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BACKGROUND: Differentiating wide complex tachycardias (WCTs) into ventricular tachycardia (VT) and supraventricular wide tachycardia via 12-lead ECG interpretation is a crucial but difficult task. Automated algorithms show promise as alternatives to manual ECG interpretation, but direct comparison of their diagnostic performance has not been undertaken. METHODS: Two electrophysiologists applied 3 manual WCT differentiation approaches (ie, Brugada, Vereckei aVR, and VT score). Simultaneously, computerized data from paired WCT and baseline ECGs were processed by 5 automated WCT differentiation algorithms (WCT Formula, WCT Formula II, VT Prediction Model, Solo Model, and Paired Model). The diagnostic performance of automated algorithms was compared with manual ECG interpretation approaches. RESULTS: A total of 212 WCTs (111 VT and 101 supraventricular wide tachycardia) from 104 patients were analyzed. WCT Formula demonstrated superior accuracy (85.8%) and specificity (87.1%) compared with Brugada (75.2% and 57.4%, respectively) and Vereckei aVR (65.3% and 36.4%, respectively). WCT Formula II achieved higher accuracy (89.6%) and specificity (85.1%) against Brugada and Vereckei aVR. Performance metrics of the WCT Formula (accuracy 85.8%, sensitivity 84.7%, and specificity 87.1%) and WCT Formula II (accuracy 89.8%, sensitivity 89.6%, and specificity 85.1%) were similar to the VT score (accuracy 84.4%, sensitivity 93.8%, and specificity 74.2%). Paired Model was superior to Brugada in accuracy (89.6% versus 75.2%), specificity (97.0% versus 57.4%), and F1 score (0.89 versus 0.80). Paired Model surpassed Vereckei aVR in accuracy (89.6% versus 65.3%), specificity (97.0% versus 75.2%), and F1 score (0.89 versus 0.74). Paired Model demonstrated similar accuracy (89.6% versus 84.4%), inferior sensitivity (79.3% versus 93.8%), but superior specificity (97.0% versus 74.2%) to the VT score. Solo Model and VT Prediction Model accuracy (82.5% and 77.4%, respectively) was superior to the Vereckei aVR (65.3%) but similar to Brugada (75.2%) and the VT score (84.4%). CONCLUSIONS: Automated WCT differentiation algorithms demonstrated favorable diagnostic performance compared with traditional manual ECG interpretation approaches. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Cerebral small vessel disease (SVD) and amyloid beta (Aβ) pathology frequently co‐exist. The impact of concurrent pathology on the pattern of hippocampal atrophy, a key substrate of memory impacted early and extensively in dementia, remains poorly understood. METHODS: In a unique cohort of mixed Alzheimer's disease and moderate–severe SVD, we examined whether total and regional neuroimaging measures of SVD, white matter hyperintensities (WMH), and Aβ, as assessed by 18F‐AV45 positron emission tomography, exert additive or synergistic effects on hippocampal volume and shape. RESULTS: Frontal WMH, occipital WMH, and Aβ were independently associated with smaller hippocampal volume. Frontal WMH had a spatially distinct impact on hippocampal shape relative to Aβ. In contrast, hippocampal shape alterations associated with occipital WMH spatially overlapped with Aβ‐vulnerable subregions. DISCUSSION: Hippocampal degeneration is differentially sensitive to SVD and Aβ pathology. The pattern of hippocampal atrophy could serve as a disease‐specific biomarker, and thus guide clinical diagnosis and individualized treatment strategies for mixed dementia. [ABSTRACT FROM AUTHOR]

The finding of unexpected variations in treatment benefits by geographic region in international clinical trials raises complex questions about the interpretation and generalizability of trial findings. We observed such geographical variations in outcome and in the effectiveness of atrial fibrillation (AF) ablation versus drug therapy in the Catheter Ablation vs Antiarrhythmic Drug Therapy for Atrial Fibrillation (CABANA) trial. This paper describes these differences and investigates potential causes. The examination of treatment effects by geographic region was a prespecified analysis. CABANA enrolled patients from 10 countries, with 1,285 patients at 85 North American (NA) sites and 919 at 41 non-NA sites. The primary endpoint was a composite of death, disabling stroke, serious bleeding, or cardiac arrest. Death and first atrial fibrillation recurrence were secondary endpoints. At least 1 primary endpoint event occurred in 157 patients (12.2%) from NA and 33 (3.6%) from non-NA sites over a median 54.9 and 40.5 months of follow-up, respectively (NA/non-NA adjusted hazard ratio (HR) 2.18, 95% confidence interval (CI) 1.48-3.21, P <.001). In NA patients, 78 events occurred in the ablation and 79 in the drug arm, (HR 0.91, 95% CI 0.66, 1.24) while 11 and 22 events occurred in non-NA patients (HR 0.51, 95% CI 0.25,1.05, interaction P =.154). Death occurred in 53 ablation and 51 drug therapy patients in the NA group (HR 0.96, 95% CI 0.65,1.42) and in 5 ablation and 16 drug therapy patients in the non-NA group (HR 0.32, 95% CI 0.12,0.86, interaction P =.044). Adjusting for baseline regional differences or prognostic risk variables did not account for the regional differences in treatment effects. Atrial fibrillation recurrence was reduced by ablation in both regions (NA: HR 0.54, 95% CI 0.46, 0.63; non-NA: HR 0.44, 95% CI 0.30, 0.64, interaction P =.322). In CABANA, primary outcome events occurred significantly more often in the NA group but assignment to ablation significantly reduced all-cause mortality in the non-NA group only. These differences were not explained by regional variations in procedure effectiveness, safety, or patient characteristics. ClinicalTrials.gov Identifier: NCT0091150; https://clinicaltrials.gov/study/NCT00911508 [ABSTRACT FROM AUTHOR]

Paroxysmal supraventricular tachycardia (PSVT) is a common episodic arrhythmia characterized by unpredictable onset and burdensome symptoms including palpitations, dizziness, chest pain, distress, and shortness of breath. Treatment of acute episodes of PSVT in the clinical setting consists of intravenous adenosine, beta-blockers, and calcium channel blockers (CCBs). Etripamil is an intranasally self-administered L-type CCB in development for acute treatment of AV-nodal dependent PSVT in a nonmedical supervised setting. This paper summarizes the rationale and study design of NODE-303 that will assess the efficacy and safety of etripamil. In the randomized, double-blinded, placebo-controlled, Phase 3 RAPID trial, etripamil was superior to placebo in the conversion of single PSVT episodes by 30 minutes post initial dose when administered in the nonhealthcare setting; this study required a mandatory and observed test dosing prior to randomization. The primary objective of NODE-303 is to evaluate the safety of symptom-prompted, self-administered etripamil for multiple PSVT episodes in real-world settings, without the need for test dosing prior to first use during PSVT. Secondary endpoints include efficacy and disease burden. Upon perceiving a PSVT episode, the patient applies an electrocardiographic monitor, performs a vagal maneuver, and, if the vagal maneuver is unsuccessful, self-administers etripamil 70 mg, with an optional repeat dose if symptoms do not resolve within 10 minutes after the first dose. A patient may treat up to four PSVT episodes during the study. Adverse events are recorded as treatment-emergent if they occur within 24 hours after the administration of etripamil. Efficacy endpoints include time to conversion to sinus rhythm within 30 and 60 minutes after etripamil administration, and the proportion of patients who convert at 3, 5, 10, 20, 30, and 60 minutes. Patient-reported outcomes are captured by the Brief Illness Perception Questionnaire, the Cardiac Anxiety Questionnaire, the Short Form Health Survey 36, the Treatment Satisfaction Questionnaire for Medication and a PSVT survey. Overall, these data will support the development of a potentially paradigm-changing long-term management strategy for recurrent PSVT. [ABSTRACT FROM AUTHOR]

