Far-red and sensitive sensor for monitoring real time H2O2 dynamics with subcellular resolution and in multi-parametric imaging applications.

H2O2 is a key oxidant in mammalian biology and a pleiotropic signaling molecule at the physiological level, and its excessive accumulation in conjunction with decreased cellular reduction capacity is often found to be a common pathological marker. Here, we present a red fluorescent Genetically Encoded H2O2 Indicator (GEHI) allowing versatile optogenetic dissection of redox biology. Our new GEHI, oROS-HT, is a chemigenetic sensor utilizing a HaloTag and Janelia Fluor (JF) rhodamine dye as fluorescent reporters. We developed oROS-HT through a structure-guided approach aided by classic protein structures and recent protein structure prediction tools. Optimized with JF635, oROS-HT is a sensor with 635 nm excitation and 650 nm emission peaks, allowing it to retain its brightness while monitoring intracellular H2O2 dynamics. Furthermore, it enables multi-color imaging in combination with blue-green fluorescent sensors for orthogonal analytes and low auto-fluorescence interference in biological tissues. Other advantages of oROS-HT over alternative GEHIs are its fast kinetics, oxygen-independent maturation, low pH sensitivity, lack of photo-artifact, and lack of intracellular aggregation. Here, we demonstrated efficient subcellular targeting and how oROS-HT can map inter and intracellular H2O2 diffusion at subcellular resolution. Lastly, we used oROS-HT with the green fluorescent calcium indicator Fluo-4 to investigate the transient effect of the anti-inflammatory agent auranofin on cellular redox physiology and calcium levels via multi-parametric, dual-color imaging.

Machine learning identifies esophageal luminal temperature patterns associated with thermal injury in catheter ablation for atrial fibrillation.

INTRODUCTION: Luminal esophageal temperature (LET) monitoring during atrial fibrillation (AF) ablation is widely used to reduce the incidence of endoscopically detected esophageal lesion (EDEL). We sought to assess whether specific patterns of LET variation are associated with EDEL. METHODS: A high-fidelity multisensor probe was used to record LET in AF patients undergoing radiofrequency ablation (RFA) or cryoballoon ablation (CBA). Explainable machine learning and SHapley Additive exPlanations (SHAP) analysis were used to predict EDEL and assess feature importance. RESULTS: A total of 94 patients (38.3% persistent AF, 71.3% male, 72 RFA, and 22 CBA) were included. EDEL was detected in 11 patients (10 RFA and one CBA). In the RFA group, the highest LET recorded was similar between patients with and without EDEL (40.6 [40.1-41]°C vs. 40.2 [39.1-40.9]°C; p = .313), however, the rate of LET rise for the highest recorded peak was higher (0.08 [0.03-0.12]°C/s vs. 0.02 [0.01-0.05]°C/s; p = .033), and the area under the curve (AUC) for the highest peak was smaller (412.5 [206.8-634.1] vs. 588.6 [380.4-861.1]; p = .047) in patients who had EDEL. In case of CBA, the patient with EDEL had a faster LET decline (0.12 vs. 0.07 [0.02-0.14]°C/s), and a smaller AUC for the lowest trough (2491.3 vs. 2629.3 [1712.6-5283.2]). SHAP analysis revealed that a rate of LET change higher than 0.05°C/s and an AUC less than 600 were more predictive of EDEL in RFA. CONCLUSION: The rate of LET change and AUC for the recorded temperature predicted EDEL, whereas absolute peak temperatures did not.

Reduced-order models of wall shear stress patterns in the left atrial appendage from a data-augmented atrial database.

