Search

Aims Direct remuscularization with pluripotent stem cell-derived cardiomyocytes (PSC-CMs) seeks to address the onset of heart failure post-myocardial infarction (MI) by treating the persistent muscle deficiency that underlies it. However, direct remuscularization with PSC-CMs could potentially be arrhythmogenic. We investigated two possible mechanisms of arrhythmogenesis-focal vs reentrant-arising from direct remuscularization with PSC-CM patches in two personalized, human ventricular computer models of post-MI. Moreover, we developed a principled approach for evaluating arrhythmogenicity of direct remuscularization that factors in the VT propensity of the patient-specific post-MI fibrotic substrate and use it to investigate different conditions of patch remuscularization. Methods & results Two personalized, human ventricular models of post-MI (P1 & P2) were constructed from late gadolinium enhanced (LGE)-magnetic resonance images (MRI). In each model, remuscularization with PSC-CM patches were simulated under different treatment conditions that included patch engraftment, patch myofibril orientation, remuscularization site, patch size (thickness and diameter), and patch maturation. To determine arrhythmogenicity of treatment conditions, VT burden of heart models was quantified prior to and after simulated remuscularization and compared. VT burden was quantified based on inducibility (i.e., weighted sum of pacing sites that induced) and severity (i.e., the number of distinct VT morphologies induced). Prior to remuscularization, VT burden was significant in P1 (0.275) and not in P2 (0.0, not VT inducible). We highlight that reentrant VT mechanisms would dominate over focal mechanisms; spontaneous beats emerging from PSC-CM grafts were always a fraction of resting sinus rate. Moreover, incomplete patch engraftment can be particularly arrhythmogenic, giving rise to particularly aberrant electrical activation and conduction slowing across the PSC-CM patches along with elevated VT burden when compared to complete engraftment. Under conditions of complete patch engraftment, remuscularization was almost always arrhythmogenic in P2 but certain treatment conditions could be anti-arrhythmogenic in P1. Moreover, the remuscularization site was the most important factor affecting VT burden in both P1 and P2. Complete maturation of PSC-CM patches, both ionically and electrotonically, at the appropriate site could completely alleviate VT burden. Conclusion We identified that reentrant VT would be the primary VT mechanism in patch remuscularization. To evaluate the arrhythmogenicity of remuscularization, we developed a principled approach that factors in the propensity of the patient-specific fibrotic substrate for VT. We showed that arrhythmogenicity is sensitive to the patient-specific fibrotic substrate and remuscularization site. We demonstrate that targeted remuscularization can be safe in the appropriate individual and holds the potential to nondestructively eliminate VT post-MI in addition to addressing muscle deficiency underlying heart failure progression. Translational perspective If safety from ventricular arrhythmias can be addressed, direct remuscularization with PSC-CMs-achieved either through engineered myocardial patches or intramyocardial injections-holds the potential to halt heart failure progression post-MI. Using personalized 3 D models of the post-MI ventricles derived from LGE-MRI, we provide evidence that arrhythmogenesis following remuscularization with PSC-CM patches is driven by a reentrant as opposed to focal VT mechanism. Moreover, the existing patient-specific fibrotic substrate together with the remuscularization site were primary determinants of arrhythmogenesis. These results suggest that the clinical safety of remuscularization can be achieved through patient-specific optimization guided in-part by computational modeling.

Right ventricle (RV) dysfunction is an independent predictor of patient survival in heart failure (HF). However, the mechanisms of RV progression towards failing are not well understood. We studied cellular mechanisms of RV remodelling in a rat model of left ventricle myocardial infarction (MI)-caused HF. RV myocytes from HF rats show significant cellular hypertrophy accompanied with a disruption of transverse-axial tubular network and surface flattening. Functionally these cells exhibit higher contractility with lower Ca2+ transients. The structural changes in HF RV myocytes correlate with more frequent spontaneous Ca2+ release activity than in control RV myocytes. This is accompanied by hyperactivated L-type Ca2+ channels (LTCCs) located specifically in the T-tubules of HF RV myocytes. The increased open probability of tubular LTCCs and Ca2+ sparks activation is linked to protein kinase A-mediated channel phosphorylation that occurs locally in T-tubules. Thus, our approach revealed that alterations in RV myocytes in heart failure are specifically localized in microdomains. Our findings may indicate the development of compensatory, though potentially arrhythmogenic, RV remodelling in the setting of LV failure. These data will foster better understanding of mechanisms of heart failure and it could promote an optimized treatment of patients.

Introduction We recently developed two noninvasive methodologies to help guide VT ablation: population-derived automated VT exit localization (PAVEL) and virtual-heart arrhythmia ablation targeting (VAAT). We hypothesized that while very different in their nature, limitations, and type of ablation targets (substrate-based vs. clinical VT), the image-based VAAT and the ECG-based PAVEL technologies would be spatially concordant in their predictions. Objective The objective is to test this hypothesis in ischemic cardiomyopathy patients in a retrospective feasibility study. Methods Four post-infarct patients who underwent LV VT ablation and had pre-procedural LGE-CMRs were enrolled. Virtual hearts with patient-specific scar and border zone identified potential VTs and ablation targets. Patient-specific PAVEL based on a population-derived statistical method localized VT exit sites onto a patient-specific 238-triangle LV endocardial surface. Results Ten induced VTs were analyzed and 9-exit sites were localized by PAVEL onto the patient-specific LV endocardial surface. All nine predicted VT exit sites were in the scar border zone defined by voltage mapping and spatially correlated with successful clinical lesions. There were 2.3 ± 1.9 VTs per patient in the models. All five VAAT lesions fell within regions ablated clinically. VAAT targets correlated well with 6 PAVEL-predicted VT exit sites. The distance between the center of the predicted VT-exit-site triangle and nearest corresponding VAAT ablation lesion was 10.7 ± 7.3 mm. Conclusions VAAT targets are concordant with the patient-specific PAVEL-predicted VT exit sites. These findings support investigation into combining these two complementary technologies as a noninvasive, clinical tool for targeting clinically induced VTs and regions likely to harbor potential VTs.

Pulmonary hypertension is a complex disorder characterized by pulmonary vascular remodeling and right ventricular hypertrophy, leading to right heart failure. The mechanisms underlying this process are not well understood. We hypothesize that the structural remodeling occurring in the cardiomyocytes of the right ventricle affects the cytosolic Ca 2+ handling leading to arrhythmias. After 12 days of monocrotaline-induced pulmonary hypertension in rats, epicardial mapping showed electrical remodeling in both ventricles. In myocytes isolated from the hypertensive rats, a combination of high-speed camera and confocal line-scan documented a prolongation of Ca 2+ transients along with a higher local Ca 2+ -release activity. These Ca 2+ transients were less synchronous than in controls, likely due to disorganized transverse-axial tubular system. In fact, following pulmonary hypertension, hypertrophied right ventricular myocytes showed significantly reduced number of transverse tubules and increased number of axial tubules; however, Stimulation Emission Depletion microscopy demonstrated that the colocalization of L-type Ca 2+ channels and RyR2 (ryanodine receptor 2) remained unchanged. Finally, Stimulation Emission Depletion microscopy and super-resolution scanning patch-clamp analysis uncovered a decrease in the density of active L-type Ca 2+ channels in right ventricular myocytes with an elevated open probability of the T-tubule anchored channels. This may represent a general mechanism of how nanoscale structural changes at the early stage of pulmonary hypertension impact on the development of the end stage failing phenotype in the right ventricle.

Background Adults with repaired tetralogy of Fallot (rTOF) are at increased risk for ventricular tachycardia (VT) due to fibrotic remodeling of the myocardium. However, the current clinical guidelines for VT risk stratification and subsequent implantable cardioverter-defibrillator deployment for primary prevention of sudden cardiac death in rTOF remain inadequate. Objective The purpose of this study was to determine the feasibility of using an rTOF-specific virtual-heart approach to identify patients stratified incorrectly as being at low VT risk by current clinical criteria. Methods This multicenter retrospective pilot study included 7 adult rTOF patients who were considered low risk for VT based on clinical criteria. Patient-specific computational heart models were generated from late gadolinium enhanced magnetic resonance imaging (LGE-MRI), incorporating the individual distribution of rTOF fibrotic remodeling in both ventricles. Simulations of rapid pacing determined VT inducibility. Model creation and simulations were performed by operators blinded to clinical outcome. Results Two patients in the study experienced clinical VT. The virtual hearts constructed from LGE-MRI scans of 7 rTOF patients correctly predicted reentrant VT in the models from VT-positive patients and no arrhythmia in those from VT-negative patients. There were no statistically significant differences in clinical criteria commonly used to assess VT risk, including QRS duration and age, between patients who did and those who did not experience clinical VT. Conclusion This study demonstrates the feasibility of image-based virtual-heart modeling in patients with congenital heart disease and structurally abnormal hearts. It highlights the potential of the methodology to improve VT risk stratification in patients with rTOF.

