Predicting long-term freedom from atrial fibrillation after catheter ablation by a machine learning algorithm: Validation of the CAAP-AF score

Role of Machine Learning and Artificial Intelligence in Arrhythmias and Electrophysiology

Machine learning (ML), a subset of artificial intelligence (AI) centered on machines learning from extensive datasets, stands at the forefront of a technological revolution shaping various facets of society. Cardiovascular medicine has emerged as a key domain for ML applications, with considerable efforts to integrate these innovations into routine clinical practice. Within cardiac electrophysiology, ML applications, especially in the automated interpretation of electrocardiograms, have garnered substantial attention in existing literature. However, less recognized are the diverse applications of ML in cardiac electrophysiology and arrhythmias, spanning basic science research on arrhythmia mechanisms, both experimental and computational, as well as contributions to enhanced techniques for mapping cardiac electrical function and translational research related to arrhythmia management. This comprehensive review delves into various ML applications within the scope of this journal, organized into 3 parts. The first section provides a fundamental understanding of general ML principles and methodologies, serving as a foundational resource for readers interested in exploring ML applications in arrhythmia research. The second part offers an in-depth review of studies in arrhythmia and electrophysiology that leverage ML methodologies, showcasing the broad potential of ML approaches. Each subject is thoroughly outlined, accompanied by a review of notable ML research advancements. Finally, the review delves into the primary challenges and future perspectives surrounding ML-driven cardiac electrophysiology and arrhythmias research.

Evaluation of the effectiveness and safety of a novel substrate-based radiofrequency ablation for persistent atrial fibrillation: a prospective, randomised, parallel-controlled, single-blinded study protocol

INTRODUCTION

Pulmonary vein isolation (PVI) is the cornerstone of radiofrequency (RF) ablation for atrial fibrillation (AF). However, a single ablation strategy does not always achieve the desired therapeutic effect in all patients with persistent AF, and individualised strategies are required for different clinical characteristics.

METHODS AND ANALYSIS

This study aimed to determine the optimal catheter ablation strategy for persistent AF by comparing the efficacy of PVI and BCXL (BC: big circles encircling pulmonary vein isolation; XL: unfixed number of lines based on the left atrial substrate). The BCXL-AF study (clinical trial no. ChiCTR2200067081) was designed as a prospective, randomised, parallel-controlled, single-blinded clinical trial. Overall, 400 patients with persistent AF were randomised in a 1:1 ratio into PVI-only and BCXL-individualised ablation groups. Patients randomised to the individualised ablation group will be further categorised into risk strata according to their clinical condition using the actual ablation method determined by the strata. Seven postoperative visits were conducted from discharge to 24 months of age. The primary observation endpoint will be the incidence of atrial tachyarrhythmia (including AF, atrial flutter and atrial tachycardia with a duration of ≥30 s) without using antiarrhythmic drugs after a blank period of 3 months following a single ablation procedure. The BCXL-AF study will assess an optimal approach for persistent AF RF ablation and evaluate the effectiveness of individualised RF ablation strategies in reducing the recurrence rate of AF.

ETHICS AND DISSEMINATION

The study protocol was reviewed, and ethical approval was obtained from the Army Medical University Human Ethics Committee (approval number: 2022-484-01). All the participants provided written informed consent. This study was conducted according to the principles of the Declaration of Helsinki and its amendments. The results of this study will be disseminated through manuscript publication and conference presentations.

TRIAL REGISTRATION NUMBER

ChiCTR2200067081.

Uplift modeling to identify patients who require extensive catheter ablation procedures among patients with persistent atrial fibrillation

Identifying patients who would benefit from extensive catheter ablation along with pulmonary vein isolation (PVI) among those with persistent atrial fibrillation (AF) has been a subject of controversy. The objective of this study was to apply uplift modeling, a machine learning method for analyzing individual causal effect, to identify such patients in the EARNEST-PVI trial, a randomized trial in patients with persistent AF. We developed 16 uplift models using different machine learning algorithms, and determined that the best performing model was adaptive boosting using Qini coefficients. The optimal uplift score threshold was 0.0124. Among patients with an uplift score ≥ 0.0124, those who underwent extensive catheter ablation (PVI-plus) showed a significantly lower recurrence rate of AF compared to those who received only PVI (PVI-alone) (HR 0.40; 95% CI 0.19-0.84; P-value = 0.015). In contrast, among patients with an uplift score < 0.0124, recurrence of AF did not significantly differ between PVI-plus and PVI-alone (HR 1.17; 95% CI 0.57-2.39; P-value = 0.661). By employing uplift modeling, we could effectively identify a subset of patients with persistent AF who would benefit from PVI-plus. This model could be valuable in stratifying patients with persistent AF who need extensive catheter ablation before the procedure.

