2024 European Heart Rhythm Association/Heart Rhythm Society/Asia Pacific Heart Rhythm Society/Latin American Heart Rhythm Society expert consensus statement on catheter and surgical ablation of atrial fibrillation.

In the last three decades, ablation of atrial fibrillation (AF) has become an evidence-based safe and efficacious treatment for managing the most common cardiac arrhythmia. In 2007, the first joint expert consensus document was issued, guiding healthcare professionals involved in catheter or surgical AF ablation. Mounting research evidence and technological advances have resulted in a rapidly changing landscape in the field of catheter and surgical AF ablation, thus stressing the need for regularly updated versions of this partnership which were issued in 2012 and 2017. Seven years after the last consensus, an updated document was considered necessary to define a contemporary framework for selection and management of patients considered for or undergoing catheter or surgical AF ablation. This consensus is a joint effort from collaborating cardiac electrophysiology societies, namely the European Heart Rhythm Association, the Heart Rhythm Society (HRS), the Asia Pacific HRS, and the Latin American HRS.

2024 European Heart Rhythm Association/Heart Rhythm Society/Asia Pacific Heart Rhythm Society/Latin American Heart Rhythm Society expert consensus statement on catheter and surgical ablation of atrial fibrillation.

In the last three decades, ablation of atrial fibrillation (AF) has become an evidence-based safe and efficacious treatment for managing the most common cardiac arrhythmia. In 2007, the first joint expert consensus document was issued, guiding healthcare professionals involved in catheter or surgical AF ablation. Mounting research evidence and technological advances have resulted in a rapidly changing landscape in the field of catheter and surgical AF ablation, thus stressing the need for regularly updated versions of this partnership which were issued in 2012 and 2017. Seven years after the last consensus, an updated document was considered necessary to define a contemporary framework for selection and management of patients considered for or undergoing catheter or surgical AF ablation. This consensus is a joint effort from collaborating cardiac electrophysiology societies, namely the European Heart Rhythm Association, the Heart Rhythm Society, the Asia Pacific Heart Rhythm Society, and the Latin American Heart Rhythm Society .

Cardioneuroablation for the management of patients with recurrent vasovagal syncope and symptomatic bradyarrhythmias: the CNA-FWRD Registry.

BACKGROUND: Cardioneuroablation has been emerging as a potential treatment alternative in appropriately selected patients with cardioinhibitory vasovagal syncope (VVS) and functional AV block (AVB). However the majority of available evidence has been derived from retrospective cohort studies performed by experienced operators. METHODS: The Cardioneuroablation for the Management of Patients with Recurrent Vasovagal Syncope and Symptomatic Bradyarrhythmias (CNA-FWRD) Registry is a multicenter prospective registry with cross-over design evaluating acute and long-term outcomes of VVS and AVB patients treated by conservative therapy and CNA. RESULTS: The study is a prospective observational registry with cross-over design for analysis of outcomes between a control group (i.e., behavioral and medical therapy only) and intervention group (Cardioneuroablation). Primary and secondary outcomes will only be assessed after enrollment in the registry. The follow-up period will be 3 years after enrollment. CONCLUSIONS: There remains a lack of prospective multicentered data for long-term outcomes comparing conservative therapy to radiofrequency CNA procedures particularly for key outcomes including recurrence of syncope, AV block, durable impact of disruption of the autonomic nervous system, and long-term complications after CNA. The CNA-FWRD registry has the potential to help fill this information gap.

Spatially Conserved Spiral Wave Activity During Human Atrial Fibrillation.

BACKGROUND: Atrial fibrillation is the most common cardiac arrhythmia in the world and increases the risk for stroke and morbidity. During atrial fibrillation, the electric activation fronts are no longer coherently propagating through the tissue and, instead, show rotational activity, consistent with spiral wave activation, focal activity, collision, or partial versions of these spatial patterns. An unexplained phenomenon is that although simulations of cardiac models abundantly demonstrate spiral waves, clinical recordings often show only intermittent spiral wave activity. METHODS: In silico data were generated using simulations in which spiral waves were continuously created and annihilated and in simulations in which a spiral wave was intermittently trapped at a heterogeneity. Clinically, spatio-temporal activation maps were constructed using 60 s recordings from a 64 electrode catheter within the atrium of N=34 patients (n=24 persistent atrial fibrillation). The location of clockwise and counterclockwise rotating spiral waves was quantified and all intervals during which these spiral waves were present were determined. For each interval, the angle of rotation as a function of time was computed and used to determine whether the spiral wave returned in step or changed phase at the start of each interval. RESULTS: In both simulations, spiral waves did not come back in phase and were out of step." In contrast, spiral waves returned in step in the majority (68%; P=0.05) of patients. Thus, the intermittently observed rotational activity in these patients is due to a temporally and spatially conserved spiral wave and not due to ones that are newly created at the onset of each interval. CONCLUSIONS: Intermittency of spiral wave activity represents conserved spiral wave activity of long, but interrupted duration or transient spiral activity, in the majority of patients. This finding could have important ramifications for identifying clinically important forms of atrial fibrillation and in guiding treatment.

