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.

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.

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.

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.

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.

Quantifying the spatiotemporal influence of acute myocardial ischemia on volumetric conduction velocity.

INTRODUCTION: Acute myocardial ischemia occurs when coronary perfusion to the heart is inadequate, which can perturb the highly organized electrical activation of the heart and can result in adverse cardiac events including sudden cardiac death. Ischemia is known to influence the ST and repolarization phases of the ECG, but it also has a marked effect on propagation (QRS); however, studies investigating propagation during ischemia have been limited. METHODS: We estimated conduction velocity (CV) and ischemic stress prior to and throughout 20 episodes of experimentally induced ischemia in order to quantify the progression and correlation of volumetric conduction changes during ischemia. To estimate volumetric CV, we 1) reconstructed the activation wavefront; 2) calculated the elementwise gradient to approximate propagation direction; and 3) estimated conduction speed (CS) with an inverse-gradient technique. RESULTS: We found that acute ischemia induces significant conduction slowing, reducing the global median speed by 20 cm/s. We observed a biphasic response in CS (acceleration then deceleration) early in some ischemic episodes. Furthermore, we noted a high temporal correlation between ST-segment changes and CS slowing; however, when comparing these changes over space, we found only moderate correlation (corr. = 0.60). DISCUSSION: This study is the first to report volumetric CS changes (acceleration and slowing) during episodes of acute ischemia in the whole heart. We showed that while CS changes progress in a similar time course to ischemic stress (measured by ST-segment shifts), the spatial overlap is complex and variable, showing extreme conduction slowing both in and around regions experiencing severe ischemia.

Patient-reported outcomes and subsequent management in atrial fibrillation clinical practice: Results from the Utah mEVAL AF program.

BACKGROUND: Atrial fibrillation (AF) significantly reduces health-related quality of life (HRQoL), previously measured in clinical trials using patient-reported outcomes (PROs). We examined AF PROs in clinical practice and their association with subsequent clinical management. METHODS: The Utah My Evaluation (mEVAL) program collects the Toronto AF Symptom Severity Scale (AFSS) in AF outpatients at the University of Utah. Baseline factors associated with worse AF symptom score (range 0-35, higher is worse) were identified in univariate and multivariable analyses. Secondary outcomes included AF burden and AF healthcare utilization. We also compared subsequent clinical management at 6 months between patients with better versus worse AF HRQoL. RESULTS: Overall, 1338 patients completed the AFSS symptom score, which varied by sex (mean 7.26 for males vs. 10.27 for females; p < .001), age (<65, 9.73; 65-74, 7.66; ≥75, 7.58; p < .001), heart failure (9.39 with HF vs. 7.67 without; p < .001), and prior ablation (7.28 with prior ablation vs. 8.84; p < .001). In multivariable analysis, younger age (mean difference 2.92 for <65 vs. ≥75; p < .001), female sex (mean difference 2.57; p < .001), pulmonary disease (mean difference 1.88; p < .001), and depression (mean difference 2.46; p < .001) were associated with higher scores. At 6-months, worse baseline symptom score was associated with the use of rhythm control (37.1% vs. 24.5%; p < .001). Similar cofactors and results were associated with increased AF burden and health care utilization scores. CONCLUSIONS: AF PROs in clinical practice identify highly-symptomatic patients, corroborating findings in more controlled, clinical trials. Increased AFSS score correlates with more aggressive clinical management, supporting the utility of disease-specific PROs guiding clinical practice.

Systematic collection of patient-reported outcomes in atrial fibrillation: feasibility and initial results of the Utah mEVAL AF programme.

