ischemic sources that accurately replicated epicardial potential distributions. Such models are essential in understanding the underlying mechanisms of the bioelectric fields that arise during ischemia and are the basis for more sophisticated simulations of body surface ECGs.

Cardiac ischemia-insights from computational models

BACKGROUND

Complementary to clinical and experimental studies, computational cardiac modeling serves to obtain a comprehensive understanding of the cardiovascular system in order to analyze dysfunction, evaluate existing, and develop novel treatment strategies.

OBJECTIVES

We describe the basics of multiscale computational modeling of cardiac electrophysiology from the molecular ion channel to the whole body scale. By modeling cardiac ischemia, we illustrate how in silico experiments can contribute to our understanding of how the pathophysiological mechanisms translate into changes observed in diagnostic tools such as the electrocardiogram (ECG).

MATERIALS AND METHODS

Quantitative in silico modeling spans a wide range of scales from ion channel biophysics to ECG signals. For each of the scales, a set of mathematical equations describes electrophysiology in relation to the other scales. Integration of ischemia-induced changes is performed on the ion channel, single-cell, and tissue level. This approach allows us to study how effects simulated at molecular scales translate to changes in the ECG.

RESULTS

Ischemia induces action potential shortening and conduction slowing. Hence, ischemic myocardium has distinct and significant effects on propagation and repolarization of excitation, depending on the intramural extent of the ischemic region. For transmural and subendocardial ischemic regions, ST segment elevation and depression, respectively, were observed, whereas intermediate ischemic regions were found to be electrically silent (NSTEMI).

CONCLUSIONS

In silico modeling contributes quantitative and mechanistic insight into fundamental ischemia-related arrhythmogenic mechanisms. In addition, computational modeling can help to translate experimental findings at the (sub-)cellular level to the organ and body context (e. g., ECG), thereby providing a thorough understanding of this routinely used diagnostic tool that may translate into optimized applications.

Quantifying the effect of uncertainty in input parameters in a simplified bidomain model of partial thickness ischaemia

Reduced blood flow in the coronary arteries can lead to damaged heart tissue (myocardial ischaemia). Although one method for detecting myocardial ischaemia involves changes in the ST segment of the electrocardiogram, the relationship between these changes and subendocardial ischaemia is not fully understood. In this study, we modelled ST-segment epicardial potentials in a slab model of cardiac ventricular tissue, with a central ischaemic region, using the bidomain model, which considers conduction longitudinal, transverse and normal to the cardiac fibres. We systematically quantified the effect of uncertainty on the input parameters, fibre rotation angle, ischaemic depth, blood conductivity and six bidomain conductivities, on outputs that characterise the epicardial potential distribution. We found that three typical types of epicardial potential distributions (one minimum over the central ischaemic region, a tripole of minima, and two minima flanking a central maximum) could all occur for a wide range of ischaemic depths. In addition, the positions of the minima were affected by both the fibre rotation angle and the ischaemic depth, but not by changes in the conductivity values. We also showed that the magnitude of ST depression is affected only by changes in the longitudinal and normal conductivities, but not by the transverse conductivities.

Engineering Biodegradable and Biocompatible Bio-ionic Liquid Conjugated Hydrogels with Tunable Conductivity and Mechanical Properties

Conventional methods to engineer electroconductive hydrogels (ECHs) through the incorporation of conductive nanomaterials and polymers exhibit major technical limitations. These are mainly associated with the cytotoxicity, as well as poor solubility, processability, and biodegradability of their components. Here, we describe the engineering of a new class of ECHs through the functionalization of non-conductive polymers with a conductive choline-based bio-ionic liquid (Bio-IL). Bio-IL conjugated hydrogels exhibited a wide range of highly tunable physical properties, remarkable in vitro and in vivo biocompatibility, and high electrical conductivity without the need for additional conductive components. The engineered hydrogels could support the growth and function of primary cardiomyocytes in both two dimentinal (2D) and three dimensional (3D) cultures in vitro. Furthermore, they were shown to be efficiently biodegraded and possess low immunogenicity when implanted subcutaneously in rats. Taken together, our results suggest that Bio-IL conjugated hydrogels could be implemented and readily tailored to different biomedical and tissue engineering applications.