An Artificial Intelligence-Enabled ECG Algorithm for Predicting the Risk of Recurrence in Patients with Paroxysmal Atrial Fibrillation after Catheter Ablation

Background: Catheter ablation (CA) is an important treatment strategy to reduce the burden and complications of atrial fibrillation (AF). This study aims to predict the risk of recurrence in patients with paroxysmal AF (pAF) after CA by an artificial intelligence (AI)-enabled electrocardiography (ECG) algorithm. Methods and Results: 1618 ≥ 18 years old patients with pAF who underwent CA in Guangdong Provincial People's Hospital from 1 January 2012 to 31 May 2019 were enrolled in this study. All patients underwent pulmonary vein isolation (PVI) by experienced operators. Baseline clinical features were recorded in detail before the operation and standard follow-up (≥12 months) was conducted. The convolutional neural network (CNN) was trained and validated by 12-lead ECGs within 30 days before CA to predict the risk of recurrence. A receiver operating characteristic curve (ROC) was created for the testing and validation sets, and the predictive performance of AI-enabled ECG was assessed by the area under the curve (AUC). After training and internal validation, the AUC of the AI algorithm was 0.84 (95% CI: 0.78-0.89), with a sensitivity, specificity, accuracy, precision and balanced F Score (F1 score) of 72.3%, 95.0%, 92.0%, 69.1% and 0.707, respectively. Compared with current prognostic models (APPLE, BASE-AF2, CAAP-AF, DR-FLASH and MB-LATER), the performance of the AI algorithm was better (p < 0.01). Conclusions: The AI-enabled ECG algorithm seemed to be an effective method to predict the risk of recurrence in patients with pAF after CA. This is of great clinical significance in decision-making for personalized ablation strategies and postoperative treatment plans in patients with pAF.

Outcome of catheter ablation for paroxysmal atrial fibrillation in patients with stable coronary artery disease

BACKGROUND

Atrial fibrillation (AF) and stable coronary artery disease (SCAD) frequently coexist. This study aimed to assess the long-term outcome of catheter ablation in patients with paroxysmal AF and SCAD.

METHODS

In total, 12104 patients with paroxysmal AF underwent catheter ablation in the Chinese Atrial Fibrillation Registry between 2011 and 2019 were screened. A total of 441 patients with SCAD were matched with patients without SCAD in a 1:4 ratio. The primary endpoint was AF recurrence after single ablation. The composite secondary endpoints were thromboembolism, coronary events, major bleeding, all-cause death. RESULTS;: Over a mean follow-up of 46.0±18.9 months, the recurrence rate in patients with SCAD was significantly higher after a single ablation (49.0% vs. 41.9%, P = 0.03). The very late recurrence rate of AF in the SCAD group was also significantly higher than that in the control group (38.9% vs. 31.2%;P = 0.04). In multivariate analysis, adjusted with the female, smoking, duration of AF, previous thromboembolism, COPD, and statins, SCAD was independently associated with AF recurrence (adjusted HR, 1.19 [1.02-1.40], P = 0.03). The composite secondary endpoints were significantly higher in the SCAD group (12.70% vs. 8.54%, P = 0.02), mainly due to thromboembolism events (8.16% vs. 4.41%, P<0.01).

CONCLUSIONS

SCAD significantly increased the risk of recurrence after catheter ablation of paroxysmal AF. The incidence of thromboembolic events after catheter ablation of paroxysmal AF in the patients with SCAD was significantly higher than that in those without SCAD. This article is protected by copyright. All rights reserved.