Atrial fibrillation (AF) is associated with increased risks of stroke and dementia. Early diagnosis and treatment could reduce the disease burden, but AF is often undiagnosed. An artificial intelligence (AI) algorithm has been shown to identify patients with previously unrecognized AF; however, monitoring these high-risk patients has been challenging. Consumer wearable devices could be an alternative to enable long-term follow-up. To test whether Apple Watch, used as a long-term monitoring device, can enable early diagnosis of AF in patients who were identified as having high risk based on AI-ECG. The R ealtime diagnosis from E lectrocardiogram (ECG) Artificial Intelligence (AI)- G uided Screening for A trial Fibrillation (AF) with L ong Follow-up (REGAL) study is a pragmatic trial that will accrue up to 2,000 older adults with a high likelihood of unrecognized AF determined by AI-ECG to reach our target of 1,420 completed participants. Participants will be 1:1 randomized to intervention or control and will be followed up for 2 years. Patients in the intervention arm will receive or use their existing Apple Watch and iPhone and record a 30-second ECG using the watch routinely or if an abnormal heart rate notification is prompted. The primary outcome is newly diagnosed AF. Secondary outcomes include changes in cognitive function, stroke, major bleeding, and all-cause mortality. The trial will utilize a pragmatic, digitally-enabled, decentralized design to allow patients to consent and receive follow-up remotely without traveling to the study sites. The REGAL trial will examine whether a consumer wearable device can serve as a long-term monitoring approach in older adults to detect AF and prevent cognitive function decline. If successful, the approach could have significant implications on how future clinical practice can leverage consumer devices for early diagnosis and disease prevention. : NCT05923359 [ABSTRACT FROM AUTHOR]

There has been an increasing uptake of transcatheter left atrial appendage occlusion (LAAO) for stroke reduction in atrial fibrillation. To investigate the perceptions and approaches among a nationally representative sample of physicians. Using the American Medical Association Physician Masterfile, we selected a random sample of 500 physicians from each of the specialties: general cardiologists, interventional cardiologists, electrophysiologists, and vascular neurologists. The participants received the survey by mail up to three times from November 9, 2021 to January 14, 2022. In addition to the questions about experiences, perceptions, and approaches, physicians were randomly assigned to 1 of the 4 versions of a patient vignette: white man, white woman, black man, and black woman, to investigate potential bias in decision-making. The top three reasons for considering LAAO were: a history of intracranial bleeding (94.3%), a history of major extracranial bleeding (91.8%), and gastrointestinal lesions (59.0%), whereas the top three reasons for withholding LAAO were: other indications for long-term oral anticoagulation (87.7%), a low bleeding risk (77.0%), and a low stroke risk (65.6%). For the reasons limiting recommendations for LAAO, 59.8% mentioned procedural risks, 42.6% mentioned "limiting efficacy data comparing LAAO to NOAC" and 32.8% mentioned "limited safety data comparing LAAO to NOAC." There was no difference in physicians' decision-making by patients' race, gender, or the concordance between patients' and physicians' race or gender. In the first U.S. national physician survey of LAAO, individual physicians' perspectives varied greatly, which provided information that will help customize future educational activities for different audiences. Although diverse practice patterns of LAAO have been documented, little is known about the reasoning or perceptions that drive these variations. Unlike prior surveys that were directed to Centers that performed LAAO, the current survey obtained insights from individual physicians, not only those who perform the procedures (interventional cardiologists and electrophysiologists) but also those who are closely involved in the decision-making and referral process (general cardiologists and vascular neurologists). The findings identify key evidence gaps and help prioritize future studies to establish a consistent and evidence-based best practice for AF stroke prevention. [ABSTRACT FROM AUTHOR]

Accurate differentiation of wide complex tachycardias (WCTs) into ventricular tachycardia (VT) or supraventricular wide complex tachycardia (SWCT) using non-invasive methods such as 12‑lead electrocardiogram (ECG) interpretation is crucial in clinical practice. Recent studies have demonstrated the potential for automated approaches utilizing computerized ECG interpretation software to achieve accurate WCT differentiation. In this review, we provide a comprehensive analysis of contemporary automated methods for VT and SWCT differentiation. Our objectives include: (i) presenting a general overview of the emergence of automated WCT differentiation methods, (ii) examining the role of machine learning techniques in automated WCT differentiation, (iii) reviewing the electrophysiology concepts leveraged existing automated algorithms, (iv) discussing recently developed automated WCT differentiation solutions, and (v) considering future directions that will enable the successful integration of automated methods into computerized ECG interpretation platforms. [ABSTRACT FROM AUTHOR]

A 12‑lead electrocardiography (ECG)-based convolutional neural network (CNN) model can detect hypertrophic cardiomyopathy (HCM). However, since these models do not rely on discrete measurements as inputs, it is not apparent what drives their performance. We hypothesized that saliency maps could be used to visually identify ECG segments that contribute to a CNN's robust classification of HCM. We derived a new one‑lead (lead I) CNN model based on median beats using the same methodology and cohort used for the original 12‑lead CNN model (3047 patients with HCM, and 63,926 sex- and age-matched non-HCM controls). One‑lead, median-beat saliency maps were generated and visually evaluated in an independent cohort of 100 patients with a diagnosis of HCM and a high artificial intelligence (AI)-ECG-HCM probability score to determine which ECG segments contributed to the model's detection of HCM. The one‑lead, median-beat CNN had an AUC of 0.90 (95% CI 0.89–0.92) for HCM detection, similar to the original 12‑lead ECG model. In the independent HCM cohort (n = 100), saliency maps highlighted the ST-T segment in 92 ECGs, the atrial depolarization segment in 12 ECGs, and the QRS complex in 5 ECGs. Saliency maps of a one‑lead, median-beat-based CNN model identified perturbations in ventricular repolarization as the main region of interest in detecting HCM. [ABSTRACT FROM AUTHOR]