Comparative arrhythmia patterns among patients on tyrosine kinase inhibitors.

BACKGROUND: Tyrosine kinase inhibitors (TKIs) are widely used in the treatment of hematologic malignancies. Limited studies have shown an association between treatment-limiting arrhythmias and TKI, particularly ibrutinib, a Bruton's tyrosine kinase (BTK) inhibitor. We sought to comprehensively assess the arrhythmia burden in patients receiving ibrutinib vs non-BTK TKI vs non-TKI therapies. METHODS: We performed a retrospective analysis of consecutive patients who received long-term cardiac event monitors while on ibrutinib, non-BTK TKIs, or non-TKI therapy for a hematologic malignancy between 2014 and 2022. RESULTS: One hundred ninety-three patients with hematologic malignancies were included (ibrutinib = 72, non-BTK TKI = 46, non-TKI therapy = 75). The average duration of TKI therapy was 32 months in the ibrutinib group vs 64 months in the non-BTK TKI group (p = 0.003). The ibrutinib group had a higher prevalence of atrial fibrillation (n = 32 [44%]) compared to the non-BTK TKI (n = 7 [15%], p = 0.001) and non-TKI (n = 15 [20%], p = 0.002) groups. Similarly, the prevalence of non-sustained ventricular tachycardia was higher in the ibrutinib group (n = 31, 43%) than the non-BTK TKI (n = 8 [17%], p = 0.004) and non-TKI groups (n = 20 [27%], p = 0.04). TKI therapy was held in 25% (n = 18) of patients on ibrutinib vs 4% (n = 2) on non-BTK TKIs (p = 0.005) secondary to arrhythmias. CONCLUSIONS: In this large retrospective analysis of patients with hematologic malignancies, patients receiving ibrutinib had a higher prevalence of atrial and ventricular arrhythmias compared to those receiving other TKI, with a higher rate of treatment interruption due to arrhythmias.

Segmenting computed tomograms for cardiac ablation using machine learning leveraged by domain knowledge encoding.

Background: Segmentation of computed tomography (CT) is important for many clinical procedures including personalized cardiac ablation for the management of cardiac arrhythmias. While segmentation can be automated by machine learning (ML), it is limited by the need for large, labeled training data that may be difficult to obtain. We set out to combine ML of cardiac CT with domain knowledge, which reduces the need for large training datasets by encoding cardiac geometry, which we then tested in independent datasets and in a prospective study of atrial fibrillation (AF) ablation. Methods: We mathematically represented atrial anatomy with simple geometric shapes and derived a model to parse cardiac structures in a small set of N = 6 digital hearts. The model, termed "virtual dissection," was used to train ML to segment cardiac CT in N = 20 patients, then tested in independent datasets and in a prospective study. Results: In independent test cohorts (N = 160) from 2 Institutions with different CT scanners, atrial structures were accurately segmented with Dice scores of 96.7% in internal (IQR: 95.3%-97.7%) and 93.5% in external (IQR: 91.9%-94.7%) test data, with good agreement with experts (r = 0.99; p < 0.0001). In a prospective study of 42 patients at ablation, this approach reduced segmentation time by 85% (2.3 ± 0.8 vs. 15.0 ± 6.9 min, p < 0.0001), yet provided similar Dice scores to experts (93.9% (IQR: 93.0%-94.6%) vs. 94.4% (IQR: 92.8%-95.7%), p = NS). Conclusions: Encoding cardiac geometry using mathematical models greatly accelerated training of ML to segment CT, reducing the need for large training sets while retaining accuracy in independent test data. Combining ML with domain knowledge may have broad applications.

Atrial fibrillation ablation outcome prediction with a machine learning fusion framework incorporating cardiac computed tomography.