Repolarization Heterogeneity of Magnetocardiography Predicts Long-Term Prognosis in Patients with Acute Myocardial Infarction

PURPOSE

Magnetocardiography (MCG) has been proposed as a noninvasive, diagnostic tool for risk-stratifying patients with acute myocardial infarction (AMI). This study evaluated whether MCG predicts long-term prognosis in AMI.

MATERIALS AND METHODS

In 124 AMI patients (95 males, mean age 60±11 years), including 39 with ST-elevation myocardial infarction, a 64-channel MCG was performed within 2 days after AMI. During a mean follow-up period of 6.1 years, major adverse cardiac events (MACE) were evaluated.

RESULTS

MACE occurred in 31 (25%) patients, including 20 revascularizations, 8 deaths, and 3 re-infarctions. Non-dipole patterns were observed at the end of the T wave in every patients. However, they were observed at T-peak in 77% (24/31) and 54% (50/93) of patients with and without MACE, respectively (p=0.03). Maximum current, field map angles, and distance dynamics were not different between groups. In the multivariate analysis, patients with non-dipole patterns at T-peak had increased age- and gender-adjusted hazard ratios for MACE (hazard ratio 2.89, 95% confidence interval 1.20-6.97, p=0.02) and lower cumulative MACE-free survival than those with dipole patterns (p=0.02).

CONCLUSION

Non-dipole patterns at T-peak were more frequently observed in patients with MACE and were related to poor long-term prognosis. Thus, repolarization heterogeneity measured by MCG may be a useful predictor for AMI prognosis.

The role of blood vessels in high-resolution volume conductor head modeling of EEG

Reconstruction of the electrical sources of human EEG activity at high spatio-temporal accuracy is an important aim in neuroscience and neurological diagnostics. Over the last decades, numerous studies have demonstrated that realistic modeling of head anatomy improves the accuracy of source reconstruction of EEG signals. For example, including a cerebro-spinal fluid compartment and the anisotropy of white matter electrical conductivity were both shown to significantly reduce modeling errors. Here, we for the first time quantify the role of detailed reconstructions of the cerebral blood vessels in volume conductor head modeling for EEG. To study the role of the highly arborized cerebral blood vessels, we created a submillimeter head model based on ultra-high-field-strength (7T) structural MRI datasets. Blood vessels (arteries and emissary/intraosseous veins) were segmented using Frangi multi-scale vesselness filtering. The final head model consisted of a geometry-adapted cubic mesh with over 17×10(6) nodes. We solved the forward model using a finite-element-method (FEM) transfer matrix approach, which allowed reducing computation times substantially and quantified the importance of the blood vessel compartment by computing forward and inverse errors resulting from ignoring the blood vessels. Our results show that ignoring emissary veins piercing the skull leads to focal localization errors of approx. 5 to 15mm. Large errors (>2cm) were observed due to the carotid arteries and the dense arterial vasculature in areas such as in the insula or in the medial temporal lobe. Thus, in such predisposed areas, errors caused by neglecting blood vessels can reach similar magnitudes as those previously reported for neglecting white matter anisotropy, the CSF or the dura - structures which are generally considered important components of realistic EEG head models. Our findings thus imply that including a realistic blood vessel compartment in EEG head models will be helpful to improve the accuracy of EEG source analyses particularly when high accuracies in brain areas with dense vasculature are required.

Computational generation of the Purkinje network driven by clinical measurements: the case of pathological propagations

To properly describe the electrical activity of the left ventricle, it is necessary to model the Purkinje fibers, responsible for the fast and coordinate ventricular activation, and their interaction with the muscular propagation. The aim of this work is to propose a methodology for the generation of a patient-specific Purkinje network driven by clinical measurements of the activation times related to pathological propagations. In this case, one needs to consider a strongly coupled problem between the network and the muscle, where the feedback from the latter to the former cannot be neglected as in a normal propagation. We apply the proposed strategy to data acquired on three subjects, one of them suffering from muscular conduction problems owing to a scar and the other two with a muscular pre-excitation syndrome (Wolff-Parkinson-White). To assess the accuracy of the proposed method, we compare the results obtained by using the patient-specific Purkinje network generated by our strategy with the ones obtained by using a non-patient-specific network. The results show that the mean absolute errors in the activation time is reduced for all the cases, highlighting the importance of including a patient-specific Purkinje network in computational models.

