Comprehensive analysis of same day discharge after atrial fibrillation ablation: Clinical, cost, and patient reported outcomes.

BACKGROUND: Same day discharge (SDD) following atrial fibrillation (AF) ablation procedure has emerged as routine practice, and primarily driven by operator discretion. However, the impacts of SDD on clinical outcomes, healthcare system costs, and patient reported outcomes (PROs) have not been systematically studied. METHODS: We retrospectively analyzed patients undergoing routine AF ablation procedures with SDD versus overnight observation (NSDD). After propensity adjustment we compared postprocedure adverse events (AEs), healthcare system costs, and changes in PROs. RESULTS: We identified 310 cases, with 159 undergoing SDD and 151 staying at least one midnight in the hospital (NSDD). Compared with NSDD, SDD patients were similar age (mean 64 vs. 66, p = 0.3), sex (26% female vs. 27%, p = 0.8), and with lower mean CHADS2-VA2Sc scores (2.0 vs. 2.7; p < 0.011). The primary outcome of AEs was noninferior in SDD versus NSDD patients (odds ratio 0.45, 95% confidence interval 0.21-0.99; noninferiority margin of 10%). There were also no differences in overall cost to the healthcare system between SDD and NSDD (p = 0.11). PROs numerically favored SDD (p = NS for all scores). CONCLUSIONS: Physician selection for SDD appears at least as safe as NSDD with respect to clinical outcomes and SDD is not significantly less costly to the health system. There is a trend towards more favorable, general PROs among SDD patients. Routine SDD should be strongly considered for patients undergoing routine AF ablation procedures.

Characteristics of a transgender and gender-diverse patient population in Utah: Use of electronic health records to advance clinical and health equity research.

Transgender and gender-diverse (TGD) people, individuals whose gender identity differs from their sex assigned at birth, face unique challenges in accessing gender-affirming care and often experience disparities in a variety of health outcomes. Clinical research on TGD health is limited by a lack of standardization on how to best identify these individuals. The objective of this retrospective cohort analysis was to accurately identify and describe TGD adults and their use of gender-affirming care from 2003-2023 in a healthcare system in Utah, United States. International Classification of Disease (ICD)-9 and 10 codes and surgical procedure codes, along with sexual orientation and gender identity data were used to develop a dataset of 4,587 TGD adults. During this time frame, 2,985 adults received gender-affirming hormone therapy (GAHT) and/or gender-affirming surgery (GAS) within one healthcare system. There was no significant difference in race or ethnicity between TGD adults who received GAHT and/or GAS compared to TGD adults who did not receive such care. TGD adults who received GAHT and/or GAS were more likely to have commercial insurance coverage, and adults from rural communities were underrepresented. Patients seeking estradiol-based GAHT tended to be older than those seeking testosterone-based GAHT. The first GAS occurred in 2013, and uptake of GAS have doubled since 2018. This study provides a methodology to identify and examine TGD patients in other health systems and offers insights into emerging trends and access to gender-affirming care.

Uncertainty quantification of the effect of cardiac position variability in the inverse problem of electrocardiographic imaging.

Objective.Electrocardiographic imaging (ECGI) is a functional imaging modality that consists of two related problems, the forward problem of reconstructing body surface electrical signals given cardiac bioelectric activity, and the inverse problem of reconstructing cardiac bioelectric activity given measured body surface signals. ECGI relies on a model for how the heart generates bioelectric signals which is subject to variability in inputs. The study of how uncertainty in model inputs affects the model output is known as uncertainty quantification (UQ). This study establishes develops, and characterizes the application of UQ to ECGI.Approach.We establish two formulations for applying UQ to ECGI: a polynomial chaos expansion (PCE) based parametric UQ formulation (PCE-UQ formulation), and a novel UQ-aware inverse formulation which leverages our previously established 'joint-inverse' formulation (UQ joint-inverse formulation). We apply these to evaluate the effect of uncertainty in the heart position on the ECGI solutions across a range of ECGI datasets.Main results.We demonstrated the ability of our UQ-ECGI formulations to characterize the effect of parameter uncertainty on the ECGI inverse problem. We found that while the PCE-UQ inverse solution provided more complex outputs such as sensitivities and standard deviation, the UQ joint-inverse solution provided a more interpretable output in the form of a single ECGI solution. We find that between these two methods we are able to assess a wide range of effects that heart position variability has on the ECGI solution.Significance.This study, for the first time, characterizes in detail the application of UQ to the ECGI inverse problem. We demonstrated how UQ can provide insight into the behavior of ECGI using variability in cardiac position as a test case. This study lays the groundwork for future development of UQ-ECGI studies, as well as future development of ECGI formulations which are robust to input parameter variability.

