One-year real-world experience with mavacamten and its physiologic effects on obstructive hypertrophic cardiomyopathy.

Mavacamten is a first-in-class cardiac myosin ATPase inhibitor, approved by the United States Food and Drug Administration for the treatment of hypertrophic cardiomyopathy with obstructive physiology (oHCM). Here, we present the real-world use of mavacamten in 50 patients with oHCM at a tertiary care referral center. In both our highlighted case and in our aggregate data, we report significant improvement in wall thickness, mitral regurgitation, left ventricular outflow tract obstruction and New York Heart Association symptom class. Moreover, in our center's experience, neither arrhythmia burden, nor contractility have worsened in the vast majority of patients: we note a clinically insignificant mean decrease in left ventricular ejection fraction (LVEF), with only two patients requiring temporary mavacamten discontinuance for LVEF < 50%. Adverse events were rare, unrelated to mavacamten itself, and seen solely in patients with disease too advanced to have been represented in clinical trials. Moreover, our multidisciplinary pathway enabled us to provide a large number of patients with a novel closely-monitored therapeutic within just a few months of commercial availability. These data lead us to conclude that mavacamten, as a first-in-class cardiac myosin inhibitor, is safe and efficacious in real-world settings.

Classification of Premature Ventricular Contractions in Athletes During Routine Preparticipation Exams.

BACKGROUND: Large-scale data on incidental premature ventricular contraction (PVC) prevalence and morphologies have been lacking, leaving many providers without guidance on further cardiac testing for patients with incidental PVCs on ECG. Athletes offer an intriguing cohort to understand the clinical significance, prevalence, and common morphologies of incidental PVCs because they often undergo ECG screening during preparticipation exams. METHODS: Digital ECGs were obtained from 10 728 screened athletes aged 14 to 35 years during mass screenings in schools and professional sports teams between 2014 and 2021. A retrospective analysis of ECGs with PVCs was performed using the simultaneous display of frontal (limb) and horizontal (precordial) plane leads. PVCs were coded for morphology and categorized as benign or nonbenign using recommended criteria. RESULTS: Twenty-six athletes (0.24%) were found to have at least 1 PVC. Among these, 50% were female, 65% were White, 8% were Asian, 4% were Hispanic, and 23% were Black. Nineteen of the 26 (73%) ECGs had PVCs with a left bundle branch block pattern compared with 7 (27%) with a right bundle branch block pattern. Twenty-four ECGs (96%) had PVCs with benign patterns, including 18 with right ventricular outflow tract, 5 with left anterior fascicle, and 2 with left posterior fascicle morphology. CONCLUSIONS: There is a low prevalence of PVCs on routine ECG screening of young athletes, and most PVCs are of benign morphology in this population. This study highlights the value of using digital ECG recorders with simultaneous lead display to guide decision-making about further cardiac testing and referrals in young athletes with PVCs. Using our results and review of the literature, we propose methods and algorithms of PVC evaluation on screening ECGs to help guide many providers with risk stratification and decision-making about further cardiac testing and electrophysiology referrals in young athletes with PVCs.

Simple models vs. deep learning in detecting low ejection fraction from the electrocardiogram.

Aims: Deep learning methods have recently gained success in detecting left ventricular systolic dysfunction (LVSD) from electrocardiogram (ECG) waveforms. Despite their high level of accuracy, they are difficult to interpret and deploy broadly in the clinical setting. In this study, we set out to determine whether simpler models based on standard ECG measurements could detect LVSD with similar accuracy to that of deep learning models. Methods and results: Using an observational data set of 40 994 matched 12-lead ECGs and transthoracic echocardiograms, we trained a range of models with increasing complexity to detect LVSD based on ECG waveforms and derived measurements. The training data were acquired from the Stanford University Medical Center. External validation data were acquired from the Columbia Medical Center and the UK Biobank. The Stanford data set consisted of 40 994 matched ECGs and echocardiograms, of which 9.72% had LVSD. A random forest model using 555 discrete, automated measurements achieved an area under the receiver operator characteristic curve (AUC) of 0.92 (0.91-0.93), similar to a deep learning waveform model with an AUC of 0.94 (0.93-0.94). A logistic regression model based on five measurements achieved high performance [AUC of 0.86 (0.85-0.87)], close to a deep learning model and better than N-terminal prohormone brain natriuretic peptide (NT-proBNP). Finally, we found that simpler models were more portable across sites, with experiments at two independent, external sites. Conclusion: Our study demonstrates the value of simple electrocardiographic models that perform nearly as well as deep learning models, while being much easier to implement and interpret.