Electrocardiogram (ECG) interpretation training is a fundamental component of medical education across disciplines. However, the skill of interpreting ECGs is not universal among medical graduates, and numerous barriers and challenges exist in medical training and clinical practice. An evidence-based and widely accessible learning solution is needed. The EDUcation Curriculum Assessment for Teaching Electrocardiography (EDUCATE) Trial is a prospective, international, investigator-initiated, open-label, randomized controlled trial designed to determine the efficacy of self-directed and active-learning approaches of a web-based educational platform for improving ECG interpretation proficiency. Target enrollment is 1000 medical professionals from a variety of medical disciplines and training levels. Participants will complete a pre-intervention baseline survey and an ECG interpretation proficiency test. After completion, participants will be randomized into one of four groups in a 1:1:1:1 fashion: (i) an online, question-based learning resource, (ii) an online, lecture-based learning resource, (iii) an online, hybrid question- and lecture-based learning resource, or (iv) a control group with no ECG learning resources. The primary endpoint will be the change in overall ECG interpretation performance according to pre- and post-intervention tests, and it will be measured within and compared between medical professional groups. Secondary endpoints will include changes in ECG interpretation time, self-reported confidence, and interpretation accuracy for specific ECG findings. The EDUCATE Trial is a pioneering initiative aiming to establish a practical, widely available, evidence-based solution to enhance ECG interpretation proficiency among medical professionals. Through its innovative study design, it tackles the currently unaddressed challenges of ECG interpretation education in the modern era. The trial seeks to pinpoint performance gaps across medical professions, compare the effectiveness of different web-based ECG content delivery methods, and create initial evidence for competency-based standards. If successful, the EDUCATE Trial will represent a significant stride towards data-driven solutions for improving ECG interpretation skills in the medical community. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI), and more specifically deep learning, models have demonstrated the potential to augment physician diagnostic capabilities and improve cardiovascular health if incorporated into routine clinical practice. However, many of these tools are yet to be evaluated prospectively in the setting of a rigorous clinical trial–a critical step prior to implementing broadly in routine clinical practice. To describe the rationale and design of a proposed clinical trial aimed at evaluating an AI-enabled electrocardiogram (AI-ECG) for cardiomyopathy detection in an obstetric population in Nigeria. The protocol will enroll 1,000 pregnant and postpartum women who reside in Nigeria in a prospective randomized clinical trial. Nigeria has the highest reported incidence of peripartum cardiomyopathy worldwide. Women aged 18 and older, seen for routine obstetric care at 6 sites (2 Northern and 4 Southern) in Nigeria will be included. Participants will be randomized to the study intervention or control arm in a 1:1 fashion. This study aims to enroll participants representative of the general obstetric population at each site. The primary outcome is a new diagnosis of cardiomyopathy, defined as left ventricular ejection fraction (LVEF) < 50% during pregnancy or within 12 months postpartum. Secondary outcomes will include the detection of impaired left ventricular function (at different LVEF cut-offs), and exploratory outcomes will include the effectiveness of AI-ECG tools for cardiomyopathy detection, new diagnosis of cardiovascular disease, and the development of composite adverse maternal cardiovascular outcomes. This clinical trial focuses on the emerging field of cardio-obstetrics and will serve as foundational data for the use of AI-ECG tools in an obstetric population in Nigeria. This study will gather essential data regarding the utility of the AI-ECG for cardiomyopathy detection in a predominantly Black population of women and pave the way for clinical implementation of these models in routine practice. Clinicaltrials.gov: NCT05438576. [ABSTRACT FROM AUTHOR]

Artificial intelligence-augmented ECG (AI-ECG) refers to the application of novel AI solutions for complex ECG interpretation tasks. A broad variety of AI-ECG approaches exist, each having differing advantages and limitations relating to their creation and application. To provide illustrative comparison of two general AI-ECG modeling approaches: machine learning (ML) and deep learning (DL). Two AI-ECG algorithms were developed to carry out two separate tasks using ML and DL, respectively. ML modeling techniques were used to create algorithms designed for automatic wide QRS complex tachycardia differentiation into ventricular tachycardia and supraventricular tachycardia. A DL algorithm was formulated for the task of comprehensive 12‑lead ECG interpretation. First, we describe the ML models for WCT differentiation, which rely upon expert domain knowledge to identify and formulate ECG features (e.g., percent monophasic time-voltage area [PMonoTVA]) that enable strong diagnostic performance. Second, we describe the DL method for comprehensive 12‑lead ECG interpretation, which relies upon the independent recognition and analysis of a virtually incalculable number of ECG features from a vast collection of standard 12‑lead ECGs. We have showcased two different AI-ECG methods, namely ML and DL respectively. In doing so, we highlighted the strengths and weaknesses of each approach. It is essential for investigators to understand these differences when attempting to create and apply novel AI-ECG solutions. [ABSTRACT FROM AUTHOR]

Lifelong oral anticoagulation is recommended in patients with atrial fibrillation (AF) to prevent stroke. Over the last decade, multiple new oral anticoagulants (OACs) have expanded the number of treatment options for these patients. While population-level effectiveness of OACs has been compared, it is unclear if there is variability in benefit and risk across patient subgroups. We analyzed claims and medical data for 34,569 patients who initiated a nonvitamin K antagonist oral anticoagulant (non-vitamin K antagonist oral anticoagulant (NOAC); apixaban, dabigatran, and rivaroxaban) or warfarin for nonvalvular AF between 08/01/2010 and 11/29/2017 from the OptumLabs Data Warehouse. A machine learning (ML) method was applied to match different OAC groups on several baseline variables including, age, sex, race, renal function, and CHA 2 DS 2 -VASC score. A causal ML method was then used to discover patient subgroups characterizing the head-to-head treatment effects of the OACs on a primary composite outcome of ischemic stroke, intracranial hemorrhage, and all-cause mortality. The mean age, number of females and white race in the entire cohort of 34,569 patients were 71.2 (SD, 10.7) years, 14,916 (43.1%), and 25,051 (72.5%) respectively. During a mean follow-up of 8.3 (SD, 9.0) months, 2,110 (6.1%) of patients experienced the composite outcome, of whom 1,675 (4.8%) died. The causal ML method identified 5 subgroups with variables favoring apixaban over dabigatran; 2 subgroups favoring apixaban over rivaroxaban; 1 subgroup favoring dabigatran over rivaroxaban; and 1 subgroup favoring rivaroxaban over dabigatran in terms of risk reduction of the primary endpoint. No subgroup favored warfarin and most dabigatran vs warfarin users favored neither drug. The variables that most influenced favoring one subgroup over another included Age, history of ischemic stroke, thromboembolism, estimated glomerular filtration rate, Race, and myocardial infarction. Among patients with AF treated with a NOAC or warfarin, a causal ML method identified patient subgroups with differences in outcomes associated with OAC use. The findings suggest that the effects of OACs are heterogeneous across subgroups of AF patients, which could help personalize the choice of OAC. Future prospective studies are needed to better understand the clinical impact of the subgroups with respect to OAC selection. [Display omitted] [ABSTRACT FROM AUTHOR]

Timely and accurate discrimination of wide complex tachycardias (WCTs) into ventricular tachycardia (VT) or supraventricular WCT (SWCT) is critically important. Previously we developed and validated an automated VT Prediction Model that provides a VT probability estimate using the paired WCT and baseline 12-lead ECGs. Whether this model improves physicians' diagnostic accuracy has not been evaluated. We sought to determine whether the VT Prediction Model improves physicians' WCT differentiation accuracy. Over four consecutive days, nine physicians independently interpreted fifty WCT ECGs (25 VTs and 25 SWCTs confirmed by electrophysiological study) as either VT or SWCT. Day 1 used the WCT ECG only, Day 2 used the WCT and baseline ECG, Day 3 used the WCT ECG and the VT Prediction Model's estimation of VT probability, and Day 4 used the WCT ECG, baseline ECG, and the VT Prediction Model's estimation of VT probability. Inclusion of the VT Prediction Model data increased diagnostic accuracy versus the WCT ECG alone (Day 3: 84.2% vs. Day 1: 68.7%, p 0.009) and WCT and baseline ECGs together (Day 3: 84.2% vs. Day 2: 76.4%, p 0.003). There was no further improvement of accuracy with addition of the baseline ECG comparison to the VT Prediction Model (Day 3: 84.2% vs. Day 4: 84.0%, p 0.928). Overall sensitivity (Day 3: 78.2% vs. Day 1: 67.6%, p 0.005) and specificity (Day 3: 90.2% vs. Day 1: 69.8%, p 0.016) for VT were superior after the addition of the VT Prediction Model. The VT Prediction Model improves physician ECG diagnostic accuracy for discriminating WCTs. • Management decisions reply on accurate wide complex tachycardia discrimination. • A shortcoming of manual ECG approaches is complete reliance on the ECG interpreter. • Automated differentiation methods remove the reliance on ECG interpreter competency. • Automated differentiation methods improve physician accuracy and confidence. [ABSTRACT FROM AUTHOR]

