Noseworthy, Peter A

Background This study aimed to compare percutaneous left atrial appendage occlusion (LAAO) with non‐vitamin K antagonist oral anticoagulants among patients with atrial fibrillation. Methods and Results Using a US administrative database, 562 850 patients with atrial fibrillation were identified, among whom 8397 were treated with LAAO and 554 453 were treated with non‐vitamin K antagonist oral anticoagulants between March 13, 2015 and December 31, 2018. Propensity score overlap weighting was used to balance baseline characteristics. The primary outcome was a composite end point of ischemic stroke or systemic embolism, major bleeding, and all‐cause mortality. The mean age was 76.4±7.6 years; 280 097 (49.8%) were female. Mean follow‐up was 1.5±1.0 years. LAAO was associated with no significant difference in the risk of the primary composite end point (hazard ratio [HR], 0.93 [0.84–1.03]), or the secondary outcomes including ischemic stroke/systemic embolism (HR, 1.07 [0.81–1.41]), and intracranial bleeding (HR, 1.08 [0.72–1.61]). LAAO was associated with a higher risk of major bleeding (HR, 1.22 [1.05–1.42], P =0.01) and a lower risk of mortality (HR, 0.73 [0.64–0.84], P <0.001). The lower risk of mortality associated with LAAO was most pronounced in patients with a prior history of intracranial bleeding. Conclusions In comparison to non‐vitamin K antagonist oral anticoagulants, LAAO was associated with no significant difference in the risk of the composite outcome and a lower risk of mortality, which suggests LAAO might be a reasonable option in select patients with atrial fibrillation. The observation of higher bleeding risk associated with LAAO highlights the need to optimize postprocedural antithrombotic regimens as well as systematic efforts to assess and address bleeding predispositions.

AbstractAimsAn artificial intelligence algorithm detecting age from 12-lead electrocardiogram (ECG) has been suggested to reflect ‘physiological age’. An increased physiological age has been associated with a higher risk of cardiac mortality in the non-transplant population. We aimed to investigate the utility of this algorithm in patients who underwent heart transplantation (HTx).Methods and resultsA total of 540 patients were studied. The average ECG ages within 1 year before and after HTx were used to represent pre- and post-HTx ECG ages. Major adverse cardiovascular event (MACE) was defined as any coronary revascularization, heart failure hospitalization, re-transplantation, and mortality. Recipient pre-transplant ECG age (mean 63 ± 11 years) correlated significantly with recipient chronological age (mean 49 ± 14 years, R = 0.63, P &lt; 0.0001), while post-transplant ECG age (mean 54 ± 10 years) correlated with both the donor (mean 32 ± 13 years, R = 0.45, P &lt; 0.0001) and the recipient ages (R = 0.38, P &lt; 0.0001). During a median follow-up of 8.8 years, 307 patients experienced MACE. Patients with an increase in ECG age post-transplant showed an increased risk of MACE [hazard ratio (HR): 1.58, 95% confidence interval (CI): (1.24, 2.01), P = 0.0002], even after adjusting for potential confounders [HR: 1.58, 95% CI: (1.19, 2.10), P = 0.002].ConclusionElectrocardiogram age-derived cardiac ageing after transplantation is associated with a higher risk of MACE. This study suggests that physiological age change of the heart might be an important determinant of MACE risk post-HTx.

Background: Transcatheter aortic valve replacement (TAVR) is now an approved alternative to surgical aortic valve replacement for the treatment of severe aortic stenosis. As the clinical adoption of TAVR expands, it remains important to identify predictors of mortality after TAVR. We aimed to evaluate the impact of sex differences in aortic valve calcium score (AVCS) on long-term mortality following TAVR in a large patient sample. Methods: We included consecutive patients who successfully underwent TAVR for treatment of severe native aortic valve stenosis from June 2010 to May 2021 across all US Mayo Clinic sites with follow-up through July 2021. AVCS values were obtained from preoperative computed tomography of the chest. Additional clinical data were abstracted from medical records. Kaplan-Meier curves and Cox-proportional hazard regression models were employed to evaluate the effect of AVCS on long-term mortality. Results: A total of 2543 patients were evaluated in the final analysis. Forty-one percent were women, median age was 82 years (Q1: 76, Q3: 86), 18.4% received a permanent pacemaker following TAVR, and 88.5% received a balloon expandable valve. We demonstrate an increase in mortality risk with higher AVCS after multivariable adjustment ( P <0.001). When stratified by sex, every 500-unit increase in AVCS was associated with a 7% increase in mortality risk among women (adjusted hazard ratio, 1.07 [95% CI, 1.02–1.12]) but not in men. Conclusions: We demonstrate a notable sex difference in the association between AVCS and long-term mortality in a large TAVR patient sample. This study highlights the potential value of AVCS in preprocedural risk stratification, specifically among women undergoing TAVR. Additional studies are needed to validate this finding.