Background: Patients with cardiac sarcoidosis (CS) are at increased risk of life-threatening ventricular arrhythmias (VA). Current approaches to risk stratification have limited predictive value. Objectives: To assess the utility of spatial dispersion analysis of LGE-CMR, as a quantitative measure of myocardial tissue heterogeneity, in risk stratifying patients with CS for ventricular VA and death. Methods: 62 patients with CS underwent LGE-CMR. LGE images were segmented and dispersion maps of the left and right ventricles were generated as follows. Based on signal intensity (SI), each pixel was categorized as abnormal (SI {greater than or equal to}3SD above the mean), intermediate (SI 1-3 SD above the mean) or normal (SI

Hypertrophic cardiomyopathy (HCM) causes sudden cardiac death (SCD) due to ventricular arrhythmias (VA) manifesting from myocardial fibrosis proliferation. Current clinical risk stratification criteria inadequately identify at-risk patients in need of primary prevention of VA. Here, we use mechanistic computational modeling of the heart to analyze how HCM-specific remodeling of the heart promotes arrhythmogenesis and to develop a personalized strategy to forecast risk of VAs in these patients. We combine contrast-enhanced cardiac magnetic-resonance (CMR) imaging and T1 mapping data to construct digital replicas of HCM patient hearts that represent the patient-specific distribution of focal and diffuse fibrosis and evaluate the substrate propensity to VA. Our analysis indicates that the presence of diffuse fibrosis, which is rarely assessed in these patients, increases arrhythmogenic propensity. In forecasting future VA events in HCM patients, the imaging-based computational heart approach achieved 84.6%, 76.9%, and 80.1% sensitivity, specificity, and accuracy, respectively, and significantly outperformed current clinical risk predictors. This novel VA risk assessment may have the potential to prevent SCD and help deploy primary prevention appropriately in HCM patients.

Background We have previously developed an intraprocedural automatic arrhythmia‐origin localization (AAOL) system to identify idiopathic ventricular arrhythmia origins in real time using a 3‐lead ECG. The objective was to assess the localization accuracy of ventricular tachycardia (VT) exit and premature ventricular contraction (PVC) origin sites in patients with structural heart disease using the AAOL system. Methods and Results In retrospective and prospective case series studies, a total of 42 patients who underwent VT/PVC ablation in the setting of structural heart disease were recruited at 2 different centers. The AAOL system combines 120‐ms QRS integrals of 3 leads (III, V2, V6) with pace mapping to predict VT exit/PVC origin site and projects that site onto the patient‐specific electroanatomic mapping surface. VT exit/PVC origin sites were clinically identified by activation mapping and/or pace mapping. The localization error of the VT exit/PVC origin site was assessed by the distance between the clinically identified site and the estimated site. In the retrospective study of 19 patients with structural heart disease, the AAOL system achieved a mean localization accuracy of 6.5±2.6 mm for 25 induced VTs. In the prospective study with 23 patients, mean localization accuracy was 5.9±2.6 mm for 26 VT exit and PVC origin sites. There was no difference in mean localization error in epicardial sites compared with endocardial sites using the AAOL system (6.0 versus 5.8 mm, P =0.895). Conclusions The AAOL system achieved accurate localization of VT exit/PVC origin sites in patients with structural heart disease; its performance is superior to current systems, and thus, it promises to have potential clinical utility.

Aims: Disease-induced repolarization heterogeneity in infarcted myocardium contributes to VT arrhythmogenesis but how apicobasal and transmural (AB-TM) repolarization gradients additionally affect post-infarct VT dynamics is unknown. The goal of this study is to assess how AB-TM repolarization gradients impact post-infarct VT dynamics using patient-specific heart models.Method: 3D late gadolinium-enhanced cardiac magnetic resonance images were acquired from seven post-infarct patients. Models representing the patient-specific scar and infarct border zone distributions were reconstructed without (baseline) and with repolarization gradients along both the AB-TM axes. AB only and TM only models were created to assess the effects of each ventricular gradient on VT dynamics. VTs were induced in all models via rapid pacing.Results: Ten baseline VTs were induced. VT inducibility in AB-TM models was not significantly different from baseline (p>0.05). Reentry pathways in AB-TM models were different than baseline pathways due to alterations in the location of conduction block (pp>0.05) or AB-TM models (p>0.05). Reentry pathways and VT exit sites in AB only and TM only models were different than in baseline (pConclusion: VT inducibility was not impacted by the addition of AB-TM repolarization gradients, but the VT reentrant pathway and exit sites were greatly affected due to modulation of conduction block. Thus, during ablation procedures, physiological and pharmacological factors that impact the ventricular repolarization gradient might need to be considered when targeting the VTs.

Patients with myocardial infarction have an abundance of conduction channels (CC); however, only a small subset of these CCs sustain ventricular tachycardia (VT). Identifying these critical CCs (CCCs) in the clinic so that they can be targeted by ablation remains a significant challenge. The objective of this study is to use a personalized virtual-heart approach to conduct a three-dimensional (3D) assessment of CCCs sustaining VTs of different morphologies in these patients, to investigate their 3D structural features, and to determine the optimal ablation strategy for each VT. To achieve these goals, ventricular models were constructed from contrast enhanced magnetic resonance imagings of six postinfarction patients. Rapid pacing induced VTs in each model. CCCs that sustained different VT morphologies were identified. CCCs’ 3D structure and type and the resulting rotational electrical activity were examined. Ablation was performed at the optimal part of each CCC, aiming to terminate each VT with a minimal lesion size. Predicted ablation locations were compared to clinical. Analyzing the simulation results, we found that the observed VTs in each patient model were sustained by a limited number (2.7 ± 1.2) of CCCs. Further, we identified three types of CCCs sustaining VTs: I-type and T-type channels, with all channel branches bounded by scar, and functional reentry channels, which were fully or partially bounded by conduction block surfaces. The different types of CCCs accounted for 43.8, 18.8, and 37.4% of all CCCs, respectively. The mean narrowest width of CCCs or a branch of CCC was 9.7 ± 3.6 mm. Ablation of the narrowest part of each CCC was sufficient to terminate VT. Our results demonstrate that a personalized virtual-heart approach can determine the possible VT morphologies in each patient and identify the CCCs that sustain reentry. The approach can aid clinicians in identifying accurately the optimal VT ablation targets in postinfarction patients.

Aims Inadequate modification of the atrial fibrotic substrate necessary to sustain re-entrant drivers (RDs) may explain atrial fibrillation (AF) recurrence following failed pulmonary vein isolation (PVI). Personalized computational models of the fibrotic atrial substrate derived from late gadolinium enhanced (LGE)-magnetic resonance imaging (MRI) can be used to non-invasively determine the presence of RDs. The objective of this study is to assess the changes of the arrhythmogenic propensity of the fibrotic substrate after PVI. Methods and results Pre- and post-ablation individualized left atrial models were constructed from 12 AF patients who underwent pre- and post-PVI LGE-MRI, in six of whom PVI failed. Pre-ablation AF sustained by RDs was induced in 10 models. RDs in the post-ablation models were classified as either preserved or emergent. Pre-ablation models derived from patients for whom the procedure failed exhibited a higher number of RDs and larger areas defined as promoting RD formation when compared with atrial models from patients who had successful ablation, 2.6 ± 0.9 vs. 1.8 ± 0.2 and 18.9 ± 1.6% vs. 13.8 ± 1.5%, respectively. In cases of successful ablation, PVI eliminated completely the RDs sustaining AF. Preserved RDs unaffected by ablation were documented only in post-ablation models of patients who experienced recurrent AF (2/5 models); all of these models had also one or more emergent RDs at locations distinct from those of pre-ablation RDs. Emergent RDs occurred in regions that had the same characteristics of the fibrosis spatial distribution (entropy and density) as regions that harboured RDs in pre-ablation models. Conclusion Recurrent AF after PVI in the fibrotic atria may be attributable to both preserved RDs that sustain AF pre- and post-ablation, and the emergence of new RDs following ablation. The same levels of fibrosis entropy and density underlie the pro-RD propensity in both pre- and post-ablation substrates.

