Impact of artificial intelligence arrhythmia mapping on time to first ablation, procedure duration, and fluoroscopy use.

INTRODUCTION: Artificial intelligence (AI) ECG arrhythmia mapping provides arrhythmia source localization using 12-lead ECG data; whether this information impacts procedural efficiency is unknown. We performed a retrospective, case-control study to evaluate the hypothesis that AI ECG mapping may reduce time to ablation, procedural duration, and fluoroscopy. MATERIALS AND METHODS: Cases in which system output was used were retrospectively enrolled according to IRB-approved protocols at each site. Matched control cases were enrolled in reverse chronological order beginning on the last day for which the technology was unavailable. Controls were matched based upon physician, institution, arrhythmia, and a predetermined complexity rating. Procedural metrics, fluoroscopy data, and clinical outcomes were assessed from time-stamped medical records. RESULTS: The study group consisted of 28 patients (age 65 ± 11 years, 46% female, left atrial dimension 4.1 ± 0.9 cm, LVEF 50 ± 18%) and was similar to 28 controls. The most common arrhythmia types were atrial fibrillation (n = 10), premature ventricular complexes (n = 8), and ventricular tachycardia (n = 6). Use of the system was associated with a 19.0% reduction in time to ablation (133 ± 48 vs. 165 ± 49 min, p = 0.02), a 22.6% reduction in procedure duration (233 ± 51 vs. 301 ± 83 min, p < 0.001), and a 43.7% reduction in fluoroscopy (18.7 ± 13.3 vs. 33.2 ± 18.0 min, p < 0.001) versus controls. At 6 months follow-up, arrhythmia-free survival was 73.5% in the study group and 63.3% in the control group (p = 0.56). CONCLUSION: Use of forward-solution AI ECG mapping is associated with reductions in time to first ablation, procedure duration, and fluoroscopy without an adverse impact on procedure outcomes or complications.

Coexistence of Rhabdomyolysis, Myocarditis and Arrhythmia after Spider Bite: A Case Report.

BACKGROUND: Rhabdomyolysis after spider bite has been reported in a small number of patients, and myocarditis in even fewer. However, arrhythmia associated with latrodectism in children has not been described in the literature to date. CASE SUMMARY: A girl presented approximately 4.5 h after being bitten on the left ankle by a black spider. Two unifocal premature ventricular contractions (PVCs) were observed on the electrocardiogram. In laboratory tests, creatine kinase was elevated. On day 2, levels of troponin, pro-brain and natriuretic peptide were elevated. Electrocardiogram revealed inverted and biphasic T waves. Echocardiography revealed mild left ventricular dilation, mitral and aortic valve regurgitation. Holter electrocardiogram showed PVCs. Her laboratory and echocardiography findings completely normalized after discharge, and no arrhythmia was observed on the Holter electrocardiogram during outpatient follow-up. CONCLUSION: Although spider bites are uncommon, they can cause serious systemic effects. These patients should be evaluated for arrhythmia, rhabdomyolysis and myocarditis.

Rarely, spider bites can cause serious systemic effects, severe morbidity and death. In a small number of patients, spider envenomation causes rhabdomyolysis and myocarditis. In the present case, the elevated troponin and pro-brain natriuretic peptide levels and electrocardiogram/echocardiography findings were consistent with myocarditis, and an increase in creatinine kinase level indicated rhabdomyolysis. In addition, the electrocardiogram and Holter electrocardiogram revealed unifocal premature ventricular contraction. To our knowledge, arrhythmia due to Latrodectus spider bite has not been described in children to date. In addition, this case demonstrates the coexistence of two serious systemic effects, rhabdomyolysis and myocarditis, with full recovery after appropriate treatment.

Spatiotemporal approximation of cardiac activation and recovery isochrones.