Objective: An artificial intelligence (AI) algorithm has been developed to detect the electrocardiographic signature of atrial fibrillation (AF) present on an electrocardiogram (ECG) obtained during normal sinus rhythm. We evaluated the ability of this algorithm to predict incident AF in an emergency department (ED) cohort of patients presenting with palpitations without concurrent AF.Methods: This retrospective study included patients 18 years and older who presented with palpitations to one of 15 ED sites and had a 12‑lead ECG performed. Patients with prior AF or newly diagnosed AF during the ED visit were excluded. Of the remaining patients, those with a follow up ECG or Holter monitor in the subsequent year were included. We evaluated the performance of the AI-ECG output to predict incident AF within one year of the index ECG by estimating an area under the receiver operating characteristics curve (AUC). Sensitivity, specificity, and positive and negative predictive values were determined at the optimum threshold (maximizing sensitivity and specificity), and thresholds by output decile for the sample.Results: A total of 1403 patients were included. Forty-three (3.1%) patients were diagnosed with new AF during the following year. The AI-ECG algorithm predicted AF with an AUC of 0.74 (95% CI 0.68-0.80), and an optimum threshold with sensitivity 79.1% (95% Confidence Interval (CI) 66.9%-91.2%), and specificity 66.1% (95% CI 63.6%-68.6%).Conclusions: We found this AI-ECG AF algorithm to maintain statistical significance in predicting incident AF, with clinical utility for screening purposes limited in this ED population with a low incidence of AF. [ABSTRACT FROM AUTHOR]

As ECG technology rapidly evolves to improve patient care, accurate ECG interpretation will continue to be foundational for maintaining high clinical standards. Recent studies have exposed significant educational gaps, with many healthcare professionals lacking sufficient training and proficiency. Furthermore, integrating new software and hardware ECG technologies poses challenges about potential knowledge and skill erosion. This underscores the need for clinicians who are adept at integrating clinical expertise with technological proficiency. It also highlights the need for innovative solutions to enhance ECG interpretation among healthcare professionals in this rapidly evolving environment. This work explores the importance of aligning ECG education with technological advancements and proposes how this synergy could advance patient care in the future. [ABSTRACT FROM AUTHOR]

Background: Shared decision making (SDM) improves the likelihood that patients will receive care in a manner consistent with their priorities. To facilitate SDM, decision aids (DA) are commonly used, both to prepare a patient before their clinician visit, as well as to facilitate discussion during the visit. However, the relative efficacy of patient-focused or encounter-based DAs on SDM and patient outcomes remains largely unknown. We aim to directly estimate the comparative effectiveness of two DA's on SDM observed in encounters to discuss stroke prevention strategies in patients with atrial fibrillation (AF).Methods: The study aims to recruit 1200 adult patients with non-valvular AF who qualify for anticoagulation therapy, and their clinicians who manage stroke prevention strategies, in a 2x2 cluster randomized multi-center trial at six sites. Two DA's were developed as interactive, online, non-linear tools: a patient decision aid (PDA) to be used by patients before the encounter, and an encounter decision aid (EDA) to be used by clinicians with their patients during the encounter. Patients will be randomized to PDA or usual care; clinicians will be randomized to EDA or usual care.Results: Primary outcomes are quality of SDM, patient decision making, and patient knowledge. Secondary outcomes include anticoagulation choice, adherence, and clinical events.Conclusion: This trial is the first randomized, head-to-head comparison of the effects of an EDA versus a PDA on SDM. Our results will help to inform future SDM interventions to improve patients' AF outcomes and experiences with stroke prevention strategies. [ABSTRACT FROM AUTHOR]

Background: Clinical trials are a fundamental tool to evaluate medical interventions but are time-consuming and resource-intensive.Objectives: To build infrastructure for digital trials to improve efficiency and generalizability and test it using a study to validate an artificial intelligence algorithm to detect atrial fibrillation (AF).Design: We will prospectively enroll 1,000 patients who underwent an electrocardiogram for any clinical reason in routine practice, do not have a previous diagnosis of AF or atrial flutter and would be eligible for anticoagulation if AF is detected. Eligible patients will be identified using digital phenotyping algorithms, including natural language processing that runs on the electronic health records. Study invitations will be sent in batches via patient portal or letter, which will direct patients to a website to verify eligibility, learn about the study (including video-based informed consent), and consent electronically. The method aims to enroll participants representative of the general patient population, rather than a convenience sample of patients presenting to clinic. A device will be mailed to patients to continuously monitor for up to 30 days. The primary outcome is AF diagnosis and burden; secondary outcomes include patients' experience with the trial conduct methods and the monitoring device. The enrollment, intervention, and follow-up will be conducted remotely, ie, a patient-centered site-less trial.Summary: This is among the first wave of trials to adopt digital technologies, artificial intelligence, and other pragmatic features to create efficiencies, which will pave the way for future trials in a broad range of disease and treatment areas. Clinicaltrials.gov: NCT04208971. [ABSTRACT FROM AUTHOR]

Rationale and Objectives: 129Xe MRI has been developed to noninvasively visualize and quantify the functional consequence of airway obstruction in asthma. Its widespread application requires evidence of intersite reproducibility and agreement. Our objective was to evaluate reproducibility and agreement of 129Xe ventilation MRI measurements in severe asthmatics at two sites.Materials and Methods: In seven adults with severe asthma, 129Xe ventilation MRI was acquired pre- and post-bronchodilator at two geographic sites within 24-hours. 129Xe MRI signal-to-noise ratio (SNR) was calculated and ventilation abnormalities were quantified as the whole-lung and slice-by-slice ventilation defect percent (VDP). Intraclass correlation coefficients (ICC) and Bland-Altman analysis were used to determine intersite 129Xe VDP reproducibility and agreement.Results: Whole-lung and slice-by-slice 129Xe VDP measured at both sites were correlated and reproducible (pre-bronchodilator: whole-lung ICC = 0.90, p = 0.005, slice-by-slice ICC = 0.78, p < 0.0001; post-bronchodilator: whole-lung ICC = 0.94, p < 0.0001, slice-by-slice ICC = 0.83, p < 0.0001) notwithstanding intersite differences in the 129Xe-dose-equivalent-volume (101 ± 15 mL site 1, 49 ± 6 mL site 2, p < 0.0001), gas-mixture (129Xe/4He site 1; 129Xe/N2 site 2) and SNR (40 ± 19 site 1, 23 ± 5 site 2, p = 0.02). Qualitative 129Xe gas distribution differences were observed between sites and slice-by-slice 129Xe VDP, but not whole-lung 129Xe VDP, was significantly lower at site 1 (pre-bronchodilator VDP: whole-lung bias = -3%, p > 0.99, slice-by-slice bias = -3%, p = 0.0001; post-bronchodilator VDP: whole-lung bias = -2%, p = 0.59, slice-by-slice-bias = -2%, p = 0.0003).Conclusion: 129Xe MRI VDP at two different sites measured within 24-hours in the same severe asthmatics were correlated. Qualitative and quantitative intersite differences in 129Xe regional gas distribution and VDP point to site-specific variability that may be due to differences in gas-mixture composition or SNR. [ABSTRACT FROM AUTHOR]