Inequities in atrial fibrillation trials: An analysis of participant race, ethnicity, and sex over time.

BACKGROUND: Despite the importance of racial and ethnic representation in clinical trials, limited data exist about the enrollment trends of these groups in atrial fibrillation (AF) trials over time. OBJECTIVE: The purpose of this study was to examine the characteristics of contemporary AF clinical trials and to evaluate their association with race and ethnicity over time. METHODS: We performed a systematic search of all completed AF trials registered in ClinicalTrials.gov from conception to December 31, 2023, and manually extracted composition of race/ethnicity. We stratified trials by study characteristics, including impact factor, publication status, funding source, and location. We calculated the participation to prevalence ratio (PPR) by dividing the percentage of non-White participants by the percentage of non-White participants in the disease population (PPR of 0.8-1.2 suggests proportional representation) over time. RESULTS: We identified 277 completed AF trials encompassing a total of 1,933,441 adults, with a median proportion of non-White at 12% (interquartile range, 6%-27%), 121 (43.7%) device focused, and 184 (66.4%) funded by industry. Only 36.1% of trials reported comprehensive race information. Overall, non-White participants were underrepresented (PPR = 0.511; P < .001), including Black (PPR = 0.263) and Hispanic (PPR = 0.337) participants. The proportion of non-White participants did not change significantly between 2000 and 2023 (11% vs 9%; P = .343). CONCLUSION: Despite greater awareness, race/ethnicity reporting and representation of non-White groups in AF clinical trials are poor and have not improved significantly over time. These findings demand additional recruitment efforts and novel recruitment policies to ensure adequate representation of these demographic subgroups in future AF clinical trials.

Generation of induced pluripotent stem cell lines from patients with LQT1 caused by heterozygous mutations in the KCNQ1 gene.

Long QT Syndrome (LQTS) is a genetic heart disorder that can induce cardiac arrhythmias. The most prevalent subtype, LQT1, stems from rare variants in the KCNQ1 gene. Utilizing induced pluripotent stem cells (iPSCs) enables detailed cellular studies and personalized medicine approaches for this life-threatening condition. We generated two LQT1 iPSC lines with single nucleotide nonsense mutations, c.1031 C > T and c.1121 T > A in KCNQ1. Both lines exhibited typical iPSC morphology, expressed high levels of pluripotent markers, maintained normal karyotype, and possessed the capability to differentiate into three germ layers. These cell lines serve as important tools for investigating the biological mechanisms underlying LQT1 due to mutations in the KCNQ1 gene.

Characteristics of contemporary atrial fibrillation clinical trials and their association with industry sponsorship.

BACKGROUND: Industry sponsorship is an important source of funding for atrial fibrillation (AF) clinical trials, the implications of which have not been analyzed. OBJECTIVE: The purpose of this study was to determine the characteristics of contemporary AF clinical trials and to evaluate their association with funding source. METHODS: We systematically assessed all completed AF trials registered in the ClinicalTrials.gov database between conception to October 31, 2023, and extracted publicly available information including funding source, trial size, demographic distribution, intervention, location, and publication status. Trial characteristics were compared using the Wilcoxon rank-sum test and Fisher exact test for continuous and categorical variables, respectively. RESULTS: Of the 253 clinical trials assessed, 171 (68%) reported industry funding. Industry funding was associated with a greater median number of patients enrolled (172 vs 80; P <.001), publication rate (56.7% vs 42.7%; P = .04), probability of being product-focused (48.0% vs 24.4%; P <.001), and multicontinental recruitment location (25.2% vs 2.4%; P <.001) when compared to nonindustry-funded trials. However, industry funding was not associated with a significant difference in median impact factor (7.7 vs 7.7; P = .723). The overall proportion of industry-funded trials did not change over time (P = 1). CONCLUSION: Industry-funded clinical trials in AF often are larger, more frequently published, multicontinental, and product-focused. Industry funding was found to be associated with significant differences in study enrollment and publication metrics.