AbstractIntroductionCardiac sarcoidosis (CS) is a nonischemic cardiomyopathy (NICM) characterized by infiltration of noncaseating granulomas involving the heart with highly variable clinical manifestations that can include conduction abnormalities and systolic heart failure. Cardiac resynchronization therapy (CRT) has shown significant promise in NICM, though little is known about its efficacy in patients with CS.ObjectiveTo determine if CRT improved cardiac remodeling in patients with CS.MethodsWe retrospectively reviewed all patients with a clinical or histological diagnosis of CS who underwent CRT implantation at the Mayo Clinic enterprise from 2000 to 2021. Baseline characteristics, imaging parameters, heart failure hospitalizations and need for advanced therapies, and major adverse cardiac events (MACE) were assessed.ResultsOur cohort was comprised of 55 patients with 61.8% male and a mean age of 58.7 ± 10.9 years. Eighteen (32.7%) patients had definite CS, 21 (38.2%) had probable CS, while 16 (29.1%) had presumed CS, and 26 (47.3%) with extracardiac sarcoidosis. The majority underwent CRT‐D implantation (n = 52, 94.5%) and 3 (5.5%) underwent CRT‐P implantation with 67.3% of implanted devices being upgrades from prior pacemakers or implantable cardioverter defibrillators. At 6 months postimplantation there was no significant improvement in ejection fraction (34.8 ± 10.9% vs. 37.7 ± 14.2%, p = .331) or left ventricular end‐diastolic diameter (58.5 ± 10.2 vs. 57.5 ± 8.1 mm, p = .236), though mild improvement in left ventricular end systolic diameter (49.1 ± 9.9 vs. 45.7± 9.9 mm, p < .0001). Within the first 6 months postimplantation, 5 (9.1%) patients sustained a heart failure hospitalization. At a mean follow‐up of 4.1± 3.7 years, 14 (25.5%) patients experienced a heart failure hospitalization, 11 (20.0%) underwent cardiac transplantation, 1 (1.8%) underwent left ventricular assist device implantation and 7 (12.7%) patients died.ConclusionsOur findings suggest variable response to CRT in patients with CS with no overall improvement in ventricular function within 6 months and a substantial proportion of patients progressing to advanced heart failure therapies.

Abstract Objective To compare the artificial intelligence‐enabled electrocardiogram (AI‐ECG) atrial fibrillation (AF) prediction model output in patients with migraine with aura (MwA) and migraine without aura (MwoA). Background MwA is associated with an approximately twofold risk of ischemic stroke. Longitudinal cohort studies showed that patients with MwA have a higher incidence of developing AF compared to those with MwoA. The Mayo Clinic Cardiology team developed an AI‐ECG algorithm that calculates the probability of concurrent paroxysmal or impending AF in ECGs with normal sinus rhythm (NSR). Methods Adult patients with an MwA or MwoA diagnosis and at least one NSR ECG within the past 20 years at Mayo Clinic were identified. Patients with an ECG‐confirmed diagnosis of AF were excluded. For each patient, the ECG with the highest AF prediction model output was used as the index ECG. Comparisons between MwA and MwoA were conducted in the overall group (including men and women of all ages), women only, and men only in each age range (18 to <35, 35 to <55, 55 to <75, ≥75 years), and adjusted for age, sex, and six common vascular comorbidities that increase risk for AF. Results The final analysis of our cross‐sectional study included 40,002 patients (17,840 with MwA, 22,162 with MwoA). The mean (SD) age at the index ECG was 48.2 (16.0) years for MwA and 45.9 (15.0) years for MwoA ( p < 0.001). The AF prediction model output was significantly higher in the MwA group compared to MwoA (mean [SD] 7.3% [15.0%] vs. 5.6% [12.4%], mean difference [95% CI] 1.7% [1.5%, 2.0%], p < 0.001). After adjusting for vascular comorbidities, the difference between MwA and MwoA remained significant in the overall group (least square means of difference [95% CI] 0.7% [0.4%, 0.9%], p < 0.001), 18 to <35 (0.4% [0.1%, 0.7%], p = 0.022), and 35 to <55 (0.5% [0.2%, 0.8%], p < 0.001), women of all ages (0.6% [0.3%, 0.8%], p < 0.001), men of all ages (1.0% [0.4%, 1.6%], p = 0.002), women 35 to <55 (0.6% [0.3%, 0.9%], p < 0.001), and men 18 to <35 (1.2% [0.3%, 2.1%], p = 0.008). Conclusions Utilizing a novel AI‐ECG algorithm on a large group of patients, we demonstrated that patients with MwA have a significantly higher AF prediction model output, implying a higher probability of concurrent paroxysmal or impending AF, compared to MwoA in both women and men. Our results suggest that MwA is an independent risk factor for AF, especially in patients <55 years old, and that AF‐mediated cardioembolism may play a role in the migraine–stroke association for some patients.