BACKGROUND: Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia, affecting over 1% of the population. It is usually triggered by irregular electrical impulses that cause the atria to contract irregularly and ineffectively. It increases blood stasis and the risk of thrombus formation within the left atrial appendage (LAA) and aggravates adverse atrial remodeling. Despite recent efforts, LAA flow patterns representative of AF conditions and their association with LAA stasis remain poorly characterized. AIM: To develop reduced-order data-driven models of LAA flow patterns during atrial remodeling in order to uncover flow disturbances concurrent with LAA stasis that could add granularity to clinical decision criteria. METHODS: We combined a geometric data augmentation process with projection of results from 180 CFD atrial simulations on a universal LAA coordinate (ULAAC) system. The projection approach enhances data visualization and facilitates direct comparison between different anatomical and functional states. ULAAC projections were used as input for a proper orthogonal decomposition (POD) algorithm to build reduced-order models of hemodynamic metrics, extracting flow characteristics associated with AF and non-AF anatomies. RESULTS: We verified that the ULAAC system provides an adequate representation to visualize data distributions on the LAA surface and to build POD-based reduced-order models. These models revealed significant differences in LAA flow patterns for atrial geometries that underwent adverse atrial remodeling and experienced elevated blood stasis. Together with anatomical morphing-based patient-specific data augmentation, this approach could facilitate data-driven analyses to identify flow features associated with thrombosis risk due to atrial remodeling.

Personalized biomechanical insights in atrial fibrillation: opportunities & challenges.

INTRODUCTION: Atrial fibrillation (AF) is an increasingly prevalent and significant worldwide health problem. Manifested as an irregular atrial electrophysiological activation, it is associated with many serious health complications. AF affects the biomechanical function of the heart as contraction follows the electrical activation, subsequently leading to reduced blood flow. The underlying mechanisms behind AF are not fully understood, but it is known that AF is highly correlated with the presence of atrial fibrosis, and with a manifold increase in risk of stroke. AREAS COVERED: In this review, we focus on biomechanical aspects in atrial fibrillation, current and emerging use of clinical images, and personalized computational models. We also discuss how these can be used to provide patient-specific care. EXPERT OPINION: Understanding the connection betweenatrial fibrillation and atrial remodeling might lead to valuable understanding of stroke and heart failure pathophysiology. Established and emerging imaging modalities can bring us closer to this understanding, especially with continued advancements in processing accuracy, reproducibility, and clinical relevance of the associated technologies. Computational models of cardiac electromechanics can be used to glean additional insights on the roles of AF and remodeling in heart function.

People with atrial fibrillation (AF) experience a fast, chaotic heartbeat. AF greatly increases the risk of stroke. The hearts of AF patients often have an accumulation of fibrous tissue (fibrosis). Fibrosis patterns can be detected via medical imaging scans, like MRI. These images can be used to build patient-specific digital representations. These models can be used to explore how fibrosis might cause AF, stroke, and other health risks. Insights from imaging and modeling are becoming more and more useful as tools for personalizing AF treatment.

Optogenetic Modulation of Arrhythmia Triggers: Proof-of-Concept from Computational Modeling.

Introduction: Early afterdepolarizations (EADs) are secondary voltage depolarizations associated with reduced repolarization reserve (RRR) that can trigger lethal arrhythmias. Relating EADs to triggered activity is difficult to study, so the ability to suppress or provoke EADs would be experimentally useful. Here, we use computational simulations to assess the feasibility of subthreshold optogenetic stimulation modulating the propensity for EADs (cell-scale) and EAD-associated ectopic beats (organ-scale). Methods: We modified a ventricular ionic model by reducing rapid delayed rectifier potassium (0.25-0.1 × baseline) and increasing L-type calcium (1.0-3.5 × baseline) currents to create RRR conditions with varying severity. We ran simulations in models of single cardiomyocytes and left ventricles from post-myocardial infarction patient MRI scans. Optogenetic stimulation was simulated using either ChR2 (depolarizing) or GtACR1 (repolarizing) opsins. Results: In cell-scale simulations without illumination, EADs were seen for 164 of 416 RRR conditions. Subthreshold stimulation of GtACR1 reduced EAD incidence by up to 84.8% (25/416 RRR conditions; 0.1 µW/mm2); in contrast, subthreshold ChR2 excitation increased EAD incidence by up to 136.6% (388/416 RRR conditions; 50 µW/mm2). At the organ scale, we assumed simultaneous, uniform illumination of the epicardial and endocardial surfaces. GtACR1-mediated suppression (10-50 µW/mm2) and ChR2-mediated unmasking (50-100 µW/mm2) of EAD-associated ectopic beats were feasible in three distinct ventricular models. Conclusions: Our findings suggest that optogenetics could be used to silence or provoke both EADs and EAD-associated ectopic beats. Validation in animal models could lead to exciting new experimental regimes and potentially to novel anti-arrhythmia treatments. Supplementary Information: The online version contains supplementary material available at 10.1007/s12195-023-00781-z.