Performance of Off-the-Shelf Machine Learning Architectures and Biases in Detection of Low Left Ventricular Ejection Fraction.

Artificial intelligence - machine learning (AI-ML) is a computational technique that has been demonstrated to be able to extract meaningful clinical information from diagnostic data that are not available using either human interpretation or more simple analysis methods. Recent developments have shown that AI-ML approaches applied to ECGs can accurately predict different patient characteristics and pathologies not detectable by expert physician readers. There is an extensive body of literature surrounding the use of AI-ML in other fields, which has given rise to an array of predefined open-source AI-ML architectures which can be translated to new problems in an "off-the-shelf" manner. Applying "off-the-shelf" AI-ML architectures to ECG-based datasets opens the door for rapid development and identification of previously unknown disease biomarkers. Despite the excellent opportunity, the ideal open-source AI-ML architecture for ECG related problems is not known. Furthermore, there has been limited investigation on how and when these AI-ML approaches fail and possible bias or disparities associated with particular network architectures. In this study, we aimed to: (1) determine if open-source, "off-the-shelf" AI-ML architectures could be trained to classify low LVEF from ECGs, (2) assess the accuracy of different AI-ML architectures compared to each other, and (3) to identify which, if any, patient characteristics are associated with poor AI-ML performance.

Major drivers of healthcare system costs and cost variability for routine atrial fibrillation ablation.

Background: Catheter ablation is an effective treatment for atrial fibrillation (AF) but incurs significant financial costs to payers. Reducing variability may improve cost effectiveness. Objectives: We aimed to measure (1) the components of direct and indirect costs for routine AF ablation procedures, (2) the variability of those costs, and (3) the main factors driving ablation cost variability. Methods: Using data from the University of Utah Health Value Driven Outcomes system, we were able to measure direct, inflation-adjusted costs of uncomplicated, routine AF ablation to the healthcare system. Direct costs were considered costs incurred by pharmacy, disposable supplies, patient labs, implants, and other services categories (primarily anesthesia support) and indirect costs were considered within imaging, facility, and electrophysiology lab management categories. Results: A total of 910 patients with 1060 outpatient ablation encounters were included from January 1, 2013, to December 31, 2020. Disposable supplies accounted for the largest component of cost with 44.8 ± 9.7%, followed by other services (primarily anesthesia support) with 30.4 ± 7.7% and facility costs with 16.1 ± 5.6%; pharmacy, imaging, and implant costs each contributed <5%. Direct costs were larger than indirect costs (82.4 ± 5.6% vs 17.6 ± 5.6%). Multivariable regression showed that procedure operator was the primary factor associated with AF ablation overall cost (up to 12% differences depending on operator). Conclusions: Direct costs and other services (primarily anesthesia) drive the majority costs associated with AF ablations. There is significant variability in costs for these routine, uncomplicated AF ablation procedures. The procedure operator, and not patient characteristic, is the main driver for cost variability.

This is Your Brain, and This is Your Brain on Atrial Fibrillation: The Roles of Cardiac Malperfusion Events and Vascular Dysfunction in Cognitive Impairment.

