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Abstract Stroke, a major global health concern often rooted in cardiac dynamics, demands precise risk evaluation for targeted intervention. Current risk models, like the $$\text {CHA}_2\text {DS}_2\text {-VASc}$$ CHA 2 DS 2 -VASc score, often lack the granularity required for personalized predictions. In this study, we present a nuanced and thorough stroke risk assessment by integrating functional insights from cardiac magnetic resonance (CMR) with patient-specific computational fluid dynamics (CFD) simulations. Our cohort, evenly split between control and stroke groups, comprises eight patients. Utilizing CINE CMR, we compute kinematic features, revealing smaller left atrial volumes for stroke patients. The incorporation of patient-specific atrial displacement into our hemodynamic simulations unveils the influence of atrial compliance on the flow fields, emphasizing the importance of LA motion in CFD simulations and challenging the conventional rigid wall assumption in hemodynamics models. Standardizing hemodynamic features with functional metrics enhances the differentiation between stroke and control cases. While standalone assessments provide limited clarity, the synergistic fusion of CMR-derived functional data and patient-informed CFD simulations offers a personalized and mechanistic understanding, distinctly segregating stroke from control cases. Specifically, our investigation reveals a crucial clinical insight: normalizing hemodynamic features based on ejection fraction fails to differentiate between stroke and control patients. Differently, when normalized with stroke volume, a clear and clinically significant distinction emerges and this holds true for both the left atrium and its appendage, providing valuable implications for precise stroke risk assessment in clinical settings. This work introduces a novel framework for seamlessly integrating hemodynamic and functional metrics, laying the groundwork for improved predictive models, and highlighting the significance of motion-informed, personalized risk assessments.

Background: Atrial fibrillation (AF) patients are at high risk of stroke with ∼90% clots originating from the left atrial appendage (LAA). Clinical understanding of blood-flow based parameters and their potential association with stroke for AF patients remains poorly understood. We hypothesize that slow blood-flow either in the LA or the LAA could lead to the formation of blood clots and is associated with stroke for AF patients. Methods: We retrospectively collected cardiac CT images of paroxysmal AF patients and dichotomized them based on clinical event of previous embolic event into stroke and non-stroke groups. After image segmentation to obtain 3D LA geometry, patient-specific blood-flow analysis was performed to model LA hemodynamics. In terms of geometry, we calculated area of the pulmonary veins (PVs), mitral valve, LA and LAA, orifice area of LAA and volumes of LA and LAA and classified LAA morphologies. For hemodynamic assessment, we quantified blood flow velocity, wall shear stress (WSS, blood-friction on LA wall), oscillatory shear index (OSI, directional change of WSS) and endothelial cell activation potential (ECAP, ratio of OSI and WSS quantifying slow and oscillatory flow) in the LA as well as the LAA. Statistical analysis was performed to compare the parameters between the groups. Results: Twenty-seven patients were included in the stroke and 28 in the non-stroke group. Examining geometrical parameters, area of left inferior PV was found to be significantly higher in the stroke group as compared to non-stroke group (p = 0.026). In terms of hemodynamics, stroke group had significantly lower blood velocity (p = 0.027), WSS (p = 0.018) and higher ECAP (p = 0.032) in the LAA as compared to non-stroke group. However, LAA morphologic type did not differ between the two groups. This suggests that stroke patients had significantly slow and oscillatory circulating blood-flow in the LAA, which might expose it to potential thrombogenesis. Conclusion: Slow flow in the LAA alone was associated with stroke in this paroxysmal AF cohort. Patient-specific blood-flow analysis can potentially identify such hemodynamic conditions, aiding in clinical stroke risk stratification of AF patients.

Background: Visualizing fibrosis on cardiac magnetic resonance (CMR) imaging with contrast enhancement (late gadolinium enhancement; LGE) is paramount in characterizing disease progression and identifying arrhythmia substrates. Segmentation and fibrosis quantification from LGE-CMR is intensive, manual, and prone to interobserver variability. There is an unmet need for automated LGE-CMR image segmentation that ensures anatomical accuracy and seamless extraction of clinical features. Objective: This study aimed to develop a novel deep learning solution for analysis of contrast-enhanced CMR images that produces anatomically accurate myocardium and scar/fibrosis segmentations and uses these to calculate features of clinical interest. Methods: Data sources were 155 2-dimensional LGE-CMR patient scans (1124 slices) and 246 synthetic “LGE-like” scans (1360 slices) obtained from cine CMR using a novel style-transfer algorithm. We trained and tested a 3-stage neural network that identified the left ventricle (LV) region of interest (ROI), segmented ROI into viable myocardium and regions of enhancement, and postprocessed the segmentation results to enforce conforming to anatomical constraints. The segmentations were used to directly compute clinical features, such as LV volume and scar burden. Results: Predicted LV and scar segmentations achieved 96% and 75% balanced accuracy, respectively, and 0.93 and 0.57 Dice coefficient when compared to trained expert segmentations. The mean scar burden difference between manual and predicted segmentations was 2%. Conclusion: We developed and validated a deep neural network for automatic, anatomically accurate expert-level LGE- CMR myocardium and scar/fibrosis segmentation, allowing direct calculation of clinical measures. Given the training set heterogeneity, our approach could be extended to multiple imaging modalities and patient pathologies.