BACKGROUND: Structural changes in the left atrium (LA) modestly predict outcomes in patients undergoing catheter ablation for atrial fibrillation (AF). Machine learning (ML) is a promising approach to personalize AF management strategies and improve predictive risk models after catheter ablation by integrating atrial geometry from cardiac computed tomography (CT) scans and patient-specific clinical data. We hypothesized that ML approaches based on a patient's specific data can identify responders to AF ablation. METHODS: Consecutive patients undergoing AF ablation, who had preprocedural CT scans, demographics, and 1-year follow-up data, were included in the study for a retrospective analysis. The inputs of models were CT-derived morphological features from left atrial segmentation (including the shape, volume of the LA, LA appendage, and pulmonary vein ostia) along with deep features learned directly from raw CT images, and clinical data. These were merged intelligently in a framework to learn their individual importance and produce the optimal classification. RESULTS: Three hundred twenty-one patients (64.2 ± 10.6 years, 69% male, 40% paroxysmal AF) were analyzed. Post 10-fold nested cross-validation, the model trained to intelligently merge and learn appropriate weights for clinical, morphological, and imaging data (AUC 0.821) outperformed those trained solely on clinical data (AUC 0.626), morphological (AUC 0.659), or imaging data (AUC 0.764). CONCLUSION: Our ML approach provides an end-to-end automated technique to predict AF ablation outcomes using deep learning from CT images, derived structural properties of LA, augmented by incorporation of clinical data in a merged ML framework. This can help develop personalized strategies for patient selection in invasive management of AF.

Design and development of a digital shared decision-making tool for stroke prevention in atrial fibrillation.

Background: Shared decision-making (SDM) is an approach in which patients and clinicians act as partners in making medical decisions. Patients receive the information needed to decide and are encouraged to balance risks, benefits, and preferences. Informative materials are vital to SDM. Atrial fibrillation (AF) is the most common cardiac arrhythmia and responsible for 10% of ischemic strokes, however 1/3 of patients are not on appropriate anticoagulation. Decision sharing may facilitate treatment acceptance, improving outcomes. Aims: To develop a framework of the components needed to create novel SDM tools and to provide practical examples through a case-study of stroke prevention in AF. Methods: We analyze the design values of a web-based SDM tool created to better inform AF patients about anticoagulation. The tool was developed in partnership with patient advocates, multi-disciplinary investigators, and private design firms. It was refined through iterative, recursive testing in patients with AF. Its effectiveness is being evaluated in a multisite clinical trial led by Stanford University and sponsored by the American Heart Association. Findings: The main components considered when creating the Stanford AFib tool included: design and software; content identification; information delivery; inclusive communication, user engagement; patient feedback; clinician experience; and anticipation of implementation and dissemination. We also highlight the ethical principles underlying SDM; matters of diversity and inclusion, linguistic variety, accessibility, and health literacy. The Stanford AFib Guide patient tool is available at: https://afibguide.com and the clinician tool at https://afibguide.com/clinician. Conclusion: Attention to a range of vital development and design factors can facilitate tool adoption and information acquisition by diverse cultural, educational, and socioeconomic subpopulations. With thoughtful design, digital tools may decrease decision regret and improve treatment outcomes across many decision-making situations in healthcare.

Tyrosine kinase inhibitor-associated ventricular arrhythmias: a case series and review of literature.

BACKGROUND: Tyrosine kinase inhibitors (TKIs) have been increasingly used as first-line therapy in hematologic and solid-organ malignancies. Multiple TKIs have been linked with the development of cardiovascular complications, especially atrial arrhythmias, but data on ventricular arrhythmias (VAs) is scarce. METHODS: Herein we describe five detailed cases of VAs related to TKI use in patients with varied baseline cardiovascular risk factors between 2019 and 2022 at three centers. Individual chart review was conducted retrospectively. RESULTS: Patient ages ranged from 43 to 83 years. Three patients were on Bruton's TKI (2 ibrutinib and 1 zanubrutinib) at the time of VAs; other TKIs involved were afatinib and dasatinib. Three patients had a high burden of non-sustained ventricular tachycardia (NSVT) requiring interventions, whereas two patients had sustained VAs. While all patients in our case series had significant improvement in VA burden after TKI cessation, two patients required new long-term antiarrhythmic drug therapy, and one had an implantable defibrillator cardioverter (ICD) placed due to persistent VAs after cessation of TKI therapy. One patient reinitiated TKI therapy after control of arrhythmia was achieved with antiarrhythmic drug therapy. CONCLUSIONS: Given the expanding long-term use of TKIs among a growing population of cancer patients, it is critical to acknowledge the association of TKIs with cardiovascular complications such as VAs, to characterize those at risk, and deploy preventive and therapeutic measures to avoid such complications and interference with oncologic therapy. Further efforts are warranted to develop monitoring protocols and optimal treatment strategies for TKI-induced VAs.