AF is an independent and strong predictor of long-term cognitive decline. However, the mechanism for this cognitive decline is difficult to define and likely multifactorial, leading to many different hypotheses. Examples include macro- or microvascular stroke events, biochemical changes to the blood-brain barrier related to anticoagulation, or hypo-hyperperfusion events. This review explores and discusses the hypothesis that AF contributes to cognitive decline and dementia through hypo-hyperperfusion events occurring during cardiac arrhythmias. We briefly explain several brain perfusion imaging techniques and further examine the novel findings associated with changes in brain perfusion in patients with AF. Finally, we discuss the implications and areas requiring more research to further understand and treat patients with cognitive decline related to AF.

Tipping the scales of understanding: An engineering approach to design and implement whole-body cardiac electrophysiology experimental models.

The study of cardiac electrophysiology is built on experimental models that span all scales, from ion channels to whole-body preparations. Novel discoveries made at each scale have contributed to our fundamental understanding of human cardiac electrophysiology, which informs clinicians as they detect, diagnose, and treat complex cardiac pathologies. This expert review describes an engineering approach to developing experimental models that is applicable across scales. The review also outlines how we applied the approach to create a set of multiscale whole-body experimental models of cardiac electrophysiology, models that are driving new insights into the response of the myocardium to acute ischemia. Specifically, we propose that researchers must address three critical requirements to develop an effective experimental model: 1) how the experimental model replicates and maintains human physiological conditions, 2) how the interventions possible with the experimental model capture human pathophysiology, and 3) what signals need to be measured, at which levels of resolution and fidelity, and what are the resulting requirements of the measurement system and the access to the organs of interest. We will discuss these requirements in the context of two examples of whole-body experimental models, a closed chest in situ model of cardiac ischemia and an isolated-heart, torso-tank preparation, both of which we have developed over decades and used to gather valuable insights from hundreds of experiments.

Atrial fibrillation and the risk of early-onset dementia and cognitive decline: An updated review.

The relationship between atrial fibrillation (AF) and dementia has been well described; however, recent data suggest that AF confers a greater risk for the development of early-onset dementia irrespective of clinical stroke. Numerous mechanisms have been hypothesized to explain cognitive decline in the setting of AF, including silent cerebral ischemia, cerebral hypoperfusion, and cerebral microvascular disease. Despite the emergence of data supporting the increased risk of early-onset dementia in patients with AF, the underlying mechanism remains unclear. Furthermore, the mechanism may be influenced by survival bias, genetic susceptibility, or early dysfunction of brain adaptation. Investigation into why this relationship exists could change how prevention and treatment are evaluated.

Improving Generalization by Learning Geometry-Dependent and Physics-Based Reconstruction of Image Sequences.

Deep neural networks have shown promise in image reconstruction tasks, although often on the premise of large amounts of training data. In this paper, we present a new approach to exploit the geometry and physics underlying electrocardiographic imaging (ECGI) to learn efficiently with a relatively small dataset. We first introduce a non-Euclidean encoding-decoding network that allows us to describe the unknown and measurement variables over their respective geometrical domains. We then explicitly model the geometry-dependent physics in between the two domains via a bipartite graph over their graphical embeddings. We applied the resulting network to reconstruct electrical activity on the heart surface from body-surface potentials. In a series of generalization tasks with increasing difficulty, we demonstrated the improved ability of the network to generalize across geometrical changes underlying the data using less than 10% of training data and fewer variations of training geometry in comparison to its Euclidean alternatives. In both simulation and real-data experiments, we further demonstrated its ability to be quickly fine-tuned to new geometry using a modest amount of data.

All Roads Lead to Rome: Diverse Etiologies of Tricuspid Regurgitation Create a Predictable Constellation of Right Ventricular Shape Changes.

