Noseworthy, Peter A.

Abstract Background Vagally mediated syncope and functional bradyarrhythmias can cause recurrent symptoms, injury and a need for permanent pacing, yet standard therapies do not address the underlying autonomic reflex. Cardioneuroablation (CNA) targets this mechanism, but adoption has been limited by uncertainty about patient selection, procedural endpoints and durable outcomes. Methods We studied 95 consecutive patients with vagally mediated syncope (n=78) or functional bradyarrhythmia (n=17) who underwent CNA at a single center by a single lead operator under a uniform, prospectively maintained protocol. Extracardiac vagal stimulation (ECVS) was performed before and after ablation to confirm vagally mediated sinus or atrioventricular (AV) nodal responses and to verify their attenuation. Outcomes through 1 year included clinical recurrence, quality of life, pacing burden and pacemaker extraction. Results Before ablation, ECVS provoked sinus pauses in 95.8% of patients and AV block in 91.6%; after ablation, sinus pauses were abolished in every patient and residual AV block was present in 4.2%. One-year freedom from recurrent syncope and from bradyarrhythmia-related events was 93.8% and 94.1%. Syncope burden fell from a median of 2.7 episodes per year to none, and disease-specific quality of life improved substantially (P<0.001). Among patients with pre-existing devices, atrial pacing burden fell from a median of 22% to 0%. There were no strokes, deaths or myocardial infarctions. Conclusions In this single-center cohort, physiology-guided CNA was associated with durable symptom control, improved quality of life and reduced pacing across vagally mediated bradyarrhythmia syndromes. Multicenter controlled studies are needed to confirm these findings.

ABSTRACT Background Superior vena cava isolation (SVCI) is performed as an adjunct to pulmonary vein isolation (PVI) for atrial fibrillation, yet whether an empiric or trigger‐guided strategy is preferable remains uncertain. Prior meta‐analyses have been largely restricted to index procedures in paroxysmal AF and have not explicitly stratified by ablation strategy. We sought to address these gaps through a systematic review of available randomized evidence. Methods Major databases were searched through November 2025 for RCTs comparing PVI+SVCI versus PVI alone. Trials were stratified by strategy: empiric SVCI (performed irrespective of SVC ectopy) or trigger‐guided SVCI (performed only upon demonstrated SVC triggers). The primary outcome was atrial tachyarrhythmia recurrence (≥30 s) beyond the blanking period. Random‐effects models, pre‐specified subgroup analyses, meta‐regression, and GRADE certainty assessment were performed. Results Eight RCTs ( n = 1231) were included; most enrolled paroxysmal AF, while two incorporated non‐paroxysmal AF subtypes and one evaluated repeat ablation procedures. Overall, SVCI+PVI significantly reduced recurrence versus PVI alone (97/604 vs. 135/627; RR 0.77, 95% CI 0.61–0.97; p = 0.03; I 2 = 0%). On stratified analysis, empiric SVCI demonstrated a significant recurrence reduction (RR 0.70, 95% CI 0.53–0.92; p = 0.01; I 2 = 0%), while trigger‐guided SVCI did not reach statistical significance (RR 0.80, 95% CI 0.37–1.74; p = 0.57; I 2 = 52%). In paroxysmal AF ( n = 1038), adjunctive SVCI significantly reduced recurrence compared with PVI alone (RR 0.68, 95% CI 0.52–0.90; p = 0.01; I 2 = 0%). Fluoroscopy time was longer with SVCI; total procedure duration was comparable. Major complications were infrequent but numerically higher with SVCI+PVI (15/604 vs. 7/627; RR 1.96, 95% CI 0.87–4.38; p = 0.10). Meta‐regression identified no significant effect modifiers. GRADE certainty was high for overall and empiric analyses, and moderate for trigger‐guided SVCI. Conclusions Empiric SVCI significantly reduced AT/AF recurrence as an adjunct to PVI, while trigger‐guided SVCI did not demonstrate a significant benefit. These findings suggest SVC isolation confers benefit independent of prior trigger documentation, supporting the role of empiric SVCI as an adjunct to AF ablation.