Summary: Atrial fibrillation (AF) is the most common human arrhythmia, affecting millions of patients worldwide. A combination of risk factors and comorbidities results in complex atrial remodeling, which increases AF vulnerability and persistence. Insights from animal models, clinical studies, and computational modeling have advanced the understanding of the mechanisms and pathophysiology of AF. Areas of heterogeneous pathological remodeling, as well as altered electrophysiological properties, serve as a substrate for AF drivers and spontaneous activations. The complex and individualized presentation of this arrhythmia suggests that mechanisms-based personalized approaches will likely be needed to overcome current challenges in AF management. In this paper, we review the insights on the mechanisms of AF obtained from animal models and clinical studies and how computational models integrate this knowledge to advance AF clinical management. We also assess the challenges that need to be overcome to implement these mechanistic models in clinical practice.

Abstract Intercellular electrical coupling is an essential means of communication between cells. It is important to obtain quantitative knowledge of such coupling between cardiomyocytes and non-excitable cells when, for example, pathological electrical coupling between myofibroblasts and cardiomyocytes yields increased arrhythmia risk or during the integration of donor (e.g., cardiac progenitor) cells with native cardiomyocytes in cell-therapy approaches. Currently, there is no direct method for assessing heterocellular coupling within multicellular tissue. Here we demonstrate experimentally and computationally a new contactless assay for electrical coupling, OptoGap, based on selective illumination of inexcitable cells that express optogenetic actuators and optical sensing of the response of coupled excitable cells (e.g., cardiomyocytes) that are light-insensitive. Cell–cell coupling is quantified by the energy required to elicit an action potential via junctional current from the light-stimulated cell(s). The proposed technique is experimentally validated against the standard indirect approach, GapFRAP, using light-sensitive cardiac fibroblasts and non-transformed cardiomyocytes in a two-dimensional setting. Its potential applicability to the complex three-dimensional setting of the native heart is corroborated by computational modelling and proper calibration. Lastly, the sensitivity of OptoGap to intrinsic cell-scale excitability is robustly characterized via computational analysis.

Abstract SARS‐CoV‐2 infection is associated with increased risk for pulmonary embolism (PE), a fatal complication that can cause right ventricular (RV) dysfunction. Serum D‐dimer levels are a sensitive test to suggest PE, however lacks specificity in COVID‐19 patients. The goal of this study was to identify a model that better predicts PE diagnosis in hospitalized COVID‐19 patients using clinical, laboratory, and echocardiographic imaging predictors. We performed a cross‐sectional study of 302 adult patients admitted to the Johns Hopkins Hospital (March 2020–February 2021) for COVID‐19 infection who underwent transthoracic echocardiography and D‐dimer testing; 204 patients had CT angiography. Clinical, laboratory and imaging predictors including, but not limited to, D‐dimer and RV dysfunction were used to build prediction models for PE using logistic regression. Model discrimination was assessed using area under the receiver operator curve (AUC) and calibration using Hosmer‐Lemeshow χ2 statistic. Internal validation was performed. The prevalence of PE was 7.6%. The model with positive D‐dimer above 5 mg/L, RV dysfunction on echocardiography, and troponin had an AUC of 0.77, and cross‐validated AUC of 0.74. D‐dimer (>5 mg/L) had a positive association with PE (adj odds ratio = 4.40; 95% confidence interval: [1.80, 10.78]). We identified a model including clinical, imaging and laboratory variables that predicted PE in hospitalized COVID‐19 patients. Positive D‐dimer >5, RV dysfunction on echocardiography, and troponin were important predictors for calculating likelihood of PE diagnosis. This approach may be useful to aid in clinical decision‐making related to diagnostic imaging and treatment. Prospective studies are needed to evaluate impact on patient outcomes.

