Emerging role of artificial intelligence in cardiac electrophysiology

The potential of artificial intelligence to revolutionize health care delivery, research, and education in cardiac electrophysiology

The field of electrophysiology (EP) has benefited from numerous seminal innovations and discoveries that have enabled clinicians to deliver therapies and interventions that save lives and promote quality of life. The rapid pace of innovation in EP may be hindered by several challenges including the aging population with increasing morbidity, the availability of multiple costly therapies that, in many instances, confer minor incremental benefit, the limitations of healthcare reimbursement, the lack of response to therapies by some patients, and the complications of the invasive procedures performed. To overcome these challenges and continue on a steadfast path of transformative innovation, the EP community must comprehensively explore how artificial intelligence (AI) can be applied to healthcare delivery, research, and education and consider all opportunities in which AI can catalyze innovation; create workflow, research, and education efficiencies; and improve patient outcomes at a lower cost. In this white paper, we define AI and discuss the potential of AI to revolutionize the EP field. We also address the requirements for implementing, maintaining, and enhancing quality when using AI and consider ethical, operational, and regulatory aspects of AI implementation. This manuscript will be followed by several perspective papers that will expand on some of these topics.

High-throughput methods for cardiac cellular electrophysiology studies: the road to personalized medicine

Personalized medicine refers to the tailored application of medical treatment at an individual level, considering the specific genotype or phenotype of each patient for targeted therapy. In the context of cardiovascular diseases, implementing personalized medicine is challenging due to the high costs involved and the slow pace of identifying the pathogenicity of genetic variants, deciphering molecular mechanisms of disease, and testing treatment approaches. Scalable cellular models such as human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) serve as useful in vitro tools that reflect individual patient genetics and retain clinical phenotypes. High-throughput functional assessment of these constructs is necessary to rapidly assess cardiac pathogenicity and test new therapeutics if personalized medicine is to become a reality. High-throughput photometry recordings of single cells coupled with potentiometric probes offer cost-effective alternatives to traditional patch-clamp assessments of cardiomyocyte action potential characteristics. Importantly, automated patch-clamp (APC) is rapidly emerging in the pharmaceutical industry and academia as a powerful method to assess individual membrane-bound ionic currents and ion channel biophysics over multiple cells in parallel. Now amenable to primary cell and hiPSC-CM measurement, APC represents an exciting leap forward in the characterization of a multitude of molecular mechanisms that underlie clinical cardiac phenotypes. This review provides a summary of state-of-the-art high-throughput electrophysiological techniques to assess cardiac electrophysiology and an overview of recent works that successfully integrate these methods into basic science research that could potentially facilitate future implementation of personalized medicine at a clinical level.

Artificial intelligence-enhanced electrocardiography for accurate diagnosis and management of cardiovascular diseases

Electrocardiography (ECG), improved by artificial intelligence (AI), has become a potential technique for the precise diagnosis and treatment of cardiovascular disorders. The conventional ECG is a frequently used, inexpensive, and easily accessible test that offers important information about the physiological and anatomical state of the heart. However, the ECG can be interpreted differently by humans depending on the interpreter's level of training and experience, which could make diagnosis more difficult. Using AI, especially deep learning convolutional neural networks (CNNs), to look at single, continuous, and intermittent ECG leads that has led to fully automated AI models that can interpret the ECG like a human, possibly more accurately and consistently. These AI algorithms are effective non-invasive biomarkers for cardiovascular illnesses because they can identify subtle patterns and signals in the ECG that may not be readily apparent to human interpreters. The use of AI in ECG analysis has several benefits, including the quick and precise detection of problems like arrhythmias, silent cardiac illnesses, and left ventricular failure. It has the potential to help doctors with interpretation, diagnosis, risk assessment, and illness management. Aside from that, AI-enhanced ECGs have been demonstrated to boost the identification of heart failure and other cardiovascular disorders, particularly in emergency department settings, allowing for quicker and more precise treatment options. The use of AI in cardiology, however, has several limitations and obstacles, despite its potential. The effective implementation of AI-powered ECG analysis is limited by issues such as systematic bias. Biases based on age, gender, and race result from unbalanced datasets. A model's performance is impacted when diverse demographics are inadequately represented. Potentially disregarded age-related ECG variations may result from skewed age data in training sets. ECG patterns are affected by physiological differences between the sexes; a dataset that is inclined toward one sex may compromise the accuracy of the others. Genetic variations influence ECG readings, so racial diversity in datasets is significant. Furthermore, issues such as inadequate generalization, regulatory barriers, and interpretability concerns contribute to deployment difficulties. The lack of robustness in models when applied to disparate populations frequently hinders their practical applicability. The exhaustive validation required by regulatory requirements causes a delay in deployment. Difficult models that are not interpretable erode the confidence of clinicians. Diverse dataset curation, bias mitigation strategies, continuous validation across populations, and collaborative efforts for regulatory approval are essential for the successful deployment of AI ECG in clinical settings and must be undertaken to address these issues. To guarantee a safe and successful deployment in clinical practice, the use of AI in cardiology must be done with a thorough understanding of the algorithms and their limits. In summary, AI-enhanced electrocardiography has enormous potential to improve the management of cardiovascular illness by delivering precise and timely diagnostic insights, aiding clinicians, and enhancing patient outcomes. Further study and development are required to fully realize AI's promise for improving cardiology practices and patient care as technology continues to advance.