Inverse Electrocardiographic Source Localization of Ischemia: An Optimization Framework and Finite Element Solution

With the goal of non-invasively localizing cardiac ischemic disease using body-surface potential recordings, we attempted to reconstruct the transmembrane potential (TMP) throughout the myocardium with the bidomain heart model. The task is an inverse source problem governed by partial differential equations (PDE). Our main contribution is solving the inverse problem within a PDE-constrained optimization framework that enables various physically-based constraints in both equality and inequality forms. We formulated the optimality conditions rigorously in the continuum before deriving finite element discretization, thereby making the optimization independent of discretization choice. Such a formulation was derived for the L₂-norm Tikhonov regularization and the total variation minimization. The subsequent numerical optimization was fulfilled by a primal-dual interior-point method tailored to our problem's specific structure. Our simulations used realistic, fiber-included heart models consisting of up to 18,000 nodes, much finer than any inverse models previously reported. With synthetic ischemia data we localized ischemic regions with roughly a 10% false-negative rate or a 20% false-positive rate under conditions up to 5% input noise. With ischemia data measured from animal experiments, we reconstructed TMPs with roughly 0.9 correlation with the ground truth. While precisely estimating the TMP in general cases remains an open problem, our study shows the feasibility of reconstructing TMP during the ST interval as a means of ischemia localization.

Influence of ischemic core muscle fibers on surface depolarization potentials in superfused cardiac tissue preparations: a simulation study

Thin-walled cardiac tissue samples superfused with oxygenated solutions are widely used in experimental studies. However, due to decreased oxygen supply and insufficient wash out of waste products in the inner layers of such preparations, electrophysiological functions could be compromised. Although the cascade of events triggered by cutting off perfusion is well known, it remains unclear as to which degree electrophysiological function in viable surface layers is affected by pathological processes occurring in adjacent tissue. Using a 3D numerical bidomain model, we aim to quantify the impact of superfusion-induced heterogeneities occurring in the depth of the tissue on impulse propagation in superficial layers. Simulations demonstrated that both the pattern of activation as well as the distribution of extracellular potentials close to the surface remain essentially unchanged. This was true also for the electrophysiological properties of cells in the surface layer, where most relevant depolarization parameters varied by less than 5.5 %. The main observed effect on the surface was related to action potential duration that shortened noticeably by 53 % as hypoxia deteriorated. Despite the known limitations of such experimental methods, we conclude that superfusion is adequate for studying impulse propagation and depolarization whereas repolarization studies should consider the influence of pathological processes taking place at the core of tissue sample.

Incorporating histology into a 3D microscopic computer model of myocardium to study propagation at a cellular level

We introduce a 3D model of cardiac tissue to study at a microscopic level the relationship between tissue morphology and propagation of depolarization. Unlike the classical bidomain approach, in which tissue properties are described by the apparent conductivity of the tissue, in this "microdomain" approach, we included histology by modeling the actual shape of the intracellular and extracellular spaces that contain spatially distributed gap-junctions and membranes. The histological model of the tissue was generated by a computer algorithm that can be tuned to model different histological changes. For healthy tissue, the model predicted a realistic conduction velocity of 0.42 m/s based solely on the parameters derived from histology. A comparison with a brick-shaped, simplified model showed that conduction depended to a moderate extent on the shape of myocytes; a comparison with a one-dimensional bidomain model with the same overall shape and structure showed that the apparent conductivity of the tissue can be used to create an equivalent bidomain model. In summary, the microdomain approach offers a means of directly incorporating structural and functional parameters into models of cardiac activation and propagation and thus provides a valuable bridge between the cellular and tissue domains in the myocardium.

