Determinants of global cardiac implantable electrical device remote monitoring utilization - Results from an international survey.

Background: Despite near-global availability of remote monitoring (RM) in patients with cardiac implantable electronic devices (CIED), there is a high geographical variability in the uptake and use of RM. The underlying reasons for this geographic disparity remain largely unknown. Objectives: To study the determinants of worldwide RM utilization and identify locoregional barriers of RM uptake. Methods: An international survey was administered to all CIED clinic personnel using the Heart Rhythm Society global network collecting demographic information, as well as information on the use of RM, the organization of the CIED clinic, and details on local reimbursement and clinic funding. The most complete response from each center was included in the current analysis. Stepwise forward multivariate linear regression was performed to identify determinants of the percentage of patients with a CIED on RM. Results: A total of 302 responses from 47 different countries were included, 61.3% by physicians and 62.3% from hospital-based CIED clinics. The median percentage of CIED patients on RM was 80% (interquartile range, 40-90). Predictors of RM use were gross national income per capita (0.76% per US$1000, 95% CI 0.72-1.00, P < .001), office-based clinics (7.48%, 95% CI 1.53-13.44, P = .014), and presence of clinic funding (per-patient payment model 7.90% [95% CI 0.63-15.17, P = .033); global budget 3.56% (95% CI -6.14 to 13.25, P = .471]). Conclusion: The high variability in RM utilization can partly be explained by economic and structural barriers that may warrant specific efforts by all stakeholders to increase RM utilization.

Factors of Engagement in Research After Graduation from EP Fellowship: an HRS Survey.

BACKGROUND: The declining number of electrophysiologists pursuing academic research careers could negatively impact innovation for patients with heart rhythm disorders in the coming decades. OBJECTIVE: To explore determinants of research engagement after graduation from EP fellowship programs and evaluate associated barriers and opportunities. METHODS: A mixed methods survey of EP fellows and early career electrophysiologists was conducted, drawing from Heart Rhythm Society members. The survey encompassed 20 questions on demographics, research involvement, perceived research barriers, and perspectives on research time and opportunities. Responses were analyzed with robust Poisson regression. RESULTS: Among 259 respondents, those with dedicated research blocks during their fellowship had a significantly higher interest in future research (RR 1.15, p=0.04). The number of peer-reviewed publications modestly influenced interest in continued research (RR 1.0034 per publication, p < 0.0001), but there was no relationship to gender or race. Educational resources, networking opportunities, mentorship, funding, and protected time to enhance research engagement were important themes in the qualitative analysis, while key barriers to post-fellowship research were lack of mentorship, insufficient resources and time constraints in that order, particular with respect to women in research. Notably, no significant differences in barriers were observed between community training programs and academic centers. CONCLUSIONS: Research experience and mentorship during EP fellowship were key determinants of subsequent research success after training, with similar findings by sex and race. These findings explain how fellowship training influences a physician's research practice post training and highlights opportunities to modify EP fellowships and augment research retention.

Mechanistic Insights From Trials of Atrial Fibrillation Ablation: Charting a Course for the Future.

Success rates for catheter ablation of atrial fibrillation (AF), particularly persistent AF, remain suboptimal. Pulmonary vein isolation has been the cornerstone for catheter ablation of AF for over a decade. While successful for most patients, pulmonary vein isolation alone is still insufficient for a substantial minority. Frustratingly, multiple clinical trials testing a diverse array of additional ablation approaches have led to mixed results, with no current strategy that improves AF outcomes beyond pulmonary vein isolation in all patients. Nevertheless, this large collection of data could be used to extract important insights regarding AF mechanisms and the diversity of the AF syndrome. Mechanistically, the general model for arrhythmogenesis prompts the need for tools to individually assess triggers, drivers, and substrates in individual patients. A key goal is to identify those who will not respond to pulmonary vein isolation, with novel approaches to phenotyping that may include mapping to identify alternative drivers or critical substrates. This, in turn, can allow for the implementation of phenotype-based, targeted approaches that may categorize patients into groups who would or would not be likely to respond to catheter ablation, pharmacological therapy, and risk factor modification programs. One major goal is to predict individuals in whom additional empirical ablation, while feasible, may be futile or lead to atrial scarring or proarrhythmia. This work attempts to integrate key lessons from successful and failed trials of catheter ablation, as well as models of AF, to suggest future paradigms for AF treatment.

