Electrical Stimulation for Low-Energy Termination of Cardiac Arrhythmias: a Review.

Cardiac arrhythmias are a leading cause of morbidity and mortality in the developed world, estimated to be responsible for hundreds of thousands of deaths annually. Our understanding of the electrophysiological mechanisms of such arrhythmias has grown since they were formally characterized in the late nineteenth century, and this has led to the development of numerous devices and therapies that have markedly improved outcomes for patients affected by such conditions. Despite these advancements, the application of a single large shock remains the clinical standard for treating deadly tachyarrhythmias. Such defibrillating shocks are undoubtedly effective in terminating such arrhythmias; however, they are applied without forewarning, contributing to the patient's stress and anxiety; they can be intensely painful; and they can have adverse psychological and physiological effects on patients. In recent years, there has been interest in developing defibrillation protocols that can terminate arrhythmias without crossing the human pain threshold for energy delivery, generally estimated to be between 0.1 and 1 J. In this article, we review existing literature on the development of such low-energy defibrillation methods and their underlying mechanisms, in an attempt to broadly describe the current landscape of these technologies.

Insurance lesions: Does a second lesion make a difference?

INTRODUCTION: In radiofrequency ablation procedures for cardiac arrhythmia, the efficacy of creating repeated lesions at the same location ("insurance lesions") remains poorly studied. We assessed the effect of type of tissue, power, and time on the resulting lesion geometry during such multiple ablation procedures. METHODS: A custom ex vivo ablation model was used to assess lesion formation. An ablation catheter was oriented perpendicular to the tissue and used to create lesions that varied by type of tissue (atrial or ventricular free wall), power (30 or 50 W), and time (30, 40, or 50 s for standard ablations and 5, 10, or 15 s for high-power, short-duration [HPSD] ablations). Lesion dimensions were recorded and then analyzed. Radiofrequency ablations were performed on 57 atrial tissue samples (28 HPSD, 29 standard) and 28 ventricular tissue samples (all standard). RESULTS: With ablation parameters held constant, performing multiple ablations significantly increased lesion depth in ventricular tissue when ablations were performed at 30 W for 50 s. No other set of ablation parameters was shown to affect the width or depth of the resulting lesions in either tissue type. CONCLUSION: Multiple ablations created with the same power and time, delivered within 30 s of each other at the same exact location, offer no meaningful benefit in lesion depth or width over single ablations, with the exception of ventricular ablation at 30 W for 50 s. Given the risks associated with excessive ablation, our results suggest that this practice should be re-evaluated by clinical electrophysiologists.

Reconstituting electrical conduction in soft tissue: the path to replace the ablationist.

Cardiac arrhythmias are a leading cause of morbidity and mortality in the developed world. A common mechanism underlying many of these arrhythmias is re-entry, which may occur when native conduction pathways are disrupted, often by myocardial infarction. Presently, re-entrant arrhythmias are most commonly treated with antiarrhythmic drugs and myocardial ablation, although both treatment methods are associated with adverse side effects and limited efficacy. In recent years, significant advancements in the field of biomaterials science have spurred increased interest in the development of novel therapies that enable restoration of native conduction in damaged or diseased myocardium. In this review, we assess the current landscape of materials-based approaches to eliminating re-entrant arrhythmias. These approaches potentially pave the way for the eventual replacement of myocardial ablation as a preferred therapy for such pathologies.

Injectable hydrogel electrodes as conduction highways to restore native pacing.

There is an urgent clinical need for a treatment regimen that addresses the underlying pathophysiology of ventricular arrhythmias, the leading cause of sudden cardiac death. The current report describes the design of an injectable hydrogel electrode and successful deployment in a pig model with access far more refined than any current pacing modalities allow. In addition to successful cardiac capture and pacing, analysis of surface ECG tracings and three-dimensional electroanatomic mapping revealed a QRS morphology comparable to native sinus rhythm, strongly suggesting the hydrogel electrode captures the deep septal bundle branches and Purkinje fibers. In an ablation model, electroanatomic mapping data demonstrated that the activation wavefront from the hydrogel reaches the mid-myocardium and endocardium much earlier than current single-point pacing modalities. Such uniform activation of broad swaths of tissue enables an opportunity to minimize the delayed myocardial conduction of heterogeneous tissue that underpins re-entry. Collectively, these studies demonstrate the feasibility of a new pacing modality that most closely resembles native conduction with the potential to eliminate lethal re-entrant arrhythmias and provide painless defibrillation.

Artificial Intelligence and Machine Learning in Cardiac Electrophysiology.

Cardiac electrophysiology requires the processing of several patient-specific data points in real time to provide an accurate diagnosis and determine an optimal therapy. Expanding beyond the traditional tools that have been used to extract information from patient-specific data, machine learning offers a new set of advanced tools capable of revealing previously unknown data patterns and features. This new tool set can substantially improve the speed and level of confidence with which electrophysiologists can determine patient-specific diagnoses and therapies. The ability to process substantial amounts of data in real time also paves the way to novel techniques for data collection and visualization. Extended realities such as virtual and augmented reality can now enable the real-time visualization of 3-dimensional images in space. This enables improved preprocedural planning and intraprocedural interventions. Machine learning supplemented with novel visualization technologies could substantially improve patient care and outcomes by helping physicians to make more informed patient-specific decisions. This article presents current applications of machine learning and their use in cardiac electrophysiology.