BACKGROUND: The sequence of myocardial activation and recovery can be studied in detail by invasive catheter recordings of cardiac electrograms (EGMs), or noninvasive inverse reconstructions thereof with electrocardiographic imaging (ECGI). Local activation and recovery times are obtained from a unipolar EGM by the moment of maximum downslope of the QRS complex or maximum upslope of the T wave, respectively. However, both invasive and noninvasive recordings of intracardiac EGMs may suffer from noise and fractionation, making reliable detection of these deflections nontrivial. METHODS: Here, we introduce a novel method that benefits from the spatial coupling of these processes, and incorporate not only the temporal EGM deflection, but also the spatial gradients. We validated this approach in computer simulations, in animal data with ECGI and invasive electrode recordings, and illustrated its use in a clinical case. RESULTS: In the simulated data, the spatiotemporal approach was able to incorporate spatial information to better select the correct deflection in artificially fractionated EGMs and outperformed the traditional temporal-only method. In experimental data, the accuracy of time estimation from ECGI compared to invasive recordings significantly increased from R = 0.73 (activation) and R = 0.58 (recovery) with the temporal-only method to R = 0.79 (activation) and R = 0.72 (recovery) with the novel approach. Localization of the pacing origin of paced beats improved significantly from 36 mm mean error with the temporal-only approach to 23 mm with the spatiotemporal approach. CONCLUSION: The spatiotemporal method to compute activation and recovery times from EGMs outperformed the traditional temporal-only approach in which spatial information was not taken into account.

Shared Decision Making in Cardiac Electrophysiology Procedures and Arrhythmia Management.

Shared decision making (SDM) has been advocated to improve patient care, patient decision acceptance, patient-provider communication, patient motivation, adherence, and patient reported outcomes. Documentation of SDM is endorsed in several society guidelines and is a condition of reimbursement for selected cardiovascular and cardiac arrhythmia procedures. However, many clinicians argue that SDM already occurs with clinical encounter discussions or the process of obtaining informed consent and note the additional imposed workload of using and documenting decision aids without validated tools or evidence that they improve clinical outcomes. In reality, SDM is a process and can be done without decision tools, although the process may be variable. Also, SDM advocates counter that the low-risk process of SDM need not be held to the high bar of demonstrating clinical benefit and that increasing the quality of decision making should be sufficient. Our review leverages a multidisciplinary group of experts in cardiology, cardiac electrophysiology, epidemiology, and SDM, as well as a patient advocate. Our goal is to examine and assess SDM methodology, tools, and available evidence on outcomes in patients with heart rhythm disorders to help determine the value of SDM, assess its possible impact on electrophysiological procedures and cardiac arrhythmia management, better inform regulatory requirements, and identify gaps in knowledge and future needs.

QT Interval Dynamics and Cardiovascular Outcomes: A Cohort Study in an Integrated Health Care Delivery System.

Background Long QT has been associated with ventricular dysrhythmias, cardiovascular disease (CVD) mortality, and sudden cardiac death. However, no studies to date have investigated the dynamics of within-person QT change over time in relation to risk of incident CVD and all-cause mortality in a real-world setting. Methods and Results A cohort study among members of an integrated health care delivery system in Northern California including 61 455 people (mean age, 62 years; 60% women, 42% non-White) with 3 or more ECGs (baseline in 2005-2009; mean±SD follow-up time, 7.6±2.6 years). In fully adjusted models, tertile 3 versus tertile 1 of average QT corrected (using the Fridericia correction) was associated with cardiac arrest (hazard ratio [HR], 1.66), heart failure (HR, 1.62), ventricular dysrhythmias (HR, 1.56), all CVD (HR, 1.31), ischemic heart disease (HR, 1.28), total stroke (HR, 1.18), and all-cause mortality (HR, 1.24). Tertile 3 versus tertile 2 of the QT corrected linear slope was associated with cardiac arrest (HR, 1.22), ventricular dysrhythmias (HR, 1.12), and all-cause mortality (HR, 1.09). Tertile 3 versus tertile 1 of the QT corrected root mean squared error was associated with ventricular dysrhythmias (HR, 1.34), heart failure (HR, 1.28), all-cause mortality (HR, 1.20), all CVD (HR, 1.14), total stroke (HR, 1.08), and ischemic heart disease (HR, 1.07). Conclusions Our results demonstrate improved predictive ability for CVD outcomes using longitudinal information from serial ECGs. Long-term average QT corrected was more strongly associated with CVD outcomes than the linear slope or the root mean squared error. This new evidence is clinically relevant because ECGs are frequently used, noninvasive, and inexpensive.

