Dominik K. Linz

Background: Multimorbidity is common among patients with atrial fibrillation (AF) and is associated with worse outcomes. We aimed to investigate the association between multimorbidity, AF progression and AF symptom severity in patients with paroxysmal AF. Results: The RACE V (Reappraisal of AF: Interaction Between Hypercoagulability, Electrical Remodeling, and Vascular Destabilization in the Progression of AF) study included patients with paroxysmal AF and continuous rhythm monitoring. Multimorbidity was defined as ≥2 comorbidities (heart failure, hypertension, diabetes, coronary heart disease, kidney dysfunction, moderate or severe mitral valve regurgitation, or obesity). AF symptom severity was assessed via the University of Toronto AF Severity Scale questionnaire. The associations between multimorbidity, AF progression, and AF symptom severity were determined using logistic regression analyses. Median age was 65 (58-71) years and 179 of 417 patients (43%) were women, with a median of 1 (1-2) comorbidities. Median follow-up was 2.2 (1.6-2.8) years. Multimorbidity was associated with AF progression (odds ratio [OR], 2.02 [95% CI, 1.10-3.72], P=0.024) and increased AF symptom severity (OR, 2.67 [95% CI, 1.79-3.99], P<0.001). There was a positive dose-response relation between the number of comorbidities and AF progression (OR, 1.40 [95% CI, 1.09-1.79], P=0.008), as well as AF symptom severity (OR, 1.64 [95% CI, 1.35-1.99], P<0.001). These results remained significant after adjusting for age. Conclusions: In patients with paroxysmal AF, multimorbidity was associated with AF progression and AF symptom severity. The risk of AF progression and AF symptom severity increased with every additional comorbidity. Background: URL: clinicaltrials.gov. Unique Identifier: NCT02726698.

Advances have been made in identifying biomarkers for atrial fibrillation (AF) outcomes. The link between clinical determinants, especially AF burden, and blood biomarkers remains underexplored. We conducted a cross-sectional analysis of AF patients scheduled for catheter ablation in the ISOLATION study (July 2020-May 2022, NCT04342312). Patient characteristics and blood samples were collected before ablation. AF burden was assessed using hand-held electrocardiograms (ECGs) over 4 weeks. Blood samples were analyzed for biomarkers, including bone morphogenetic protein 10 (BMP10), angiopoietin-2 (Ang-2), fibroblast growth factor 23 (FGF23), and others. We trained elastic net regression models to identify the most important clinical determinants out of 64 available clinical features. We analyzed blood samples from 508 patients with a mean age of 63 ±9 years; 31.1% were female. Of these, 70% had paroxysmal AF and 30% persistent AF. Heart failure was present in 15% of patients. In 140 patients (28%), AF was observed during blood draw. AF burden before ablation was available in 389 patients. After multivariable analysis, the following clinical determinants were independently associated with biomarker levels: AF burden, AF during blood draw, age, heart failure, decreased kidney function, and female sex. Most notably, AF burden and AF rhythm at the time of sampling were strongly associated with various biomarker levels. Female sex was positively associated with BMP10 and FGF23, but negatively associated with high sensitive Troponin-T (hs-TNT). AF burden is a strong determinant of many biomarkers, underpinning their relevance as covariates in biomarker studies. Pro-fibrotic biomarkers are increased in female patients, whereas male patients more often show elevated biomarkers of myocardial injury.

Heart rate variability (HRV) has been reported to predict overall mortality and the risk of cardiovascular disease events in patients, including those with heart failure. However, inconsistent methods of recording and analyzing HRV parameters, along with a lack of randomized data substantiating its clinical efficacy and potential to guide treatment decisions for improved patient outcomes, have limited its use in clinical settings. With the advancements in technologies such as artificial intelligence and machine learning, and emergence of ablation procedures that can alter autonomic function, this article re-explores HRV assessment methods, their potential for clinical application, the issues encountered in using them in clinical research, and potential approaches to studying HRV in the future (Graphical Abstract).

Background: Omny-IRE (A Study For Treatment of Paroxysmal Atrial Fibrillation [PAF] With the OMNYPULSE Catheter and the TRUPULSE Generator; NCT05971693) evaluated safety and effectiveness of a novel large-tip focal, multielectrode, contact force-sensing, pulsed field ablation catheter with electroanatomic mapping integration. Objective: This study sought to assess 3-month safety and effectiveness of the platform for treating symptomatic paroxysmal atrial fibrillation. Methods: Pulmonary vein isolation (PVI) was performed using the OMNYPULSE Platform. Primary effectiveness was adenosine/isoproterenol-proof entrance block. Primary safety was occurrence of primary adverse events. Prespecified patient subsets underwent systematic brain imaging, esophageal endoscopy, cardiac computed tomography/magnetic resonance angiogram, and mandatory 3-month remapping for PVI durability assessment. Results: Of 188 patients enrolled, 136 were included in the per-protocol analysis. Primary effectiveness was 100% (136 of 136). Median (Q1-Q3) procedure, left atrial dwell, total ablation, and total fluoroscopy times were 105.5 (91.0-124.0), 70.0 (56.0-81.5), 46.9 (37.1-58.8), and 5.0 (3.1-9.8) minutes, respectively. The primary adverse event rate was 3.0% (4 of 135 patients with 3-month follow-up; 3 major vascular access complications, 1 pericarditis). Brain imaging (n = 30) revealed 1 patient (3.3%) with an asymptomatic silent cerebral event at discharge, which resolved at 1 month without neurological change. No esophageal injury was observed. Computed tomography/magnetic resonance angiogram imaging (n = 24) showed no incidences of pulmonary vein narrowing >70%. During remapping, PVI was durable in 84.5% (98 of 116) of veins and 62.1% (18 of 29) of patients. With an optimized workflow, PVI durability improved to 89.3% (75 of 84) and 71.4% (15 of 21) of veins and patients, respectively. Conclusions: The force-sensing, large-focal pulsed field ablation catheter with 3-dimensional electroanatomic mapping integration showed 100% acute success with a promising safety profile for treating paroxysmal atrial fibrillation. Prespecified remapping showed good PVI durability. (A Study For Treatment of Paroxysmal Atrial Fibrillation [PAF] With the OMNYPULSE Catheter and the TRUPULSE Generator; NCT05971693).

Objective: Artificial intelligence (AI) has the potential to transform cardiac electrophysiology (EP), particularly in arrhythmia detection, procedural optimization, and patient outcome prediction. However, a standardized approach to reporting and understanding AI-related research in EP is lacking. This scientific statement aims to develop and apply a checklist for AI-related research reporting in EP to enhance transparency, reproducibility, and understandability in the field. Results: An AI checklist specific to EP was developed with expert input from the writing group and voted on using a modified Delphi process, leading to the development of a 29-item checklist. The checklist was subsequently applied to assess reporting practices to identify areas where improvements could be made and provide an overview of the state of the art in AI-related EP research in three domains from May 2021 until May 2024: atrial fibrillation (AF) management, sudden cardiac death (SCD), and EP lab applications. The EHRA AI checklist was applied to 31 studies in AF management, 18 studies in SCD, and 6 studies in EP lab applications. Results differed between the different domains, but in no domain reporting of a specific item exceeded 55% of included papers. Key areas such as trial registration, participant details, data handling, and training performance were underreported (<20%). The checklist application highlighted areas where reporting practices could be improved to promote clearer, more comprehensive AI research in EP. Conclusions: The EHRA AI checklist provides a structured framework for reporting AI research in EP. Its use can improve understanding but also enhance the reproducibility and transparency of AI studies, fostering more robust and reliable integration of AI into clinical EP practice.