Manifold Approximating Graph Interpolation of Cardiac Local Activation Time.

OBJECTIVE: Local activation time (LAT) mapping of cardiac chambers is vital for targeted treatment of cardiac arrhythmias in catheter ablation procedures. Current methods require too many LAT observations for an accurate interpolation of the necessarily sparse LAT signal extracted from intracardiac electrograms (EGMs). Additionally, conventional performance metrics for LAT interpolation algorithms do not accurately measure the quality of interpolated maps. We propose, first, a novel method for spatial interpolation of the LAT signal which requires relatively few observations; second, a realistic sub-sampling protocol for LAT interpolation testing; and third, a new color-based metric for evaluation of interpolation quality that quantifies perceived differences in LAT maps. METHODS: We utilize a graph signal processing framework to reformulate the irregular spatial interpolation problem into a semi-supervised learning problem on the manifold with a closed-form solution. The metric proposed uses a color difference equation and color theory to quantify visual differences in generated LAT maps. RESULTS: We evaluate our approach on a dataset consisting of seven LAT maps from four patients obtained by the CARTO electroanatomic mapping system during premature ventricular complex (PVC) ablation procedures. Random sub-sampling and re-interpolation of the LAT observations show excellent accuracy for relatively few observations, achieving on average 6% lower error than state-of-the-art techniques for only 100 observations. CONCLUSION: Our study suggests that graph signal processing methods can improve LAT mapping for cardiac ablation procedures. SIGNIFICANCE: The proposed method can reduce patient time in surgery by decreasing the number of LAT observations needed for an accurate LAT map.

Seven-day in vivo testing of a novel, low-resistance, pumpless pediatric artificial lung for long-term support.

INTRODUCTION: For children with end-stage lung disease that cannot wean from extracorporeal life support (ECLS), a wearable artificial lung would permit extubation and provide a bridge to recovery or transplantation. We evaluate the function of the novel Pediatric MLung-a low-resistance, pumpless artificial lung developed specifically for children-in healthy animal subjects. METHODS: Adolescent "mini sheep" weighing 12-20 kg underwent left thoracotomy, cannulation of the main pulmonary artery (PA; inflow) and left atrium (outflow), and connection to the MLung. RESULTS: Thirteen sheep were studied; 6 were supported for 7 days. Mean PA pressure was 23.9 ± 6.9 mmHg. MLung blood flow was 633±258 mL/min or 30.0 ± 16.0% of CO. MLung pressure drop was 4.4 ± 3.4 mmHg. Resistance was 7.2 ± 5.2 mmHg/L/min. Device outlet oxygen saturation was 99.0 ± 3.3% with inlet saturation 53.8 ± 7.3%. Oxygen delivery was 41.1 ± 18.4 mL O2/min (maximum 84.9 mL/min) or 2.8 ± 1.5 mL O2/min/kg. Platelet count significantly decreased; no platelet transfusions were required. Plasma free hemoglobin significantly increased only on day 7, at which point 2 of the animals had plasma free hemoglobin levels above 50 mg/dL. CONCLUSION: The MLung provides adequate gas exchange at appropriate blood flows for the pediatric population in a PA-to-LA configuration. Further work remains to improve the biocompatibility of the device. LEVEL OF EVIDENCE: N/A.

A novel convolutional neural network for reconstructing surface electrocardiograms from intracardiac electrograms and vice versa.

We propose a novel convolutional neural network framework for mapping a multivariate input to a multivariate output. In particular, we implement our algorithm within the scope of 12-lead surface electrocardiogram (ECG) reconstruction from intracardiac electrograms (EGM) and vice versa. The goal of performing this task is to allow for improved point-of-care monitoring of patients with an implanted device to treat cardiac pathologies. We will achieve this goal with 12-lead ECG reconstruction and by providing a new diagnostic tool for classifying five different ECG types. The algorithm is evaluated on a dataset retroactively collected from 14 patients. Correlation coefficients calculated between the reconstructed and the actual ECG show that the proposed convolutional neural network model represents an efficient, accurate, and superior way to synthesize a 12-lead ECG when compared to previous methods. We can also achieve the same reconstruction accuracy with only one EGM lead as input. We also tested the model in a non-patient specific way and saw a reasonable correlation coefficient. The model was also executed in the reverse direction to produce EGM signals from a 12-lead ECG and found that the correlation was comparable to the forward direction. Lastly, we analyzed the features learned in the model and determined that the model learns an overcomplete basis of our 12-lead ECG space. We then use this basis of features to create a new diagnostic tool for classifying different ECG arrhythmia's on the MIT-BIH arrhythmia database with an average accuracy of 0.98.

Low-Resistance, Concentric-Gated Pediatric Artificial Lung for End-Stage Lung Failure.

Children with end-stage lung failure awaiting lung transplant would benefit from improvements in artificial lung technology allowing for wearable pulmonary support as a bridge-to-transplant therapy. In this work, we designed, fabricated, and tested the Pediatric MLung-a dual-inlet hollow fiber artificial lung based on concentric gating, which has a rated flow of 1 L/min, and a pressure drop of 25 mm Hg at rated flow. This device and future iterations of the current design are designed to relieve pulmonary arterial hypertension, provide pulmonary support, reduce ventilator-associated injury, and allow for more effective therapy of patients with end-stage lung disease, including bridge-to-transplant treatment.