Oral anticoagulants (OACs) are medications commonly used in patients with atrial fibrillation and other cardiovascular conditions. Both warfarin and direct oral anticoagulants are susceptible to drug-drug interactions (DDIs). DDIs are an important cause of adverse drug reactions and exact a large toll on the health care system. DDI for warfarin mainly involve moderate to strong inhibitors/inducers of cytochrome P450 (CYP) 2C9, which is responsible for the elimination of the more potent S-isomer of warfarin. However, inhibitor/inducers of CYP3A4 and CYP1A2 may also cause DDI with warfarin. Recognition of these precipitating agents along with increased frequency of monitoring when these agents are initiated or discontinued will minimize the impact of warfarin DDI. Direct oral anticoagulants are mainly affected by medications strongly affecting the permeability glycoprotein (P-gp), and to a lesser extent, strong CYP3A4 inhibitors/inducers. Dabigatran and edoxaban are affected by P-gp modulation. Strong inducers of CYP3A4 or P-gp should be avoided in all patients taking direct oral anticoagulant unless previously proven to be otherwise safe. Simultaneous strong CYP3A4 and P-gp inhibitors should be avoided in patients taking apixaban and rivaroxaban. Concomitant antiplatelet/anticoagulant use confers additive risk for bleeding, but their combination is unavoidable in many cases. Minimizing duration of concomitant anticoagulant/antiplatelet therapy as indicated by evidence-based clinical guidelines is the best way to reduce the risk of bleeding.

Background EAST‐AFNET 4 (Early Treatment of Atrial Fibrillation for Stroke Prevention Trial) demonstrated clinical benefit of early rhythm‐control therapy (ERC) in patients with new‐onset atrial fibrillation (AF) and concomitant cardiovascular conditions compared with current guideline‐based practice. This study aimed to evaluate the generalizability of EAST‐AFNET 4 in routine practice. Methods and Results Using a US administrative database, we identified 109 739 patients with newly diagnosed AF during the enrollment period of EAST‐AFNET 4. Patients were classified as either receiving ERC, using AF ablation or antiarrhythmic drug therapy, within the first year after AF diagnosis (n=27 106) or not receiving ERC (control group, n=82 633). After propensity score overlap weighting, Cox proportional hazards regression was used to compare groups for the primary composite outcome of all‐cause mortality, stroke, or hospitalization with the diagnoses heart failure or myocardial infarction. Most patients (79 948 of 109 739; 72.9%) met the inclusion criteria for EAST‐AFNET 4. ERC was associated with a reduced risk for the primary composite outcome (hazard ratio [HR], 0.85; 95% CI, 0.75–0.97 [ P =0.02]) with largely consistent results between eligible (HR, 0.89; 95% CI, 0.76–1.04 [ P =0.14]) or ineligible (HR, 0.77; 95% CI, 0.60–0.98 [ P =0.04]) patients for EAST‐AFNET 4 trial inclusion. ERC was associated with lower risk of stroke in the overall cohort and in trial‐eligible patients. Conclusions This analysis replicates the clinical benefit of ERC seen in EAST‐AFNET 4. The results support adoption of ERC as part of the management of recently diagnosed AF in the United States.

