Assessing the ability of substrate mapping techniques to guide ventricular tachycardia ablation using computational modelling

Predicting postinfarct ventricular tachycardia by integrating cardiac MRI and advanced computational reentrant pathway analysis

BACKGROUND

Implantable cardiac defibrillator (ICD) implantation can protect against sudden cardiac death after myocardial infarction. However, improved risk stratification for device requirement is still needed.

OBJECTIVE

The purpose of this study was to improve assessment of postinfarct ventricular electropathology and prediction of appropriate ICD therapy by combining late gadolinium enhancement (LGE) and advanced computational modeling.

METHODS

ADAS 3D LV (ADAS LV Medical, Barcelona, Spain) and custom-made software were used to generate 3-dimensional patient-specific ventricular models in a prospective cohort of patients with a myocardial infarction (N = 40) having undergone LGE imaging before ICD implantation. Corridor metrics and 3-dimensional surface features were computed from LGE images. The Virtual Induction and Treatment of Arrhythmias (VITA) framework was applied to patient-specific models to comprehensively probe the vulnerability of the scar substrate to sustaining reentrant circuits. Imaging and VITA metrics, related to the numbers of induced ventricular tachycardias and their corresponding round trip times (RTTs), were compared with ICD therapy during follow-up.

RESULTS

Patients with an event (n = 17) had a larger interface between healthy myocardium and scar and higher VITA metrics. Cox regression analysis demonstrated a significant independent association with an event: interface (hazard ratio [HR] 2.79; 95% confidence interval [CI] 1.44-5.44; P < .01), unique ventricular tachycardias (HR 1.67; 95% CI 1.04-2.68; P = .03), mean RTT (HR 2.14; 95% CI 1.11-4.12; P = .02), and maximum RTT (HR 2.13; 95% CI 1.19-3.81; P = .01).

CONCLUSION

A detailed quantitative analysis of LGE-based scar maps, combined with advanced computational modeling, can accurately predict ICD therapy and could facilitate the early identification of high-risk patients in addition to left ventricular ejection fraction.

Computational modeling of cardiac electrophysiology and arrhythmogenesis: toward clinical translation

The complexity of cardiac electrophysiology, involving dynamic changes in numerous components across multiple spatial (from ion channel to organ) and temporal (from milliseconds to days) scales, makes an intuitive or empirical analysis of cardiac arrhythmogenesis challenging. Multiscale mechanistic computational models of cardiac electrophysiology provide precise control over individual parameters, and their reproducibility enables a thorough assessment of arrhythmia mechanisms. This review provides a comprehensive analysis of models of cardiac electrophysiology and arrhythmias, from the single cell to the organ level, and how they can be leveraged to better understand rhythm disorders in cardiac disease and to improve heart patient care. Key issues related to model development based on experimental data are discussed, and major families of human cardiomyocyte models and their applications are highlighted. An overview of organ-level computational modeling of cardiac electrophysiology and its clinical applications in personalized arrhythmia risk assessment and patient-specific therapy of atrial and ventricular arrhythmias is provided. The advancements presented here highlight how patient-specific computational models of the heart reconstructed from patient data have achieved success in predicting risk of sudden cardiac death and guiding optimal treatments of heart rhythm disorders. Finally, an outlook toward potential future advances, including the combination of mechanistic modeling and machine learning/artificial intelligence, is provided. As the field of cardiology is embarking on a journey toward precision medicine, personalized modeling of the heart is expected to become a key technology to guide pharmaceutical therapy, deployment of devices, and surgical interventions.

Role of activation signatures in re-entrant ventricular tachycardia circuits

INTRODUCTION

Development of a rapid means to verify the ventricular tachycardia (VT) isthmus location from heart surface electrogram recordings would be a helpful tool for the electrophysiologist.

METHOD

Myocardial infarction was induced in 22 canines by left anterior descending coronary artery ligation under general anesthesia. After 3-5 days, VT was inducible via programmed electrical stimulation at the anterior left ventricular epicardial surface. Bipolar VT electrograms were acquired from 196 to 312 recording sites using a multielectrode array. Electrograms were marked for activation time, and activation maps were constructed. The activation signal, or signature, is defined as the cumulative number of recording sites that have activated per millisecond, and it was utilized to segment each circuit into inner and outer circuit pathways, and as an estimate of best ablation lesion location to prevent VT.