A novel convolutional neural network for reconstructing surface electrocardiograms from intracardiac electrograms and vice versa.

We propose a novel convolutional neural network framework for mapping a multivariate input to a multivariate output. In particular, we implement our algorithm within the scope of 12-lead surface electrocardiogram (ECG) reconstruction from intracardiac electrograms (EGM) and vice versa. The goal of performing this task is to allow for improved point-of-care monitoring of patients with an implanted device to treat cardiac pathologies. We will achieve this goal with 12-lead ECG reconstruction and by providing a new diagnostic tool for classifying five different ECG types. The algorithm is evaluated on a dataset retroactively collected from 14 patients. Correlation coefficients calculated between the reconstructed and the actual ECG show that the proposed convolutional neural network model represents an efficient, accurate, and superior way to synthesize a 12-lead ECG when compared to previous methods. We can also achieve the same reconstruction accuracy with only one EGM lead as input. We also tested the model in a non-patient specific way and saw a reasonable correlation coefficient. The model was also executed in the reverse direction to produce EGM signals from a 12-lead ECG and found that the correlation was comparable to the forward direction. Lastly, we analyzed the features learned in the model and determined that the model learns an overcomplete basis of our 12-lead ECG space. We then use this basis of features to create a new diagnostic tool for classifying different ECG arrhythmia's on the MIT-BIH arrhythmia database with an average accuracy of 0.98.

Contemporary procedure characteristics and outcomes of accessory atrioventricular pathway ablations in an integrated community-based health care system using a tiered approach.

BACKGROUND: Since the early descriptions of large series of accessory atrioventricular pathway ablations in adults and adolescents over 20 years ago, there have been limited published reports based on more recent experiences of large referral centers. We aimed to characterize accessory pathway distribution and features in a large community-based population that influence ablation outcomes using a tiered approach to ablation. METHODS: Retrospective analysis of 289 patients (age 14-81) who underwent accessory ablation from 2015-2019 was performed. Pathways were categorized into anteroseptal, left freewall, posteroseptal, and right freewall locations. We analyzed patient and pathway features to identify factors associated with prolonged procedure time parameters. RESULTS: Initial ablation success rate was 94.7% with long-term success rate of 93.4% and median follow-up of 931 days. Accessory pathways were in left freewall (61.6%), posteroseptal (24.6%), right freewall (9.6%), and anteroseptal (4.3%) locations. Procedure outcome was dependent on pathway location. Acute success was highest for left freewall pathways (97.1%) with lowest case times (144 ± 68 min) and fluoroscopy times (15 ± 19 min). Longest procedure time parameters were seen with anteroseptal, left anterolateral, epicardial-coronary sinus, and right anterolateral pathway ablations. CONCLUSIONS: In this community-based adult and adolescent population, majority of the accessory pathways are in the left freewall and posteroseptal region and tend to be more easily ablated. A tiered approach with initial use of standard ablation equipment before the deployment of more advance tools, such as irrigated tips and 3D mapping, is cost effective without sacrificing overall efficacy.

An electrographic AV optimization for the maximum integrative atrioventricular and ventricular resynchronization in CRT.