AIMS: Incorporating patient-reported outcomes (PROs) into routine care of atrial fibrillation (AF) enables direct integration of symptoms, function, and health-related quality of life (HRQoL) into practice. We report our initial experience with a system-wide PRO initiative among AF patients. METHODS AND RESULTS: All patients with AF in our practice undergo PRO assessment with the Toronto AF Severity Scale (AFSS), and generic PROs, prior to electrophysiology clinic visits. We describe the implementation, feasibility, and results of clinic-based, electronic AF PRO collection, and compare AF-specific and generic HRQoL assessments. From October 2016 to February 2019, 1586 unique AF patients initiated 2379 PRO assessments, 2145 of which had all PRO measures completed (90%). The median completion time for all PRO measures per visit was 7.3 min (1st, 3rd quartiles: 6, 10). Overall, 38% of patients were female (n = 589), mean age was 68 (SD 12) years, and mean CHA2DS2-VASc score was 3.8 (SD 2.0). The mean AFSS symptom score was 8.6 (SD 6.6, 1st, 3rd quartiles: 3, 13), and the full range of values was observed (0, 35). Generic PROs of physical function, general health, and depression were impacted at the most severe quartiles of AF symptom score (P < 0.0001 for each vs. AFSS quartile). CONCLUSION: Routine clinic-based, PRO collection for AF is feasible in clinical practice and patient time investment was acceptable. Disease-specific AF PROs add value to generic HRQoL instruments. Further research into the relationship between PROs, heart rhythm, and AF burden, as well as PRO-guided management, is necessary to optimize PRO utilization.

Temporal Performance of Laplacian Eigenmaps and 3D Conduction Velocity in Detecting Ischemic Stress.

BACKGROUND: Myocardial ischemia has a complex and time-varying electrocardiographic signature that is used to diagnose and stratify severity. Despite the ubiquitous clinical use of the ECG to detect ischemia, the sensitivity and specificity of ECG based detection of myocardial ischemia are still inadequate. PURPOSE: The purpose of this study was to compare, using animal models, the performance of several traditional ECG-based metrics for detecting acute ischemia against two novel metrics, the Laplacian Eigenmap (LE) parameters and a three-dimensional estimate of Conduction Velocity (CV). METHODS: LE is a machine learning technique that reduces the dimensions of simultaneously recorded time signals using a non-linear embedding followed by an singular value decomposition to represent each multichannel recording as a single trajectory on a manifold. Perturbations in the trajectories suggest the presence of myocardial ischemia. CV was computed using a tetrahedral mesh created from the electrode locations of transmural plunge needles. To validate the results, we used electrograms collected over 95 episodes of acutely induced myocardial ischemia in 15 canine and 2 porcine subjects. The LE and CV metrics were compared against traditional metrics derived from the ST segment, the T wave, the QRS of the same electrograms. The response time and robustness of each metric was quantified using parameters we defined as time to threshold (TTT) and contrast ratio (CR). RESULTS: The temporal performance of the metrics evaluated throughout the ischemic episodes showed a consistent relationship; the LE metrics changed earlier than those from the T wave, which were followed by those from the ST segment, and finally from the QRS. The CV results showed median drops in conduction velocity throughout the perfusion bed of more than 23% in canines and over 12% during half of the induced ischemia episodes in swine. The other half of the episodes in swine produced a 76% drop. CONCLUSIONS: Our results suggest that the LE metric is more sensitive to acute ischemia than traditional single parameters used in previous studies, likely because it incorporates the entire QRST across multiple electrodes in a way that captures their most salient features in a low-dimensional space. The estimates of conduction velocity suggest substantial, in some cases dramatic slowing of the spread of activation, a finding that is not surprising but has not been documented in such three-dimensional detail before. The experiments and these new metrics provide a means to both explore details of the acute ischemic response not available from humans and suggest a path to translate this knowledge into improvements in clinical scoring of ischemia.

Discordant Treatment Goals for Patients With Atrial Fibrillation and Clinical Trials Metrics.