Introduction: Myriad disorders cause right ventricular (RV) dilation and lead to tricuspid regurgitation (TR). Because the thin-walled, flexible RV is mechanically coupled to the pulmonary circulation and the left ventricular septum, it distorts with any disturbance in the cardiopulmonary system. TR, therefore, can result from pulmonary hypertension, left heart failure, or intrinsic RV dysfunction; but once it occurs, TR initiates a cycle of worsening RV volume overload, potentially progressing to right heart failure. Characteristic three-dimensional RV shape-changes from this process, and changes particular to individual TR causes, have not been defined in detail. Methods: Cardiac MRI was obtained in 6 healthy volunteers, 41 patients with ≥ moderate TR, and 31 control patients with cardiac disease without TR. The mean shape of each group was constructed using a three-dimensional statistical shape model via the particle-based shape modeling approach. Changes in shape were examined across pulmonary hypertension and congestive heart failure subgroups using principal component analysis (PCA). A logistic regression approach based on these PCA modes identified patients with TR using RV shape alone. Results: Mean RV shape in patients with TR exhibited free wall bulging, narrowing of the base, and blunting of the RV apex compared to controls (p < 0.05). Using four primary PCA modes, a logistic regression algorithm identified patients with TR correctly with 82% recall and 87% precision. In patients with pulmonary hypertension without TR, RV shape was narrower and more streamlined than in healthy volunteers. However, in RVs with TR and pulmonary hypertension, overall RV shape continued to demonstrate the free wall bulging characteristic of TR. In the subgroup of patients with congestive heart failure without TR, this intermediate state of RV muscular hypertrophy was not present. Conclusion: The multiple causes of TR examined in this study changed RV shape in similar ways. Logistic regression classification based on these shape changes reliably identified patients with TR regardless of etiology. Furthermore, pulmonary hypertension without TR had unique shape features, described here as the "well compensated" RV. These results suggest shape modeling as a promising tool for defining severity of RV disease and risk of decompensation, particularly in patients with pulmonary hypertension.

Patient-reported outcomes and costs associated with vascular closure and same-day discharge following atrial fibrillation ablation.

BACKGROUND: We aimed to measure patient-reported outcomes (PROs) and costs associated with same-day discharge (SDD) for atrial fibrillation (AF) ablation and vascular closure device implantation in clinical practice. METHODS: PROs were prospectively measured in 50 AF ablation patients, comparing complete vascular device closure (n = 25) versus manual compression hemostasis (n = 25). Health-system costs for SDD patients receiving vascular device closure were compared to matched controls with one-night stays who did not receive any closure device. RESULTS: Prospectively enrolled patients receiving vascular device closure for AF ablation had a mean age of 65 years, 17% were female, with a mean CHA2 DS2 -VASc score of 3. The mean number of venous sheaths was higher among patients receiving vascular device closure (3.8 vs. 3.1, p < 0.001), and there was one case of rebleeding in a patient receiving a vascular closure device (no other complications). Same-day discharge rates (76% vs. 8.3%, p < 0.001), patient satisfaction with bedrest time (8.5 vs. 6, p = 0.004) and with pain (8 vs. 5.1, p = 0.009) were significantly better among patients receiving vascular closure. In matched analyses of health-system costs, patients with vascular closure had mean age 66, 32% were female, and the mean CHA2 DS2 -VASc score was 2 (p = NS vs. controls). SDD with vascular closure was associated with the significantly lower facility, pharmacy, and disposable costs, but higher implant costs. Overall costs for ablation were not significantly different (mean difference 1.10%, 95% confidence interval [CI] -3.03 to 5.42). CONCLUSIONS: Vascular closure for AF ablation improves patient experience in routine care. The use of vascular closure and SDD after AF ablation reduces several components of healthcare system costs, without an overall increase.

Reconstruction of cardiac position using body surface potentials.

Electrocardiographic imaging (ECGI) is a noninvasive technique to assess the bioelectric activity of the heart which has been applied to aid in clinical diagnosis and management of cardiac dysfunction. ECGI is built on mathematical models that take into account several patient specific factors including the position of the heart within the torso. Errors in the localization of the heart within the torso, as might arise due to natural changes in heart position from respiration or changes in body position, contribute to errors in ECGI reconstructions of the cardiac activity, thereby reducing the clinical utility of ECGI. In this study we present a novel method for the reconstruction of cardiac geometry utilizing noninvasively acquired body surface potential measurements. Our geometric correction method simultaneously estimates the cardiac position over a series of heartbeats by leveraging an iterative approach which alternates between estimating the cardiac bioelectric source across all heartbeats and then estimating cardiac positions for each heartbeat. We demonstrate that our geometric correction method is able to reduce geometric error and improve ECGI accuracy in a wide range of testing scenarios. We examine the performance of our geometric correction method using different activation sequences, ranges of cardiac motion, and body surface electrode configurations. We find that after geometric correction resulting ECGI solution accuracy is improved and variability of the ECGI solutions between heartbeats is substantially reduced.

