Vigorous Exercise in Patients With Congenital Long QT Syndrome: Results of the Prospective, Observational, Multinational LIVE-LQTS Study.

BACKGROUND: Whether vigorous exercise increases risk of ventricular arrhythmias for individuals diagnosed and treated for congenital long QT syndrome (LQTS) remains unknown. METHODS: The National Institutes of Health-funded LIVE-LQTS study (Lifestyle and Exercise in the Long QT Syndrome) prospectively enrolled individuals 8 to 60 years of age with phenotypic and/or genotypic LQTS from 37 sites in 5 countries from May 2015 to February 2019. Participants (or parents) answered physical activity and clinical events surveys every 6 months for 3 years with follow-up completed in February 2022. Vigorous exercise was defined as ≥6 metabolic equivalents for >60 hours per year. A blinded Clinical Events Committee adjudicated the composite end point of sudden death, sudden cardiac arrest, ventricular arrhythmia treated by an implantable cardioverter defibrillator, and likely arrhythmic syncope. A National Death Index search ascertained vital status for those with incomplete follow-up. A noninferiority hypothesis (boundary of 1.5) between vigorous exercisers and others was tested with multivariable Cox regression analysis. RESULTS: Among the 1413 participants (13% <18 years of age, 35% 18-25 years of age, 67% female, 25% with implantable cardioverter defibrillators, 90% genotype positive, 49% with LQT1, 91% were treated with beta-blockers, left cardiac sympathetic denervation, and/or implantable cardioverter defibrillator), 52% participated in vigorous exercise (55% of these competitively). Thirty-seven individuals experienced the composite end point (including one sudden cardiac arrest and one sudden death in the nonvigorous group, one sudden cardiac arrest in the vigorous group) with overall event rates at 3 years of 2.6% in the vigorous and 2.7% in the nonvigorous exercise groups. The unadjusted hazard ratio for experience of events for the vigorous group compared with the nonvigorous group was 0.97 (90% CI, 0.57-1.67), with an adjusted hazard ratio of 1.17 (90% CI, 0.67-2.04). The upper 95% one-sided confidence level extended beyond the 1.5 boundary. Neither vigorous or nonvigorous exercise was found to be superior in any group or subgroup. CONCLUSIONS: Among individuals diagnosed with phenotypic and/or genotypic LQTS who were risk assessed and treated in experienced centers, LQTS-associated cardiac event rates were low and similar between those exercising vigorously and those not exercising vigorously. Consistent with the low event rate, CIs are wide, and noninferiority was not demonstrated. These data further inform shared decision-making discussions between patient and physician about exercise and competitive sports participation. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT02549664.

Subclinical Atrial Fibrillation: A Silent Threat with Uncertain Implications.

Atrial fibrillation (AF) is one of the most common cardiac arrhythmias. Implantable and wearable cardiac devices have enabled the detection of asymptomatic AF episodes-termed subclinical AF (SCAF). SCAF, the prevalence of which is likely significantly underestimated, is associated with increased cardiovascular and all-cause mortality and a significant stroke risk. Recent advances in machine learning, namely artificial intelligence-enabled ECG (AI-ECG), have enabled identification of patients at higher likelihood of SCAF. Leveraging the capabilities of AI-ECG algorithms to drive screening protocols could eventually allow for earlier detection and treatment and help reduce the burden associated with AF.

Performance of AI-Enabled Electrocardiogram in the Prediction of Metabolic Dysfunction-Associated Steatotic Liver Disease.

BACKGROUND AND AIMS: Accessible noninvasive screening tools for metabolic dysfunction-associated steatotic liver disease (MASLD) are needed. We aim to explore the performance of a deep learning-based artificial intelligence (AI) model in distinguishing the presence of MASLD using 12-lead electrocardiogram (ECG). METHODS: This is a retrospective study of adults diagnosed with MASLD in Olmsted County, Minnesota, between 1996 and 2019. Both cases and controls had ECGs performed within 6 years before and 1 year after study entry. An AI-based ECG model using a convolutional neural network was trained, validated, and tested in 70%, 10%, and 20% of the cohort, respectively. External validation was performed in an independent cohort from Mayo Clinic Enterprise. The primary outcome was the performance of ECG to identify MASLD, alone or when added to clinical parameters. RESULTS: A total of 3468 MASLD cases and 25,407 controls were identified. The AI-ECG model predicted the presence of MASLD with an area under the curve (AUC) of 0.69 (original cohort) and 0.62 (validation cohort). The performance was similar or superior to age- and sex-adjusted models using body mass index (AUC, 0.71), presence of diabetes, hypertension or hyperlipidemia (AUC, 0.68), or diabetes alone (AUC, 0.66). The model combining ECG, age, sex, body mass index, diabetes, and alanine aminotransferase had the highest AUC: 0.76 (original) and 0.72 (validation). CONCLUSIONS: This is a proof-of-concept study that an AI-based ECG model can detect MASLD with a comparable or superior performance as compared with the models using a single clinical parameter but not superior to the combination of clinical parameters. ECG can serve as another screening tool for MASLD in the nonhepatology space.