Multimodal explainable artificial intelligence identifies patients with non-ischaemic cardiomyopathy at risk of lethal ventricular arrhythmias.

The efficacy of an implantable cardioverter-defibrillator (ICD) in patients with a non-ischaemic cardiomyopathy for primary prevention of sudden cardiac death is increasingly debated. We developed a multimodal deep learning model for arrhythmic risk prediction that integrated late gadolinium enhanced (LGE) cardiac magnetic resonance imaging (MRI), electrocardiography (ECG) and clinical data. Short-axis LGE-MRI scans and 12-lead ECGs were retrospectively collected from a cohort of 289 patients prior to ICD implantation, across two tertiary hospitals. A residual variational autoencoder was developed to extract physiological features from LGE-MRI and ECG, and used as inputs for a machine learning model (DEEP RISK) to predict malignant ventricular arrhythmia onset. In the validation cohort, the multimodal DEEP RISK model predicted malignant ventricular arrhythmias with an area under the receiver operating characteristic curve (AUROC) of 0.84 (95% confidence interval (CI) 0.71-0.96), a sensitivity of 0.98 (95% CI 0.75-1.00) and a specificity of 0.73 (95% CI 0.58-0.97). The models trained on individual modalities exhibited lower AUROC values compared to DEEP RISK [MRI branch: 0.80 (95% CI 0.65-0.94), ECG branch: 0.54 (95% CI 0.26-0.82), Clinical branch: 0.64 (95% CI 0.39-0.87)]. These results suggest that a multimodal model achieves high prognostic accuracy in predicting ventricular arrhythmias in a cohort of patients with non-ischaemic systolic heart failure, using data collected prior to ICD implantation.

Competing risks of monomorphic vs. non-monomorphic ventricular arrhythmias in primary prevention implantable cardioverter-defibrillator recipients: Global Electrical Heterogeneity and Clinical Outcomes (GEHCO) study.

AIMS: Ablation of monomorphic ventricular tachycardia (MMVT) has been shown to reduce shock frequency and improve survival. We aimed to compare cause-specific risk factors for MMVT and polymorphic ventricular tachycardia (PVT)/ventricular fibrillation (VF) and to develop predictive models. METHODS AND RESULTS: The multicentre retrospective cohort study included 2668 patients (age 63.1 ± 13.0 years; 23% female; 78% white; 43% non-ischaemic cardiomyopathy; left ventricular ejection fraction 28.2 ± 11.1%). Cox models were adjusted for demographic characteristics, heart failure severity and treatment, device programming, and electrocardiogram metrics. Global electrical heterogeneity was measured by spatial QRS-T angle (QRSTa), spatial ventricular gradient elevation (SVGel), azimuth, magnitude (SVGmag), and sum absolute QRST integral (SAIQRST). We compared the out-of-sample performance of the lasso and elastic net for Cox proportional hazards and the Fine-Gray competing risk model. During a median follow-up of 4 years, 359 patients experienced their first sustained MMVT with appropriate implantable cardioverter-defibrillator (ICD) therapy, and 129 patients had their first PVT/VF with appropriate ICD shock. The risk of MMVT was associated with wider QRSTa [hazard ratio (HR) 1.16; 95% confidence interval (CI) 1.01-1.34], larger SVGel (HR 1.17; 95% CI 1.05-1.30), and smaller SVGmag (HR 0.74; 95% CI 0.63-0.86) and SAIQRST (HR 0.84; 95% CI 0.71-0.99). The best-performing 3-year competing risk Fine-Gray model for MMVT [time-dependent area under the receiver operating characteristic curve (ROC(t)AUC) 0.728; 95% CI 0.668-0.788] identified high-risk (> 50%) patients with 75% sensitivity and 65% specificity, and PVT/VF prediction model had ROC(t)AUC 0.915 (95% CI 0.868-0.962), both satisfactory calibration. CONCLUSION: We developed and validated models to predict the competing risks of MMVT or PVT/VF that could inform procedural planning and future randomized controlled trials of prophylactic ventricular tachycardia ablation. CLINICAL TRIAL REGISTRATION: URL:www.clinicaltrials.gov Unique identifier:NCT03210883.

Successful Cardiac Resynchronization Therapy Reduces Negative Septal Work in Patient-Specific Models of Dyssynchronous Heart Failure.