Statistical Shape Modeling of Biventricular Anatomy with Shared Boundaries.

Statistical shape modeling (SSM) is a valuable and powerful tool to generate a detailed representation of complex anatomy that enables quantitative analysis and the comparison of shapes and their variations. SSM applies mathematics, statistics, and computing to parse the shape into a quantitative representation (such as correspondence points or landmarks) that will help answer various questions about the anatomical variations across the population. Complex anatomical structures have many diverse parts with varying interactions or intricate architecture. For example, the heart is a four-chambered anatomy with several shared boundaries between chambers. Coordinated and efficient contraction of the chambers of the heart is necessary to adequately perfuse end organs throughout the body. Subtle shape changes within these shared boundaries of the heart can indicate potential pathological changes that lead to uncoordinated contraction and poor end-organ perfusion. Early detection and robust quantification could provide insight into ideal treatment techniques and intervention timing. However, existing SSM approaches fall short of explicitly modeling the statistics of shared boundaries. In this paper, we present a general and flexible data-driven approach for building statistical shape models of multi-organ anatomies with shared boundaries that captures morphological and alignment changes of individual anatomies and their shared boundary surfaces throughout the population. We demonstrate the effectiveness of the proposed methods using a biventricular heart dataset by developing shape models that consistently parameterize the cardiac biventricular structure and the interventricular septum (shared boundary surface) across the population data.

Reducing Line-of-Block Artifacts in Cardiac Activation Maps Estimated Using ECG Imaging: A Comparison of Source Models and Estimation Methods.

OBJECTIVE: To investigatecardiac activation maps estimated using electrocardiographic imaging and to find methods reducing line-of-block (LoB) artifacts, while preserving real LoBs. METHODS: Body surface potentials were computed for 137 simulated ventricular excitations. Subsequently, the inverse problem was solved to obtain extracellular potentials (EP) and transmembrane voltages (TMV). From these, activation times (AT) were estimated using four methods and compared to the ground truth. This process was evaluated with two cardiac mesh resolutions. Factors contributing to LoB artifacts were identified by analyzing the impact of spatial and temporal smoothing on the morphology of source signals. RESULTS: AT estimation using a spatiotemporal derivative performed better than using a temporal derivative. Compared to deflection-based AT estimation, correlation-based methods were less prone to LoB artifacts but performed worse in identifying real LoBs. Temporal smoothing could eliminate artifacts for TMVs but not for EPs, which could be linked to their temporal morphology. TMVs led to more accurate ATs on the septum than EPs. Mesh resolution had anegligible effect on inverse reconstructions, but small distances were important for cross-correlation-based estimation of AT delays. CONCLUSION: LoB artifacts are mainly caused by the inherent spatial smoothing effect of the inverse reconstruction. Among the configurations evaluated, only deflection-based AT estimation in combination with TMVs and strong temporal smoothing can prevent LoB artifacts, while preserving real LoBs. SIGNIFICANCE: Regions of slow conduction are of considerable clinical interest and LoB artifacts observed in non-invasive ATs can lead to misinterpretations. We addressed this problem by identifying factors causing such artifacts.

Statistical shape modeling of multi-organ anatomies with shared boundaries.