RESULTS

VT circuit components were differentiable by activation signals as: inner pathway (mean: 0.30 sites activating/ms) and outer pathway (mean: 2.68 sites activating/ms). These variables were linearly related (p < .001). Activation signal characteristics were dependent in part upon the isthmus exit site. The inner circuit pathway determined by the activation signal overlapped and often extended beyond the activation map isthmus location for each circuit. The best lesion location estimated by the activation signal would likely block an electrical impulse traveling through the isthmus, to prevent VT in all circuits.

CONCLUSIONS

The activation signal algorithm, simple to implement for real-time computer display, approximates the VT isthmus location and shape as determined from activation marking, and best ablation lesion location to prevent reinduction.

Arrhythmogenic vulnerability of re-entrant pathways in post-infarct ventricular tachycardia assessed by advanced computational modelling

AIMS

Substrate assessment of scar-mediated ventricular tachycardia (VT) is frequently performed using late gadolinium enhancement (LGE) images. Although this provides structural information about critical pathways through the scar, assessing the vulnerability of these pathways for sustaining VT is not possible with imaging alone.This study evaluated the performance of a novel automated re-entrant pathway finding algorithm to non-invasively predict VT circuit and inducibility.

METHODS

Twenty post-infarct VT-ablation patients were included for retrospective analysis. Commercially available software (ADAS3D left ventricular) was used to generate scar maps from 2D-LGE images using the default 40-60 pixel-signal-intensity (PSI) threshold. In addition, algorithm sensitivity for altered thresholds was explored using PSI 45-55, 35-65, and 30-70. Simulations were performed on the Virtual Induction and Treatment of Arrhythmias (VITA) framework to identify potential sites of block and assess their vulnerability depending on the automatically computed round-trip-time (RTT). Metrics, indicative of substrate complexity, were correlated with VT-recurrence during follow-up.

RESULTS

Total VTs (85 ± 43 vs. 42 ± 27) and unique VTs (9 ± 4 vs. 5 ± 4) were significantly higher in patients with- compared to patients without recurrence, and were predictive of recurrence with area under the curve of 0.820 and 0.770, respectively. VITA was robust to scar threshold variations with no significant impact on total and unique VTs, and mean RTT between the four models. Simulation metrics derived from PSI 45-55 model had the highest number of parameters predictive for post-ablation VT-recurrence.

CONCLUSION

Advanced computational metrics can non-invasively and robustly assess VT substrate complexity, which may aid personalized clinical planning and decision-making in the treatment of post-infarction VT.

Frequency Domain Analysis of Endocardial Electrograms for Detection of Nontransmural Myocardial Fibrosis in Nonischemic Cardiomyopathy

BACKGROUND

Voltage mapping in nonischemic cardiomyopathy can fail to identify midmyocardial substrate for ventricular arrhythmias, an important cause of ablation failure.

OBJECTIVES

The aim of this study was to assess whether frequency domain analysis of endocardial left ventricular electrograms (EGMs) can better predict the presence of midmyocardial fibrosis (MMF) compared with voltage amplitude.

METHODS

Nonischemic cardiomyopathy patients undergoing ventricular tachycardia ablation with registered preprocedural cardiac computed tomography and late iodine enhancement were included. Presence of fibrosis at each EGM site was assessed. Bipolar and unipolar EGMs were transformed to the frequency domain using multitaper spectral analysis. Singular value decomposition of the EGM frequency spectrum was used within a supervised machine learning process to select features to predict the presence of MMF and compare against predictions using voltage amplitude.

RESULTS

Thirteen patients were included (median age 57 years [IQR: 28-73 years], median ejection fraction 40% [IQR: 15%-57%]). A total of 6,015 EGM pairs were processed: 2,459 EGM pairs in MMF areas and 3,556 EGM pairs in non-MMF areas. Supervised classifiers were trained with stratified k-fold cross-validation within patients. The distribution of mean area under the curve metrics using frequency features, f, was significantly greater than voltage feature area under the curve metrics, v, (mean f = 0.841 [95% CI: 0.789-0.884] vs mean v = 0.591 [95% CI: 0.530-0.658]; P < 0.001), indicating that frequency-trained classifiers better predicted the presence of MMF.

CONCLUSIONS

These data indicate the promising discriminatory value of endocardial EGM frequency content in the assessment of concealed myocardial substrate. Further studies are needed to investigate the importance of the specific frequency features identified.