In patients with dyssynchronous heart failure (DHF), cardiac conduction abnormalities cause the regional distribution of myocardial work to be non-homogeneous. Cardiac resynchronization therapy (CRT) using an implantable, programmed biventricular pacemaker/defibrillator, can improve the synchrony of contraction between the right and left ventricles in DHF, resulting in reduced morbidity and mortality and increased quality of life. Since regional work depends on wall stress, which cannot be measured in patients, we used computational methods to investigate regional work distributions and their changes after CRT. We used three-dimensional multi-scale patient-specific computational models parameterized by anatomic, functional, hemodynamic, and electrophysiological measurements in eight patients with heart failure and left bundle branch block (LBBB) who received CRT. To increase clinical translatability, we also explored whether streamlined computational methods provide accurate estimates of regional myocardial work. We found that CRT increased global myocardial work efficiency with significant improvements in non-responders. Reverse ventricular remodeling after CRT was greatest in patients with the highest heterogeneity of regional work at baseline, however the efficacy of CRT was not related to the decrease in overall work heterogeneity or to the reduction in late-activated regions of high myocardial work. Rather, decreases in early-activated regions of myocardium performing negative myocardial work following CRT best explained patient variations in reverse remodeling. These findings were also observed when regional myocardial work was estimated using ventricular pressure as a surrogate for myocardial stress and changes in endocardial surface area as a surrogate for strain. These new findings suggest that CRT promotes reverse ventricular remodeling in human dyssynchronous heart failure by increasing regional myocardial work in early-activated regions of the ventricles, where dyssynchrony is specifically associated with hypoperfusion, late systolic stretch, and altered metabolic activity and that measurement of these changes can be performed using streamlined approaches.

Data Interoperability for Ambulatory Monitoring of Cardiovascular Disease: A Scientific Statement From the American Heart Association.

Wearable devices are increasingly used by a growing portion of the population to track health and illnesses. The data emerging from these devices can potentially transform health care. This requires an interoperability framework that enables the deployment of platforms, sensors, devices, and software applications within diverse health systems, aiming to facilitate innovation in preventing and treating cardiovascular disease. However, the current data ecosystem includes several noninteroperable systems that inhibit such objectives. The design of clinically meaningful systems for accessing and incorporating these data into clinical workflows requires strategies to ensure the quality of data and clinical content and patient and caregiver accessibility. This scientific statement aims to address the best practices, gaps, and challenges pertaining to data interoperability in this area, with considerations for (1) data integration and the scope of measures, (2) application of these data into clinical approaches/strategies, and (3) regulatory/ethical/legal issues.

Catheter Ablation as First-Line Therapy in Persistent Atrial Fibrillation: Patient Characteristics and Clinical Outcomes.

BACKGROUND: In patients with persistent atrial fibrillation (PerAF), antiarrhythmic drugs (AADs) are considered a first-line rhythm-control strategy, whereas catheter ablation is a reasonable alternative. OBJECTIVES: This study sought to examine the prevalence, patient characteristics, and clinical outcomes of patients with PerAF who underwent catheter ablation as a first or second-line strategy. METHODS: This multicenter observational study included consecutive patients with PerAF who underwent first-time ablation between January 2020 and September 2021 in 9 medical centers in the United States. Patients were divided into those who underwent ablation as first-line therapy and those who had ablation as second-line therapy. Patient characteristics and clinical outcomes were compared between the groups. RESULTS: A total of 2,083 patients underwent first-time ablation for PerAF. Of these, 1,086 (52%) underwent ablation as a first-line rhythm-control treatment. Compared with patients treated with AADs as first-line therapy, these patients were predominantly male (72.6% vs 68.1%; P = 0.03), with a lower frequency of hypertension (64.0% vs 73.4%; P < 0.001) and heart failure (19.1% vs 30.5%; P < 0.001). During a mean follow-up of 325.9 ± 81.6 days, arrhythmia-free survival was similar between the groups (HR: 1.13; 95% CI: 0.92-1.41); however, patients in the second-line ablation strategy were more likely to continue receiving AAD therapy (41.5% vs 15.9%; P < 0.001). CONCLUSIONS: A first-line ablation strategy for PerAF is prevalent in the United States, particularly in men with fewer comorbidities. More data are needed to identify patients with PerAF who derive benefit from an early intervention strategy.

Spatially Conserved Spiral Wave Activity During Human Atrial Fibrillation.