Probabilistic estimation of cardiac electrophysiological model parameters serves an important step toward model personalization and uncertain quantification. The expensive computation associated with these model simulations, however, makes direct Markov Chain Monte Carlo (MCMC) sampling of the posterior probability density function (pdf) of model parameters computationally intensive. Approximated posterior pdfs resulting from replacing the simulation model with a computationally efficient surrogate, on the other hand, have seen limited accuracy. In this study, we present a Bayesian active learning method to directly approximate the posterior pdf function of cardiac model parameters, in which we intelligently select training points to query the simulation model in order to learn the posterior pdf using a small number of samples. We integrate a generative model into Bayesian active learning to allow approximating posterior pdf of high-dimensional model parameters at the resolution of the cardiac mesh. We further introduce new acquisition functions to focus the selection of training points on better approximating the shape rather than the modes of the posterior pdf of interest. We evaluated the presented method in estimating tissue excitability in a 3D cardiac electrophysiological model in a range of synthetic and real-data experiments. We demonstrated its improved accuracy in approximating the posterior pdf compared to Bayesian active learning using regular acquisition functions, and substantially reduced computational cost in comparison to existing standard or accelerated MCMC sampling.

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 (p0.05) 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 (p

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.

RationalePatients with ischemic cardiomyopathy (ICMP) are at high risk for malignant arrhythmias, largely due to electrophysiological remodeling of the non-infarcted myocardium. The electrophysiological properties of the non-infarcted myocardium of patients with ICMP remain largely unknown.ObjectivesTo assess the pro-arrhythmic behavior of non-infarcted myocardium in ICMP patients and couple computational simulations with machine learning to establish a methodology for the development of disease-specific action potential models based on clinically measured action potential duration restitution (APDR) data.Methods and ResultsWe enrolled 22 patients undergoing left-sided ablation (10 ICMP) and compared APDRs between ICMP and structurally normal left ventricles (SNLVs). APDRs were clinically assessed with a decremental pacing protocol. Using genetic algorithms (GAs), we constructed populations of action potential models that incorporate the cohort-specific APDRs. The variability in the populations of ICMP and SNLV models was captured by clustering models based on their similarity using unsupervised machine learning. The pro-arrhythmic potential of ICMP and SNLV models was assessed in cell- and tissue-level simulations. Clinical measurements established that ICMP patients have a steeper APDR slope compared to SNLV (by 38%, p < 0.01). In cell-level simulations, APD alternans were induced in ICMP models at a longer cycle length compared to SNLV models (385–400 vs 355 ms). In tissue-level simulations, ICMP models were more susceptible for sustained functional re-entry compared to SNLV models.ConclusionMyocardial remodeling in ICMP patients is manifested as a steeper APDR compared to SNLV, which underlies the greater arrhythmogenic propensity in these patients, as demonstrated by cell- and tissue-level simulations using action potential models developed by GAs from clinical measurements. The methodology presented here captures the uncertainty inherent to GAs model development and provides a blueprint for use in future studies aimed at evaluating electrophysiological remodeling resulting from other cardiac diseases.

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.

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.

Heart failure (HF) is one of the most common causes of morbidity and mortality worldwide. Although many patients suffering from HF die from sudden cardiac death caused by arrhythmias, the mechanism linking HF remodeling to an increased arrhythmogenic propensity remains incomplete. HF is typically characterized by a progressive loss of transverse tubule (T-tubule) domains, which leads to an altered distribution of L-type calcium channels (LTCCs). Microdomain degradation also causes the disruption of the β2 adrenergic receptor (β2AR) and phosphodiesterase (PDE) signaling localization, normally confined to the dyadic space. The goal of this study was to analyze how these subcellular changes affect the function of LTCCs and lead to the emergence of ventricular cell-level triggers of arrhythmias. To accomplish this, we developed a novel computational model of a human ventricular HF myocyte in which LTCCs were divided into six different populations, based on their location and signaling environment they experience. To do so, we included T-tubular microdomain remodeling which led to a subset of LTCCs to be redistributed from the T-tubular to the surface membrane and allowed for different levels of phosphorylation of LTCCs by PKA, based on the presence of β2ARs and PDEs. The model was used to study the behavior of the LTCC current (ICaL) under basal and sympathetic stimulation and its effect on cellular action potential. Our results showed that channels redistributed from the T-tubular membrane to the bulk of the sarcolemma displayed an altered function in their new, non-native signaling domain. Incomplete calcium dependent inactivation, which resulted in a longer-lasting and larger-in-magnitude LTCC current, was observed when we decoupled LTCCs from ryanodine receptors and removed them from the dyadic space. The magnitude of the LTCC current, especially in the surface sarcolemma, was also increased via phosphorylation by the redistributed β2ARs and PDEs. These changes in LTCC current led to the development of early afterdepolarizations. Thus, our study shows that altered LTCC function is a potential cause for the emergence of cell-level triggers of arrhythmia, and that β2ARs and PDEs present useful therapeutic targets for treatment of HF and prevention of sudden cardiac death.

