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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.

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.

Abstract Aims Artificial intelligence (AI) enabled electrocardiography (ECG) can detect latent atrial fibrillation (AF) in patients with sinus rhythm (SR). However, the change of AI-ECG probability before and after the first AF episode is not well characterized. We sought to characterize the temporal trend of AI-ECG AF probability around the first episode of AF. Methods and results We retrospectively studied adults who had at least one ECG in SR prior to an ECG that documented AF. An AI network calculated the AF probability from ECGs during SR (positive defined &gt;8.7%, based on optimal sensitivity and specificity). The AI-ECG probability was reported prior to and after the first episode of AF and stratified by age and CHA2DS2-VASc score. Mixed effect models were used to assess the rate of change between time points. A total of 59 212 patients with 544 330 ECGs prior to AF and 413 486 ECGs after AF were included. The mean time between the first positive AI-ECG and first AF was 5.4 ± 5.7 years. The mean AI-ECG probability was 19.8% 2–5 years prior to AF, 23.6% 1–2 years prior to AF, 34.0% 0–3 months prior to AF, 40.9% 0–3 months after AF, 35.2% 1–2 years after AF, and 42.2% 2–5 years after AF (P &lt; 0.001). The rate of increase prior to AF was higher for age &gt;50 years CHA2DS2-VASc score ≥4. Conclusion The AI-ECG probability progressively increases with time prior to the first AF episode, transiently decreases 1–2 years following AF and continues to increase thereafter.

Introduction: OAC are recommended for AF patients with CHA 2 DS 2 -VASc ≥2 in men or ≥3 in women (high-risk group), may be considered in patients with CHA 2 DS 2 -VASc 1 in men or 2 in women (medium-risk group), and are reasonable to omit in patients with CHA 2 DS 2 -VASc 0 in men or 1 in women (low-risk group). Furthermore, NOACs are recommended over warfarin in most AF patients. This study aimed to investigate AF stroke prevention treatment patterns in patients managed in routine practice, in particular, the under-use of OAC in the high-risk group and over-use in the low-risk group. Methods: Adult patients with AF were identified using de-identified claims for commercial and Medicare Advantage enrollees in the OptumLabs Data Warehouse. Prescription fills were assessed within 90 days of a patient’s AF encounter between 1/1/2016-12/31/2020. Patients with other indications for OAC, such as VTE, recent joint replacement, prior mechanical or bioprosthetic valve replacement, rheumatic mitral stenosis, and patients who underwent LAAO were excluded. Results: Among the 339,197 AF patients, 4.4%, 8.0%, and 87.6% were in the low-, medium-, and high-risk groups, respectively. The mean age was 51.2 ±10.5 yr, 59.9 ±9.4 yr, and 75.1 ±8.8 yr. In the low-risk group, 29.6% were treated with OAC, whereas in the high-risk group, 40.3% were not treated with any antithrombotic therapy and 7.5% used thienopyridine only. Although 71% of patients on OAC were on a NOAC, the proportion of patients on NOAC decreased from 91% in the low-risk group to 69% in the high-risk group. Conclusions: There are potentially both under-use of guideline-recommended stroke prevention therapies in the high-risk patients and over-use in the low-risk group. Because patients at a high risk of stroke are also at a high risk of major and intracranial bleeding who might benefit from NOACs, the decreased use of NOAC in high-risk patients might highlight the need for future quality improvement efforts.

Introduction: Transthyretin cardiac amyloidosis (TTR-CA) can cause heart failure with preserved EF (HFpEF). We previously reported a clinical/echo-based score (TCAS) and defined a cut-off that predicted risk of TTR-CA in HFpEF. Herein, we assess the comparative performance of an artificial-intelligence ECG-based screening tool (AI-ECG). Hypothesis: AI-ECG successfully identifies TTR-CA in HFpEF cohorts across a range of TTR-CA prevalence Methods: Referral HFpEF cohort was constructed from a referral population of HFpEF patients (EF ≥ 40%) undergoing 99m Tc-pyrophosphate scintigraphy (PYP) at Mayo Clinic (2013-2020, n=484, 38% with TTR-CA). Community HFpEF cohort was prospectively screened with PYP (n=275; age ≥ 60, LV wall thickness (WT) ≥ 12 mm, 4% with TTR-CA). TCAS was built using a multivariable logistic regression model and converted to a simple point score comprised of age (60-69, 70-79, ≥ 80: +2, +3, +4 points respectively), male sex (+2), relative WT > 0.57 (+2), posterior WT ≥ 12 mm (+1), EF < 60% (+1), and hypertension diagnosis (-1). TCAS was validated in the community cohort. A deep neural network trained to predict cardiac amyloidosis from a single 12-lead ECG, was applied to both referral and community cohorts. Patients were excluded if ECG was not available ≤ 12 months before PYP or if used in development of the AI-ECG. Standard ROC methods were applied. Youden’s index was used to define optimal cut-off. Results: Predictive characteristics of AI-ECG were strong and compared favorably with TCAS across a range of true TTR-CA prevalence (Table). Conclusions: Using a single ECG, AI-ECG effectively predicts TTR-CA with an excellent negative predictive value to rule out TTR-CA and a positive predictive value sufficient to warrant specific TTR-CA testing. This was particularly evident in the community. AI-ECG holds promise for simple, cost-effective global screening of at-risk populations, especially those with limited access to advanced cardiac imaging.

AbstractIntroductionThe extravascular implantable cardioverter‐defibrillator (EV ICD) system with substernal lead placement is a novel nontransvenous alternative to current commercially available ICD systems. The EV ICD provides defibrillation and pacing therapies without the potential long‐term complications of endovascular lead placement but requires a new procedure for implantation with a safety profile under evaluation.MethodsThis paper summarizes the development of the EV ICD, including the preclinical and clinical evaluations that have contributed to the system and procedural refinements to date.ResultsExtensive preclinical research evaluations and four human clinical studies with >140 combined acute and chronic implants have enabled the development and refinement of the EV ICD system, currently in worldwide pivotal study.ConclusionThe EV ICD may represent a clinically valuable solution in protecting patients from sudden cardiac death while avoiding the long‐term consequences of transvenous hardware. The EV ICD offers advantages over transvenous and subcutaneous systems by avoiding placement in the heart and vasculature; relative to subcutaneous systems, EV ICD requires less energy for defibrillation, enabling a smaller device, and provides pacing features such as antitachycardia and asystole pacing in a single system.

Abstract Digital technology is now an integral part of medicine. Tools for detecting, screening, diagnosis, and monitoring health-related parameters have improved patient care and enabled individuals to identify issues leading to better management of their own health. Wearable technologies have integrated sensors and can measure physical activity, heart rate and rhythm, and glucose and electrolytes. For individuals at risk, wearables or other devices may be useful for early detection of atrial fibrillation or sub-clinical states of cardiovascular disease, disease management of cardiovascular diseases such as hypertension and heart failure, and lifestyle modification. Health data are available from a multitude of sources, namely clinical, laboratory and imaging data, genetic profiles, wearables, implantable devices, patient-generated measurements, and social and environmental data. Artificial intelligence is needed to efficiently extract value from this constantly increasing volume and variety of data and to help in its interpretation. Indeed, it is not the acquisition of digital information, but rather the smart handling and analysis that is challenging. There are multiple stakeholder groups involved in the development and effective implementation of digital tools. While the needs of these groups may vary, they also have many commonalities, including the following: a desire for data privacy and security; the need for understandable, trustworthy, and transparent systems; standardized processes for regulatory and reimbursement assessments; and better ways of rapidly assessing value.