BACKGROUND: Most clinical trials define successful atrial fibrillation (AF) treatment as no AF episodes longer than 30 seconds. Yet, there has been minimal study of how patients define successful treatment and whether their perspectives align with trial outcomes. OBJECTIVES: Survey patients with AF to identify: 1) what aspect of AF is most important to address (frequency, duration, or severity of AF episodes); 2) what AF burden would be considered acceptable to consider treatment successful; and 3) to establish patient preferences for successful treatment thresholds for a validated patient-reported outcome (PRO) score. METHODS: We surveyed patients receiving active care for AF at a single tertiary care center modeled after the Toronto AF Severity Scale (AFSS). The survey consisted of current and "successful treatment" AF frequency, burden, and symptom domains; and baseline socioeconomic information. RESULTS: Of 7,000 invitations, 852 individuals completed the survey (12% response) with a mean age of 65 ± 13 years, 36.5% were female, and they had a mean CHA2DS2-VAsc score of 2.9 ± 1.9. Overall, 114 (13%) selected a decrease in AF episode duration as their top treatment priority, 505 (59%) episode frequency, and 230 (27%) episode severity. Overall, 207 (24%) patients would only consider a treatment successful if they never had AF again, whereas 645 (76%) patients considered success to be fewer AF episodes. A total of 341 (40%) patients would only consider a treatment successful if AF episodes lasted less than a few minutes, whereas 509 (60%) patients would accept AF episodes lasting >30 minutes. An AFSS symptom score ≤5 was considered a good outcome by 80% of respondents. CONCLUSIONS: Patients prioritize decreased AF frequency over improvements in severity or duration, and an AFSS ≤5 would be a reasonable outcome of AF treatment. Most patients would consider treatment successful if they had more than 1 AF episode lasting longer than 30 seconds. Future clinical trial design should consider patients' perspectives when designing outcomes.

Performance of off-the-shelf machine learning architectures and biases in low left ventricular ejection fraction detection.

Background: Artificial intelligence-machine learning (AI-ML) has demonstrated the ability to extract clinically useful information from electrocardiograms (ECGs) not available using traditional interpretation methods. There exists an extensive body of AI-ML research in fields outside of cardiology including several open-source AI-ML architectures that can be translated to new problems in an "off-the-shelf" manner. Objective: We sought to address the limited investigation of which if any of these off-the-shelf architectures could be useful in ECG analysis as well as how and when these AI-ML approaches fail. Methods: We applied 6 off-the-shelf AI-ML architectures to detect low left ventricular ejection fraction (LVEF) in a cohort of ECGs from 24,868 patients. We assessed LVEF classification and explored patient characteristics associated with inaccurate (false positive or false negative) LVEF prediction. Results: We found that all of these network architectures produced LVEF detection area under the receiver-operating characteristic curve values above 0.9 (averaged over 5 instances per network), with the ResNet 18 network performing the highest (average area under the receiver-operating characteristic curve of 0.917). We also observed that some patient-specific characteristics such as race, sex, and presence of several comorbidities were associated with lower LVEF prediction performance. Conclusions: This demonstrates the ability of off-the-shelf AI-ML architectures to detect clinically useful information from ECGs with performance matching contemporary custom-build AI-ML architectures. We also highlighted the presence of possible biases in these AI-ML approaches in the context of patient characteristics. These findings should be considered in the pursuit of efficient and equitable deployment of AI-ML technologies moving forward.

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.

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.

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.

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.

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.

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.

Uncertainty Quantification of the Effect of Variable Conductivity in Ventricular Fibrotic Regions on Ventricular Tachycardia.

Ventricular tachycardia (VT) is a life-threatening cardiac arrhythmia for which a common treatment pathway is electroanatomical mapping and ablation. Recent advances in both noninvasive ablation techniques and computational modeling have motivated the development of patient-specific computational models of VT. Such models are parameterized by a wide range of inputs, each of which is associated with an often unknown amount of error and uncertainty. Uncertainty quantification (UQ) is a technique to assess how variability in the inputs to a model affects its outputs. UQ has seen increased attention in computational cardiology as an avenue to further improve, understand, and develop patient-specific models. In this study we applied polynomial chaos-based UQ to explore the effect of varying the tissue conductivity of fibrotic border zones in a patient-specific model on the resulting VT simulation. We found that over a range of inputs, the model was most sensitive to fibrotic sheet direction, and uncertainty in fibrotic conductivity resulted in substantial variability in the VT reentry duration and cycle length. Overall, this study paves the way for future UQ applications to improve and understand VT models.

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.