Realtime Diagnosis from Electrocardiogram Artificial Intelligence-Guided Screening for Atrial Fibrillation with Long Follow-Up (REGAL): Rationale and design of a pragmatic, decentralized, randomized controlled trial.

BACKGROUND: 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. OBJECTIVES: 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. DESIGN: The Realtime diagnosis from Electrocardiogram (ECG) Artificial Intelligence (AI)-Guided Screening for Atrial Fibrillation (AF) with Long 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. SUMMARY: 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. CLINICALTRIALS: GOV: : NCT05923359.

Rationale and design of the NODE-303 study: evaluating the safety of symptom-prompted, self-administered etripamil for paroxysmal supraventricular tachycardia episodes in real-world settings.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: Overall, these data will support the development of a potentially paradigm-changing long-term management strategy for recurrent PSVT.

Regional differences in outcomes with ablation versus drug therapy for atrial fibrillation: Results from the CABANA trial.

BACKGROUND: 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. METHODS: 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. RESULTS: 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). CONCLUSIONS: 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. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT0091150; https://clinicaltrials.gov/study/NCT00911508.

First-in-man report of transsubclavian venous implantation of the Aveir leadless cardiac pacing system.

INTRODUCTION: Transsubclavian venous implantation of the Aveir leadless cardiac pacemaker (LCP) has not been previously reported. METHODS AND RESULTS: Three cases of transsubclavian implantation of the Aveir LCP are reported. Two cases were postbilateral orthotopic lung transplant, without appropriate femoral or jugular access due to recent ECMO cannulation and jugular central venous catheters. In one case, there was strong patient preference for same-day discharge. Stability testing confirmed adequate fixation and electrical testing confirmed stable parameters in all cases. All patients tolerated the procedure well without significant immediate complications. CONCLUSIONS: We demonstrate the feasibility of transsubclavian implantation of the Aveir LCP.

Cardiovascular Disease Screening in Women: Leveraging Artificial Intelligence and Digital Tools.

Cardiovascular disease remains the leading cause of death in women. Given accumulating evidence on sex- and gender-based differences in cardiovascular disease development and outcomes, the need for more effective approaches to screening for risk factors and phenotypes in women is ever urgent. Public health surveillance and health care delivery systems now continuously generate massive amounts of data that could be leveraged to enable both screening of cardiovascular risk and implementation of tailored preventive interventions across a woman's life span. However, health care providers, clinical guidelines committees, and health policy experts are not yet sufficiently equipped to optimize the collection of data on women, use or interpret these data, or develop approaches to targeting interventions. Therefore, we provide a broad overview of the key opportunities for cardiovascular screening in women while highlighting the potential applications of artificial intelligence along with digital technologies and tools.

Bridging ECG learning with emerging technologies: Advancing clinical excellence.

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.

Research Opportunities in Stroke Prevention for Atrial Fibrillation: A Report From a National Heart, Lung, and Blood Institute Virtual Workshop.

Atrial fibrillation (AF) is one of the strongest risk factors for ischemic stroke, which is a leading cause of disability and death. Given the aging population, increasing prevalence of AF risk factors, and improved survival in those with cardiovascular disease, the number of individuals affected by AF will continue increasing over time. While multiple proven stroke prevention therapies exist, important questions remain about the optimal approach to stroke prevention at the population and individual patient levels. Our report summarizes the National Heart, Lung, and Blood Institute virtual workshop focused on identifying key research opportunities related to stroke prevention in AF. The workshop reviewed major knowledge gaps and identified targeted research opportunities to advance stroke prevention in AF in the following areas: (1) improving risk stratification tools for stroke and intracranial hemorrhage; (2) addressing challenges with oral anticoagulants; and (3) delineating the optimal roles of percutaneous left atrial appendage occlusion and surgical left atrial appendage closure/excision. This report aims to promote innovative, impactful research that will lead to more personalized, effective use of stroke prevention strategies in people with AF.

Artificial intelligence-guided screening for atrial fibrillation using electrocardiogram during sinus rhythm: a prospective non-randomised interventional trial.