Contemporary trends in cardiac electrophysiology procedures in the United States, and impact of a global pandemic.

Background: There are limited data on trends in nationwide cardiac electrophysiology (EP) procedures in the United States before and during the global COVID-19 pandemic. Objective: We aimed to understand contemporary EP procedural trends and how the COVID-19 pandemic impacted them. Methods: Trends were obtained from publicly reported Centers for Medicare and Medicaid Services data from 2013 to 2020 (latest available). Rates of catheter-based EP procedures (EP studies and ablations) and cardiac implantable electronic device (CIED) procedures were analyzed. All procedural rates were calculated per 100,000 Medicare beneficiaries (year specific). Procedure physician subspecialty was also reported. Results: From 2013 to 2019, annual rate of all cardiac EP procedures increased from 817.91 to 1089.68 per 100,000 beneficiaries. Catheter-based EP procedures increased from 323.73 to 675.01, while CIED rates decreased from 494.18 to 414.67. While all ablation procedures increased over time, relative proportion of ablation procedures being pulmonary vein isolation (PVI) increased (9.9% of ablations in 2013, to 18.2% in 2019). In 2020, rates of both catheter-based EP procedures and CIED procedures decreased; however, PVI share of ablation continued to increase in 2020 comprising 25.2% of ablation procedures. Conclusion: Rates of EP procedures have increased among Medicare beneficiaries, with catheter-based procedures now eclipsing CIEDs. Additionally, a greater proportion of catheter-based EP procedures are PVI, but they still represent a minority of all ablations. In 2020, rates of EP procedures were attenuated, yet the proportion of PVI ablations increased to over one-fourth of ablation procedures. These data have important implications for the EP workforce.

Racial, ethnic, and sex disparities in atrial fibrillation management: rate and rhythm control.

BACKGROUND: Atrial fibrillation (AF) affects around 6 million Americans. AF management involves pharmacologic therapy and/or interventional procedures to control rate and rhythm, as well as anticoagulation for stroke prevention. Different populations may respond differently to distinct management strategies. This review will describe disparities in rate and rhythm control and their impact on outcomes among women and historically underrepresented racial and/or ethnic groups. METHODS: This is a narrative review exploring the topic of sex and racial and/or ethnic disparities in rate and rhythm management of AF. We describe basic terminology, summarize AF epidemiology, discuss diversity in clinical research, and review landmark clinical trials. RESULTS: Despite having higher rates of traditional AF risk factors, Black and Hispanic adults have lower risk of AF than non-Hispanic White (NHW) patients, although those with AF experience more severe symptoms and report lower quality-of-life scores than NHW patients with AF. NHW patients receive antiarrhythmic drugs, cardioversions, and invasive therapies more frequently than Black and Hispanic patients. Women have lower rates of AF than men, but experience more severe symptoms, heart failure, stroke, and death after AF diagnosis. Women and people from diverse racial and ethnic backgrounds are inadequately represented in AF trials; prevalence findings may be a result of underdetection. CONCLUSION: Race, ethnicity, and gender are social determinants of health that may impact the prevalence, evolution, and management of AF. This impact reflects differences in biology as well as disparities in treatment and representation in clinical trials.

Randomized Clinical Trial to Evaluate an Atrial Fibrillation Stroke Prevention Shared Decision-Making Pathway.