Optogenetic defibrillation of hearts expressing light-sensitive cation channels (e.g., ChR2) has been proposed as an alternative to conventional electrotherapy. Past modeling work has shown that ChR2 stimulation can depolarize enough myocardium to interrupt arrhythmia, but its efficacy is limited by light attenuation and high energy needs. These shortcomings may be mitigated by using new optogenetic proteins like Guillardia theta Anion Channelrhodopsin (GtACR1), which produces a repolarizing outward current upon illumination. Accordingly, we designed a study to assess the feasibility of GtACR1-based optogenetic arrhythmia termination in human hearts. We conducted electrophysiological simulations in MRI-based atrial or ventricular models (n = 3 each), with pathological remodeling from atrial fibrillation or ischemic cardiomyopathy, respectively. We simulated light sensitization via viral gene delivery of three different opsins (ChR2, red-shifted ChR2, GtACR1) and uniform endocardial illumination at the appropriate wavelengths (blue, red, or green light, respectively). To analyze consistency of arrhythmia termination, we varied pulse timing (three evenly spaced intervals spanning the reentrant cycle) and intensity (atrial: 0.001–1 mW/mm2; ventricular: 0.001–10 mW/mm2). In atrial models, GtACR1 stimulation with 0.005 mW/mm2 green light consistently terminated reentry; this was 10–100x weaker than the threshold levels for ChR2-mediated defibrillation. In ventricular models, defibrillation was observed in 2/3 models for GtACR1 stimulation at 0.005 mW/mm2 (100–200x weaker than ChR2 cases). In the third ventricular model, defibrillation failed in nearly all cases, suggesting that attenuation issues and patient-specific organ/scar geometry may thwart termination in some cases. Across all models, the mechanism of GtACR1-mediated defibrillation was voltage forcing of illuminated tissue toward the modeled channel reversal potential of −40 mV, which made propagation through affected regions impossible. Thus, our findings suggest GtACR1-based optogenetic defibrillation of the human heart may be feasible with ≈2–3 orders of magnitude less energy than ChR2.

Combining mechanistic modeling and machine learning, a multivariable sudden cardiac death predictor outperforms clinical metrics., Cardiac sarcoidosis (CS), an inflammatory disease characterized by formation of granulomas in the heart, is associated with high risk of sudden cardiac death (SCD) from ventricular arrhythmias. Current “one-size-fits-all” guidelines for SCD risk assessment in CS result in insufficient appropriate primary prevention. Here, we present a two-step precision risk prediction technology for patients with CS. First, a patient’s arrhythmogenic propensity arising from heterogeneous CS-induced ventricular remodeling is assessed using a novel personalized magnetic-resonance imaging and positron-emission tomography fusion mechanistic model. The resulting simulations of arrhythmogenesis are fed, together with a set of imaging and clinical biomarkers, into a supervised classifier. In a retrospective study of 45 patients, the technology achieved testing results of 60% sensitivity [95% confidence interval (CI): 57-63%], 72% specificity [95% CI: 70-74%], and 0.754 area under the receiver operating characteristic curve [95% CI: 0.710-0.797]. It outperformed clinical metrics, highlighting its potential to transform CS risk stratification.

Atrial fibrillation (AF) patients are at high risk of stroke, with the left atrial appendage (LAA) found to be the most common site of clot formation. Presence of left atrial (LA) fibrosis has also been associated with higher stroke risk. However, the mechanisms for increased stroke risk in patients with atrial fibrotic remodeling are poorly understood. We sought to explore these mechanisms using fluid dynamic analysis and to test the hypothesis that the presence of LA fibrosis leads to aberrant hemodynamics in the LA, contributing to increased stroke risk in AF patients. We retrospectively collected late-gadolinium-enhanced MRI (LGE-MRI) images of eight AF patients (four persistent and four paroxysmal) and reconstructed their 3D LA surfaces. Personalized computational fluid dynamic simulations were performed, and hemodynamics at the LA wall were quantified by wall shear stress (WSS, friction of blood), oscillatory shear index (OSI, temporal directional change of WSS), endothelial cell activation potential (ECAP, ratio of OSI and WSS), and relative residence time (RRT, residence time of blood near the LA wall). For each case, these hemodynamic metrics were compared between fibrotic and non-fibrotic portions of the wall. Our results showed that WSS was lower, and OSI, ECAP, and RRT was higher in the fibrotic region as compared to the non-fibrotic region, with ECAP (p = 0.001) and RRT (p = 0.002) having significant differences. Case-wise analysis showed that these differences in hemodynamics were statistically significant for seven cases. Furthermore, patients with higher fibrotic burden were exposed to larger regions of high ECAP, which represents regions of low WSS and high OSI. Consistently, high ECAP in the vicinity of the fibrotic wall suggest that local blood flow was slow and oscillating that represents aberrant hemodynamic conditions, thus enabling prothrombotic conditions for circulating blood. AF patients with high LA fibrotic burden had more prothrombotic regions, providing more sites for potential clot formation, thus increasing their risk of stroke.

RATIONALE: Susceptibility to ventricular arrhythmias (VT/VF) is difficult to predict in patients with ischemic cardiomyopathy either by clinical tools or by attempting to translate cellular mechanisms to the bedside. OBJECTIVE: To develop computational phenotypes of patients with ischemic cardiomyopathy, by training then interpreting machine learning (ML) of ventricular monophasic action potentials (MAPs) to reveal phenotypes that predict long-term outcomes. METHODS AND RESULTS: We recorded 5706 ventricular MAPs in 42 patients with coronary disease (CAD) and left ventricular ejection fraction (LVEF) ≤40% during steady-state pacing. Patients were randomly allocated to independent training and testing cohorts in a 70:30 ratio, repeated K=10 fold. Support vector machines (SVM) and convolutional neural networks (CNN) were trained to 2 endpoints: (i) sustained VT/VF or (ii) mortality at 3 years. SVM provided superior classification. For patient-level predictions, we computed personalized MAP scores as the proportion of MAP beats predicting each endpoint. Patient-level predictions in independent test cohorts yielded c-statistics of 0.90 for sustained VT/VF (95% CI: 0.76-1.00) and 0.91 for mortality (95% CI: 0.83-1.00) and were the most significant multivariate predictors. Interpreting trained SVM revealed MAP morphologies that, using in silico modeling, revealed higher L-type calcium current or sodium calcium exchanger as predominant phenotypes for VT/VF. CONCLUSION: Machine learning of action potential recordings in patients revealed novel phenotypes for long-term outcomes in ischemic cardiomyopathy. Such computational phenotypes provide an approach which may reveal cellular mechanisms for clinical outcomes and could be applied to other conditions.

We developed a novel patient-specific computational model for the numerical simulation of ventricular electromechanics in patients with ischemic cardiomyopathy (ICM). This model reproduces the activity both in sinus rhythm (SR) and in ventricular tachycardia (VT). The presence of scars, grey zones and non-remodeled regions of the myocardium is accounted for by the introduction of a spatially heterogeneous coefficient in the 3D electromechanics model. This 3D electromechanics model is firstly coupled with a 2-element Windkessel afterload model to fit the pressure-volume (PV) loop of a patient-specific left ventricle (LV) with ICM in SR. Then, we employ the coupling with a 0D closed-loop circulation model to analyze a VT circuit over multiple heartbeats on the same LV. We highlight similarities and differences on the solutions obtained by the electrophysiology model and those of the electromechanics model, while considering different scenarios for the circulatory system. We observe that very different parametrizations of the circulation model induce the same hemodynamical considerations for the patient at hand. Specifically, we classify this VT as unstable. We conclude by stressing the importance of combining electrophysiological, mechanical and hemodynamical models to provide relevant clinical indicators in how arrhythmias evolve and can potentially lead to sudden cardiac death.

OBJECTIVE: Higher mortality in COVID-19 in men compared to women is recognized, but sex differences in cardiovascular events are less well established. We aimed to determine the independent contribution of sex to stroke, myocardial infarction and death in the setting of COVID-19 infection. METHODS: We performed a retrospective cohort study of hospitalized COVID-19 patients in a racially/ethnically diverse population. Clinical features, laboratory markers and clinical events were initially abstracted from medical records, with subsequent clinician adjudication. RESULTS: Of 2060 patients, myocardial injury (32% vs 23%, p = 0.019), acute myocardial infarction (2.7% vs 1.6%, p = 0.114), and ischemic stroke (1.8% vs 0.7%, p = 0.007) were more common in men vs women. In-hospital death occurred in 160 men (15%) vs 117 women (12%, p = 0.091). Men had higher odds of myocardial injury (odds ratio (OR) 2.04 [95% CI 1.43-2.91], p < 0.001), myocardial infarction (1.72 [95% CI 0.93-3.20], p = 0.085) and ischemic stroke (2.76 [95% CI 1.29-5.92], p = 0.009). Despite adjustment for demographics and cardiovascular risk factors, male sex predicted mortality (HR 1.33; 95% CI:1.01-1.74; p = 0.041). While men had significantly higher markers of inflammation, in sex-stratified analyses, increase in interleukin-6, C-reactive protein, ferritin and d-dimer were predictive of mortality and myocardial injury similarly in both sexes. CONCLUSIONS: Adjusted odds of myocardial injury, ischemic stroke and all-cause mortality, but not myocardial infarction, are significantly higher in men compared to women with COVID-19. Higher inflammatory markers are present in men but associated similarly with risk in both men and women. These data suggest that adverse cardiovascular outcomes in men vs. women are independent of cardiovascular comorbidities.