BACKGROUND: Previous atrial fibrillation screening trials have highlighted the need for more targeted approaches. We did a pragmatic study to evaluate the effectiveness of an artificial intelligence (AI) algorithm-guided targeted screening approach for identifying previously unrecognised atrial fibrillation. METHODS: For this non-randomised interventional trial, we prospectively recruited patients with stroke risk factors but with no known atrial fibrillation who had an electrocardiogram (ECG) done in routine practice. Participants wore a continuous ambulatory heart rhythm monitor for up to 30 days, with the data transmitted in near real time through a cellular connection. The AI algorithm was applied to the ECGs to divide patients into high-risk or low-risk groups. The primary outcome was newly diagnosed atrial fibrillation. In a secondary analysis, trial participants were propensity-score matched (1:1) to individuals from the eligible but unenrolled population who served as real-world controls. This study is registered with ClinicalTrials.gov, NCT04208971. FINDINGS: 1003 patients with a mean age of 74 years (SD 8·8) from 40 US states completed the study. Over a mean 22·3 days of continuous monitoring, atrial fibrillation was detected in six (1·6%) of 370 patients with low risk and 48 (7·6%) of 633 with high risk (odds ratio 4·98, 95% CI 2·11-11·75, p=0·0002). Compared with usual care, AI-guided screening was associated with increased detection of atrial fibrillation (high-risk group: 3·6% [95% CI 2·3-5·4] with usual care vs 10·6% [8·3-13·2] with AI-guided screening, p<0·0001; low-risk group: 0·9% vs 2·4%, p=0·12) over a median follow-up of 9·9 months (IQR 7·1-11·0). INTERPRETATION: An AI-guided targeted screening approach that leverages existing clinical data increased the yield for atrial fibrillation detection and could improve the effectiveness of atrial fibrillation screening. FUNDING: Mayo Clinic Robert D and Patricia E Kern Center for the Science of Health Care Delivery.

Physicians' perspectives on percutaneous left atrial appendage occlusion for patients with atrial fibrillation.

BACKGROUND: There has been an increasing uptake of transcatheter left atrial appendage occlusion (LAAO) for stroke reduction in atrial fibrillation. OBJECTIVES: To investigate the perceptions and approaches among a nationally representative sample of physicians. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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. CONDENSED ABSTRACT: 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.

Identifying treatment heterogeneity in atrial fibrillation using a novel causal machine learning method.

BACKGROUND: 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. METHODS: 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 CHA2DS2 -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. RESULTS: 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. CONCLUSIONS: 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.

Screening for peripartum cardiomyopathies using artificial intelligence in Nigeria (SPEC-AI Nigeria): Clinical trial rationale and design.

BACKGROUND: 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. OBJECTIVES: 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. DESIGN: 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. SUMMARY: 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. TRIAL REGISTRATION: Clinicaltrials.gov: NCT05438576.

Mayo Clinic VT calculator: A practical tool for accurate wide complex tachycardia differentiation.

The discrimination of ventricular tachycardia (VT) versus supraventricular wide complex tachycardia (SWCT) via 12-lead electrocardiogram (ECG) is crucial for achieving appropriate, high-quality, and cost-effective care in patients presenting with wide QRS complex tachycardia (WCT). Decades of rigorous research have brought forth an expanding arsenal of applicable manual algorithm methods for differentiating WCTs. However, these algorithms are limited by their heavy reliance on the ECG interpreter for their proper execution. Herein, we introduce the Mayo Clinic ventricular tachycardia calculator (MC-VTcalc) as a novel generalizable, accurate, and easy-to-use means to estimate VT probability independent of ECG interpreter competency. The MC-VTcalc, through the use of web-based and mobile device platforms, only requires the entry of computerized measurements (i.e., QRS duration, QRS axis, and T-wave axis) that are routinely displayed on standard 12-lead ECG recordings.

Computerized electrocardiogram data transformation enables effective algorithmic differentiation of wide QRS complex tachycardias.