Wavefront directionality and decremental stimuli synergistically improve identification of ventricular tachycardia substrate: insights from personalized computational heart models

AIMS

Multiple wavefront pacing (MWP) and decremental pacing (DP) are two electroanatomic mapping (EAM) strategies that have emerged to better characterize the ventricular tachycardia (VT) substrate. The aim of this study was to assess how well MWP, DP, and their combination improve identification of electrophysiological abnormalities on EAM that reflect infarct remodelling and critical VT sites.

METHODS AND RESULTS

Forty-eight personalized computational heart models were reconstructed using images from post-infarct patients undergoing VT ablation. Paced rhythms were simulated by delivering an initial (S1) and an extra-stimulus (S2) from one of 100 locations throughout each heart model. For each pacing, unipolar signals were computed along the myocardial surface to simulate substrate EAM. Six EAM features were extracted and compared with the infarct remodelling and critical VT sites. Concordance of S1 EAM features between different maps was lower in hearts with smaller amounts of remodelling. Incorporating S1 EAM features from multiple maps greatly improved the detection of remodelling, especially in hearts with less remodelling. Adding S2 EAM features from multiple maps decreased the number of maps required to achieve the same detection accuracy. S1 EAM features from multiple maps poorly identified critical VT sites. However, combining S1 and S2 EAM features from multiple maps paced near VT circuits greatly improved identification of critical VT sites.

CONCLUSION

Electroanatomic mapping with MWP is more advantageous for characterization of substrate in hearts with less remodelling. During substrate EAM, MWP and DP should be combined and delivered from locations proximal to a suspected VT circuit to optimize identification of the critical VT site.

An automated near-real time computational method for induction and treatment of scar-related ventricular tachycardias

Catheter ablation is currently the only curative treatment for scar-related ventricular tachycardias (VTs). However, not only are ablation procedures long, with relatively high risk, but success rates are punitively low, with frequent VT recurrence. Personalized in-silico approaches have the opportunity to address these limitations. However, state-of-the-art reaction diffusion (R-D) simulations of VT induction and subsequent circuits used for in-silico ablation target identification require long execution times, along with vast computational resources, which are incompatible with the clinical workflow. Here, we present the Virtual Induction and Treatment of Arrhythmias (VITA), a novel, rapid and fully automated computational approach that uses reaction-Eikonal methodology to induce VT and identify subsequent ablation targets. The rationale for VITA is based on finding isosurfaces associated with an activation wavefront that splits in the ventricles due to the presence of an isolated isthmus of conduction within the scar; once identified, each isthmus may be assessed for their vulnerability to sustain a reentrant circuit, and the corresponding exit site automatically identified for potential ablation targeting. VITA was tested on a virtual cohort of 7 post-infarcted porcine hearts and the results compared to R-D simulations. Using only a standard desktop machine, VITA could detect all scar-related VTs, simulating activation time maps and ECGs (for clinical comparison) as well as computing ablation targets in 48 minutes. The comparable VTs probed by the R-D simulations took 68.5 hours on 256 cores of high-performance computing infrastructure. The set of lesions computed by VITA was shown to render the ventricular model VT-free. VITA could be used in near real-time as a complementary modality aiding in clinical decision-making in the treatment of post-infarction VTs.

Assessing Noninvasive Delineation of Low-Voltage Zones Using ECG Imaging in Patients With Structural Heart Disease

OBJECTIVES

This study sought to assess the association between electrocardiographic imaging (ECGI) parameters and voltage from simultaneous electroanatomic mapping (EAM).

BACKGROUND

ECGI offers noninvasive assessment of electrophysiologic features relevant for mapping ventricular arrhythmia and its substrate, but the accuracy of ECGI in the delineation of scar is unclear.

METHODS

Sixteen patients with structural heart disease underwent simultaneous ECGI (CardioInsight, Medtronic) and contact EAM (CARTO, Biosense-Webster) during ventricular tachycardia catheter ablation, with 7 mapped epicardially. ECGI and EAM geometries were coregistered using anatomic landmarks. ECGI points were paired to the closest site on the EAM within 10 mm. The association between EAM voltage and ECGI features from reconstructed epicardial unipolar electrograms was assessed by mixed-effects regression models. The classification of low-voltage regions was performed using receiver-operating characteristic analysis.

RESULTS

A total of 9,541 ECGI points (median: 596; interquartile range: 377-737 across patients) were paired to an EAM site. Epicardial EAM voltage was associated with ECGI features of signal fractionation and local repolarization dispersion (N = 7; P < 0.05), but they poorly classified sites with bipolar voltage of <1.5 mV or <0.5 mV thresholds (median area under the curve across patients: 0.50-0.62). No association was found between bipolar EAM voltage and low-amplitude reconstructed epicardial unipolar electrograms or ECGI-derived bipolar electrograms. Similar results were found in the combined cohort (n = 16), including endocardial EAM voltage compared to epicardial ECGI features (n = 9).