Background: Neighbourhood nature spaces are associated with brain health. Biological sex moderates the associations of various lifestyle and environmental factors with brain health, but it is unclear whether sex moderates associations between nature exposure and brain structure. We thus investigated whether sex moderates the association of residential nature exposure with brain structure in the UK Biobank cohort. Methods: We conducted a cross‐sectional analysis of the UK Biobank study. We included cognitively healthy participants with available nature exposure data at study entry (2006‐2010) who completed a 3T structural brain MRI (2014‐2021; N = 37,880). Residential nature exposure was estimated as the percentage of land classified as natural environment within 1000m of a participant's home. We indexed total grey matter volume and total white matter volume, normalized for head size, and hippocampal volume averaged from both hemispheres. We performed linear regressions to assess the independent associations of residential nature exposure and biological sex with brain structure. We then assessed whether sex moderated the association between residential nature exposure and brain structure, and plotted simple slopes to interpret interaction effects. All models adjusted for age, body mass index (kg/m2), educational attainment, income, physical activity (min/day), time spent outdoors (hours/day), smoking status, disability status, Townsend deprivation index, and population density. Results: At study entry, mean age was 55 years (SD = 7.53 years) and 53% of participants were female. Average residential nature exposure was 44% (SD = 26%). Every 10% increment in residential nature exposure was associated with 737 mm3 greater grey matter volume (p<0.0001) and 601 mm3 greater white matter volume (p<0.0001). Females had 24,270 mm3 greater grey matter volume (p<0.0001), 9,703 mm3 less white matter volume (p<0.0001), and 223 mm3 less hippocampal volume than males (p<0.0001). Sex moderated the association of residential nature exposure with total grey matter volume, such that every 10% increment in nature exposure attenuated sex differences in grey matter volume by 422 mm3 (p = 0.020) (Figure 1). Conclusion: These results suggest that neighbourhood nature spaces may benefit brain health, and that males may be more sensitive than females to these potential benefits. Longitudinal analyses are needed to verify these findings. [ABSTRACT FROM AUTHOR]

Accurate wide QRS complex tachycardia (WCT) differentiation into either ventricular tachycardia or supraventricular wide complex tachycardia using 12‑lead electrocardiogram (ECG) interpretation is essential for diagnostic, therapeutic, and prognostic reasons. There is an ever-expanding variety of WCT differentiation methods and criteria available to clinicians. However, only a few make use of the diagnostic value of comparing the ECG during WCT to that of the patient's baseline ECG. Therefore, we highlight the conceptual rationale and scientific literature supporting the diagnostic value of WCT and baseline ECG comparison. [ABSTRACT FROM AUTHOR]

Objectives: To assess the effect of ankle, knee, and hip kinematics on patellofemoral and tibiofemoral joint reaction forces (JRF) during bicycling. Secondarily, to assess if sex, horizontal saddle position, or crank arm length were related to JRFs, after accounting for kinematics.Design: Experimental cross-sectional study.Methods: Forty healthy adults (mean (SD); 28.6 (7.2) years, 24.2 (2.6)kg/m2, 17 women) bicycled under 18 bicycling positions. One position used commercial guidelines and 17 randomly deviated from commercial. Resultant patellofemoral as well as compressive and shear tibiofemoral JRFs were calculated. Linear mixed-effects models with a random intercept of leg-nested-in-participant were used.Results: Patellofemoral resultant forces were most sensitive to all joint kinematics (i.e., sensitivity was defined as the slope of single predictor models); all JRFs were least sensitive to minimum knee flexion. Tibiofemoral compression was predicted by minimum hip flexion and maximum ankle dorsiflexion (R2=0.90). Tibiofemoral shear (R2=0.86) and the resultant patellofemoral JRF (R2=0.90) were predicted by minimum hip flexion, maximum ankle dorsiflexion, minimum knee flexion, and the interaction between minimum hip flexion and minimum knee flexion. Adding sex as a factor improved fit of all models. This sex-effect was driven by differences in cycling intensity, reflected by the tangential crank arm force. Horizontal saddle position and crank arm length were not related to JRFs.Conclusions: Optimizing joint kinematics should be the primary goal of bicycle-fit. JRFs were least sensitive to the current gold standard for assessing bicycle-fit, minimum knee flexion. Bicycle-fit is of particular importance for those working at high intensities. [ABSTRACT FROM AUTHOR]

The World Heart Federation (WHF) commenced a Roadmap initiative in 2015 to reduce the global burden of cardiovascular disease and resultant burgeoning of healthcare costs. Roadmaps provide a blueprint for implementation of priority solutions for the principal cardiovascular diseases leading to death and disability. Atrial fibrillation (AF) is one of these conditions and is an increasing problem due to ageing of the world's population and an increase in cardiovascular risk factors that predispose to AF. The goal of the AF roadmap was to provide guidance on priority interventions that are feasible in multiple countries, and to identify roadblocks and potential strategies to overcome them. Since publication of the AF Roadmap in 2017, there have been many technological advances including devices and artificial intelligence for identification and prediction of unknown AF, better methods to achieve rhythm control, and widespread uptake of smartphones and apps that could facilitate new approaches to healthcare delivery and increasing community AF awareness. In addition, the World Health Organisation added the non-vitamin K antagonist oral anticoagulants (NOACs) to the Essential Medicines List, making it possible to increase advocacy for their widespread adoption as therapy to prevent stroke. These advances motivated the WHF to commission a 2020 AF Roadmap update. Three years after the original Roadmap publication, the identified barriers and solutions were judged still relevant, and progress has been slow. This 2020 Roadmap update reviews the significant changes since 2017 and identifies priority areas for achieving the goals of reducing death and disability related to AF, particularly targeted at low-middle income countries. These include advocacy to increase appreciation of the scope of the problem; plugging gaps in guideline management and prevention through physician education, increasing patient health literacy, and novel ways to increase access to integrated healthcare including mHealth and digital transformations; and greater emphasis on achieving practical solutions to national and regional entrenched barriers. Despite the advances reviewed in this update, the task will not be easy, but the health rewards of implementing solutions that are both innovative and practical will be great. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) is a global pandemic that is wreaking havoc on the health and economy of much of human civilization. Electrophysiologists have been impacted personally and professionally by this global catastrophe. In this joint article from representatives of the Heart Rhythm Society, the American College of Cardiology, and the American Heart Association, we identify the potential risks of exposure to patients, allied healthcare staff, industry representatives, and hospital administrators. We also describe the impact of COVID-19 on cardiac arrhythmias and methods of triage based on acuity and patient comorbidities. We provide guidance for managing invasive and noninvasive electrophysiology procedures, clinic visits, and cardiac device interrogations. In addition, we discuss resource conservation and the role of telemedicine in remote patient care along with management strategies for affected patients. [ABSTRACT FROM AUTHOR]

Background: In the Catheter Ablation for Atrial Fibrillation with Heart Failure (CASTLE-AF) trial, catheter ablation reduced the risk of death and heart failure (HF) hospitalization in patients with atrial fibrillation and HF by 40%.Objectives: The study aimed to assess the generalizability of CASTLE-AF to routine clinical practice.Methods: Using a large US administrative database, we identified 289,831 patients with atrial fibrillation and HF treated with ablation (n = 7465) or medical therapy alone (n = 282,366) from January 1, 2008, through August 31, 2018. Patients were divided into 3 groups on the basis of trial eligibility: (1) eligible for CASTLE-AF, (2) failing to meet the inclusion criteria, and (3) meeting at least 1 of the exclusion criteria. Propensity score overlap weighting was used to balance ablated and drug-treated patients on 90 baseline characteristics. Cox proportional hazards regression was used to compare ablation with medical therapy for the primary outcome of a composite end point of all-cause mortality and HF hospitalization.Results: Only 7.8% of patients would have been eligible for the trial; 91.0% failed to meet the trial inclusion criteria; and 15.5% met the exclusion criteria. Ablation was associated with a lower risk of the primary outcome in the overall cohort (hazard ratio [HR] 0.81; 95% confidence interval [CI] 0.76-0.87; P < .001), in the trial-eligible cohort (HR 0.82; 95% CI 0.70-0.96; P = .01), and in patients who failed to meet inclusion criteria (HR 0.79; 95% CI 0.73-0.86; P < .001) but not in patients who met the exclusion criteria (HR 0.97; 95% CI 0.81-1.17). The relative risk reduction was consistent regardless of whether patients had HF with reduced left ventricular ejection fraction.Conclusion: The benefit associated with ablation appears to be more modest in practice than that reported in the CASTLE-AF trial. [ABSTRACT FROM AUTHOR]