Background: Infiltrating adipose tissue (inFAT) is a newly recognized proarrhythmic substrate for postinfarct ventricular tachycardias (VT) identifiable on contrast-enhanced computed tomography. This study presents novel digital-heart technology that incorporates inFAT from contrast-enhanced computed tomography to noninvasively predict VT ablation targets and assesses the capability of the technology by comparing its predictions with VT ablation procedure data from patients with ischemic cardiomyopathy. Methods: Digital-heart models reflecting patient-specific inFAT distributions were reconstructed from contrast-enhanced computed tomography. The digital-heart identification of fat-based ablation targeting (DIFAT) technology evaluated the rapid-pacing–induced VTs in each personalized inFAT-based substrate. DIFAT targets that render the inFAT substrate noninducible to VT, including VTs that arise postablation, were determined. DIFAT predictions were compared with corresponding clinical ablations to assess the capabilities of the technology. Results: DIFAT was developed and applied retrospectively to 29 ischemic cardiomyopathy patients with contrast-enhanced computed tomography. DIFAT ablation volumes were significantly less than the estimated clinical ablation volumes (1.87±0.35 versus 7.05±0.88 cm 3 , P Conclusions: DIFAT is a novel digital-heart technology for individualized VT ablation guidance designed to eliminate VT inducibility following initial ablation. DIFAT predictions colocalized well with clinical ablation locations but provided significantly smaller lesions. DIFAT also predicted VTs targeted in redo procedures years later. As DIFAT uses widely accessible computed tomography, its integration into clinical workflows may augment therapeutic precision and reduce redo procedures.

BACKGROUND: We previously developed an intraprocedural automated site of origin localization system to identify the origin of early left ventricular (LV) activation using 12-lead ECGs. However, it has limitations, as it could not identify the site of origin in the right ventricle (RV), and relied on acquiring a complete electroanatomic map (EAM). OBJECTIVE: The objective of this study was to present a new system, the Automatic Arrhythmia Origin Localization (AAOL) system, which utilized incomplete EAM for localization of idiopathic ventricular arrhythmia (IVA) origin on the patient-specific geometry of LV, RV and neighboring vessels. The accuracy of the system in localizing IVA source sites on cardiac structures where pace-mapping is challenging was assessed. METHODS: Twenty patients undergoing IVA catheter ablation had a 12-lead ECG recorded during clinical arrhythmia and during pacing at various locations identified on EAM geometries. The new system combined 3-lead (III, V2, V6) 120-ms QRS integrals and patient-specific EAM geometry with pace mapping to predict the site of earliest ventricular activation. The predicted site was projected onto EAM geometry. RESULTS: Twenty-three IVA origin sites were clinically identified by activation mapping and/or pace mapping (8 RV; 15 LV, including 8 from the posteromedial papillary muscle; 2 from the aortic root; and 1 from the distal coronary sinus). The new system achieved a mean localization accuracy of 3.6 mm for the 23 mapped IVAs. CONCLUSIONS: The new intraprocedural AAOL system achieved accurate localization of IVA origin in ventricles and neighbouring vessels, which could facilitate ablation procedures for patients with IVAs. Key Words: Idiopathic ventricular arrhythmias (IVA); Premature ventricular complexes (PVCs); idiopathic ventricular tachycardia (IVT); Pace mapping; Activation mapping; Radiofrequency (RF) Ablation; ECG.

Introduction: Left atrial appendage (LAA) is the primary source of clot formation that can cause stroke or transient ischemic attack (TIA) in patients with atrial fibrillation (AF). LAA closure devices have emerged as alternatives to traditional anticoagulation therapy for reducing stroke risk in AF patients. However, ~1-2% of AF patients undergoing LAA closure have subsequent stroke/TIA event. Hypothesis: The presence of a persistent low-flow zone in LA, distinct from LAA, explains why some patients have stroke/TIA events after LAA closure. Methods: We developed a personalized stroke risk prediction tool that uses patient’s CT image to perform computational fluid dynamics simulation and determine presence of low blood-flow in the LA before and after LAA closure. Low flow is quantified by the establishment of low velocity fraction (LVF) in the LA volume, and low wall shear stress fraction (LWSSF) on the surface of the LA wall. The tool was applied retrospectively on 4 AF patients who had undergone LAA closure: 2 patients with TIA 4 months after LAA closure, and 2 controls matched for age, gender, CHA2DS2-VASc score and LAA dimensions with no complications at follow-up up to 2 years. Results: Before LAA closure, TIA and control groups had similar LVF (0.07 and 0.06, respectively) and LWSSF (0.12 and 0.16, respectively). However, after LAA closure, the TIA group had smaller reductions as compared to controls in both LVF (67% vs 79%) and LWSSF (17% vs 52%). This suggests that the LA low-flow region (especially at LA wall) was not substantially reduced after LAA closure, explaining why these patients might have experienced TIA. Conclusion: This proof-of-concept study demonstrates that LAA closure might not substantially reduce low-flow zones for all AF patients, as some retain low-flow zones in LA. Our stroke prediction tool has the potential to identify patients at risk of future stroke/TIA, thus enabling personalized patient selection to increase efficacy of LAA closure devices.

Introduction: Deep learning (DL) has achieved promising performance on common heart rhythms classification using 12-lead electrocardiogram (ECG). However, two major concerns hinder the DL’s application - lack of interpretability and overfitting caused by using the full 12-lead ECG as input. Objective: We proposed a hybrid DL model with enhanced interpretability to detect 9 common types of heart rhythms from an optimal ECG lead subset, and to quantitively analyze the overfitting. Methods: We used a multicenter dataset of 6,877 annotated 12-lead ECG recordings. The proposed model (Fig. 1A) consists of a feature extraction and a decision-making. The feature extraction used 12 separate neural networks to extract features from each lead. The features were then fed into a random-forest classifier in the decision-making step to classify heart-rhythm types. The classifier was used to interpret the correlations between the heart rhythms and the ECG leads, to find an optimal subset of ECG leads, and to analyze whether using 12-lead ECG added unnecessary complexity to the model and undermined its generalizability. Results: The proposed model detected the correlations between the heart-rhythm types and the ECG leads (Fig. 1B), and identified an optimal ECG lead subset (leads II, aVR, V1, V4). The optimal subset was, in comparison with using 12-lead ECG, significantly better (F1 =0.776 vs. F1 = 0.767, P=0.02) on the validation set for classifying the 9 common heart rhythms. There was no statistical difference on the test set. No overfitting caused by 12-lead ECG was detected in this study. Conclusion: The hybrid DL model based on an optimal 4-lead ECG can interpret rhythm types without significant loss of accuracy in comparison with the 12-lead ECG.

Introduction: Few intraprocedural localization systems have been developed to predict idiopathic ventricular arrhythmia (IVA) source sites. However, an accurate and bi-ventricular patient-specific automated site of origin localization system remains elusive. To address this issue, we have developed a new automatic arrhythmia origin localization (AAOL) system that determines the sites of earliest activation in both ventricles and provides superior accuracy. Hypothesis: We hypothesized that the AAOL system can use electroanatomic mapping (EAM) geometry and accurately localize IVA source sites on patient-specific geometry of LV, RV and neighboring vessels using 3-lead ECGs. Methods: Twenty patients undergoing IVA catheter ablation had a 12-lead ECG recorded during clinical arrhythmia and during pacing at various locations identified on EAM geometries. The AAOL system combined 3-lead (III, V2, V6) 120-ms QRS integrals and patient-specific EAM geometry with intracardiac pacing to predict the site of earliest ventricular activation. The predicted site was projected onto the EAM geometry using the EAM triangular-mesh site nearest to the tip of the predicted site. Results: Twenty-three IVA source sites were clinically identified by activation mapping and/or pace mapping (8 RV, 15 LV, including 8 from the posteromedial papillary muscle; 2 from the aortic root; and 1 from the distal coronary sinus). The new system achieved a mean localization accuracy of 3.6 mm for the 23 mapped IVAs (Figure 1D), better than that achieved by previous systems. Conclusions: The new AAOL system offers highly accurate localization of IVA source sites in both ventricles and neighboring vessels, which could facilitate ablation procedures for patients with IVAs.