BACKGROUND: Radiofrequency (RF) ablation of premature ventricular complexes (PVCs) originating from the left ventricular summit may be unsuccessful, highlighting the need for alternative approaches. We aimed to assess the efficacy and safety of a novel dual-modality focal catheter in treating left ventricular summit PVCs in patients who had failed previous RF ablation. METHODS: Patients were prospectively enrolled and underwent a redo ablation procedure using an irrigated, contact-force sensing, dual-modality focal catheter (TactiFlex Duo, Abbott) under compassionate use indication. The ablation strategy, including the choice of energy modality (monopolar pulsed field [PF], RF, or both), was left to the operator’s discretion. Safety assessments included serial biomarkers, renal function, and CMR within 36 hours. Efficacy was defined as ≥80% reduction in PVC burden with symptom resolution at 3-month follow-up. RESULTS: Six consecutive patients (4 men; mean age, 53.8±15 years) with symptomatic left ventricular summit PVCs and a prior failed RF ablation (range, 1–2) were prospectively enrolled. All PVCs had an inferior axis, with a left bundle V1 morphology observed in 4 of 6 cases (mean burden, 30.1±5.3%). Acute suppression of PVCs was achieved in all cases. Acute suppression with a single PF application in the great cardiac vein-anterior interventricular vein occurred in 2 patients, while 3 patients required additional RF, PF, or combined RF+PF applications at adjacent sites. One patient with an inaccessible great cardiac vein-anterior interventricular vein was successfully treated in the left ventricular outflow tract with RF+PF application. Transient, reversible left anterior descending vasospasm was observed during PF delivery in 2 of 5 cases that underwent PF application adjacent to a coronary artery, despite pretreatment with intracoronary nitroglycerin without ECG or hemodynamic sequelae. All patients had durable suppression of PVCs at follow-up. CONCLUSIONS: In its first human application, dual-modality ablation was feasible and effective for refractory left ventricular summit PVCs. Transient coronary vasospasm occurred, though it was reversible and without acute clinical consequences.

Abstract Aims Early detection of structural heart disease (SHD) improves patient outcomes. However, population-based screening is not recommended due to the lack of accurate and cost-effective tools. We evaluated the costs of artificial intelligence–enabled electrocardiogram (AI-ECG) alone vs. AI-ECG followed by handheld cardiac ultrasound (HCU) for SHD screening. Methods and results We performed a model-based cost analysis using data from 286 adult patients who underwent ECG and same-day HCU performed by a novice operator. Transthoracic echocardiogram (TTE) was the reference standard. We compared two screening strategies: (i) AI-ECG alone and (ii) a stepwise approach (AI-ECG followed by HCU). We assessed costs per diagnosis of aortic stenosis (AS), increased left ventricular wall thickness (ILVWT), and left ventricular systolic dysfunction (LVSD). Sensitivity analyses were conducted for varying disease prevalence. The stepwise approach decreased the cost per diagnosis of AS from $6386 (AI-ECG alone) to $2746 (57.0% savings), ILVWT from $4448 to $2895 (34.9% savings), and LVSD from $1469 to $1296 (11.8% savings). Overall, the cost per diagnosis for all SHDs combined decreased from $1940 to $1570 (19.1% savings). Sensitivity analysis demonstrated that cost savings were inversely proportional to disease prevalence. Nevertheless, stepwise screening remained cost-saving compared with AI-ECG alone until prevalence exceeded ∼55.9% for AS, 28.9% for ILVWT, 20.7% for LVSD, and 40.8% for all SHDs combined. Conclusion A stepwise screening strategy incorporating HCU after a positive AI-ECG reduces the immediate costs of SHD detection by minimizing unnecessary TTEs. This approach may enhance the feasibility of population-based SHD screening, particularly in lower-prevalence settings.