Ventricular Tachycardia Catheter Ablation: Retrospective Analysis and Prospective Outlooks-A Comprehensive Review

Ventricular tachycardia is a potentially life-threatening arrhythmia associated with an overall high morbi-mortality, particularly in patients with structural heart disease. Despite their pivotal role in preventing sudden cardiac death, implantable cardioverter-defibrillators, although a guideline-based class I recommendation, are unable to prevent arrhythmic episodes and significantly alter the quality of life by delivering recurrent therapies. From open-heart surgical ablation to the currently widely used percutaneous approach, catheter ablation is a safe and effective procedure able to target the responsible re-entry myocardial circuit from both the endocardium and the epicardium. There are four main mapping strategies, activation, entrainment, pace, and substrate mapping, each of them with their own advantages and limitations. The contemporary guideline-based recommendations for VT ablation primarily apply to patients experiencing antiarrhythmic drug ineffectiveness or those intolerant to the pharmacological treatment. Although highly effective in most cases of scar-related VTs, the traditional approach may sometimes be insufficient, especially in patients with nonischemic cardiomyopathies, where circuits may be unmappable using the classic techniques. Alternative methods have been proposed, such as stereotactic arrhythmia radioablation or radiotherapy ablation, surgical ablation, needle ablation, transarterial coronary ethanol ablation, and retrograde coronary venous ethanol ablation, with promising results. Further studies are needed in order to prove the overall efficacy of these methods in comparison to standard radiofrequency delivery. Nevertheless, as the field of cardiac electrophysiology continues to evolve, it is important to acknowledge the role of artificial intelligence in both the pre-procedural planning and the intervention itself.

Advancing Cardiovascular Risk Assessment with Artificial Intelligence: Opportunities and Implications in North Carolina

Cardiovascular disease mortality is increasing in North Carolina with persistent inequality by race, income, and location. Artificial intelligence (AI) can repurpose the widely available electrocardiogram (ECG) for enhanced assessment of cardiac dysfunction. By identifying accelerated cardiac aging from the ECG, AI offers novel insights into risk assessment and prevention.

Artificial intelligence-enhanced 12-lead electrocardiography for identifying atrial fibrillation during sinus rhythm (AIAFib) trial: protocol for a multicenter retrospective study

Introduction

Atrial fibrillation (AF) is the most common arrhythmia, contributing significantly to morbidity and mortality. In a previous study, we developed a deep neural network for predicting paroxysmal atrial fibrillation (PAF) during sinus rhythm (SR) using digital data from standard 12-lead electrocardiography (ECG). The primary aim of this study is to validate an existing artificial intelligence (AI)-enhanced ECG algorithm for predicting PAF in a multicenter tertiary hospital. The secondary objective is to investigate whether the AI-enhanced ECG is associated with AF-related clinical outcomes.

Methods and analysis

We will conduct a retrospective cohort study of more than 50,000 12-lead ECGs from November 1, 2012, to December 31, 2021, at 10 Korean University Hospitals. Data will be collected from patient records, including baseline demographics, comorbidities, laboratory findings, echocardiographic findings, hospitalizations, and related procedural outcomes, such as AF ablation and mortality. De-identification of ECG data through data encryption and anonymization will be conducted and the data will be analyzed using the AI algorithm previously developed for AF prediction. An area under the receiver operating characteristic curve will be created to test and validate the datasets and assess the AI-enabled ECGs acquired during the sinus rhythm to determine whether AF is present. Kaplan-Meier survival functions will be used to estimate the time to hospitalization, AF-related procedure outcomes, and mortality, with log-rank tests to compare patients with low and high risk of AF by AI. Multivariate Cox proportional hazards regression will estimate the effect of AI-enhanced ECG multimorbidity on clinical outcomes after stratifying patients by AF probability by AI.

Discussion

This study will advance PAF prediction based on AI-enhanced ECGs. This approach is a novel method for risk stratification and emphasizes shared decision-making for early detection and management of patients with newly diagnosed AF. The results may revolutionize PAF management and unveil the wider potential of AI in predicting and managing cardiovascular diseases.

Ethics and dissemination

The study findings will be published in peer-reviewed publications and disseminated at national and international conferences and through social media. This study was approved by the institutional review boards of all participating university hospitals. Data extraction, storage, and management were approved by the data review committees of all institutions.

Clinical Trial Registration

[cris.nih.go.kr], identifier (KCT0007881).