Comparing Inducibility of Re-Entrant Arrhythmia in Patient-Specific Computational Models to Clinical Atrial Fibrillation Phenotypes.

BACKGROUND: Computational models of fibrosis-mediated, re-entrant left atrial (LA) arrhythmia can identify possible substrate for persistent atrial fibrillation (AF) ablation. Contemporary models use a 1-size-fits-all approach to represent electrophysiological properties, limiting agreement between simulations and patient outcomes. OBJECTIVES: The goal of this study was to test the hypothesis that conduction velocity (Ï´) modulation in persistent AF models can improve simulation agreement with clinical arrhythmias. METHODS: Patients with persistent AF (n = 37) underwent ablation and were followed up for ≥2 years to determine post-ablation outcomes: AF, atrial flutter (AFL), or no recurrence. Patient-specific LA models (n = 74) were constructed using pre-ablation and ≥90 days' post-ablation magnetic resonance imaging data. Simulated pacing gauged in silico arrhythmia inducibility due to AF-like rotors or AFL-like macro re-entrant tachycardias. A physiologically plausible range of Ï´ values (±10 or 20% vs. baseline) was tested, and model/clinical agreement was assessed. RESULTS: Fifteen (41%) patients had a recurrence with AF and 6 (16%) with AFL. Arrhythmia was induced in 1,078 of 5,550 simulations. Using baseline Ï´, model/clinical agreement was 46% (34 of 74 models), improving to 65% (48 of 74) when any possible Ï´ value was used (McNemar's test, P = 0.014). Ï´ modulation improved model/clinical agreement in both pre-ablation and post-ablation models. Pre-ablation model/clinical agreement was significantly greater for patients with extensive LA fibrosis (>17.2%) and an elevated body mass index (>32.0 kg/m2). CONCLUSIONS: Simulations in persistent AF models show a 41% relative improvement in model/clinical agreement when Ï´ is modulated. Patient-specific calibration of Ï´ values could improve model/clinical agreement and model usefulness, especially in patients with higher body mass index or LA fibrosis burden. This could ultimately facilitate better personalized modeling, with immediate clinical implications.

Explainable Machine Learning to Predict Anchored Reentry Substrate Created by Persistent Atrial Fibrillation Ablation in Computational Models.

Background Postablation arrhythmia recurrence occurs in ~40% of patients with persistent atrial fibrillation. Fibrotic remodeling exacerbates arrhythmic activity in persistent atrial fibrillation and can play a key role in reentrant arrhythmia, but emergent interaction between nonconductive ablation-induced scar and native fibrosis (ie, residual fibrosis) is poorly understood. Methods and Results We conducted computational simulations in pre- and postablation left atrial models reconstructed from late gadolinium enhanced magnetic resonance imaging scans to test the hypothesis that ablation in patients with persistent atrial fibrillation creates new substrate conducive to recurrent arrhythmia mediated by anchored reentry. We trained a random forest machine learning classifier to accurately pinpoint specific nonconductive tissue regions (ie, areas of ablation-delivered scar or vein/valve boundaries) with the capacity to serve as substrate for anchored reentry-driven recurrent arrhythmia (area under the curve: 0.91±0.03). Our analysis suggests there is a distinctive nonconductive tissue pattern prone to serving as arrhythmogenic substrate in postablation models, defined by a specific size and proximity to residual fibrosis. Conclusions Overall, this suggests persistent atrial fibrillation ablation transforms substrate that favors functional reentry (ie, rotors meandering in excitable tissue) into an arrhythmogenic milieu more conducive to anchored reentry. Our work also indicates that explainable machine learning and computational simulations can be combined to effectively probe mechanisms of recurrent arrhythmia.