ABSTRACT Background Acute heart failure (AHF) exhibits marked heterogeneity in diastolic hemodynamics, yet comprehensive echocardiographic assessment of diastolic function (DF) and filling pressure (FP) is often infeasible. We evaluated whether artificial intelligence–enabled electrocardiography (AI-ECG) could provide scalable DF grading and FP estimation in hospitalized AHF patients. Methods We retrospectively studied adults hospitalized for AHF across Mayo Clinic sites (2013–2023) who received ≥1 dose of intravenous loop diuretic and had paired 12-lead ECG and TTE. The previously validated AI-ECG DF model was applied without retraining to generate four DF grades and a continuous FP probability. Clinical outcomes were all-cause mortality and heart failure rehospitalization. Associations with clinical severity markers and echocardiographic indices were examined. Kaplan–Meier survival analysis and adjusted multivariable Cox proportional hazards models were performed. Exploratory analyses examine the kinetics of change in FP probability and impact on mortality. Results Among 11,513 patients (median age 75 years, 39% female), AI-ECG DF grading was feasible in 100%, whereas echocardiographic DF was indeterminate in 44% of clinically eligible patients. In 2,582 patients with determinate echocardiographic DF, AI-ECG FP probability discriminated TTE Grade 2–3 dysfunction with AUC 0.85 (95% CI 0.83 – 0.86). Higher AI-ECG DF grades were associated with higher comorbidity burden, worse NYHA class, elevated NT-proBNP, higher MAGGIC scores, elevated PCWP, and more advanced structural remodeling. After multivariable adjustment, AI-ECG DF remained independently associated with mortality (hazard ratio [HR] 1.25, 95% CI 1.16–1.35 for Grade 2; HR 1.44, 95% CI 1.33–1.56 for Grade 3 versus Normal/Grade 1). Combining AI-ECG DF with MAGGIC scores yielded ordered risk gradients, with highest mortality in patients with both high MAGGIC and Grade 2–3 DF. Among patients with serial ECGs, improvement in FP probability was independently associated with lower mortality (HR 0.85, 95% CI 0.79–0.91), whereas worsening did not show a consistent adverse gradient beyond baseline DF. Conclusions In a large, geographically diverse AHF cohort, AI-ECG DF grading was universally feasible, correlated with established hemodynamic severity markers, and provided independent prognostic information beyond established risk factors, supporting its role as a pragmatic, scalable diastolic biomarker in AHF. CLINICAL PERSPECTIVE What Is New? In 11,513 hospitalized acute heart failure (HF) patients, artificial intelligence-enabled electrocardiography provided diastolic function grading in 100% of patients from a single 12-lead ECG without requiring additional clinical variables, compared with 56% feasibility for guideline-based echocardiography grading. AI-ECG diastolic function grades correlated with established marker of severity (NYHA functional class, NT-proBNP, MAGGIC risk scores, and pulmonary capillary wedge pressure) and remained independently associated with both mortality and HF rehospitalization after multivariable adjustment. Serial AI-ECG measurements identified post-discharge filling pressure trajectories, with improvement independently associated with 15% lower mortality, a first demonstration that longitudinal ECG assessment can track post-discharge hemodynamic recovery. What Are the Clinical Implications? AI-ECG transforms the universally obtained 12-lead ECG into an actionable hemodynamic biomarker that addresses the critical gap when echocardiographic diastolic function assessment is indeterminate or unavailable in acute HF patients. Despite markedly different hemodynamic severity and long-term outcomes across AI-ECG diastolic function grades, hospitalization length of stay did not differ, suggesting advanced diastolic dysfunction represents occult risk not easily recognized during routine acute care and highlighting the need for improved post-discharge risk stratification. The continuous filling pressure probability metric enables longitudinal monitoring of post-discharge hemodynamic status using serial routine ECGs, potentially identifying patients requiring intensified follow-up or specialist referral.