Sudden arrhythmic death is a leading cause of mortality, however approaches to identify at-risk patients are of low sensitivity and specificity. Here, the authors develop a personalized approach to assess arrhythmia risk in post-infarction patients based on cardiac imaging and computational modelling that significantly outperforms existing clinical metrics.

Focal impulse and rotor mapping (FIRM) involves intracardiac detection and catheter ablation of re-entrant drivers (RDs), some of which may contribute to arrhythmia perpetuation in persistent atrial fibrillation (PsAF). Patient-specific computational models derived from late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) has the potential to non-invasively identify all areas of the fibrotic substrate where RDs could potentially be sustained, including locations where RDs may not manifest during mapped AF episodes. The objective of this study was to carry out multi-modal assessment of the arrhythmogenic propensity of the fibrotic substrate in PsAF patients by comparing locations of RD-harboring regions found in simulations and detected by FIRM (RDsim and RDFIRM) and analyze implications for ablation strategies predicated on targeting RDs. For 11 PsAF patients who underwent pre-procedure LGE-MRI and FIRM-guided ablation, we retrospectively simulated AF in individualized atrial models, with geometry and fibrosis distribution reconstructed from pre-ablation LGE-MRI scans, and identified RDsim sites. Regions harboring RDsim and RDFIRM were compared. RDsim were found in 38 atrial regions (median [inter-quartile range (IQR)] = 4 [3; 4] per model). RDFIRM were identified and subsequently ablated in 24 atrial regions (2 [1; 3] per patient), which was significantly fewer than the number of RDsim-harboring regions in corresponding models (p < 0.05). Computational modeling predicted RDsim in 20 of 24 (83%) atrial regions identified as RDFIRM-harboring during clinical mapping. In a large number of cases, we uncovered RDsim-harboring regions in which RDFIRM were never observed (18/22 regions that differed between the two modalities; 82%); we termed such cases “latent” RDsim sites. During follow-up (230 [180; 326] days), AF recurrence occurred in 7/11 (64%) individuals. Interestingly, latent RDsim sites were observed in all seven computational models corresponding to patients who experienced recurrent AF (2 [2; 2] per patient); in contrast, latent RDsim sites were only discovered in two of four patients who were free from AF during follow-up (0.5 [0; 1.5] per patient; p < 0.05 vs. patients with AF recurrence). We conclude that substrate-based ablation based on computational modeling could improve outcomes.

Electrocardiographic mapping (ECGI) detects reentrant drivers (RDs) that perpetuate arrhythmia in persistent AF (PsAF). Patient-specific computational models derived from late gadolinium-enhanced magnetic resonance imaging (LGE-MRI) identify all latent sites in the fibrotic substrate that could potentially sustain RDs, not just those manifested during mapped AF. The objective of this study was to compare RDs from simulations and ECGI (RDsim/RDECGI) and analyze implications for ablation. We considered 12 PsAF patients who underwent RDECGI ablation. For the same cohort, we simulated AF and identified RDsim sites in patient-specific models with geometry and fibrosis distribution from pre-ablation LGE-MRI. RDsim- and RDECGI-harboring regions were compared, and the extent of agreement between macroscopic locations of RDs identified by simulations and ECGI was assessed. Effects of ablating RDECGI/RDsim were analyzed. RDsim were predicted in 28 atrial regions (median [inter-quartile range (IQR)] = 3.0 [1.0; 3.0] per model). ECGI detected 42 RDECGI-harboring regions (4.0 [2.0; 5.0] per patient). The number of regions with RDsim and RDECGI per individual was not significantly correlated (R = 0.46, P = ns). The overall rate of regional agreement was fair (modified Cohen's κ0 statistic = 0.11), as expected, based on the different mechanistic underpinning of RDsim- and RDECGI. nineteen regions were found to harbor both RDsim and RDECGI, suggesting that a subset of clinically observed RDs was fibrosis-mediated. The most frequent source of differences (23/32 regions) between the two modalities was the presence of RDECGI perpetuated by mechanisms other than the fibrotic substrate. In 6/12 patients, there was at least one region where a latent RD was observed in simulations but was not manifested during clinical mapping. Ablation of fibrosis-mediated RDECGI (i.e., targets in regions that also harbored RDsim) trended toward a higher rate of positive response compared to ablation of other RDECGI targets (57 vs. 41%, P = ns). Our analysis suggests that RDs in human PsAF are at least partially fibrosis-mediated. Substrate-based ablation combining simulations with ECGI could improve outcomes.