Introduction: Statistical shape modeling (SSM) is a valuable and powerful tool to generate a detailed representation of complex anatomy that enables quantitative analysis of shapes and their variations. SSM applies mathematics, statistics, and computing to parse the shape into some quantitative representation (such as correspondence points or landmarks) which can be used to study the covariance patterns of the shapes and answer various questions about the anatomical variations across the population. Complex anatomical structures have many diverse parts with varying interactions or intricate architecture. For example, the heart is a four-chambered organ with several shared boundaries between chambers. Subtle shape changes within the shared boundaries of the heart can indicate potential pathologic changes such as right ventricular overload. Early detection and robust quantification could provide insight into ideal treatment techniques and intervention timing. However, existing SSM methods do not explicitly handle shared boundaries which aid in a better understanding of the anatomy of interest. If shared boundaries are not explicitly modeled, it restricts the capability of the shape model to identify the pathological shape changes occurring at the shared boundary. Hence, this paper presents a general and flexible data-driven approach for building statistical shape models of multi-organ anatomies with shared boundaries that explicitly model contact surfaces. Methods: This work focuses on particle-based shape modeling (PSM), a state-of-art SSM approach for building shape models by optimizing the position of correspondence particles. The proposed PSM strategy for handling shared boundaries entails (a) detecting and extracting the shared boundary surface and contour (outline of the surface mesh/isoline) of the meshes of the two organs, (b) followed by a formulation for a correspondence-based optimization algorithm to build a multi-organ anatomy statistical shape model that captures morphological and alignment changes of individual organs and their shared boundary surfaces throughout the population. Results: We demonstrate the shared boundary pipeline using a toy dataset of parameterized shapes and a clinical dataset of the biventricular heart models. The shared boundary model for the cardiac biventricular data achieves consistent parameterization of the shared surface (interventricular septum) and identifies the curvature of the interventricular septum as pathological shape differences.

Impact of Catheter Ablation on Stroke, Cognitive Decline and Dementia.

AF has been consistently associated with multiple forms of dementia, including idiopathic dementia. Outcomes after catheter ablation for AF are favourable and patients experience a better quality of life, arrhythmia-free survival, and lower rates of hospitalisation compared to patients treated with antiarrhythmic drugs. Catheter ablation is consistently associated with lower rates of stroke compared to AF management without ablation in large national and healthcare system databases. Multiple observational trials have shown that catheter ablation is also associated with a lower risk of cognitive decline, dementia and improved cognitive testing that can be explained through a variety of pathways. Long-term, adequately powered, randomised trials are required to define the role of catheter ablation in the management of AF as a means to lower the risk of cognitive decline, stroke and dementia.

Combining endocardial mapping and electrocardiographic imaging (ECGI) for improving PVC localization: A feasibility study.

INTRODUCTION: Accurate reconstruction of cardiac activation wavefronts is crucial for clinical diagnosis, management, and treatment of cardiac arrhythmias. Furthermore, reconstruction of activation profiles within the intramural myocardium has long been impossible because electrical mapping was only performed on the endocardial surface. Recent advancements in electrocardiographic imaging (ECGI) have made endocardial and epicardial activation mapping possible. We propose a novel approach to use both endocardial and epicardial mapping in a combined approach to reconstruct intramural activation times. OBJECTIVE: To implement and validate a combined epicardial/endocardial intramural activation time reconstruction technique. METHODS: We used 11 simulations of ventricular activation paced from sites throughout myocardial wall and extracted endocardial and epicardial activation maps at approximate clinical resolution. From these maps, we interpolated the activation times through the myocardium using thin-plate-spline radial basis functions. We evaluated activation time reconstruction accuracy using root-mean-squared error (RMSE) of activation times and the percent of nodes within 1 ms of the ground truth. RESULTS: Reconstructed intramural activation times showed an RMSE and percentage of nodes within 1 ms of the ground truth simulations of 3 ms and 70%, respectively. In the worst case, the RMSE and percentage of nodes were 4 ms and 60%, respectively. CONCLUSION: We showed that a simple, yet effective combination of clinical endocardial and epicardial activation maps can accurately reconstruct intramural wavefronts. Furthermore, we showed that this approach provided robust reconstructions across multiple intramural stimulation sites.