CONCLUSIONS

Despite a statistically significant association between ECGI features and EAM voltage, the accuracy of the delineation of low-voltage zones was modest. This may limit ECGI use for pr-procedural substrate analysis in ventricular tachycardia ablation, but it could provide value in risk assessment for ventricular arrhythmias.

Role of Scar and Border Zone Geometry on the Genesis and Maintenance of Re-Entrant Ventricular Tachycardia in Patients With Previous Myocardial Infarction

In patients with healed myocardial infarction, the left ventricular ejection fraction is characterized by low sensitivity and specificity in the prediction of future malignant arrhythmias. Thus, there is the need for new parameters in daily practice to perform arrhythmic risk stratification. The aim of this study is to identify some features of proarrhythmic geometric configurations of scars and border zones (BZ), by means of numerical simulations based on left ventricular models derived from post myocardial infarction patients. Two patients with similar clinical characteristics were included in this study. Both patients exhibited left ventricular scars characterized by subendo- and subepicardial BZ and a transmural BZ isthmus. The scar of patient #1 was significantly larger than that of patient #2, whereas the transmural BZ isthmus and the subdendo- and subepicardial BZs of patient #2 were thicker than those of patient #1. Patient #1 was positive at electrophysiologic testing, whereas patient #2 was negative. Based on the cardiac magnetic resonance (CMR) data, we developed a geometric model of the left ventricles of the two patients, taking into account the position, extent, and topological features of scars and BZ. The numerical simulations were based on the anisotropic monodomain model of electrocardiology. In the model of patient #1, sustained ventricular tachycardia (VT) was inducible by an S2 stimulus delivered at any of the six stimulation sites considered, while in the model of patient #2 we were not able to induce sustained VT. In the model of patient #1, making the subendo- and subepicardial BZs as thick as those of patient #2 did not affect the inducibility and maintenance of VT. On the other hand, in the model of patient #2, making the subendo- and subepicardial BZs as thin as those of patient #1 yielded sustained VT. In conclusion, the results show that the numerical simulations have an effective predictive capability in discriminating patients at high arrhythmic risk. The extent of the infarct scar and the presence of transmural BZ isthmuses and thin subendo- and subepicardial BZs promote sustained VT.

Structure and function of the ventricular tachycardia isthmus

Catheter ablation of postinfarction reentrant ventricular tachycardia (VT) has received renewed interest owing to the increased availability of high-resolution electroanatomic mapping systems that can describe the VT circuits in greater detail, and the emergence and need to target noninvasive external beam radioablation. These recent advancements provide optimism for improving the clinical outcome of VT ablation in patients with postinfarction and potentially other scar-related VTs. The combination of analyses gleaned from studies in swine and canine models of postinfarction reentrant VT, and in human studies, suggests the existence of common electroanatomic properties for reentrant VT circuits. Characterizing these properties may be useful for increasing the specificity of substrate mapping techniques and for noninvasive identification to guide ablation. Herein, we describe properties of reentrant VT circuits that may assist in elucidating the mechanisms of onset and maintenance, as well as a means to localize and delineate optimal catheter ablation targets.

Whole-heart ventricular arrhythmia modeling moving forward: Mechanistic insights and translational applications

Ventricular arrhythmias are the primary cause of sudden cardiac death and one of the leading causes of mortality worldwide. Whole-heart computational modeling offers a unique approach for studying ventricular arrhythmias, offering vast potential for developing both a mechanistic understanding of ventricular arrhythmias and clinical applications for treatment. In this review, the fundamentals of whole-heart ventricular modeling and current methods of personalizing models using clinical data are presented. From this foundation, the authors summarize recent advances in whole-heart ventricular arrhythmia modeling. Efforts in gaining mechanistic insights into ventricular arrhythmias are discussed, in addition to other applications of models such as the assessment of novel therapeutics. The review emphasizes the unique benefits of computational modeling that allow for insights that are not obtainable by contemporary experimental or clinical means. Additionally, the clinical impact of modeling is explored, demonstrating how patient care is influenced by the information gained from ventricular arrhythmia models. The authors conclude with future perspectives about the direction of whole-heart ventricular arrhythmia modeling, outlining how advances in neural network methodologies hold the potential to reduce computational expense and permit for efficient whole-heart modeling.