Machine Learning and the Conundrum of Stroke Risk Prediction.

Stroke is a leading cause of death worldwide. With escalating healthcare costs, early non-invasive stroke risk stratification is vital. The current paradigm of stroke risk assessment and mitigation is focused on clinical risk factors and comorbidities. Standard algorithms predict risk using regression-based statistical associations, which, while useful and easy to use, have moderate predictive accuracy. This review summarises recent efforts to deploy machine learning (ML) to predict stroke risk and enrich the understanding of the mechanisms underlying stroke. The surveyed body of literature includes studies comparing ML algorithms with conventional statistical models for predicting cardiovascular disease and, in particular, different stroke subtypes. Another avenue of research explored is ML as a means of enriching multiscale computational modelling, which holds great promise for revealing thrombogenesis mechanisms. Overall, ML offers a new approach to stroke risk stratification that accounts for subtle physiologic variants between patients, potentially leading to more reliable and personalised predictions than standard regression-based statistical associations.

Prediction of Shock-Refractory Ventricular Fibrillation During Resuscitation of Out-of-Hospital Cardiac Arrest.

BACKGROUND: Out-of-hospital cardiac arrest due to shock-refractory ventricular fibrillation (VF) is associated with relatively poor survival. The ability to predict refractory VF (requiring ≥3 shocks) in advance of repeated shock failure could enable preemptive targeted interventions aimed at improving outcome, such as earlier administration of antiarrhythmics, reconsideration of epinephrine use or dosage, changes in shock delivery strategy, or expedited invasive treatments. METHODS: We conducted a cohort study of VF out-of-hospital cardiac arrest to develop an ECG-based algorithm to predict patients with refractory VF. Patients with available defibrillator recordings were randomized 80%/20% into training/test groups. A random forest classifier applied to 3-s ECG segments immediately before and 1 minute after the initial shock during cardiopulmonary resuscitation was used to predict the need for ≥3 shocks based on singular value decompositions of ECG wavelet transforms. Performance was quantified by area under the receiver operating characteristic curve. RESULTS: Of 1376 patients with VF out-of-hospital cardiac arrest, 311 (23%) were female, 864 (63%) experienced refractory VF, and 591 (43%) achieved functional neurological survival. Total shock count was associated with decreasing likelihood of functional neurological survival, with a relative risk of 0.95 (95% CI, 0.93-0.97) for each successive shock (P<0.001). In the 275 test patients, the area under the receiver operating characteristic curve for predicting refractory VF was 0.85 (95% CI, 0.79-0.89), with specificity of 91%, sensitivity of 63%, and a positive likelihood ratio of 6.7. CONCLUSIONS: A machine learning algorithm using ECGs surrounding the initial shock predicts patients likely to experience refractory VF, and could enable rescuers to preemptively target interventions to potentially improve resuscitation outcome.

Graft-host coupling changes can lead to engraftment arrhythmia: a computational study.