Critical appraisal of technologies to assess electrical activity during atrial fibrillation: a position paper from the European Heart Rhythm Association and European Society of Cardiology Working Group on eCardiology in collaboration with the Heart Rhythm Society, Asia Pacific Heart Rhythm Society, Latin American Heart Rhythm Society and Computing in Cardiology

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

A review of machine learning for cardiology

This paper reviews recent cardiology literature and reports how Artificial Intelligence Tools (specifically, Machine Learning techniques) are being used by physicians in the field. Each technique is introduced with enough details to allow the understanding of how it works and its intent, but without delving into details that do not add immediate benefits and require expertise in the field. We specifically focus on the principal Machine Learning based risk scores used in cardiovascular research. After introducing them and summarizing their assumptions and biases, we discuss their merits and shortcomings. We report on how frequently they are adopted in the field and suggest why this is the case based on our expertise in Machine Learning. We complete the analysis by reviewing how corresponding statistical approaches compare with them. Finally, we discuss the main open issues in applying Machine Learning tools to cardiology tasks, also drafting possible future directions. Despite the growing interest in these tools, we argue that there are many still underutilized techniques: while Neural Networks are slowly being incorporated in cardiovascular research, other important techniques such as Semi-Supervised Learning and Federated Learning are still underutilized. The former would allow practitioners to harness the information contained in large datasets that are only partially labeled, while the latter would foster collaboration between institutions allowing building larger and better models.

Computational models of atrial fibrillation: achievements, challenges, and perspectives for improving clinical care

Despite significant advances in its detection, understanding and management, atrial fibrillation (AF) remains a highly prevalent cardiac arrhythmia with a major impact on morbidity and mortality of millions of patients. AF results from complex, dynamic interactions between risk factors and comorbidities that induce diverse atrial remodelling processes. Atrial remodelling increases AF vulnerability and persistence, while promoting disease progression. The variability in presentation and wide range of mechanisms involved in initiation, maintenance and progression of AF, as well as its associated adverse outcomes, make the early identification of causal factors modifiable with therapeutic interventions challenging, likely contributing to suboptimal efficacy of current AF management. Computational modelling facilitates the multilevel integration of multiple datasets and offers new opportunities for mechanistic understanding, risk prediction and personalized therapy. Mathematical simulations of cardiac electrophysiology have been around for 60 years and are being increasingly used to improve our understanding of AF mechanisms and guide AF therapy. This narrative review focuses on the emerging and future applications of computational modelling in AF management. We summarize clinical challenges that may benefit from computational modelling, provide an overview of the different in silico approaches that are available together with their notable achievements, and discuss the major limitations that hinder the routine clinical application of these approaches. Finally, future perspectives are addressed. With the rapid progress in electronic technologies including computing, clinical applications of computational modelling are advancing rapidly. We expect that their application will progressively increase in prominence, especially if their added value can be demonstrated in clinical trials.

Machine Learning in Arrhythmia and Electrophysiology

Machine learning (ML), a branch of artificial intelligence, where machines learn from big data, is at the crest of a technological wave of change sweeping society. Cardiovascular medicine is at the forefront of many ML applications, and there is a significant effort to bring them into mainstream clinical practice. In the field of cardiac electrophysiology, ML applications have also seen a rapid growth and popularity, particularly the use of ML in the automatic interpretation of ECGs, which has been extensively covered in the literature. Much lesser known are the other aspects of ML application in cardiac electrophysiology and arrhythmias, such as those in basic science research on arrhythmia mechanisms, both experimental and computational; in the development of better techniques for mapping of cardiac electrical function; and in translational research related to arrhythmia management. In the current review, we examine comprehensively such ML applications as they match the scope of this journal. The current review is organized in 3 parts. The first provides an overview of general ML principles and methodologies that will afford readers of the necessary information on the subject, serving as the foundation for inviting further ML applications in arrhythmia research. The basic information we provide can serve as a guide on how one might design and conduct an ML study. The second part is a review of arrhythmia and electrophysiology studies in which ML has been utilized, highlighting the broad potential of ML approaches. For each subject, we outline comprehensively the general topics, while reviewing some of the research advances utilizing ML under the subject. Finally, we discuss the main challenges and the perspectives for ML-driven cardiac electrophysiology and arrhythmia research.