Abstract Aims Electrocardiogram (ECG)-enabled stethoscope (ECG-Scope) acquires a single-lead ECGs during cardiac auscultation and may facilitate real-time screening for pathologies not routinely identified by cardiac auscultation alone. We previously demonstrated an artificial intelligence (AI) algorithm can identify left ventricular dysfunction (LVSD) [defined as ejection fraction (EF) ≤ 40%] with an area under the curve (AUC) of 0.91 using a 12-lead ECG. Methods and results One hundred patients referred for clinically indicated echocardiography were prospectively recruited. ECG-Scope recordings with the patient supine and sitting were obtained in multiple electrode locations at the time of the echocardiogram. The AI algorithm for the detection of LVSD was retrained using single leads from ECG-12 and validated against ECG-Scope to determine accuracy for low EF detection (≤35%, &lt;40%, or &lt;50%). We evaluated the algorithm with respect to body position and lead location. Amongst 100 patients (aged 61.3 ± 13.8; 61% male, BMI: 30.0 ± 5.4), eight had EF≤40%, and six had EF 40–50%. The best single recording position was V2 with the patient supine [AUC: 0.88 (CI: 0.80–0.97) for EF≤35%, 0.85 (CI: 0.75–0.95) for EF≤40%, and 0.81 (CI: 0.71–0.90) for EF &lt; 50%]. When using an AI model to select the recording automatically, AUC was 0.91 (CI: 0.84–0.97) for EF≤35%, 0.89 (CI: 0.83–0.96) for EF≤40%, and 0.84 (CI: 0.73–0.94) for EF &lt; 50%. Conclusion An AI algorithm applied to an ECG-enabled stethoscope recording in standard auscultation positions reliably detected the presence of a low EF in this prospective study of patients referred for echocardiography. The ability to screen patients with a possible low EF during routine physical examination may facilitate rapid detection of LVSD.

Abstract Aims Some artificial intelligence models applied in medical practice require ongoing retraining, introduce unintended racial bias, or have variable performance among different subgroups of patients. We assessed the real-world performance of the artificial intelligence-enhanced electrocardiogram to detect left ventricular systolic dysfunction with respect to multiple patient and electrocardiogram variables to determine the algorithm’s long-term efficacy and potential bias in the absence of retraining. Methods and results Electrocardiograms acquired in 2019 at Mayo Clinic in Minnesota, Arizona, and Florida with an echocardiogram performed within 14 days were analyzed (n = 44 986 unique patients). The area under the curve (AUC) was calculated to evaluate performance of the algorithm among age groups, racial and ethnic groups, patient encounter location, electrocardiogram features, and over time. The artificial intelligence-enhanced electrocardiogram to detect left ventricular systolic dysfunction had an AUC of 0.903 for the total cohort. Time series analysis of the model validated its temporal stability. Areas under the curve were similar for all racial and ethnic groups (0.90–0.92) with minimal performance difference between sexes. Patients with a ‘normal sinus rhythm’ electrocardiogram (n = 37 047) exhibited an AUC of 0.91. All other electrocardiogram features had areas under the curve between 0.79 and 0.91, with the lowest performance occurring in the left bundle branch block group (0.79). Conclusion The artificial intelligence-enhanced electrocardiogram to detect left ventricular systolic dysfunction is stable over time in the absence of retraining and robust with respect to multiple variables including time, patient race, and electrocardiogram features.

Antiarrhythmic drugs (AAD) play an important role in the management of arrhythmias. Drug interactions involving AAD are common in clinical practice. As AADs have a narrow therapeutic window, both pharmacokinetic as well as pharmacodynamic interactions involving AAD can result in serious adverse drug reactions ranging from arrhythmia recurrence, failure of device-based therapy, and heart failure, to death. Pharmacokinetic drug interactions frequently involve the inhibition of key metabolic pathways, resulting in accumulation of a substrate drug. Additionally, over the past 2 decades, the P-gp (permeability glycoprotein) has been increasingly cited as a significant source of drug interactions. Pharmacodynamic drug interactions involving AADs commonly involve additive QT prolongation. Amiodarone, quinidine, and dofetilide are AADs with numerous and clinically significant drug interactions. Recent studies have also demonstrated increased morbidity and mortality with the use of digoxin and other AAD which interact with P-gp. QT prolongation is an important pharmacodynamic interaction involving mainly Vaughan-Williams class III AAD as many commonly used drug classes, such as macrolide antibiotics, fluoroquinolone antibiotics, antipsychotics, and antiemetics prolong the QT interval. Whenever possible, serious drug-drug interactions involving AAD should be avoided. If unavoidable, patients will require closer monitoring and the concomitant use of interacting agents should be minimized. Increasing awareness of drug interactions among clinicians will significantly improve patient safety for patients with arrhythmias.