Introduction: Atrial fibrillation (AF) patients are at high risk of stroke, with the left atrial appendage (LAA) found to be the most common site of clot formation. Left atrial (LA) fibrosis is also associated with higher stroke risk. However, the mechanisms for increased stroke risk in patients with atrial fibrotic remodeling are poorly understood. We sought to explore these specific mechanisms using fluid dynamics analysis. Hypothesis: The presence of LA fibrosis leads to aberrant hemodynamics in the atria, contributing to increased stroke risk in AF patients. Methods: We retrospectively collected LGE-MRI images of 3 AF patients and reconstructed their 3D LA endocardial surfaces. Fibrotic regions were identified as those with intensity 3 standard deviations greater than the mean LA blood-pool intensity. Personalized computational fluid dynamic simulations were performed, and hemodynamics at the LA wall were quantified by wall shear stress (WSS, friction of blood) and oscillatory shear index (OSI, temporal directional change of WSS). For each case, WSS and OSI were compared between fibrotic and normal regions. Results: WSS was significantly lower in the fibrotic region as compared to normal region for all 3 cases. Additionally, OSI was higher in the fibrotic region as compared to normal region for all 3 cases. However, this different was statistically significant for cases 1 and 3; case 2 was not statistically significantly different. Low WSS and high OSI in the vicinity of the fibrotic wall suggest that local blood-flow was slow and oscillating, enabling pro-thrombotic conditions for circulating blood. Conclusion: LA fibrosis correlates with regions of aberrant hemodynamics, which renders it susceptible to blood thrombosis. AF patients with high LA fibrosis burden will have more pro-thrombotic regions, in addition to low flow in the LAA, providing more sites for a potential clot formation. These conditions might get exacerbated during an AF event and cause stroke.

Introduction: While CI, or the inability of heart rate (HR) to adequately respond to exercise, is a strong indicator of mortality in HFpEF, treatment options remain limited as its mechanisms remain underexplored. Ionic remodeling of the SAN, both of the membrane (MC) and calcium clocks (CC), is associated with HFpEF and CI. Aim: Using an in silico approach, we investigated whether SAN ionic remodeling can contribute to 3 different CI presentations (Fig A) and explored the effectiveness of targeting the remodeling of the MC or CC (MCR and CCR, resp.) in eliminating CI. Methods: A human SAN model (control) was used, in which ionic remodeling was integrated using experimental data from HFpEF rat model with CI (Fig B top). A family of HFpEF SAN models (n = 147) was generated because the uniqueness of Ca 2+ handling parameters constrained to experimentally-based Ca 2+ transients could not be guaranteed (Fig B middle). To assess the level of CI, max beat rate (BR), BR response (τ on ), and BR recovery (τ off ) were quantified for each model in response to a transient 40s pulse of 1 μM isoproterenol (ISO) (Fig B bottom). Lastly, the effect of reversing either CCR or MCR on CI was determined. Results: Without ISO, BR of HFpEF SAN models was similar to that of control; when ISO was applied (Fig C), HFpEF models exhibited all aspects of CI: submaximal BR (Fig D), and elevated τ on and τ off (Fig E). In CCR reversed models, BR without ISO was similar to that of control; with ISO, they exhibited submaximal BR (Fig F). In MCR reversed models, BR without ISO was slightly lower compared to control; with ISO, while they exhibited submaximal BR, the increase in BR with ISO was similar to that of control (Fig F). τ on and τ off were elevated in CCR reversed models, but they were not in MCR reversed models (Fig G). In summary, CCR reversed models exhibited all aspects of CI, but MCR reversed models did not. Conclusion: Ionic remodeling of the SAN contributes to CI in HFpEF, and targeting MCR could be a promising strategy for eliminating CI.

Introduction: We previously developed an LGE-MRI-based virtual-heart arrhythmia ablation targeting (VAAT) methodology to non-invasively determine potential ablation targets for infarct-related VT. However, it is unknown whether VAAT’s predictions correspond with surface ECG predictions. Hypothesis: We hypothesized that the VAAT predicted VT circuits and potential ablation lesions would co-localize with ECG-based VT-exit predictions from a previously validated population-derived automated VT exit localization (PAVEL) system. Methods: We retrospectively enrolled 5 post-infarct patients who underwent LV endocardial VT ablation and had pre-procedural 2D LGE-MRIs. The PAVEL system based on a population-derived statistical method was used to localize VT-exit sites onto one of 238 triangles on the patient-specific virtual-heart LV endocardial surface using 8 independent ECG leads (I, II, V1-V6). The VAAT methodology incorporating patient-specific scar and infarct border zone distributions was used to identify potential VT circuits and find ablation lesions. Results: Eleven induced VTs were analyzed. Ten VT-exit sites were localized onto the patient-specific virtual-heart LV endocardial surface by the PAVEL system, and were used for the comparisons. One VT-exit site was too basal to be localized onto the virtual-heart geometry. The spatial resolution of the 10 predicted VT-exit sites was 13.8 ± 1.8 mm. VAAT-predicted VT circuits and ablation lesions correlated well with all 10 predicted VT-exit sites. Lastly, VAAT ablation lesions fell within the regions ablated clinically. Conclusions: The VAAT-predicted VT circuits and ablation lesions matched VT-exit sites predicted by the surface ECG-based PAVEL system. Combining these two complementary technologies may improve accuracy for non-invasively identifying optimal ablation targets to increase ablation efficacy.

Introduction: Post-infarct ventricular tachycardias (VT) arise due to structural remodeling (scarring). Physiological repolarization gradients (apicobasal and transmural) exist in the human heart, but the effects on post-infarct VT dynamics are unknown. Hypothesis: We hypothesized that incorporation of repolarization gradients in personalized digital hearts of post-infarct patients impacts VT exit sites without altering the location of the VTs. Methods: 3D late-gadolinium enhanced CMR images were acquired from 7 post-infarct patients. Personalized image-based computational heart models (digital hearts) representing scar and infarct border zone distributions were constructed. Apicobasal (AB) and transmural (TM) repolarization gradients were incorporated using a validated method (Fig A). VTs were induced at baseline (no repolarization gradient) via rapid pacing in the right ventricular apex, using two pacing cycle lengths, mimicking non-invasive programmed stimulation. Pacing protocols that induced baseline VTs were repeated under AB and TM conditions. Results: Ten VTs were induced in baseline digital hearts. 8 AB VTs and 8 TM VTs were induced; the remaining 2 VTs for both AB and TM digital hearts could not be induced. 5/8 induced AB VTs had VT exit sites matching baseline VT exit sites; the remaining 3/8 AB VTs had reversed VT exit and entrance sites from the corresponding baseline VTs (Fig B, VT 1 & 2). 4/8 induced TM VTs had exit sites that matched those at baseline; the remaining TM VTs had exit and entrance sites reversed from those of baseline VTs (Fig B, VT 1, 2 & 3). All 8 AB VTs and 8 TM VTs had the same location as corresponding baseline VTs. Conclusion: AB and TM repolarization gradients can act to reverse VT entrance and exit sites without changing VT location. Thus, incorporation of physiological repolarization gradients into personalized digital hearts may not impact VT ablation targeting but may affect accurate prediction of VT exit or entrance sites.

Background: To facilitate ablation of ventricular tachycardia (VT), an automated localization system to identify the site of origin of left ventricular activation in real time using the 12-lead ECG was developed. The objective of this study was to prospectively assess its accuracy. Methods: The automated site of origin localization system consists of 3 steps: (1) localization of ventricular segment based on population templates, (2) population-based localization within a segment, and (3) patient-specific site localization. Localization error was assessed by the distance between the known reference site and the estimated site. Results: In 19 patients undergoing 21 catheter ablation procedures of scar-related VT, site of origin localization accuracy was estimated using 552 left ventricular endocardial pacing sites pooled together and 25 VT-exit sites identified by contact mapping. For the 25 VT-exit sites, localization error of the population-based localization steps was within 10 mm. Patient-specific site localization achieved accuracy of within 3.5 mm after including up to 11 pacing (training) sites. Using 3 remotes (67.8±17.0 mm from the reference VT-exit site), and then 5 close pacing sites, resulted in localization error of 7.2±4.1 mm for the 25 identified VT-exit sites. In 2 emulated clinical procedure with 2 induced VTs, the site of origin localization system achieved accuracy within 4 mm. Conclusions: In this prospective validation study, the automated localization system achieved estimated accuracy within 10 mm and could thus provide clinical utility.