Background Oral anticoagulation reduces stroke and disability in atrial fibrillation (AF) but is underused. We evaluated the effects of a novel patient-clinician shared decision-making (SDM) tool in reducing oral anticoagulation patient's decisional conflict as compared with usual care. Methods and Results We designed and evaluated a new digital decision aid in a multicenter, randomized, comparative effectiveness trial, ENHANCE-AF (Engaging Patients to Help Achieve Increased Patient Choice and Engagement for AF Stroke Prevention). The digital AF shared decision-making toolkit was developed using patient-centered design with clear health communication principles (eg, meaningful images, limited text). Available in English and Spanish, the toolkit included the following: (1) a brief animated video; (2) interactive questions with answers; (3) a quiz to check on understanding; (4) a worksheet to be used by the patient during the encounter; and (5) an online guide for clinicians. The study population included English or Spanish speakers with nonvalvular AF and a CHA2DS2-VASc stroke score ≥1 for men or ≥2 for women. Participants were randomized in a 1:1 ratio to either usual care or the shared decision-making toolkit. The primary end point was the validated 16-item Decision Conflict Scale at 1 month. Secondary outcomes included Decision Conflict Scale at 6 months and the 10-item Decision Regret Scale at 1 and 6 months as well as a weighted average of Mann-Whitney U-statistics for both the Decision Conflict Scale and the Decision Regret Scale. A total of 1001 participants were enrolled and followed at 5 different sites in the United States between December 18, 2019, and August 17, 2022. The mean patient age was 69±10 years (40% women, 16.9% Black, 4.5% Hispanic, 3.6% Asian), and 50% of participants had CHA2DS2-VASc scores ≥3 (men) or ≥4 (women). The primary end point at 1 month showed a clinically meaningful reduction in decisional conflict: a 7-point difference in median scores between the 2 arms (16.4 versus 9.4; Mann-Whitney U-statistics=0.550; P=0.007). For the secondary end point of 1-month Decision Regret Scale, the difference in median scores between arms was 5 points in the direction of less decisional regret (P=0.078). The treatment effects lessened over time: at 6 months the difference in medians was 4.7 points for Decision Conflict Scale (P=0.060) and 0 points for Decision Regret Scale (P=0.35). Conclusions Implementation of a novel shared decision-making toolkit (afibguide.com; afibguide.com/clinician) achieved significantly lower decisional conflict compared with usual care in patients with AF. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT04096781.

Stochastic termination of spiral wave dynamics in cardiac tissue.

Rotating spiral waves are self-organized features in spatially extended excitable media and may play an important role in cardiac arrhythmias including atrial fibrillation (AF). In homogeneous media, spiral wave dynamics are perpetuated through spiral wave breakup, leading to the continuous birth and death of spiral waves, but have a finite probability of termination. In non-homogeneous media, however, heterogeneities can act as anchoring sources that result in sustained spiral wave activity. It is thus unclear how and if AF may terminate following the removal of putative spiral wave sources in patients. Here, we address this question using computer simulations in which a stable spiral wave is trapped by an heterogeneity and is surrounded by spiral wave breakup. We show that, following ablation of spatial heterogeneity to render that region of the medium unexcitable, termination of spiral wave dynamics is stochastic and Poisson-distributed. Furthermore, we show that the dynamics can be accurately described by a master equation using birth and death rates. To validate these predictions in vivo, we mapped spiral wave activity in patients with AF and targeted the locations of spiral wave sources using radiofrequency ablation. Targeted ablation was indeed able to terminate AF, but only after a variable delay of up to several minutes. Furthermore, and consistent with numerical simulations, termination was not accompanied by gradual temporal or spatial organization. Our results suggest that spiral wave sources and tissue heterogeneities play a critical role in the maintenance of AF and that the removal of sources results in spiral wave dynamics with a finite termination time, which could have important clinical implications.

Artificial intelligence applications in cardio-oncology: Leveraging high dimensional cardiovascular data.

Growing evidence suggests a wide spectrum of potential cardiovascular complications following cancer therapies, leading to an urgent need for better risk-stratifying and disease screening in patients undergoing oncological treatment. As many cancer patients undergo frequent surveillance through imaging as well as other diagnostic testing, there is a wealth of information that can be utilized to assess one's risk for cardiovascular complications of cancer therapies. Over the past decade, there have been remarkable advances in applying artificial intelligence (AI) to analyze cardiovascular data obtained from electrocardiograms, echocardiograms, computed tomography, and cardiac magnetic resonance imaging to detect early signs or future risk of cardiovascular diseases. Studies have shown AI-guided cardiovascular image analysis can accurately, reliably and inexpensively identify and quantify cardiovascular risk, leading to better detection of at-risk or disease features, which may open preventive and therapeutic opportunities in cardio-oncology. In this perspective, we discuss the potential for the use of AI in analyzing cardiovascular data to identify cancer patients at risk for cardiovascular complications early in treatment which would allow for rapid intervention to prevent adverse cardiovascular outcomes.