Prevalence of frequent premature ventricular contractions and nonsustained ventricular tachycardia in older women screened for atrial fibrillation in the Women's Health Initiative.

BACKGROUND: Frequent premature ventricular contractions (PVCs) and nonsustained ventricular tachycardia (NSVT) have been associated with cardiovascular disease and mortality. Their prevalence, especially in ambulatory populations, is understudied and limited by few female participants and the use of short-duration (24- to 48-hour) monitoring. OBJECTIVE: The objective of this study was to report the prevalence of frequent PVCs and NSVT in a community-based population of women likely to undergo electrocardiogram (ECG) screening by sequential patch monitoring. METHODS: Participants from the Women's Health Initiative Strong and Healthy (WHISH) trial with no history of atrial fibrillation (AF) but 5-year predicted risk of incident AF ≥5% by CHARGE-AF score were randomly selected to undergo screening with 7-day ECG patch monitors at baseline, 6 months, and 12 months. Recordings were reviewed for PVCs and NSVT (>5 beats); data were analyzed with multivariate regression models. RESULTS: There were 1067 participants who underwent ECG screening at baseline, 866 at 6 months, and 777 at 12 months. Frequent PVCs were found on at least 1 patch from 4.3% of participants, and 1 or more episodes of NSVT were found in 12 (1.1%) women. PVC frequency directly correlated with CHARGE-AF score and NSVT on any patch. Detection of frequent PVCs increased with sequential monitoring. CONCLUSION: In postmenopausal women at high risk for AF, frequent PVCs were relatively common (4.3%) and correlated with higher CHARGE-AF score. As strategies for AF screening continue to evolve, particularly in those individuals at high risk of AF, the prevalence of incidental ventricular arrhythmias is an important benchmark to guide clinical decision-making.

2023 ACC/AHA/ACCP/HRS Guideline for the Diagnosis and Management of Atrial Fibrillation: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines.

AIM: The "2023 ACC/AHA/ACCP/HRS Guideline for the Diagnosis and Management of Atrial Fibrillation" provides recommendations to guide clinicians in the treatment of patients with atrial fibrillation. METHODS: A comprehensive literature search was conducted from May 12, 2022, to November 3, 2022, encompassing studies, reviews, and other evidence conducted on human subjects that were published in English from PubMed, EMBASE, the Cochrane Library, the Agency for Healthcare Research and Quality, and other selected databases relevant to this guideline. Additional relevant studies, published through November 2022, during the guideline writing process, were also considered by the writing committee and added to the evidence tables, where appropriate. STRUCTURE: Atrial fibrillation is the most sustained common arrhythmia, and its incidence and prevalence are increasing in the United States and globally. Recommendations from the "2014 AHA/ACC/HRS Guideline for the Management of Patients With Atrial Fibrillation" and the "2019 AHA/ACC/HRS Focused Update of the 2014 AHA/ACC/HRS Guideline for the Management of Patients With Atrial Fibrillation" have been updated with new evidence to guide clinicians. In addition, new recommendations addressing atrial fibrillation and thromboembolic risk assessment, anticoagulation, left atrial appendage occlusion, atrial fibrillation catheter or surgical ablation, and risk factor modification and atrial fibrillation prevention have been developed.

2023 ACC/AHA/ACCP/HRS Guideline for the Diagnosis and Management of Atrial Fibrillation: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines.