Wide Antral Circumferential Re-Ablation for Recurrent Atrial Fibrillation after Prior Pulmonary Vein Isolation Guided by High-Density Mapping Increases Freedom from Atrial Arrhythmias

Performing repeated pulmonary vein isolation (re-PVI) after recurrent atrial fibrillation (AF) following prior PVI is a standard procedure. However, no consensus exists regarding the most effective approach in redo procedures. We assessed the efficacy of re-PVI using wide antral circumferential re-ablation (WACA) supported by high-density electroanatomical mapping (HDM) as compared to conventional re-PVI. Consecutive patients with AF recurrences showing true PV reconnection (residual intra-PV and PV antral electrical potentials within the initial ablation line) or exclusive PV antral potentials (without intra-PV potentials) in the redo procedure were prospectively enrolled and received HDM-guided WACA (Re-WACA group). Conventional re-PVI patients treated using pure ostial gap ablation guided by a circular mapping catheter served as a historical control (Re-PVI group). Patients with durable PVI and no antral PV potentials were excluded. Arrhythmia recurrences ≥30 s were calculated as recurrences. In total, 114 patients were investigated (Re-WACA: n = 56, 68 ± 10 years, Re-PVI: n = 58, 65 ± 10 years). There were no significant differences in clinical characteristics including the AF type or the number of previous PVIs. In the Re-WACA group, 11% of patients showed electrical potentials only in the antrum but not inside any PV. At 402 ± 71 days of follow-up, the estimated freedom from arrhythmia was 89% in the Re-WACA group and 69% in the Re-PVI group (p = 0.01). Re-WACA independently predicted arrhythmia-free survival (HR = 0.39, 95% CI 0.16-0.93, p = 0.03), whereas two previous PVI procedures predicted recurrences (HR = 2.35, 95% CI 1.20-4.46, p = 0.01). The Re-WACA strategy guided by HDM significantly improved arrhythmia-free survival as compared to conventional ostial re-PVI. Residual PV antral potentials after prior PVI are frequent and can be easily visualized by HDM.

Data deluge from remote monitoring of cardiac implantable electronic devices and importance of clinical stratification

Background

Remote monitoring (RM) has been accepted as a standard of care for follow-up of patients with cardiac implantable electronic devices (CIEDs). However, the resulting data deluge poses major challenge to device clinics.

Objective

This study aimed to quantify the data deluge from CIED and stratify these data based on clinical relevance.

Methods

The study included patients from 67 device clinics across the United States being remotely monitored by Octagos Health. The CIEDs included implantable loop recorders, pacemakers, implantable cardioverter-defibrillators, cardiac resynchronization therapy defibrillators, and cardiac resynchronization therapy pacemakers. Transmissions were either dismissed before reaching the clinical practice if they were repetitive or redundant or were forwarded if they were either clinically relevant or actionable transmission (alert). The alerts were further classified as level 1, 2, or 3 based on clinical urgency.

Results

A total of 32,721 patients with CIEDs were included. There were 14,465 (44.2%) patients with pacemakers, 8381 (25.6%) with implantable loop recorders, 5351 (16.4%) with implantable cardioverter-defibrillators, 3531 (10.8%) with cardiac resynchronization therapy defibrillators, and 993 (3%) with cardiac resynchronization therapy pacemakers. Over a period of 2 years of RM, 384,796 transmissions were received. Of these, 220,049 (57%) transmissions were dismissed, as they were either redundant or repetitive. Only 164,747 (43%) transmissions were transmitted to the clinicians, of which only 13% (n = 50,440) had clinical alerts, while 30.6% (n = 114,307) were routine transmissions.

Conclusion

Our study shows that data deluge from RM of CIEDs can be streamlined by utilization of appropriate screening strategies that will enhance efficiency of device clinics and provide better patient care.

Animal Disease Models and Patient-iPS-Cell-Derived In Vitro Disease Models for Cardiovascular Biology-How Close to Disease?

Currently, zebrafish, rodents, canines, and pigs are the primary disease models used in cardiovascular research. In general, larger animals have more physiological similarities to humans, making better disease models. However, they can have restricted or limited use because they are difficult to handle and maintain. Moreover, animal welfare laws regulate the use of experimental animals. Different species have different mechanisms of disease onset. Organs in each animal species have different characteristics depending on their evolutionary history and living environment. For example, mice have higher heart rates than humans. Nonetheless, preclinical studies have used animals to evaluate the safety and efficacy of human drugs because no other complementary method exists. Hence, we need to evaluate the similarities and differences in disease mechanisms between humans and experimental animals. The translation of animal data to humans contributes to eliminating the gap between these two. In vitro disease models have been used as another alternative for human disease models since the discovery of induced pluripotent stem cells (iPSCs). Human cardiomyocytes have been generated from patient-derived iPSCs, which are genetically identical to the derived patients. Researchers have attempted to develop in vivo mimicking 3D culture systems. In this review, we explore the possible uses of animal disease models, iPSC-derived in vitro disease models, humanized animals, and the recent challenges of machine learning. The combination of these methods will make disease models more similar to human disease.