Transient recovery of epicardial and torso ST-segment ischemic signals during cardiac stress tests: A possible physiological mechanism.

BACKGROUND: Acute myocardial ischemia has several characteristic ECG findings, including clinically detectable ST-segment deviations. However, the sensitivity and specificity of diagnosis based on ST-segment changes are low. Furthermore, ST-segment deviations have been shown to be transient and spontaneously recover without any indication the ischemic event has subsided. OBJECTIVE: Assess the transient recovery of ST-segment deviations on remote recording electrodes during a partial occlusion cardiac stress test and compare them to intramyocardial ST-segment deviations. METHODS: We used a previously validated porcine experimental model of acute myocardial ischemia with controllable ischemic load and simultaneous electrical measurements within the heart wall, on the epicardial surface, and on the torso surface. Simulated cardiac stress tests were induced by occluding a coronary artery while simultaneously pacing rapidly or infusing dobutamine to stimulate cardiac function. Postexperimental imaging created anatomical models for data visualization and quantification. Markers of ischemia were identified as deviations in the potentials measured at 40% of the ST-segment. Intramural cardiac conduction speed was also determined using the inverse gradient method. We assessed changes in intramyocardial ischemic volume proportion, conduction speed, clinical presence of ischemia on remote recording arrays, and regional changes to intramyocardial ischemia. We defined the peak deviation response time as the time interval after onset of ischemia at which maximum ST-segment deviation was achieved, and ST-recovery time was the interval when ST deviation returned to below thresholded of ST elevation. RESULTS: In both epicardial and torso recordings, the peak ST-segment deviation response time was 4.9±1.1 min and the ST-recovery time was approximately 7.9±2.5 min, both well before the termination of the ischemic stress. At peak response time, conduction speed was reduced by 50% and returned to near baseline at ST-recovery. The overall ischemic volume proportion initially increased, on average, to 37% at peak response time; however, it recovered to only 30% at the ST-recovery time. By contrast, the subepicardial region of the myocardial wall showed 40% ischemic volume at peak response time and recovered much more strongly to 25% as epicardial ST-segment deviations returned to baseline. CONCLUSION: Our data show that remote ischemic signal recovery correlates with a recovery of the subepicardial myocardium, whereas subendocardial ischemic development persists.

Pharmacological and simulated exercise cardiac stress tests produce different ischemic signatures in high-resolution experimental mapping studies.

OBJECTIVE: Test the hypothesis that exercise and pharmacological cardiac stressors create different electrical ischemic signatures. INTRODUCTION: Current clinical stress tests for detecting ischemia lack sensitivity and specificity. One unexplored source of the poor detection is whether pharmacological stimulation and regulated exercise produce identical cardiac stress. METHODS: We used a porcine model of acute myocardial ischemia in which animals were instrumented with transmural plunge-needle electrodes, an epicardial sock array, and torso arrays to simultaneously measure cardiac electrical signals within the heart wall, the epicardial surface, and the torso surface, respectively. Ischemic stress via simulated exercise and pharmacological stimulation were created with rapid electrical pacing and dobutamine infusion, respectively, and mimicked clinical stress tests of five 3-minute stages. Perfusion to the myocardium was regulated by a hydraulic occluder around the left anterior descending coronary artery. Ischemia was measured as deflections to the ST-segment on ECGs and electrograms. RESULTS: Across eight experiments with 30 (14 simulated exercise and 16 dobutamine) ischemic interventions, the spatial correlations between exercise and pharmacological stress diverged at stage three or four during interventions (p<0.05). We found more detectable ST-segment changes on the epicardial surface during simulated exercise than with dobutamine (p<0.05). The intramyocardial ischemia formed during simulated exercise had larger ST40 potential gradient magnitudes (p<0.05). CONCLUSION: We found significant differences on the epicardium between cardiac stress types using our experimental model, which became more pronounced at the end stages of each test. A possible mechanism for these differences was the larger ST40 potential gradient magnitudes within the myocardium during exercise. The presence of microvascular dysfunction during exercise and its absence during dobutamine stress may explain these differences.