AIM: The "2023 ACC/AHA/ACCP/HRS Guideline for the Diagnosis and Management of Patients With Atrial Fibrillation" provides recommendations to guide clinicians in the treatment of patients with atrial fibrillation. METHODS: A comprehensive literature search was conducted from May 12, 2022, to November 3, 2022, encompassing studies, reviews, and other evidence conducted on human subjects that were published in English from PubMed, EMBASE, the Cochrane Library, the Agency for Healthcare Research and Quality, and other selected databases relevant to this guideline. Additional relevant studies, published through November 2022, during the guideline writing process, were also considered by the writing committee and added to the evidence tables, where appropriate. STRUCTURE: Atrial fibrillation is the most sustained common arrhythmia, and its incidence and prevalence are increasing in the United States and globally. Recommendations from the "2014 AHA/ACC/HRS Guideline for the Management of Patients With Atrial Fibrillation" and the "2019 AHA/ACC/HRS Focused Update of the 2014 AHA/ACC/HRS Guideline for the Management of Patients With Atrial Fibrillation" have been updated with new evidence to guide clinicians. In addition, new recommendations addressing atrial fibrillation and thromboembolic risk assessment, anticoagulation, left atrial appendage occlusion, atrial fibrillation catheter or surgical ablation, and risk factor modification and atrial fibrillation prevention have been developed.

Race, Sex, and Age Disparities in the Performance of ECG Deep Learning Models Predicting Heart Failure.

BACKGROUND: Deep learning models may combat widening racial disparities in heart failure outcomes through early identification of individuals at high risk. However, demographic biases in the performance of these models have not been well-studied. METHODS: This retrospective analysis used 12-lead ECGs taken between 2008 and 2018 from 326 518 patient encounters referred for standard clinical indications to Stanford Hospital. The primary model was a convolutional neural network model trained to predict incident heart failure within 5 years. Biases were evaluated on the testing set (160 312 ECGs) using the area under the receiver operating characteristic curve, stratified across the protected attributes of race, ethnicity, age, and sex. RESULTS: There were 59 817 cases of incident heart failure observed within 5 years of ECG collection. The performance of the primary model declined with age. There were no significant differences observed between racial groups overall. However, the primary model performed significantly worse in Black patients aged 0 to 40 years compared with all other racial groups in this age group, with differences most pronounced among young Black women. Disparities in model performance did not improve with the integration of race, ethnicity, sex, and age into model architecture, by training separate models for each racial group, or by providing the model with a data set of equal racial representation. Using probability thresholds individualized for race, age, and sex offered substantial improvements in F1 scores. CONCLUSIONS: The biases found in this study warrant caution against perpetuating disparities through the development of machine learning tools for the prognosis and management of heart failure. Customizing the application of these models by using probability thresholds individualized by race, ethnicity, age, and sex may offer an avenue to mitigate existing algorithmic disparities.

Clinical Management of Brugada Syndrome: Commentary From the Experts.

Although there is consensus on the management of patients with Brugada Syndrome with high risk for sudden cardiac arrest, asymptomatic or intermediate-risk patients present clinical management challenges. This document explores the management opinions of experts throughout the world for patients with Brugada Syndrome who do not fit guideline recommendations. Four real-world clinical scenarios were presented with commentary from small expert groups for each case. All authors voted on case-specific questions to evaluate the level of consensus among the entire group in nuanced diagnostic and management decisions relevant to each case. Points of agreement, points of controversy, and gaps in knowledge are highlighted.

Improved Cardiac Performance and Decreased Arrhythmia in Hypertrophic Cardiomyopathy With Non-ß-Blocking R-Enantiomer Carvedilol.