Background: Pulmonary vein isolation (PVI) is an effective treatment strategy for patients with atrial fibrillation (AF), but many experience AF recurrence and require repeat ablation procedures. The goal of this study was to develop and evaluate a methodology that combines machine learning (ML) and personalized computational modeling to predict, before PVI, which patients are most likely to experience AF recurrence after PVI. Methods: This single-center retrospective proof-of-concept study included 32 patients with documented paroxysmal AF who underwent PVI and had preprocedural late gadolinium enhanced magnetic resonance imaging. For each patient, a personalized computational model of the left atrium simulated AF induction via rapid pacing. Features were derived from pre-PVI late gadolinium enhanced magnetic resonance images and from results of simulations of AF induction. The most predictive features were used as input to a quadratic discriminant analysis ML classifier, which was trained, optimized, and evaluated with 10-fold nested cross-validation to predict the probability of AF recurrence post-PVI. Results: In our cohort, the ML classifier predicted probability of AF recurrence with an average validation sensitivity and specificity of 82% and 89%, respectively, and a validation area under the curve of 0.82. Dissecting the relative contributions of simulations of AF induction and raw images to the predictive capability of the ML classifier, we found that when only features from simulations of AF induction were used to train the ML classifier, its performance remained similar (validation area under the curve, 0.81). However, when only features extracted from raw images were used for training, the validation area under the curve significantly decreased (0.47). Conclusions: ML and personalized computational modeling can be used together to accurately predict, using only pre-PVI late gadolinium enhanced magnetic resonance imaging scans as input, whether a patient is likely to experience AF recurrence following PVI, even when the patient cohort is small.

Advances in cardiac magnetic resonance (CMR) techniques and image acquisition have made it an excellent tool in the assessment of atrial myopathy. Remolding of the left atrium is the mainstay of atrial fibrillation (AF) development and its progression. CMR can detect phasic atrial volumes, atrial function, and atrial fibrosis using cine, and contrast-enhanced or non-contrast-enhanced images. These abilities make CMR a versatile and extraordinary tool in management of patients with AF including for risk stratification, ablation prognostication and planning, and assessment of stroke risk. We review the latest advancements in utility of CMR in management of patients with AF.

Background: Characterizing myocardial conduction velocity (CV) in patients with ischemic cardiomyopathy (ICM) and ventricular tachycardia (VT) is important for understanding the patient-specific proarrhythmic substrate of VTs and therapeutic planning. The objective of this study is to accurately assess the relation between CV and myocardial fibrosis density on late gadolinium–enhanced cardiac magnetic resonance imaging (LGE-CMR) in patients with ICM. Methods: We enrolled 6 patients with ICM undergoing VT ablation and 5 with structurally normal left ventricles (controls) undergoing premature ventricular contraction or VT ablation. All patients underwent LGE-CMR and electroanatomic mapping (EAM) in sinus rhythm (2960 electroanatomic mapping points analyzed). We estimated CV from electroanatomic mapping local activation time using the triangulation method that provides an accurate estimate of CV as it accounts for the direction of wavefront propagation. We evaluated the association between LGE-CMR intensity and CV with multilevel linear mixed models. Results: Median CV in patients with ICM and controls was 0.41 m/s and 0.65 m/s, respectively. In patients with ICM, CV in areas with no visible fibrosis was 0.81 m/s (95% CI, 0.59–1.12 m/s). For each 25% increase in normalized LGE intensity, CV decreased by 1.34-fold (95% CI, 1.25–1.43). Dense scar areas have, on average, 1.97- to 2.66-fold slower CV compared with areas without dense scar. Ablation lesions that terminated VTs were localized in areas of slow conduction on CV maps. Conclusions: CV is inversely associated with LGE-CMR fibrosis density in patients with ICM. Noninvasive derivation of CV maps from LGE-CMR is feasible. Integration of noninvasive CV maps with electroanatomic mapping during substrate mapping has the potential to improve procedural planning and outcomes. Visual Overview: A visual overview is available for this article.

Background: Transition zones between healthy myocardium and scar form a spatially complex substrate that may give rise to reentrant ventricular arrhythmias (VAs). We sought to assess the utility of a novel machine learning approach for quantifying 3-dimensional spatial complexity of grayscale patterns on late gadolinium enhanced cardiac magnetic resonance images to predict VAs in patients with ischemic cardiomyopathy. Methods: One hundred twenty-two consecutive ischemic cardiomyopathy patients with left ventricular ejection fraction ≤35% without prior history of VAs underwent late gadolinium enhanced cardiac magnetic resonance images. From raw grayscale data, we generated graphs encoding the 3-dimensional geometry of the left ventricle. A novel technique, adapted to these graphs, assessed global regularity of signal intensity patterns using Fourier-like analysis and generated a substrate spatial complexity profile for each patient. A machine learning statistical algorithm was employed to discern which substrate spatial complexity profiles correlated with VA events (appropriate implantable cardioverter-defibrillator firings and arrhythmic sudden cardiac death) at 5 years of follow-up. From the statistical machine learning results, a complexity score ranging from 0 to 1 was calculated for each patient and tested using multivariable Cox regression models. Results: At 5 years of follow-up, 40 patients had VA events. The machine learning algorithm classified with 81% overall accuracy and correctly classified 86% of those without VAs. Overall negative predictive value was 91%. Average complexity score was significantly higher in patients with VA events versus those without (0.5±0.5 versus 0.1±0.2; P P =0.002). Conclusions: Substrate spatial complexity analysis of late gadolinium enhanced cardiac magnetic resonance images may be helpful in refining VA risk in patients with ischemic cardiomyopathy, particularly to identify low-risk patients who may not benefit from prophylactic implantable cardioverter-defibrillator therapy. Visual Overview: A visual overview is available for this article.

AIMS: Efforts to improve ablation success rates in persistent atrial fibrillation (AF) patients by targeting re-entrant driver (RD) sites have been hindered by weak mechanistic understanding regarding emergent RDs localization following initial fibrotic substrate modification. This study aimed to systematically assess arrhythmia dynamics after virtual ablation of RD sites in computational models. METHODS AND RESULTS: Simulations were conducted in 12 patient-specific atrial models reconstructed from pre-procedure late gadolinium-enhanced magnetic resonance imaging scans. In a previous study involving these same models, we comprehensively characterized pre-ablation RDs in simulations conducted with either ‘average human AF’-based electrophysiology (i.e. EP(avg)) or ±10% action potential duration or conduction velocity (i.e. EP(var)). Re-entrant drivers seen under the EP(avg) condition were virtually ablated and the AF initiation protocol was re-applied. Twenty-one emergent RDs were observed in 9/12 atrial models (1.75 ± 1.35 emergent RDs per model); these dynamically localized to boundary regions between fibrotic and non-fibrotic tissue. Most emergent RD locations (15/21, 71.4%) were within 0.1 cm of sites where RDs were seen pre-ablation in simulations under EP(var) conditions. Importantly, this suggests that the level of uncertainty in our models’ ability to predict patient-specific ablation targets can be substantially mitigated by running additional simulations that include virtual ablation of RDs. In 7/12 atrial models, at least one episode of macro-reentry around ablation lesion(s) was observed. CONCLUSION: Arrhythmia episodes after virtual RD ablation are perpetuated by both emergent RDs and by macro-reentrant circuits formed around lesions. Custom-tailoring of ablation procedures based on models should take steps to mitigate these sources of AF recurrence.

The treatment of individual patients in cardiology practice increasingly relies on advanced imaging, genetic screening and devices. As the amount of imaging and other diagnostic data increases, paralleled by the greater capacity to personalize treatment, the difficulty of using the full array of measurements of a patient to determine an optimal treatment seems also to be paradoxically increasing. Computational models are progressively addressing this issue by providing a common framework for integrating multiple data sets from individual patients. These models, which are based on physiology and physics rather than on population statistics, enable computational simulations to reveal diagnostic information that would have otherwise remained concealed and to predict treatment outcomes for individual patients. The inherent need for patient-specific models in cardiology is clear and is driving the rapid development of tools and techniques for creating personalized methods to guide pharmaceutical therapy, deployment of devices and surgical interventions.

Objectives This study sought to assess the relationship between fibrosis and re-entrant activity in persistent atrial fibrillation (AF). Background The mechanisms involved in sustaining re-entrant activity during AF are poorly understood. Methods Forty-one patients with persistent AF (age 56 ± 12 years; 6 women) were evaluated. High-resolution electrocardiographic imaging (ECGI) was performed during AF by using a 252-chest electrode array, and phase mapping was applied to locate re-entrant activity. Sites of high re-entrant activity were defined as re-entrant regions. Late gadolinium-enhanced (LGE) cardiac magnetic resonance (CMR) was performed at 1.25 × 1.25 × 2.5 mm resolution to characterize atrial fibrosis and measure atrial volumes. The relationship between LGE burden and the number of re-entrant regions was analyzed. Local LGE density was computed and characterized at re-entrant sites. All patients underwent catheter ablation targeting re-entrant regions, the procedural endpoint being AF termination. Clinical, CMR, and ECGI predictors of acute procedural success were then analyzed. Results Left atrial (LA) LGE burden was 22.1 ± 5.9% of the wall, and LA volume was 74 ± 21 ml/m 2 . The number of re-entrant regions was 4.3 ± 1.7 per patient. LA LGE imaging was significantly associated with the number of re-entrant regions (R = 0.52, p = 0.001), LA volume (R = 0.62, p Conclusions The number of re-entrant regions during AF relates to the extent of LGE on CMR, with the location of these regions clustering to LGE areas. These characteristics affect procedural outcomes of ablation.