Early recognition of ST-segment elevation myocardial infarction equivalent electrocardiogram patterns is of paramount importance. Successful identification of these ischemic patterns helps ensure proper triage of patients needing urgent restoration of coronary perfusion. The so-called de Winter sign has become increasingly recognized as a ST-segment elevation myocardial infarction equivalent pattern due to proximal left anterior descending artery occlusion. Yet, despite the de Winter pattern's well-defined electrocardiographic-angiographic relationship, the electrophysiologic explanation for its characteristic electrocardiographic manifestations remains unclear. Herein, we report a case in which an ischemic lateral lead variant of the de Winter pattern emerged from a patient inflicted by an abrupt thrombotic occlusion of the ostial left anterior descending artery, which developed in series with a high-grade stenosis of the distal left main coronary artery. We examine the patient's presenting electrocardiographic findings and clinical course to (i) establish causal inferences that align with the distribution of myocardial ischemia supported by coronary angiography and (ii) provide an accompanying analysis of the relevant scientific literature. [ABSTRACT FROM AUTHOR]

Background: Differentiation of wide complex tachycardias (WCTs) into ventricular tachycardia (VT) or supraventricular wide complex tachycardia (SWCT) using conventional manually-operated electrocardiogram (ECG) interpretation methods is difficult. Recent research has shown that accurate WCT differentiation may be accomplished by automated approaches (e.g., WCT Formula) implemented by computerized ECG interpretation software.Objective: We sought to develop a new automated means to differentiate WCTs.Methods: First, a derivation cohort of paired WCT and baseline ECGs was examined to secure independent VT predictors to be incorporated into a logistic regression model (i.e., WCT Formula II). Second, the WCT Formula II was trialed against a separate validation cohort of paired WCT and baseline ECGs.Results: The derivation cohort comprised 317 paired WCT (157 VT, 160 SWCT) and baseline ECGs. The WCT Formula II was composed of baseline QRS duration (p = 0.02), WCT QRS duration (p < 0.001), frontal percent time-voltage area change (p < 0.001), and horizontal percent time-voltage area change (p < 0.001). The area under the curve (AUC) for VT and SWCT differentiation was 0.96 (95% CI 0.94-0.98) for the derivation cohort. The validation cohort consisted of 284 paired WCT (116 VT, 168 SWCT) and baseline ECGs. WCT Formula II implementation on the validation cohort yielded effective WCT differentiation (AUC 0.96; 95% CI 0.94-0.98).Conclusion: The WCT Formula II is an example of how contemporary ECG interpretation software could be used to differentiate WCTs successfully. [ABSTRACT FROM AUTHOR]

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Background: A deep learning algorithm to detect low ejection fraction (EF) using routine 12-lead electrocardiogram (ECG) has recently been developed and validated. The algorithm was incorporated into the electronic health record (EHR) to automatically screen for low EF, encouraging clinicians to obtain a confirmatory transthoracic echocardiogram (TTE) for previously undiagnosed patients, thereby facilitating early diagnosis and treatment.Objectives: To prospectively evaluate a novel artificial intelligence (AI) screening tool for detecting low EF in primary care practices.Design: The EAGLE trial is a pragmatic two-arm cluster randomized trial (NCT04000087) that will randomize >100 clinical teams (i.e., clusters) to either intervention (access to the new AI screening tool) or control (usual care) at 48 primary care practices across Minnesota and Wisconsin. The trial is expected to involve approximately 400 clinicians and 20,000 patients. The primary endpoint is newly discovered EF ≤50%. Eligible patients will include adults who undergo ECG for any reason and have not been previously diagnosed with low EF. Data will be pulled from the EHR, and no contact will be made with patients. A positive deviance qualitative study and a post-implementation survey will be conducted among select clinicians to identify facilitators and barriers to using the new screening report.Summary: This trial will examine the effectiveness of the AI-enabled ECG for detection of asymptomatic low EF in routine primary care practices and will be among the first to prospectively evaluate the value of AI in real-world practice. Its findings will inform future implementation strategies for the translation of other AI-enabled algorithms. [ABSTRACT FROM AUTHOR]

Despite the critical role of electrocardiograms (ECGs) in patient care, evident gaps exist in ECG interpretation competency among healthcare professionals across various medical disciplines and training levels. Currently, no practical, evidence-based, and easily accessible ECG learning solution is available for healthcare professionals. The aim of this study was to assess the effectiveness of web-based, learner-directed interventions in improving ECG interpretation skills in a diverse group of healthcare professionals. In an international, prospective, randomized controlled trial, 1206 healthcare professionals from various disciplines and training levels were enrolled. They underwent a pre-intervention test featuring 30 12-lead ECGs with common urgent and non-urgent findings. Participants were randomly assigned to four groups: (i) practice ECG interpretation question bank (question bank), (ii) lecture-based learning resource (lectures), (iii) hybrid question- and lecture-based learning resource (hybrid), or (iv) no ECG learning resources (control). After four months, a post-intervention test was administered. The primary outcome was the overall change in ECG interpretation performance, with secondary outcomes including changes in interpretation time, self-reported confidence, and accuracy for specific ECG findings. Both unadjusted and adjusted scores were used for performance assessment. Among 1206 participants, 863 (72 %) completed the trial. Following the intervention, the question bank, lectures, and hybrid intervention groups each exhibited significant improvements, with average unadjusted score increases of 11.4 % (95 % CI, 9.1 to 13.7; P<0.01), 9.8 % (95 % CI, 7.8 to 11.9; P<0.01), and 11.0 % (95 % CI, 9.2 to 12.9; P<0.01), respectively. In contrast, the control group demonstrated a non-significant improvement of 0.8 % (95 % CI, -1.2 to 2.8; P=0.54). While no differences were observed among intervention groups, all outperformed the control group significantly (P<0.01). Intervention groups also excelled in adjusted scores, confidence, and proficiency for specific ECG findings. Web-based, self-directed interventions markedly enhanced ECG interpretation skills across a diverse range of healthcare professionals, providing an accessible and evidence-based solution. [ABSTRACT FROM AUTHOR]

The standard 12‑lead electrocardiogram (ECG) has become a mainstay diagnostic tool in patients suspected to have myocardial ischemia. The identification of hallmark electrocardiographic abnormalities, such as ST-segment deviation or serial T wave changes, not only helps identify the presence of myocardial ischemia but also may help localize myocardial territories with an ongoing injury. Widespread ST-segment depression is commonly attributed to diffuse subendocardial ischemia precipitated by severe multivessel or left main coronary artery disease. However, among patients with prior coronary revascularization, clear electrocardiographic-angiographic relationships responsible for widespread ST-segment depressions have not been well defined. We report a case in which diffuse ST-segment depression emerged from a patient with prior coronary artery bypass grafting. In this report, we examine the patient's presenting ECG pattern as to (1) establish causal inferences which align with the distribution of myocardial ischemia supported by angiography and (2) provide an accompanying analysis of the relevant scientific literature. [ABSTRACT FROM AUTHOR]