Subject-specific, multiscale simulation of electrophysiology: a software pipeline for image-based models and application examples

Many simulation studies in biomedicine are based on a similar sequence of processing steps, starting from images and running through geometric model generation, assignment of tissue properties, numerical simulation and visualization of the results--a process known as image-based geometric modelling and simulation. We present an overview of software systems for implementing such a sequence both within highly integrated problem-solving environments and in the form of loosely integrated pipelines. Loose integration in this case indicates that individual programs function largely independently but communicate through files of a common format and support simple scripting, so as to automate multiple executions wherever possible. We then describe three specific applications of such pipelines to translational biomedical research in electrophysiology.

Usefulness of magnetocardiogram to detect unstable angina pectoris and non-ST elevation myocardial infarction

Electrophysiologic information as well as anatomic information to detect coronary artery disease is important for accurate diagnosis. A diagnostic tool that can detect patients with unstable angina pectoris (UAP) or non-ST elevation myocardial infarction (NSTEMI) with severe stenosis would be beneficial for patients and clinicians. Magnetocardiography has been recognized as a noncontact, noninvasive, fast tool to detect ischemic coronary artery disease and provide direct electrophysiologic information from the heart. In this study, 10 magnetocardiographic (MCG) parameters from 4 groups, including 185 young controls, 19 age-matched controls (AMCs), 110 patients with UAP, and 83 patients with NSTEMIs, were analyzed. A 64-channel MCG system in a magnetically shielded room was used. All 10 parameters showed significant differences (p <0.001) between controls and patients with NSTEMIs, and 6 parameters showed significant differences (p <0.01) between AMCs and patients with UAP. MCG parameters significantly increased when ischemic heart conditions worsened. Of the 10 parameters, the magnetic field map was among the easiest ways to detect the severity of coronary artery disease. Abnormal magnetic field maps were observed frequently with worsening ischemic coronary artery disease (70% of patients with UAP and 92.5% of those with NSTEMIs had abnormal maps). The combination of the binary boundaries of the 10 parameters had 96.4% sensitivity and 85% specificity to detect NSTEMI. In conclusion, the MCG parameters and magnetic field maps may detect UAP and NSTEMI easily when they are considered together.

Ischemic preconditioning protects against arrhythmogenesis through maintenance of both active as well as passive electrical properties in ischemic canine hearts

BACKGROUND

The mechanisms for the antiarrhythmogenic effects of preconditioning in ischemic hearts, although well demonstrated, are not clear. We measured indices of activation and repolarization using data from a high-resolution epicardial sock electrode array in preconditioned (PC) and non-PC hearts in an attempt to gain further insight into protective mechanisms.

METHODS AND RESULTS

Five canine hearts were subjected to a coronary artery occlusion lasting at least 1 hour, and 5 were subjected to a similar occlusion preceded by a preconditioning protocol. Epicardial electrograms were recorded using a 490-electrode sock. Representative beats were selected at intervals of 1 minute for analysis. The mean ST elevation for the PC group both rose slowly after occlusion and also resolved more slowly than the non-PC group. Electrocardiographic markers for propagation such as Total Activation Time, the QRSRMS width, and magnitude of steepest downstroke of the QRS complex all showed that the PC group maintained conduction velocity initially and also varied less dramatically than the control group. The regression line slope computed on a scatter plot of QT width vs cycle length was 0.23 for the PC group and 0.58 for non-PC. During occlusion, the incidence of premature ventricular contractions (PVCs) peaked at approximately 17 minutes followed by a second peak at approximately 27 minutes in the non-PC group, the PC group showed similar peaks at approximately 24 and approximately 53 minutes respectively.

CONCLUSION

The slower rate of resolution of ST elevation in PC hearts suggests a delay in gap junction closure, thus maintaining intracellular resistivity and reducing the likelihood of arrhythmia. The speed of conduction is adequately maintained during the early stages of ischemia in PC hearts. The mQTi-mRR regression line, a surrogate measure of rate dependency of repolarization (restitution), has a lower slope in the PC case, thus suggesting a mechanism of reduced arrhythmogenesis. The conclusions are supported by a delay of peak PVCs in PC hearts.