Deep-learning algorithms to annotate electrocardiograms (ECGs) and classify different types of cardiac arrhythmias with the use of a single-lead ECG input data set have been developed. It remains to be determined whether these algorithms can be generalized to 12-lead ECG-based rhythm classification.We used a long short-term memory (LSTM) model to detect 12 heart rhythm classes with the use of 65,932 digital 12-lead ECG signals from 38,899 patients, using annotations obtained by consensus of 3 board-certified electrophysiologists as the criterion standard.The accuracy of the LSTM model for the classification of each of the 12 heart rhythms was ≥ 0.982 (range 0.982-1.0), with an area under the receiver operating characteristic curve of ≥ 0.987 (range 0.987-1.0). The precision and recall ranged from 0.692 to 1 and from 0.625 to 1, respectively, with an FWe demonstrated the feasibility and effectiveness of the deep-learning LSTM model for interpreting 12 common heart rhythms according to 12-lead ECG signals. The findings may have clinical relevance for the early diagnosis of cardiac rhythm disorders.

KEY POINTS Optogenetics has emerged as a potential alternative to electrotherapy for treating heart rhythm disorders, but its applicability for terminating atrial arrhythmias remains largely unexplored. We used computational models reconstructed from clinical MRI scans of fibrotic patient atria to explore the feasibility of optogenetic termination of atrial tachycardia (AT), comparing two different illumination strategies: distributed vs. targeted. We show that targeted optogenetic stimulation based on automated, non-invasive flow-network analysis of patient-specific re-entry morphology may be a reliable approach for identifying the optimal illumination target in each individual (i.e. the critical AT isthmus). The above-described approach yields very high success rates (up to 100%) and requires dramatically less input power than distributed illumination We conclude that simulations in patient-specific models show that targeted light pulses lasting longer than the AT cycle length can efficiently and reliably terminate AT if the human atria can be successfully light-sensitized via gene delivery of ChR2. ABSTRACT Optogenetics has emerged as a potential alternative to electrotherapy for treating arrhythmia, but feasibility studies have been limited to ventricular defibrillation via epicardial light application. Here, we assess the efficacy of optogenetic atrial tachycardia (AT) termination in human hearts using a strategy that targets for illumination specific regions identified in an automated manner. In three patient-specific models reconstructed from late gadolinium-enhanced MRI scans, we simulated channelrhodopsin-2 (ChR2) expression via gene delivery. In all three models, we attempted to terminate re-entrant AT (induced via rapid pacing) via optogenetic stimulation. We compared two strategies: (1) distributed illumination of the endocardium by multi-optrode grids (number of optrodes, Nopt = 64, 128, 256) and (2) targeted illumination of the critical isthmus, which was identified via analysis of simulated activation patterns using an algorithm based on flow networks. The illuminated area and input power were smaller for the targeted approach (19-57.8 mm2 ; 0.6-1.8 W) compared to the sparsest distributed arrays (Nopt = 64; 124.9 ± 6.3 mm2 ; 3.9 ± 0.2 W). AT termination rates for distributed illumination were low, ranging from

This study hypothesized that a left ventricular assist device (LVAD) shortens the electromechanical delay (EMD) by mechanical unloading. The goal of this study is to examine, by computational modeling, the influence of LVAD on EMD for four heart failure (HF) cases ranging from mild HF to severe HF. We constructed an integrated model of an LVAD-implanted cardiovascular system, then we altered the Ca2+ transient magnitude, with scaling factors 1, 0.9, 0.8, and 0.7 representing HF1, HF2, HF3, and HF4, respectively, in order of increasing HF severity. The four HF conditions are classified into two groups. Group one is the four HF conditions without LVAD, and group two is the conditions treated with continuous LVAD pump. The single-cell mechanical responses showed that EMD was prolonged with the higher load. The findings indicated that in group one, the HF-induced Ca2 + transient remodeling prolonged the mechanical activation time (MAT) and decreased the contractile tension, which reduced the left ventricle (LV) pressure, and increased the end-diastolic strain. In group two, LVAD shortened MAT of the ventricles. Furthermore, LVAD reduced the contractile tension, and end-diastolic strain, but increased the aortic pressure. The computational study demonstrated that LVAD shortens EMD by mechanical unloading of the ventricle. Electronic supplementary material The online version of this article (10.1007/s11517-017-1730-y) contains supplementary material, which is available to authorized users.

Long QT syndrome (LQTS) is an inherited or drug induced condition associated with delayed repolarization and sudden cardiac death. The cardiac potassium channel, IKr, and the adrenergic-sensitive cardiac potassium current, IKs, are two primary contributors to cardiac repolarization. This study aimed to elucidate the role of β-adrenergic (β-AR) stimulation in mediating the contributions of IKr and IKs to repolarizing the human left ventricle (n = 18). Optical mapping was used to measure action potential durations (APDs) in the presence of the IKs blocker JNJ-303 and the IKr blocker E-4031. We found that JNJ-303 alone did not increase APD. However, under isoprenaline (ISO), both the application of JNJ-303 and additional E-4031 significantly increased APD. With JNJ-303, ISO decreased APD significantly more in the epicardium as compared to the endocardium, with subsequent application E-4031 increasing mid- and endocardial APD80 more significantly than in the epicardium. We found that β-AR stimulation significantly augmented the effect of IKs blocker JNJ-303, in contrast to the reduced effect of IKr blocker E-4031. We also observed synergistic augmentation of transmural repolarization gradient by the combination of ISO and E-4031. Our results suggest β-AR-mediated increase of transmural dispersion of repolarization, which could pose arrhythmogenic risk in LQTS patients.

Patients with hypertrophic cardiomyopathy (HCM), a disease associated with sarcomeric protein mutations, often suffer from sudden cardiac death (SCD) resulting from arrhythmia. In order to advance SCD prevention strategies, our understanding of how sarcomeric mutations in HCM patients contribute to enhanced arrhythmogenesis needs to be improved. Early afterdepolarizations (EADs) are an important mechanism underlying arrhythmias associated with HCM-SCD. Although the ionic mechanisms underlying EADs have been studied in general, whether myofilament protein dynamics mechanisms also underlie EADs remains unknown. Thus, our goals were to investigate if myofilament protein dynamics mechanisms underlie EADs and to uncover how those mechanisms are affected by pacing rate, sarcomere length (SL), and different levels of HCM-induced myofilament remodeling. To achieve this, a mechanistically-based bidirectionally coupled human electrophysiology-force myocyte model under the conditions of HCM was constructed. HCM ionic remodeling included a reduced repolarization reserve, while HCM myofilament modeling involved altered thin filament activation. We found that the mechanoelectric feedback (MEF) on calcium dynamics in the bidirectionally coupled model, via Troponin C buffering of cytoplasmic Ca2+, was the myofilament mechanism underlying EADs. Incorporating MEF diminished the degree of repolarization reserve reduction necessary for EADs to emerge and increased the frequency of EAD occurrence, especially at faster pacing rates. Longer SLs and enhanced thin filament activation diminished the effects of MEF on EADs. Together these findings demonstrate that myofilament protein dynamics mechanisms play an important role in EAD formation.

Plakophilin-2 (PKP2) is a component of the desmosome and known for its role in cell–cell adhesion. Mutations in human PKP2 associate with a life-threatening arrhythmogenic cardiomyopathy, often of right ventricular predominance. Here, we use a range of state-of-the-art methods and a cardiomyocyte-specific, tamoxifen-activated, PKP2 knockout mouse to demonstrate that in addition to its role in cell adhesion, PKP2 is necessary to maintain transcription of genes that control intracellular calcium cycling. Lack of PKP2 reduces expression of Ryr2 (coding for Ryanodine Receptor 2), Ank2 (coding for Ankyrin-B), Cacna1c (coding for CaV1.2) and Trdn (coding for triadin), and protein levels of calsequestrin-2 (Casq2). These factors combined lead to disruption of intracellular calcium homeostasis and isoproterenol-induced arrhythmias that are prevented by flecainide treatment. We propose a previously unrecognized arrhythmogenic mechanism related to PKP2 expression and suggest that mutations in PKP2 in humans may cause life-threatening arrhythmias even in the absence of structural disease., It is believed that mutations in desmosomal adhesion complex protein plakophilin 2 (PKP2) cause arrhythmia due to loss of cell-cell communication. Here the authors show that PKP2 controls the expression of proteins involved in calcium cycling in adult mouse hearts, and that lack of PKP2 can cause arrhythmia in a structurally normal heart.