An interdisciplinary obesity management program was established in 2007 at our quaternary hospital, including bariatric surgery for selected adolescent patients. We report the evolution of surgical management within the program and outcomes following bariatric surgery. This was a retrospective review of adolescents who underwent bariatric surgery between 2007 and 2017. All cases were performed by a pediatric surgeon and an adult bariatric surgeon. Baseline demographics, BMI, co-morbidities, and post-operative outcomes were recorded. Thirty-eight patients underwent bariatric surgery. Median age at entrance into the program was 16.5 (range, 12.1–17.4) years and at time of surgery was 17.4 (range, 13.6–18.8) years. Eight patients had laparoscopic adjustable gastric banding (LAGB) from 2007 to 10. Between 2011 and 2017, 18 had laparoscopic sleeve gastrectomy (LSG), and 12 had laparoscopic Roux-en-Y gastric bypass (RYGB). There were no intraoperative complications or conversions. Postoperative complications included wound infection, bleeding requiring transfusion and re-exploration, and internal hernia. Of patients who had LAGB, 2 required surgical revision, and 3 underwent subsequent removal. Adolescent bariatric surgery in the context of a multidisciplinary obesity management program is safe and effective. RYGB and sleeve gastrectomy are associated with superior weight loss in the immediate post-operative period and at most recent follow-up and lower reoperation rates than gastric banding. III. [ABSTRACT FROM AUTHOR]

Background: The accurate differentiation of wide complex tachycardias (WCTs) into ventricular tachycardia (VT) or supraventricular wide complex tachycardia (SWCT) remains problematic despite numerous manually-operated electrocardiogram (ECG) interpretation methods. We sought to create a new WCT differentiation method that could be automatically implemented by computerized ECG interpretation (CEI) software.Methods: In a two-part study, we developed and validated a logistic regression model (i.e. WCT Formula) that utilizes computerized measurements and computations derived from patients' paired WCT and subsequent baseline ECGs. In Part 1, a derivation cohort of paired WCT and baseline ECGs was examined to identify independent VT predictors to be incorporated into the WCT Formula. In Part 2, a separate validation cohort of paired WCT and baseline ECGs was used to prospectively evaluate the WCT Formula's diagnostic performance.Results: The derivation cohort was comprised of 317 paired WCT (157 VT, 160 SWCT) and baseline ECGs. A logistic regression model (i.e. WCT Formula) incorporating WCT QRS duration (ms) (p < 0.001), frontal percent amplitude change (%) (p < 0.001), and horizontal percent amplitude change (%) (p < 0.001) yielded effective WCT differentiation (AUC of 0.96). The validation cohort consisted of 284 paired WCT (116 VT, 168 SWCT) and baseline ECGs. The WCT Formula achieved favorable accuracy (91.5%) with strong sensitivity (89.7%) and specificity (92.9%) for VT.Conclusion: The WCT Formula is an example of how contemporary CEI software could be used to successfully differentiate WCTs. The incorporation of similar automated methods into CEI software may improve clinicians' ability to accurately distinguish VT and SWCT. [ABSTRACT FROM AUTHOR]

Background: In the FOURIER trial, evolocumab, a proprotein convertase subtilisin-kexin type 9 inhibitor, reduced cardiovascular events in patients with atherosclerotic cardiovascular disease (ASCVD). We aimed to examine how closely patients in routine practice resemble the FOURIER trial participants and to assess the observed cardiovascular risks based on trial eligibility and underrepresentativeness.Methods: Using a large US administrative database with linked laboratory data, we identified adult patients with ASCVD between January 1, 2012, and December 31, 2016. We identified the excluded and underrepresented populations and examined the risk of cardiovascular events (a composite endpoint of myocardial infarction [MI], stroke, angina, and coronary revascularization) based on trial eligibility and underrepresentativeness.Results: Only 15.2% of 233,977 patients met the FOURIER eligibility. Nearly 60% of the ineligible patients met at least 2 exclusion criteria. Among trial-eligible patients, elderly patients, women, minorities, and those without prior MI were underrepresented in FOURIER. Patients who would have been excluded from FOURIER had a diverse risk profile but, on average, had a lower cardiovascular risk than those who would have qualified (hazard ratio [HR] 0.84 [0.81-0.88], P < .001). Among the underrepresented patients, women and patients without prior MI had a lower cardiovascular risk (HR 0.77 [0.71-0.82], P < .001; HR 0.67 [0.63-0.72], P < .001, respectively). Only 47.2% of patients were on moderate-/high-intensity statins.Conclusions: One in 7 ASCVD patients in practice would have qualified for FOURIER. The excluded and underrepresented populations were at a particularly low or high cardiovascular risk. Statin therapy was underused, and physicians may need to evaluate adherence before adding a proprotein convertase subtilisin-kexin type 9 inhibitor. [ABSTRACT FROM AUTHOR]

Accurate ECG interpretation is vital, but variations in skills exist among healthcare professionals. This study aims to identify factors contributing to ECG interpretation proficiency. Survey data and ECG interpretation test scores from participants in the EDUCATE Trial were analyzed to identify predictors of performance for 30 sequential 12-lead ECGs. Nonmodifiable factors (being a physician, clinical experience, patient care impact) and modifiable factors (weekly interpretation volume, training hours, expert supervision frequency) were analyzed. Bivariate and multivariate analyses were used to generate a Comprehensive Model (incorporating all factors) and Actionable Model (incorporating modifiable factors only). Among 1206 participants analyzed, there were 72 (6.0%) primary care physicians, 146 (12.1%) cardiology fellows-in-training, 353 (29.3%) resident physicians, 182 (15.1%) medical students, 84 (7.0%) advanced practice providers, 120 (9.9%) nurses, and 249 (20.7%) allied health professionals. Among them, 571 (47.3%) were physicians and 453 (37.6%) were nonphysicians. The average test score was 56.4% ± 17.2%. Bivariate analysis demonstrated significant associations between test scores and >10 weekly ECG interpretations, being a physician, >5 training hours, patient care impact, and expert supervision but not clinical experience. In the Comprehensive Model, independent associations were found with weekly interpretation volume (9.9 score increase; 95% CI, 7.9-11.8; P < 0.001), being a physician (9.0 score increase; 95% CI, 7.2-10.8; P < 0.001), and training hours (5.7 score increase; 95% CI, 3.7-7.6; P < 0.001). In the Actionable Model, scores were independently associated with weekly interpretation volume (12.0 score increase; 95% CI, 10.0-14.0; P < 0.001) and training hours (4.7 score increase; 95% CI, 2.6-6.7; P < 0.001). The Comprehensive and Actionable Models explained 18.7% and 12.3% of the variance in test scores, respectively. Predictors of ECG interpretation proficiency include nonmodifiable factors like physician status and modifiable factors such as training hours and weekly ECG interpretation volume. [ABSTRACT FROM AUTHOR]