After myocardial infarction (MI), a significant portion of heart muscle is replaced with scar tissue, progressively leading to heart failure. Human pluripotent stem cell-derived cardiomyocytes (hPSC-CM) offer a promising option for improving cardiac function after MI. However, hPSC-CM transplantation can lead to engraftment arrhythmia (EA). EA is a transient phenomenon arising shortly after transplantation then spontaneously resolving after a few weeks. The underlying mechanism of EA is unknown. We hypothesize that EA may be explained partially by time-varying, spatially heterogeneous, graft-host electrical coupling. Here, we created computational slice models derived from histological images that reflect different configuration of grafts in the infarcted ventricle. We ran simulations with varying degrees of connection imposed upon the graft-host perimeter to assess how heterogeneous electrical coupling affected EA with non-conductive scar, slow-conducting scar and scar replaced by host myocardium. We also quantified the effect of variation in intrinsic graft conductivity. Susceptibility to EA initially increased and subsequently decreased with increasing graft-host coupling, suggesting the waxing and waning of EA is regulated by progressive increases in graft-host coupling. Different spatial distributions of graft, host and scar yielded markedly different susceptibility curves. Computationally replacing non-conductive scar with host myocardium or slow-conducting scar, and increasing intrinsic graft conductivity both demonstrated potential means to blunt EA vulnerability. These data show how graft location, especially relative to scar, along with its dynamic electrical coupling to host, can influence EA burden; moreover, they offer a rational base for further studies aimed to define the optimal delivery of hPSC-CM injection. KEY POINTS: Human pluripotent stem cell-derived cardiomyocytes (hPSC-CM) hold great cardiac regenerative potential but can also cause engraftment arrhythmias (EA). Spatiotemporal evolution in the pattern of electrical coupling between injected hPSC-CMs and surrounding host myocardium may explain the dynamics of EA observed in large animal models. We conducted simulations in histology-derived 2D slice computational models to assess the effects of heterogeneous graft-host electrical coupling on EA propensity, with or without scar tissue. Our findings suggest spatiotemporally heterogeneous graft-host coupling can create an electrophysiological milieu that favours graft-initiated host excitation, a surrogate metric of EA susceptibility. Removing scar from our models reduced but did not abolish the propensity for this phenomenon. Conversely, reduced intra-graft electrical connectedness increased the incidence of graft-initiated host excitation. The computational framework created for this study can be used to generate new hypotheses, targeted delivery of hPSC-CMs.

Cryoballoon temperature parameters during cryoballoon ablation predict pulmonary vein reconnection and atrial fibrillation recurrence.

BACKGROUND: Cryoballoon ablation (CBA) is an established approach for rhythm management of atrial fibrillation (AF). We sought to assess balloon temperature (BT) parameters as predictors of pulmonary vein (PV) reconnection within the index procedure and AF recurrence following CBA. METHODS: BT was monitored in 119 AF patients undergoing CBA. PVs were assessed for reconnection during the procedure and patients were followed for arrhythmia recurrence. RESULTS: PV reconnection was identified in 39 (8.3%) of 471 PVs. BT was significantly colder in the absence of PV reconnection (30 s: - 33.5 °C [- 36; - 30] vs - 29.5 °C [- 35; - 25.5], p = 0.001; 60 s: - 41 °C [- 44; - 37] vs - 36.5 °C [- 42; - 33.5], p < 0.001; nadir: - 47 °C [- 52; - 43] vs - 41.5 °C [- 47; - 38], p < 0.001). PV reconnection was associated with significantly longer time to reach - 15 °C and - 40 °C (14.5 s [11.5-18.5] vs 12 s [10-15.5], p = 0.023; and 75 s [40-95.5] vs 46 s [37-66.75], p = 0.005) and shorter rewarming time (5.75 s [4.75-8.5] vs 7 s [6-9], p = 0.012). ROC analysis of these procedural parameters had an AUC = 0.71 in predicting PV reconnection. AF recurrence occurred in 51 (42.8%) patients. Kaplan-Meier analysis showed better arrhythmia-free survival for patients in whom BT decreased below - 40 °C in all PVs and patients who had no early PV reconnections, compared to patients in whom BT below - 40 °C was not achieved in at least one PV (log rank = 6.3, p = 0.012) and patients who had PV reconnections (log rank = 4.1, p = 0.043). CONCLUSIONS: Slower BT decline, warmer BT nadir, and faster rewarming time predict early PV reconnection. Absence of early PV reconnections and BT dropping below - 40 °C in all PVs during CBA are associated with lower rates of AF recurrence.

Elevated fibrosis burden as assessed by MRI predicts cryoballoon ablation failure.