Ventricular tachycardia (VT), which could lead to sudden cardiac death, occurs frequently in patients with myocardial infarction. Computational modeling has emerged as a powerful platform for the non-invasive investigation of lethal heart rhythm disorders in post-infarction patients and for guiding patient VT ablation. However, it remains unclear how VT dynamics and predicted ablation targets are influenced by inter-patient variability in action potential duration (APD) and conduction velocity (CV). The goal of this study was to systematically assess the effect of changes in the electrophysiological parameters on the induced VTs and predicted ablation targets in personalized models of post-infarction hearts. Simulations were conducted in 5 patient-specific left ventricular models reconstructed from late gadolinium-enhanced magnetic resonance imaging scans. We comprehensively characterized all possible pre-ablation and post-ablation VTs in simulations conducted with either an “average human VT”-based electrophysiological representation (i.e., EPavg) or with ±10% APD or CV (i.e., EPvar); additional simulations were also executed in some models for an extended range of these parameters. The results showed that: (1) a subset of reentries (76.2–100%, depending on EP parameter set) conducted with ±10% APD/CV was observed in approximately the same locations as reentries observed in EPavg cases; (2) emergent VTs could be induced sometimes after ablation in EPavg models, and these emergent VTs often corresponded to the pre-ablation reentries in simulations with EPvar parameter sets. These findings demonstrate that the VT ablation target uncertainty in patient-specific ventricular models with an average representation of VT-remodeled electrophysiology is relatively low and the ablation targets stable, as the localization of the induced VTs was primarily driven by the remodeled structural substrate. Thus, personalized ventricular modeling with an average representation of infarct-remodeled electrophysiology may uncover most targets for VT ablation.

Ventricular tachycardia (VT), which can lead to sudden cardiac death, occurs frequently in patients with myocardial infarction. Catheter-based radiofrequency ablation of cardiac tissue has achieved only modest efficacy, owing to the inaccurate identification of ablation targets by current electrical mapping techniques, which can lead to extensive lesions and to a prolonged, poorly tolerated procedure. Here we show that personalized virtual-heart technology based on cardiac imaging and computational modelling can identify optimal infarct-related VT ablation targets in retrospective animal (5 swine) and human studies (21 patients) and in a prospective feasibility study (5 patients). We first assessed in retrospective studies (one of which included a proportion of clinical images with artifacts) the capability of the technology to determine the minimum-size ablation targets for eradicating all VTs. In the prospective study, VT sites predicted by the technology were targeted directly, without relying on prior electrical mapping. The approach could improve infarct-related VT ablation guidance, where accurate identification of patient-specific optimal targets could be achieved on a personalized virtual heart prior to the clinical procedure.

This study sought to evaluate the association between contrast-enhanced multidetector computed tomography (CE-MDCT) attenuation and local epicardial conduction speed (ECS) and electrographic abnormalities in patients with arrhythmogenic right ventricular cardiomyopathy (ARVC) and ventricular tachycardia (VT).CE-MDCT is a widely available and fast imaging technology with high spatial resolution that is less prone to defibrillator generator-related safety issues and image artifacts. However, the association between hypoattenuation on MDCT and VT substrates in ARVC remains unknown.Patients with ARVC who underwent CE-MDCT followed by endocardial (n = 30) and epicardial (n = 21) electroanatomical mapping (EAM) and VT ablation were prospectively enrolled. Right ventricular (RV) mid-myocardial attenuation was calculated from 3-dimensional MDCT images and registered to EAM. Local ECS was calculated by averaging the ECS between each point and 5 adjacent points with concordant wave front direction.A total of 17,311 epicardial and 5,204 endocardial points were included. In multivariable regression analysis clustered by patient, RV myocardial attenuation was associated with epicardial bipolar voltage amplitude (2.5% decrease in amplitude per 10 HU decrease in attenuation; p 0.001), with endocardial unipolar voltage amplitude (0.9% decrease in amplitude per 10 HU decrease in attenuation; p 0.001), and with ECS (0.4% decrease in ECS per 10 HU decrease in attenuation; p = 0.001).CE-MDCT attenuation distribution is associated with regional ECS and electrographic amplitude in ARVC. Regions with low attenuation likely reflect fibro-fatty involvement in the RV and may serve as important VT substrates in patients with ARVC who are undergoing VT ablation.

BACKGROUND: Recently, an augmented reality (AR) solution allows the physician to place the ablation catheter at the designated lesion site more accurately during cardiac electrophysiology studies. The improvement in navigation accuracy may positively affect ventricular tachycardia (VT) ablation termination, however assessment of this in the clinic would be difficult. Novel personalized virtual heart technology enables non-invasive identification of optimal lesion targets for infarct-related VT. This study aims to evaluate the potential impact of such catheter navigation accuracy improvement in virtual VT ablations. METHODS: 2 MRI-based virtual hearts with 2 in silico induced VTs (VT 1, VT 2) were included. VTs were terminated with virtual “ground truth” endocardial ablation lesions. 106 navigation error values that were previously assessed in a clinical study evaluating the improvement of ablation catheter navigation accuracy guided with AR (53 with, 53 without) were used to displace the “ground truth” ablation targets. The corresponding ablations were simulated based on these errors and VT termination for each simulation was assessed. RESULTS: In 54 VT 1 ablation simulations, smaller error with AR significantly resulted in more VT termination (25) compared to the error without AR (16) (P

Aim To predict arrhythmia susceptibility in myocardial infarction (MI) patients with left ventricular ejection fraction (LVEF) >35% using a personalized virtual heart simulation approach. Methods and results A total of four contrast enhanced magnetic resonance imaging (MRI) datasets of patient hearts with MI and average LVEF of 44.0 ± 2.6% were used in this study. Because of the preserved LVEF, the patients were not indicated for implantable cardioverter defibrillator (ICD) insertion. One patient had spontaneous ventricular tachycardia (VT) prior to the MRI scan; the others had no arrhythmic events. Simulations of arrhythmia susceptibility were blind to clinical outcome. Models were constructed from patient MRI images segmented to identify myocardium, grey zone, and scar based on pixel intensity. Grey zone was modelled as having altered electrophysiology. Programmed electrical stimulation (PES) was performed to assess VT inducibility from 19 bi-ventricular sites in each heart model. Simulations successfully predicted arrhythmia risk in all four patients. For the patient with arrhythmic event, in-silico PES resulted in VT induction. Simulations correctly predicted that VT was non-inducible for the three patients with no recorded VT events. Conclusions Results demonstrate that the personalized virtual heart simulation approach may provide a novel risk stratification modality to non-invasively and effectively identify patients with LVEF >35% who could benefit from ICD implantation.

Ventricular arrhythmias are among the most severe complications of heart disease and can result in sudden cardiac death. Patients at risk currently receive implantable defibrillators that deliver electrical shocks to terminate arrhythmias on demand. However, strong electrical shocks can damage the heart and cause severe pain. Therefore, we have tested optogenetic defibrillation using expression of the light-sensitive channel channelrhodopsin-2 (ChR2) in cardiac tissue. Epicardial illumination effectively terminated ventricular arrhythmias in hearts from transgenic mice and from WT mice after adeno-associated virus-based gene transfer of ChR2. We also explored optogenetic defibrillation for human hearts, taking advantage of a recently developed, clinically validated in silico approach for simulating infarct-related ventricular tachycardia (VT). Our analysis revealed that illumination with red light effectively terminates VT in diseased, ChR2-expressing human hearts. Mechanistically, we determined that the observed VT termination is due to ChR2-mediated transmural depolarization of the myocardium, which causes a block of voltage-dependent Na+ channels throughout the myocardial wall and interrupts wavefront propagation into illuminated tissue. Thus, our results demonstrate that optogenetic defibrillation is highly effective in the mouse heart and could potentially be translated into humans to achieve nondamaging and pain-free termination of ventricular arrhythmia.

Sudden cardiac death (SCD) from arrhythmias is a leading cause of mortality. For patients at high SCD risk, prophylactic insertion of implantable cardioverter defibrillators (ICDs) reduces mortality. Current approaches to identify patients at risk for arrhythmia are, however, of low sensitivity and specificity, which results in a low rate of appropriate ICD therapy. Here, we develop a personalized approach to assess SCD risk in post-infarction patients based on cardiac imaging and computational modelling. We construct personalized three-dimensional computer models of post-infarction hearts from patients’ clinical magnetic resonance imaging data and assess the propensity of each model to develop arrhythmia. In a proof-of-concept retrospective study, the virtual heart test significantly outperformed several existing clinical metrics in predicting future arrhythmic events. The robust and non-invasive personalized virtual heart risk assessment may have the potential to prevent SCD and avoid unnecessary ICD implantations.