The interpretation of electrocardiograms (ECGs) involves a dynamic interplay between computerized ECG interpretation (CEI) software and human overread. However, the impact of computer ECG interpretation on the performance of healthcare professionals remains largely unexplored. The aim of this study was to evaluate the interpretation proficiency of various medical professional groups, with and without access to the CEI report. Healthcare professionals from diverse disciplines, training levels, and countries sequentially interpreted 60 standard 12-lead ECGs, demonstrating both urgent and nonurgent findings. The interpretation process consisted of 2 phases. In the first phase, participants interpreted 30 ECGs with clinical statements. In the second phase, the same 30 ECGs and clinical statements were randomized and accompanied by a CEI report. Diagnostic performance was evaluated based on interpretation accuracy, time per ECG (in seconds [s]), and self-reported confidence (rated 0 [not confident], 1 [somewhat confident], or 2 [confident]). A total of 892 participants from various medical professional groups participated in the study. This cohort included 44 (4.9%) primary care physicians, 123 (13.8%) cardiology fellows-in-training, 259 (29.0%) resident physicians, 137 (15.4%) medical students, 56 (6.3%) advanced practice providers, 82 (9.2%) nurses, and 191 (21.4%) allied health professionals. The inclusion of the CEI was associated with a significant improvement in interpretation accuracy by 15.1% (95% confidence interval, 14.3-16.0; P < 0.001), decrease in interpretation time by 52 s (-56 to -48; P < 0.001), and increase in confidence by 0.06 (0.03-0.09; P = 0.003). Improvement in interpretation accuracy was seen across all professional subgroups, including primary care physicians by 12.9% (9.4-16.3; P = 0.003), cardiology fellows-in-training by 10.9% (9.1-12.7; P < 0.001), resident physicians by 14.4% (13.0-15.8; P < 0.001), medical students by 19.9% (16.8-23.0; P < 0.001), advanced practice providers by 17.1% (13.3-21.0; P < 0.001), nurses by 16.2% (13.4-18.9; P < 0.001), allied health professionals by 15% (13.4-16.6; P < 0.001), physicians by 13.2% (12.2-14.3; P < 0.001), and nonphysicians by 15.6% (14.3-17.0; P < 0.001).CEI integration improves ECG interpretation accuracy, efficiency, and confidence among healthcare professionals. [ABSTRACT FROM AUTHOR]

ECG interpretation is essential in modern medicine, yet achieving and maintaining competency can be challenging for healthcare professionals. Quantifying proficiency gaps can inform educational interventions for addressing these challenges. Medical professionals from diverse disciplines and training levels interpreted 30 12-lead ECGs with common urgent and nonurgent findings. Average accuracy (percentage of correctly identified findings), interpretation time per ECG, and self-reported confidence (rated on a scale of 0 [not confident], 1 [somewhat confident], or 2 [confident]) were evaluated. Among the 1206 participants, there were 72 (6%) primary care physicians (PCPs), 146 (12%) cardiology fellows-in-training (FITs), 353 (29%) resident physicians, 182 (15%) medical students, 84 (7%) advanced practice providers (APPs), 120 (10%) nurses, and 249 (21%) allied health professionals (AHPs). Overall, participants achieved an average overall accuracy of 56.4% ± 17.2%, interpretation time of 142 ± 67 seconds, and confidence of 0.83 ± 0.53. Cardiology FITs demonstrated superior performance across all metrics. PCPs had a higher accuracy compared to nurses and APPs (58.1% vs 46.8% and 50.6%; P < 0.01), but a lower accuracy than resident physicians (58.1% vs 59.7%; P < 0.01). AHPs outperformed nurses and APPs in every metric and showed comparable performance to resident physicians and PCPs. Our findings highlight significant gaps in the ECG interpretation proficiency among healthcare professionals. [ABSTRACT FROM AUTHOR]

The electrocardiogram (ECG) is a crucial diagnostic tool in medicine with concerns about its interpretation proficiency across various medical disciplines. Our study aimed to explore potential causes of these issues and identify areas requiring improvement. A survey was conducted among medical professionals to understand their experiences with ECG interpretation and education. A total of 2515 participants from diverse medical backgrounds were surveyed. A total of 1989 (79%) participants reported ECG interpretation as part of their practice. However, 45% expressed discomfort with independent interpretation. A significant 73% received less than 5 hours of ECG-specific education, with 45% reporting no education at all. Also, 87% reported limited or no expert supervision. Nearly all medical professionals (2461, 98%) expressed a desire for more ECG education. These findings were consistent across all groups and did not vary between primary care physicians, cardiology FIT, resident physicians, medical students, APPs, nurses, physicians, and nonphysicians. This study reveals substantial deficiencies in ECG interpretation training, supervision, and confidence among medical professionals, despite a strong interest in increased ECG education. [ABSTRACT FROM AUTHOR]

Aims: Non-cardiologists (NCs) are often responsible for the preliminary diagnosis and early management of patients presenting with ventricular tachycardia (VT) or supraventricular wide complex tachycardia (SWCT). At present, the Vereckei aVR and Brugada algorithms are the most widely recognized and frequently relied upon wide complex tachycardia (WCT) differentiation criteria by NCs. This study aimed to determine the diagnostic efficacy of the Vereckei aVR and Brugada algorithms when applied by NCs.Methods: In a blinded fashion, three internal medicine residents prospectively interpreted WCTs using the Vereckei aVR and Brugada algorithms. The diagnostic performance of each method was evaluated according to their agreement with the correct rhythm diagnosis.Results: Two-hundred sixty-nine WCTs (160 VT, 109 SWCT) from 186 patients were independently interpreted by each participant (807 separate interpretations per algorithm). The aVR and Brugada algorithms accurately classified 546 out of 807 (67.7%) and 622 out of 807 (77.1%) interpreted WCTs, respectively. Overall sensitivity and specificity of the aVR algorithm for VT was 92.1% and 31.8%, respectively. Overall sensitivity and specificity of the Brugada algorithm for VT was 89.4% and 59.0%, respectively. Both algorithms yielded modestly favorable overall positive predictive values (aVR 66.5%; Brugada 76.2%) and negative predictive values (73.3%; Brugada 79.1%).Conclusion: Non-cardiologist algorithm users correctly identified most "actual" VTs, but did not sufficiently revise VT probability to conclusively distinguish VT and SWCT. Newer WCT differentiation methods are needed to improve NC's ability to accurately differentiate WCTs. [ABSTRACT FROM AUTHOR]

Background: There is growing interest in the role for non-vitamin K antagonist oral anticoagulants (NOACs) in patients without atrial fibrillation (AF). We aimed to provide a comprehensive assessment of the risks of ischemic stroke, myocardial infarction (MI), AF, and major bleeding in patients without previously diagnosed AF.Methods: Using a large US administrative database, we identified 6,495,875 patients ≥50 years between January 1, 2011, and September 30, 2016, who were not diagnosed with AF and were not treated with oral anticoagulants or nonaspirin antiplatelet agents. We assessed the risks by age, sex, the number of risk factors, and the combination of risk factors. We also calculated the number needed to treat or harm based on the untreated risks in our data set and relative risks of NOAC treatment derived from a recent clinical trial.Results: The event rates were 0.67%/y for ischemic stroke or MI, 0.96%/y for AF, and 0.52%/y for major bleeding. Among patients who had a stroke during follow-up, 84% were not diagnosed with AF at any time, and only 5% were diagnosed with AF before the stroke. Patients who had low number needed to treat for cardiovascular risk reduction (ie, potentially benefiting the most from the addition of NOACs) also had low number needed to harm for major bleeding (ie, facing serious harm).Conclusions: Patients without diagnosed AF but with certain risk factors were at a particularly high cardiovascular risk and may require new prevention approaches. In addition to the ongoing trials, future trials in other high-risk populations, for example, diabetes and chronic kidney disease, may be warranted. [ABSTRACT FROM AUTHOR]