BACKGROUND: Accurate automated wide QRS complex tachycardia (WCT) differentiation into ventricular tachycardia (VT) and supraventricular wide complex tachycardia (SWCT) can be accomplished using calculations derived from computerized electrocardiogram (ECG) data of paired WCT and baseline ECGs. OBJECTIVE: Develop and trial novel WCT differentiation approaches for patients with and without a corresponding baseline ECG. METHODS: We developed and trialed WCT differentiation models comprised of novel and previously described parameters derived from WCT and baseline ECG data. In Part 1, a derivation cohort was used to evaluate five different classification models: logistic regression (LR), artificial neural network (ANN), Random Forests [RF], support vector machine (SVM), and ensemble learning (EL). In Part 2, a separate validation cohort was used to prospectively evaluate the performance of two LR models using parameters generated from the WCT ECG alone (Solo Model) and paired WCT and baseline ECGs (Paired Model). RESULTS: Of the 421 patients of the derivation cohort (Part 1), a favorable area under the receiver operating characteristic curve (AUC) by all modeling subtypes: LR (0.96), ANN (0.96), RF (0.96), SVM (0.96), and EL (0.97). Of the 235 patients of the validation cohort (Part 2), the Solo Model and Paired Model achieved a favorable AUC for 103 patients with (Solo Model 0.87; Paired Model 0.95) and 132 patients without (Solo Model 0.84; Paired Model 0.95) a corroborating electrophysiology procedure or intracardiac device recording. CONCLUSION: Accurate WCT differentiation may be accomplished using computerized data of (i) the WCT ECG alone and (ii) paired WCT and baseline ECGs.

Associations of Atrial Fibrillation After Noncardiac Surgery With Stroke, Subsequent Arrhythmia, and Death : A Cohort Study.

BACKGROUND: Postoperative atrial fibrillation (AF) after noncardiac surgery confers increased risks for ischemic stroke and transient ischemic attack (TIA). How outcomes for postoperative AF after noncardiac surgery compare with those for AF occurring outside of the operative setting is unknown. OBJECTIVE: To compare the risks for ischemic stroke or TIA and other outcomes in patients with postoperative AF versus those with incident AF not associated with surgery. DESIGN: Cohort study. SETTING: Olmsted County, Minnesota. PARTICIPANTS: Patients with incident AF between 2000 and 2013. MEASUREMENTS: Patients were categorized as having AF occurring within 30 days of a noncardiac surgery (postoperative AF) or having AF unrelated to surgery (nonoperative AF). RESULTS: Of 4231 patients with incident AF, 550 (13%) had postoperative AF as their first-ever documented AF presentation. Over a mean follow-up of 6.3 years, 486 patients had an ischemic stroke or TIA and 2462 had subsequent AF; a total of 2565 deaths occurred. The risk for stroke or TIA was similar between those with postoperative AF and nonoperative AF (absolute risk difference [ARD] at 5 years, 0.1% [95% CI, -2.9% to 3.1%]; hazard ratio [HR], 1.01 [CI, 0.77 to 1.32]). A lower risk for subsequent AF was seen for patients with postoperative AF (ARD at 5 years, -13.4% [CI, -17.8% to -9.0%]; HR, 0.68 [CI, 0.60 to 0.77]). Finally, no difference was seen for cardiovascular death or all-cause death between patients with postoperative AF and nonoperative AF. LIMITATION: The population consisted predominantly of White patients; caution should be used when extrapolating the results to more racially diverse populations. CONCLUSION: Postoperative AF after noncardiac surgery is associated with similar risk for thromboembolism compared with nonoperative AF. Our findings have potentially important implications for the early postsurgical and subsequent management of postoperative AF. PRIMARY FUNDING SOURCE: National Institute on Aging.

Comparison of two artificial intelligence-augmented ECG approaches: Machine learning and deep learning.

BACKGROUND: 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. PURPOSE: To provide illustrative comparison of two general AI-ECG modeling approaches: machine learning (ML) and deep learning (DL). METHOD COMPARISON: 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 12­lead 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 12­lead ECG interpretation, which relies upon the independent recognition and analysis of a virtually incalculable number of ECG features from a vast collection of standard 12­lead ECGs. CONCLUSION: 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.

The emergence and destiny of automated methods to differentiate wide QRS complex tachycardias.

Accurate differentiation of wide complex tachycardias (WCTs) into ventricular tachycardia (VT) or supraventricular wide complex tachycardia (SWCT) using non-invasive methods such as 12­lead 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.

Saliency maps provide insights into artificial intelligence-based electrocardiography models for detecting hypertrophic cardiomyopathy.

INTRODUCTION: A 12­lead 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. METHODS: We derived a new one­lead (lead I) CNN model based on median beats using the same methodology and cohort used for the original 12­lead CNN model (3047 patients with HCM, and 63,926 sex- and age-matched non-HCM controls). One­lead, 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. RESULTS: The one­lead, median-beat CNN had an AUC of 0.90 (95% CI 0.89-0.92) for HCM detection, similar to the original 12­lead 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. CONCLUSIONS: Saliency maps of a one­lead, median-beat-based CNN model identified perturbations in ventricular repolarization as the main region of interest in detecting HCM.