BACKGROUND: Atrioventricular (AV) delay could affect AV and ventricular synchrony in cardiac resynchronization therapy (CRT). Strategies to optimize AV delay according to optimal AV synchrony (AVopt-AV) or ventricular synchrony (AVopt-V) would potentially be discordant. This study aimed to explore a new AV delay optimization algorithm guided by electrograms to obtain the maximum integrative effects of AV and ventricular resynchronization (opt-AV). METHODS: Forty-nine patients with CRT were enrolled. AVopt-AV was measured through the Ritter method. AVopt-V was obtained by yielding the narrowest QRS. The opt-AV was considered to be AVopt-AV or AVopt-V when their difference was < 20 ms, and to be the AV delay with the maximal aortic velocity-time integral between AVopt-AV and AVopt-V when their difference was > 20 ms. RESULTS: The results showed that sensing/pacing AVopt-AV (SAVopt-AV/PAVopt-AV) were correlated with atrial activation time (Pend-As/Pend-Ap) (P < 0.05). Sensing/pacing AVopt-V (SAVopt-V/PAVopt-V) was correlated with the intrinsic AV conduction time (As-Vs/Ap-Vs) (P < 0.01). The percentages of patients with more than 20 ms differences between SAVopt-AV/PAVopt-AV and SAVopt-V/PAVopt-V were 62.9% and 57.1%, respectively. Among them, opt-AV was linearly correlated with SAVopt-AV/PAVopt-AV and SAVopt-V/PAVopt-V. The sensing opt-AV (opt-SAV) = 0.1 × SAVopt-AV + 0.4 × SAVopt-V + 70 ms (R2 = 0.665, P < 0.01) and the pacing opt-AV (opt-PAV) = 0.25 × PAVopt-AV + 0.5 × PAVopt-V + 30 ms (R2 = 0.560, P < 0.01). CONCLUSION: The SAVopt-AV/PAVopt-AV and SAVopt-V/PAVopt-V were correlated with the atrial activation time and the intrinsic AV conduction interval respectively. Almost half of the patients had a > 20 ms difference between SAVopt-AV/PAVopt-AV and SAVopt-V/PAVopt-V. The opt-AV could be estimated based on electrogram parameters.

[Lifestyle changes for stroke prevention]. / Schlaganfallprävention durch Lifestyle-Modifikation.

Changes of lifestyle have a substantial effect on stroke prevention, especially in high-risk patients. Maintaining a healthier lifestyle can have greater effects than most pharmacological therapies of cardiovascular prevention. For example, increasing the amount of physical activity, adopting a healthy diet, limiting alcohol consumption and quitting smoking are associated with a 70 % decrease in stroke risk. Despite the abundance of observational data and meta-analyses assessing the association of different lifestyle changes and stroke risk, the literature frequently lacks evidence from randomized controlled clinical trial. This article will provide an overview of various forms of lifestyle changes and summarize their potential to modify the risk of stroke.

Comprehensive in vitro pro-arrhythmic assays demonstrate that omecamtiv mecarbil has low pro-arrhythmic risk.

Omecamtiv mecarbil (OM) is a myosin activator (myotrope), developed as a potential therapeutic agent for heart failure with reduced ejection fraction. To characterize the potential pro-arrhythmic risk of this novel sarcomere activator, we evaluated OM in a series of International Conference on Harmonization S7B core and follow-up assays, including an in silico action potential (AP) model. OM was tested in: (i) hERG, Nav1.5 peak, and Cav1.2 channel assays; (ii) in silico computation in a human ventricular AP (hVAP) population model; (iii) AP recordings in canine cardiac Purkinje fibers (PF); and (iv) electrocardiography analysis in isolated rabbit hearts (IRHs). OM had low potency in the hERG (half-maximal inhibitory concentration [IC50 ] = 125.5 µM) and Nav1.5 and Cav1.2 assays (IC50  > 300 µM). These potency values were used as inputs to investigate the occurrence of repolarization abnormalities (biomarkers of pro-arrhythmia) in an hVAP model over a wide range of OM concentrations. The outcome of hVAP analysis indicated low pro-arrhythmia risk at OM concentration up to 30 µM (100-fold the effective free therapeutic plasma concentration). In the isolated canine PF assay, OM shortened AP duration (APD)60 and APD90 significantly from 3 to 30 µM. In perfused IRH, ventricular repolarization (corrected QT and corrected JT intervals) was decreased significantly at greater than or equal to 1 µM OM. In summary, the comprehensive proarrhythmic assessment in human and non-rodent cardiac models provided data indicative that OM did not delay ventricular repolarization at therapeutic relevant concentrations, consistent with clinical findings.