BACKGROUND: Atrial fibrillation is the most common cardiac arrhythmia in the world and increases the risk for stroke and morbidity. During atrial fibrillation, the electric activation fronts are no longer coherently propagating through the tissue and, instead, show rotational activity, consistent with spiral wave activation, focal activity, collision, or partial versions of these spatial patterns. An unexplained phenomenon is that although simulations of cardiac models abundantly demonstrate spiral waves, clinical recordings often show only intermittent spiral wave activity. METHODS: In silico data were generated using simulations in which spiral waves were continuously created and annihilated and in simulations in which a spiral wave was intermittently trapped at a heterogeneity. Clinically, spatio-temporal activation maps were constructed using 60 s recordings from a 64 electrode catheter within the atrium of N=34 patients (n=24 persistent atrial fibrillation). The location of clockwise and counterclockwise rotating spiral waves was quantified and all intervals during which these spiral waves were present were determined. For each interval, the angle of rotation as a function of time was computed and used to determine whether the spiral wave returned in step or changed phase at the start of each interval. RESULTS: In both simulations, spiral waves did not come back in phase and were out of step." In contrast, spiral waves returned in step in the majority (68%; P=0.05) of patients. Thus, the intermittently observed rotational activity in these patients is due to a temporally and spatially conserved spiral wave and not due to ones that are newly created at the onset of each interval. CONCLUSIONS: Intermittency of spiral wave activity represents conserved spiral wave activity of long, but interrupted duration or transient spiral activity, in the majority of patients. This finding could have important ramifications for identifying clinically important forms of atrial fibrillation and in guiding treatment.

Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association.

A major focus of academia, industry, and global governmental agencies is to develop and apply artificial intelligence and other advanced analytical tools to transform health care delivery. The American Heart Association supports the creation of tools and services that would further the science and practice of precision medicine by enabling more precise approaches to cardiovascular and stroke research, prevention, and care of individuals and populations. Nevertheless, several challenges exist, and few artificial intelligence tools have been shown to improve cardiovascular and stroke care sufficiently to be widely adopted. This scientific statement outlines the current state of the art on the use of artificial intelligence algorithms and data science in the diagnosis, classification, and treatment of cardiovascular disease. It also sets out to advance this mission, focusing on how digital tools and, in particular, artificial intelligence may provide clinical and mechanistic insights, address bias in clinical studies, and facilitate education and implementation science to improve cardiovascular and stroke outcomes. Last, a key objective of this scientific statement is to further the field by identifying best practices, gaps, and challenges for interested stakeholders.

Comparative arrhythmia patterns among patients on tyrosine kinase inhibitors.

BACKGROUND: Tyrosine kinase inhibitors (TKIs) are widely used in the treatment of hematologic malignancies. Limited studies have shown an association between treatment-limiting arrhythmias and TKI, particularly ibrutinib, a Bruton's tyrosine kinase (BTK) inhibitor. We sought to comprehensively assess the arrhythmia burden in patients receiving ibrutinib vs non-BTK TKI vs non-TKI therapies. METHODS: We performed a retrospective analysis of consecutive patients who received long-term cardiac event monitors while on ibrutinib, non-BTK TKIs, or non-TKI therapy for a hematologic malignancy between 2014 and 2022. RESULTS: One hundred ninety-three patients with hematologic malignancies were included (ibrutinib = 72, non-BTK TKI = 46, non-TKI therapy = 75). The average duration of TKI therapy was 32 months in the ibrutinib group vs 64 months in the non-BTK TKI group (p = 0.003). The ibrutinib group had a higher prevalence of atrial fibrillation (n = 32 [44%]) compared to the non-BTK TKI (n = 7 [15%], p = 0.001) and non-TKI (n = 15 [20%], p = 0.002) groups. Similarly, the prevalence of non-sustained ventricular tachycardia was higher in the ibrutinib group (n = 31, 43%) than the non-BTK TKI (n = 8 [17%], p = 0.004) and non-TKI groups (n = 20 [27%], p = 0.04). TKI therapy was held in 25% (n = 18) of patients on ibrutinib vs 4% (n = 2) on non-BTK TKIs (p = 0.005) secondary to arrhythmias. CONCLUSIONS: In this large retrospective analysis of patients with hematologic malignancies, patients receiving ibrutinib had a higher prevalence of atrial and ventricular arrhythmias compared to those receiving other TKI, with a higher rate of treatment interruption due to arrhythmias.

Dynamic prediction of malignant ventricular arrhythmias using neural networks in patients with an implantable cardioverter-defibrillator.