INTRODUCTION: Late-gadolinium enhancement magnetic resonance (LGE-MRI) imaging is increasingly used in management of atrial fibrillation (AFib) patients. Here, we assess the usefulness of LGE-MRI-based fibrosis quantification to predict arrhythmia recurrence in patients undergoing cryoballoon ablation. Our secondary goal was to compare two widely used fibrosis quantification methods. METHODS: In 102 AF patients undergoing LGE-MRI and cryoballoon ablation (mean age 62 years; 64% male; 59% paroxysmal AFib), atrial fibrosis was quantified using the pixel intensity histogram (PIH) and image intensity ratio (IIR) methods. PIH segmentations were completed by a third-party provider as part of the standard of care at our hospital; Image intensity ratio (IIR) segmentations of the same scans were carried out in our lab using a commercially available software package. Fibrosis burdens and spatial distributions for the two methods were compared. Patients were followed prospectively for recurrent arrhythmia following ablation. RESULTS: Average PIH fibrosis was 15.6 ± 5.8% of the left atrial (LA) volume. Depending on threshold (IIRthr ), the average IIR fibrosis (% of LA wall surface area) ranged from 5.0 ± 7.2% (IIRthr = 1.2) to 37.4 ± 10.9% (IIRthr = 0.97). An IIRthr of 1.03 demonstrated the greatest agreement between the methods, but spatial overlap of fibrotic areas delineated by the two methods was modest (Sorenson Dice coefficient: 0.49). Fourty-two patients (41.2%) had recurrent arrhythmia. PIH fibrosis successfully predicted recurrence (HR 1.07; p = .02) over a follow-up period of 362 ± 149 days; regardless of IIRthr , IIR fibrosis did not predict recurrence. CONCLUSIONS: PIH-based volumetric assessment of atrial fibrosis was modestly predictive of arrhythmia recurrence following cryoballoon ablation in this cohort. IIR-based fibrosis was not predictive of recurrence for any of the IIRthr values tested, and the overlap in designated areas of fibrosis between the PIH and IIR methods was modest. Caution must therefore be exercised when interpreting LA fibrosis from LGE-MRI, since the values and spatial pattern are methodology-dependent.

MRI-quantified left atrial epicardial adipose tissue predicts atrial fibrillation recurrence following catheter ablation.

Background: Epicardial adipose tissue (EAT) plays a significant role in promoting atrial fibrillation (AF) due to its proinflammatory properties and anatomic proximity to the myocardium. We sought to assess whether left atrial (LA) EAT volume is associated with AF recurrence following catheter ablation. Methods: EAT was assessed via the 3D MRI Dixon sequence in 101 patients undergoing AF ablation. Patients were followed for arrhythmia recurrence. Results: During an average follow-up period of 1 year, post-ablation AF recurrence occurred in 31 (30.7%) patients. LA EAT index was higher in those with compared to without recurrence (20.7 [16.9, 30.4] vs. 13.7 [10.5, 20.1] mL/m2, p < 0.001), and so was LA volume index (66 [52.6, 77.5] vs. 49.9 [37.7, 61.8] mL/m2, p = 0.001). Cox regression analysis showed LA EAT (HR = 1.089; 95% CI: [1.049-1.131], p < 0.001) to be an independent predictor of post-ablation AF recurrence. The ROC curve for LA EAT index in the prediction of AF recurrence had an AUC of 0.77 (95% CI 0.68-0.86, p < 0.001) and showed an optimal cutoff value of 14.29 mL/m2 to identify patients at risk of post-ablation AF recurrence. Integrating LA EAT with clinical risk factors improved prediction of AF recurrence (AUC increased from 0.65 to 0.79, DeLong test p = 0.044). Kaplan-Meier analysis for recurrence-free survival showed a significant difference between two groups of patients identified by the optimal LA EAT index cutoff of 14.29 mL/m2 (log rank = 14.79; p < 0.001). Conclusion: EAT quantified using cardiac MRI, a reproducible and widely accessible imaging parameter, is a strong and independent predictor of post-ablation AF recurrence.

Epicardial adipose tissue is associated with left atrial volume and fibrosis in patients with atrial fibrillation.