Machine Learning in Arrhythmia and Electrophysiology.

Machine learning (ML), a branch of artificial intelligence, where machines learn from big data, is at the crest of a technological wave of change sweeping society. Cardiovascular medicine is at the forefront of many ML applications, and there is a significant effort to bring them into mainstream clinical practice. In the field of cardiac electrophysiology, ML applications have also seen a rapid growth and popularity, particularly the use of ML in the automatic interpretation of ECGs, which has been extensively covered in the literature. Much lesser known are the other aspects of ML application in cardiac electrophysiology and arrhythmias, such as those in basic science research on arrhythmia mechanisms, both experimental and computational; in the development of better techniques for mapping of cardiac electrical function; and in translational research related to arrhythmia management. In the current review, we examine comprehensively such ML applications as they match the scope of this journal. The current review is organized in 3 parts. The first provides an overview of general ML principles and methodologies that will afford readers of the necessary information on the subject, serving as the foundation for inviting further ML applications in arrhythmia research. The basic information we provide can serve as a guide on how one might design and conduct an ML study. The second part is a review of arrhythmia and electrophysiology studies in which ML has been utilized, highlighting the broad potential of ML approaches. For each subject, we outline comprehensively the general topics, while reviewing some of the research advances utilizing ML under the subject. Finally, we discuss the main challenges and the perspectives for ML-driven cardiac electrophysiology and arrhythmia research.

Digital Health and the Care of the Patient With Arrhythmia: What Every Electrophysiologist Needs to Know.

The field of cardiac electrophysiology has been on the cutting edge of advanced digital technologies for many years. More recently, medical device development through traditional clinical trials has been supplemented by direct to consumer products with advancement of wearables and health care apps. The rapid growth of innovation along with the mega-data generated has created challenges and opportunities. This review summarizes the regulatory landscape, applications to clinical practice, opportunities for virtual clinical trials, the use of artificial intelligence to streamline and interpret data, and integration into the electronic medical records and medical practice. Preparation of the new generation of physicians, guidance and promotion by professional societies, and advancement of research in the interpretation and application of big data and the impact of digital technologies on health outcomes will help to advance the adoption and the future of digital health care.

VIEgram - Analysis and Visualization of Intracardiac Electrograms on Patient-Specific 3D Atria Model.

Over the last few years, the use of cardiac mapping for effective diagnosis and treatment of arrhythmias has increased significantly. In the clinical environment, electroanatomical mapping (EAM) is performed during the electrophysiological procedures using proprietary systems such as CARTO, EnSite Precision, RHYTHMIA, etc. These systems generate the 3D model of patient-specific atria with the electrical activity (i.e., intracardiac electrograms (iEGMs)) displayed on it, for further identification of the sources of arrhythmia and for guiding cardiac ablation therapy. Recently, several novel techniques were developed to perform iEGMs analysis to more accurately identify the arrhythmogenic sites. However, there is a difficulty in incorporating the results of iEGMs analysis back to EAM systems due to their proprietary constraints. This created a hurdle in the further development of novel techniques to help navigate patient-specific clinical ablation therapy. Thus, we developed an open source software, VIEgram1, that allows researchers to visualize the results of the various iEGMs analysis on a patient-specific 3D atria model. It eliminates the dependency of the academic environment on the proprietary EAM systems, thereby making the process of retrospective mapping extremely convenient and time efficient. Here, we demonstrate the features of VIEgram such as visual inspection of iEGMs, flexibility in implementing custom iEGMs analysis techniques and interpolation schemes, and spatial analysis.