BACKGROUND: Risk stratification for ventricular arrhythmias currently relies on static measurements that fail to adequately capture dynamic interactions between arrhythmic substrate and triggers over time. We trained and internally validated a dynamic machine learning (ML) model and neural network that extracted features from longitudinally collected electrocardiograms (ECG), and used these to predict the risk of malignant ventricular arrhythmias. METHODS: A multicentre study in patients implanted with an implantable cardioverter-defibrillator (ICD) between 2007 and 2021 in two academic hospitals was performed. Variational autoencoders (VAEs), which combine neural networks with variational inference principles, and can learn patterns and structure in data without explicit labelling, were trained to encode the mean ECG waveforms from the limb leads into 16 variables. Supervised dynamic ML models using these latent ECG representations and clinical baseline information were trained to predict malignant ventricular arrhythmias treated by the ICD. Model performance was evaluated on a hold-out set, using time-dependent receiver operating characteristic (ROC) and calibration curves. FINDINGS: 2942 patients (61.7 ± 13.9 years, 25.5% female) were included, with a total of 32,129 ECG recordings during a mean follow-up of 43.9 ± 35.9 months. The mean time-varying area under the ROC curve for the dynamic model was 0.738 ± 0.07, compared to 0.639 ± 0.03 for a static (i.e. baseline-only model). Feature analyses indicated dynamic changes in latent ECG representations, particularly those affecting the T-wave morphology, were of highest importance for model predictions. INTERPRETATION: Dynamic ML models and neural networks effectively leverage routinely collected longitudinal ECG recordings for personalised and updated predictions of malignant ventricular arrhythmias, outperforming static models. FUNDING: This publication is part of the project DEEP RISK ICD (with project number 452019308) of the research programme Rubicon which is (partly) financed by the Dutch Research Council (NWO). This research is partly funded by the Amsterdam Cardiovascular Sciences (personal grant F.V.Y.T).

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.

Constructing bilayer and volumetric atrial models at scale.

To enable large in silico trials and personalized model predictions on clinical timescales, it is imperative that models can be constructed quickly and reproducibly. First, we aimed to overcome the challenges of constructing cardiac models at scale through developing a robust, open-source pipeline for bilayer and volumetric atrial models. Second, we aimed to investigate the effects of fibres, fibrosis and model representation on fibrillatory dynamics. To construct bilayer and volumetric models, we extended our previously developed coordinate system to incorporate transmurality, atrial regions and fibres (rule-based or data driven diffusion tensor magnetic resonance imaging (MRI)). We created a cohort of 1000 biatrial bilayer and volumetric models derived from computed tomography (CT) data, as well as models from MRI, and electroanatomical mapping. Fibrillatory dynamics diverged between bilayer and volumetric simulations across the CT cohort (correlation coefficient for phase singularity maps: left atrial (LA) 0.27 ± 0.19, right atrial (RA) 0.41 ± 0.14). Adding fibrotic remodelling stabilized re-entries and reduced the impact of model type (LA: 0.52 ± 0.20, RA: 0.36 ± 0.18). The choice of fibre field has a small effect on paced activation data (less than 12 ms), but a larger effect on fibrillatory dynamics. Overall, we developed an open-source user-friendly pipeline for generating atrial models from imaging or electroanatomical mapping data enabling in silico clinical trials at scale (https://github.com/pcmlab/atrialmtk).

Tachycardia-induced metabolic rewiring as a driver of contractile dysfunction.

Prolonged tachycardia-a risk factor for cardiovascular morbidity and mortality-can induce cardiomyopathy in the absence of structural disease in the heart. Here, by leveraging human patient data, a canine model of tachycardia and engineered heart tissue generated from human induced pluripotent stem cells, we show that metabolic rewiring during tachycardia drives contractile dysfunction by promoting tissue hypoxia, elevated glucose utilization and the suppression of oxidative phosphorylation. Mechanistically, a metabolic shift towards anaerobic glycolysis disrupts the redox balance of nicotinamide adenine dinucleotide (NAD), resulting in increased global protein acetylation (and in particular the acetylation of sarcoplasmic/endoplasmic reticulum Ca2+-ATPase), a molecular signature of heart failure. Restoration of NAD redox by NAD+ supplementation reduced sarcoplasmic/endoplasmic reticulum Ca2+-ATPase acetylation and accelerated the functional recovery of the engineered heart tissue after tachycardia. Understanding how metabolic rewiring drives tachycardia-induced cardiomyopathy opens up opportunities for therapeutic intervention.