Background: Obesity is a risk factor for atrial fibrillation (AF) and strongly influences the response to treatment. Atrial fibrosis shows similar associations. Epicardial adipose tissue (EAT) may be a link between these associations. We sought to assess whether EAT is associated with body mass index (BMI), left atrial (LA) fibrosis and volume. Methods: LA fibrosis and EAT were assessed using late gadolinium enhancement, and Dixon MRI sequences, respectively. We derived 3D models incorporating fibrosis and EAT, then measured the distance of fibrotic and non-fibrotic areas to the nearest EAT to assess spatial colocalization. Results: One hundred and three AF patients (64% paroxysmal, 27% female) were analyzed. LA volume index was 54.9 (41.2, 69.7) mL/m2, LA EAT index was 17.4 (12.7, 22.9) mL/m2, and LA fibrosis was 17.1 (12.4, 23.1)%. LA EAT was significantly correlated with BMI (R = 0.557, p < 0.001); as well as with LA volume and LA fibrosis after BSA adjustment (R = 0.579 and R = 0.432, respectively, p < 0.001 for both). Multivariable analysis showed LA EAT to be independently associated with LA volume and fibrosis. 3D registration of fat and fibrosis around the LA showed no clear spatial overlap between EAT and fibrotic LA regions. Conclusion: LA EAT is associated with obesity (BMI) as well as LA volume and fibrosis. Regions of LA EAT did not colocalize with fibrotic areas, suggesting a systemic or paracrine mechanism rather than EAT infiltration of fibrotic areas.

Acute Glycogen Synthase Kinase-3 Inhibition Modulates Human Cardiac Conduction.

Glycogen synthase kinase 3 (GSK-3) inhibition has emerged as a potential therapeutic target for several diseases, including cancer. However, the role for GSK-3 regulation of human cardiac electrophysiology remains ill-defined. We demonstrate that SB216763, a GSK-3 inhibitor, can acutely reduce conduction velocity in human cardiac slices. Combined computational modeling and experimental approaches provided mechanistic insight into GSK-3 inhibition-mediated changes, revealing that decreased sodium-channel conductance and tissue conductivity may underlie the observed phenotypes. Our study demonstrates that GSK-3 inhibition in human myocardium alters electrophysiology and may predispose to an arrhythmogenic substrate; therefore, monitoring for adverse arrhythmogenic events could be considered.

Critical appraisal of technologies to assess electrical activity during atrial fibrillation: a position paper from the European heart rhythm association and European society of cardiology working group on ecardiology in collaboration with the heart rhythm society, Asia pacific heart rhythm society, Latin American Heart rhythm society and computing in cardiology

Abstract We aim to provide a critical appraisal of basic concepts underlying signal recording and processing technologies applied for (I) atrial fibrillation (AF) mapping to unravel AF mechanisms and/or identifying target sites for AF therapy and (ii) AF detection, to optimize usage of technologies, stimulate research aimed at closing knowledge gaps, and developing ideal AF recording and processing technologies. Recording and processing techniques for assessment of electrical activity during AF essential for diagnosis and guiding ablative therapy including body surface electrocardiograms (ECG) and endo- or epicardial electrograms (EGM) are evaluated. Discussion of (I) differences in uni-, bi-, and multi-polar (omnipolar/Laplacian) recording modes, (ii) impact of recording technologies on EGM morphology, (iii) global or local mapping using various types of EGM involving signal processing techniques including isochronal-, voltage- fractionation-, dipole density-, and rotor mapping, enabling derivation of parameters like atrial rate, entropy, conduction velocity/direction, (iv) value of epicardial and optical mapping, (v) AF detection by cardiac implantable electronic devices containing various detection algorithms applicable to stored EGMs, (vi) contribution of machine learning (ML) to further improvement of signals processing technologies. Recording and processing of EGM (or ECG) are the cornerstones of (body surface) mapping of AF. Currently available AF recording and processing technologies are mainly restricted to specific applications or have technological limitations. Improvements in AF mapping by obtaining highest fidelity source signals (e. g. catheter­electrode combinations) for signal processing (e. g. filtering, digitization, and noise elimination) is of utmost importance. Novel acquisition instruments (multi-polar catheters combined with improved physical modelling and ML techniques) will enable enhanced and automated interpretation of EGM recordings in the near future.