BACKGROUND: Hypercontractility and arrhythmia are key pathophysiologic features of hypertrophic cardiomyopathy (HCM), the most common inherited heart disease. ß-Adrenergic receptor antagonists (ß-blockers) are the first-line therapy for HCM. However, ß-blockers commonly selected for this disease are often poorly tolerated in patients, where heart-rate reduction and noncardiac effects can lead to reduced cardiac output and fatigue. Mavacamten, myosin ATPase inhibitor recently approved by the US Food and Drug Administration, has demonstrated the ability to ameliorate hypercontractility without lowering heart rate, but its benefits are so far limited to patients with left ventricular (LV) outflow tract obstruction, and its effect on arrhythmia is unknown. METHODS: We screened 21 ß-blockers for their impact on myocyte contractility and evaluated the antiarrhythmic properties of the most promising drug in a ventricular myocyte arrhythmia model. We then examined its in vivo effect on LV function by hemodynamic pressure-volume loop analysis. The efficacy of the drug was tested in vitro and in vivo compared with current therapeutic options (metoprolol, verapamil, and mavacamten) for HCM in an established mouse model of HCM (Myh6R403Q/+ and induced pluripotent stem cell (iPSC)-derived cardiomyocytes from patients with HCM (MYH7R403Q/+). RESULTS: We identified that carvedilol, a ß-blocker not commonly used in HCM, suppresses contractile function and arrhythmia by inhibiting RyR2 (ryanodine receptor type 2). Unlike metoprolol (a ß1-blocker), carvedilol markedly reduced LV contractility through RyR2 inhibition, while maintaining stroke volume through α1-adrenergic receptor inhibition in vivo. Clinically available carvedilol is a racemic mixture, and the R-enantiomer, devoid of ß-blocking effect, retains the ability to inhibit both α1-receptor and RyR2, thereby suppressing contractile function and arrhythmias without lowering heart rate and cardiac output. In Myh6R403Q/+ mice, R-carvedilol normalized hyperdynamic contraction, suppressed arrhythmia, and increased cardiac output better than metoprolol, verapamil, and mavacamten. The ability of R-carvedilol to suppress contractile function was well retained in MYH7R403Q/+ iPSC-derived cardiomyocytes. CONCLUSIONS: R-enantiomer carvedilol attenuates hyperdynamic contraction, suppresses arrhythmia, and at the same time, improves cardiac output without lowering heart rate by dual blockade of α1-adrenergic receptor and RyR2 in mouse and human models of HCM. This combination of therapeutic effects is unique among current therapeutic options for HCM and may particularly benefit patients without LV outflow tract obstruction.

Development and validation of a rapid visual technique for left ventricular hypertrophy detection from the electrocardiogram.

Introduction: Left ventricular hypertrophy (LVH) detection techniques on by electrocardiogram (ECG) are cumbersome to remember with modest performance. This study validated a rapid technique for LVH detection and measured its performance against other techniques. Methods: This was a retrospective cohort study of patients at Stanford Health Care who received ECGs and resting transthoracic echocardiograms (TTE) from 2006 through 2018. The novel technique, Witteles-Somani (WS), assesses for S- and R-wave overlap on adjacent precordial leads. The WS, Sokolow-Lyon, Cornell, and Peguero-Lo Presti techniques were algorithmically implemented on ECGs. Classification metrics, receiver-operator curves, and Pearson correlations measured performance. Age- and sex-adjusted Cox proportional hazard models evaluated associations between incident cardiovascular outcomes and each technique. Results: A total of 53,333 ECG-TTE pairs from 18,873 patients were identified. Of all ECG-TTE pairs, 21,638 (40.6%) had TTE-diagnosed LVH. The WS technique had a sensitivity of 0.46, specificity of 0.66, and AUROC of 0.56, compared to Sokolow-Lyon (AUROC 0.55), Cornell (AUROC 0.63), and Peguero-Lo Presti (AUROC 0.63). Patients meeting LVH by WS technique had a higher risk of cardiovascular mortality [HR 1.18, 95% CI (1.12, 1.24), P < 0.001] and a higher risk of developing any cardiovascular disease [HR 1.29, 95% CI (1.22, 1.36), P < 0.001], myocardial infarction [HR 1.60, 95% CI (1.44, 1.78), P < 0.005], and heart failure [HR 1.24, 95% CI (1.17, 1.32), P < 0.001]. Conclusions: The WS criteria is a rapid visual technique for LVH detection with performance like other LVH detection techniques and is associated with incident cardiovascular outcomes.

A deep learning-based electrocardiogram risk score for long term cardiovascular death and disease.