Background Multimorbidity is common in patients with atrial fibrillation (AF); however, the impact of the number and type of comorbid conditions on outcomes remains uncertain. Methods This cohort study included patients with new‐onset AF from a Midwest region between 2013 and 2017. Eighteen chronic conditions at the time of AF were classified into groups: cardiometabolic, other somatic, and mental health. Cox regression determined associations between the number of each condition type with death, ischemic stroke/transient ischemic attack, and congestive heart failure, stratified by age. Results Among 16 509 patients with AF (mean age, 74 years; 43% women), the mean number of cardiometabolic, other somatic, and mental health conditions was 2.7, 1.4, and 0.5, respectively. The number and type of conditions had a varying impact on outcomes and differed by age. A higher number of cardiometabolic conditions were associated with increased risk of death within 90 days only in people aged ≥85 years (≥4 conditions versus 0: hazard ratio [HR], 1.74 [95% CI, 1.05–2.87]), whereas for death after 90 days, associations were strongest in the youngest age group (<65 years: HR, 1.83 [95% CI, 1.35–2.46]; 65–74 years: HR, 1.34 [95% CI, 1.04–1.73]; 75–84 years: HR, 1.32 [95% CI, 1.09–1.60]; ≥85 years: HR, 1.14 [95% CI, 0.96–1.35]). Associations with outcomes were generally strongest in the youngest age group and attenuated with older age for higher number of other somatic conditions, whereas the pattern was less consistent for mental health conditions. Conclusions Along with cardiometabolic‐related conditions, other somatic and mental health conditions are important predictors of outcomes in AF, with effects differing by age, and should be considered when caring for these patients.

Abstract Objective To investigate the ability of artificial intelligence‐enabled electrocardiogram (AI‐ECG) atrial fibrillation (AF) prediction model output and delta age, defined as AI‐estimated age minus chronological age, to predict adverse vascular outcomes in patients with migraine. Background Migraine, especially migraine with aura, is associated with an increased risk of cardiovascular events. Using normal sinus rhythm ECG, our institution has developed AI‐ECG models that predict the probability of AF and estimate patients' age, and from that, delta age (estimated age minus actual age) has been shown to reflect cardiovascular health and physiologic aging. Methods We conducted a retrospective cohort study that included adult patients with migraine who had at least one digital, standard 12‐lead ECG from 2000 to 2020 at any Mayo Clinic site. The first normal sinus rhythm ECG was defined as the index ECG, and those with adverse vascular events or AF prior to, or within 3 days after, index ECG were excluded. AI‐ECG AF prediction model output and delta age were obtained from the index ECG. The primary outcome was a composite endpoint of adverse vascular events that included acute myocardial infarction, acute ischemic stroke, venous thromboembolism, and death. New‐onset AF was also recorded as a secondary outcome. Results A total of 29,928 patients were included. The mean age was 44.3 ± 14.5 years, and the median follow‐up time was 54 months. During the study period, 4662 (15.6%) patients had adverse vascular events, and 1384 (4.6%) patients developed new‐onset AF. A higher AF prediction model output was associated with a higher risk of adverse vascular outcomes (adjusted hazard ratio [aHR]:1.15 [95% confidence interval: 1.12 to 1.18] for every 10% increase in prediction model output), as was a larger delta age (aHR:1.16 [1.12 to 1.21] for every 10‐year increase in delta age). Moreover, AI‐ECG AF prediction model outputs of ≥1% (aHR: 1.45 [1.36 to 1.54]) and delta age ≥−1 years (aHR: 1.09 [1.02 to 1.16]) were found to be the optimal cutoffs to dichotomize patients into high‐risk and low‐risk groups for adverse vascular events. Higher AI‐ECG AF prediction model output at baseline also predicted a higher likelihood of having new‐onset AF during the study period (aHR: 1.31 [1.26 to 1.37]), and the development of new‐onset AF was associated with a higher risk of adverse vascular events (aHR: 2.43 [2.17 to 2.73]). Conclusions For patients with migraine, a higher AI‐ECG AF prediction model output and delta age are predictive of adverse vascular outcomes. A higher AF prediction model output is also predictive of future AF development. Furthermore, we identified the cutoffs for both AI‐ECG models that can distinguish patients between high‐ and low‐risk groups. Our results showed that AI‐ECG models could inform clinical practice and identify patients with migraine at risk for future adverse vascular events.