The Purkinje-myocardial junction is the anatomic origin of ventricular arrhythmia in CPVT.

Catecholaminergic polymorphic ventricular tachycardia (CPVT) is an arrhythmia syndrome caused by gene mutations that render RYR2 Ca release channels hyperactive, provoking spontaneous Ca release and delayed afterdepolarizations (DADs). What remains unknown is the cellular source of ventricular arrhythmia triggered by DADs: Purkinje cells in the conduction system or ventricular cardiomyocytes in the working myocardium. To answer this question, we used a genetic approach in mice to knock out cardiac calsequestrin either in Purkinje cells or in ventricular cardiomyocytes. Total loss of calsequestrin in the heart causes a severe CPVT phenotype in mice and humans. We found that loss of calsequestrin only in ventricular myocytes produced a full-blown CPVT phenotype, whereas mice with loss of calsequestrin only in Purkinje cells were comparable to WT mice. Subendocardial chemical ablation or restoration of calsequestrin expression in subendocardial cardiomyocytes neighboring Purkinje cells was sufficient to protect against catecholamine-induced arrhythmias. In silico modeling demonstrated that DADs in ventricular myocardium can trigger full action potentials in the Purkinje fiber, but not vice versa. Hence, ectopic beats in CPVT are likely generated at the Purkinje-myocardial junction via a heretofore unrecognized tissue mechanism, whereby DADs in the ventricular myocardium trigger full action potentials in adjacent Purkinje cells.

Identifying risk of adverse outcomes in COVID-19 patients via artificial intelligence-powered analysis of 12-lead intake electrocardiogram.

BACKGROUND: Adverse events in COVID-19 are difficult to predict. Risk stratification is encumbered by the need to protect healthcare workers. We hypothesize that artificial intelligence (AI) can help identify subtle signs of myocardial involvement in the 12-lead electrocardiogram (ECG), which could help predict complications. OBJECTIVE: Use intake ECGs from COVID-19 patients to train AI models to predict risk of mortality or major adverse cardiovascular events (MACE). METHODS: We studied intake ECGs from 1448 COVID-19 patients (60.5% male, aged 63.4 ± 16.9 years). Records were labeled by mortality (death vs discharge) or MACE (no events vs arrhythmic, heart failure [HF], or thromboembolic [TE] events), then used to train AI models; these were compared to conventional regression models developed using demographic and comorbidity data. RESULTS: A total of 245 (17.7%) patients died (67.3% male, aged 74.5 ± 14.4 years); 352 (24.4%) experienced at least 1 MACE (119 arrhythmic, 107 HF, 130 TE). AI models predicted mortality and MACE with area under the curve (AUC) values of 0.60 ± 0.05 and 0.55 ± 0.07, respectively; these were comparable to AUC values for conventional models (0.73 ± 0.07 and 0.65 ± 0.10). There were no prominent temporal trends in mortality rate or MACE incidence in our cohort; holdout testing with data from after a cutoff date (June 9, 2020) did not degrade model performance. CONCLUSION: Using intake ECGs alone, our AI models had limited ability to predict hospitalized COVID-19 patients' risk of mortality or MACE. Our models' accuracy was comparable to that of conventional models built using more in-depth information, but translation to clinical use would require higher sensitivity and positive predictive value. In the future, we hope that mixed-input AI models utilizing both ECG and clinical data may be developed to enhance predictive accuracy.

Personalized computational heart models with T1-mapped fibrotic remodeling predict sudden death risk in patients with hypertrophic cardiomyopathy.

Hypertrophic cardiomyopathy (HCM) is associated with risk of sudden cardiac death (SCD) due to ventricular arrhythmias (VAs) arising from the proliferation of fibrosis in the heart. 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 promotes arrhythmogenesis and to develop a personalized strategy to forecast risk of VAs in these patients. We combine contrast-enhanced cardiac magnetic resonance 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.