The electrocardiogram (ECG) is the most frequently performed cardiovascular diagnostic test, but it is unclear how much information resting ECGs contain about long term cardiovascular risk. Here we report that a deep convolutional neural network can accurately predict the long-term risk of cardiovascular mortality and disease based on a resting ECG alone. Using a large dataset of resting 12-lead ECGs collected at Stanford University Medical Center, we developed SEER, the Stanford Estimator of Electrocardiogram Risk. SEER predicts 5-year cardiovascular mortality with an area under the receiver operator characteristic curve (AUC) of 0.83 in a held-out test set at Stanford, and with AUCs of 0.78 and 0.83 respectively when independently evaluated at Cedars-Sinai Medical Center and Columbia University Irving Medical Center. SEER predicts 5-year atherosclerotic disease (ASCVD) with an AUC of 0.67, similar to the Pooled Cohort Equations for ASCVD Risk, while being only modestly correlated. When used in conjunction with the Pooled Cohort Equations, SEER accurately reclassified 16% of patients from low to moderate risk, uncovering a group with an actual average 9.9% 10-year ASCVD risk who would not have otherwise been indicated for statin therapy. SEER can also predict several other cardiovascular conditions such as heart failure and atrial fibrillation. Using only lead I of the ECG it predicts 5-year cardiovascular mortality with an AUC of 0.80. SEER, used alongside the Pooled Cohort Equations and other risk tools, can substantially improve cardiovascular risk stratification and aid in medical decision making.

Association Between Insomnia, Stress Events, and Other Psychosocial Factors and Incident Atrial Fibrillation in Postmenopausal Women: Insights From the Women's Health Initiative.

Background The association between psychosocial factors and atrial fibrillation (AF) is poorly understood. Methods and Results Postmenopausal women from the Women's Health Initiative were retrospectively analyzed to identify incident AF in relation to a panel of validated psychosocial exposure variables, as assessed by multivariable Cox proportional hazard regression and hierarchical cluster analysis. Among the 83 736 women included, the average age was 63.9±7.0 years. Over an average of 10.5±6.2 years follow-up, there were 23 954 cases of incident AF. Hierarchical cluster analysis generated 2 clusters of highly correlated psychosocial variables: the Stress Cluster included stressful life events, depressive symptoms, and insomnia, and the Strain Cluster included optimism, social support, social strain, cynical hostility, and emotional expressiveness. Incident AF was associated with higher values in the Stress Cluster (hazard ratio [HR], 1.07 per unit cluster score [95% CI, 1.05-1.09]) and the Strain Cluster (HR, 1.03 per unit cluster score [95% CI, 1.00-1.05]). Of the 8 individual psychosocial predictors that were tested, insomnia (HR, 1.04 [95% CI, 1.03-1.06]) and stressful life events (HR, 1.02 [95% CI, 1.01-1.04]) were most strongly associated with increased incidence of AF in Cox regression analysis after multivariate adjustment. Subgroup analyses showed that the Strain Cluster was more strongly associated with incident AF in those with lower traditional AF risks (P for interaction=0.02) as determined by the cohorts for heart and aging research in genomic epidemiology for atrial fibrillation score. Conclusions Among postmenopausal women, 2 clusters of psychosocial stressors were found to be significantly associated with incident AF. Further research is needed to validate these associations.

Generation of two induced pluripotent stem cell lines from catecholaminergic polymorphic ventricular tachycardia patients carrying RYR2 mutations.

Catecholaminergic polymorphic ventricular tachycardia (CPVT) is a congenital arrhythmic syndrome caused by the RYR2 gene encoded ryanodine receptor. Mutations on RYR2 are commonly associated with ventricular tachycardia after adrenergic stimulation, leading to lethal arrhythmias and sudden cardiac death. We generated two human induced pluripotent stem cell (iPSC) lines from CPVT affected patients carrying single missense heterozygote RYR2 mutations, c.1082 G > A and c.100 A > C. Pluripotency and differentiation capability into derivatives of three germ layers were evaluated along with karyotype stability in the report. The generated patient-specific iPSC lines provide a reliable tool to investigate the CPVT phenotype and understand underlaying mechanisms.