A Modular Communicative Leadless Pacing-Defibrillator System.

BACKGROUND: The subcutaneous implantable cardioverter-defibrillator (ICD) is associated with fewer lead-related complications than a transvenous ICD; however, the subcutaneous ICD cannot provide bradycardia and antitachycardia pacing. Whether a modular pacing-defibrillator system comprising a leadless pacemaker in wireless communication with a subcutaneous ICD to provide antitachycardia and bradycardia pacing is safe remains unknown. METHODS: We conducted a multinational, single-group study that enrolled patients at risk for sudden death from ventricular arrhythmias and followed them for 6 months after implantation of a modular pacemaker-defibrillator system. The safety end point was freedom from leadless pacemaker-related major complications, evaluated against a performance goal of 86%. The two primary performance end points were successful communication between the pacemaker and the ICD (performance goal, 88%) and a pacing threshold of up to 2.0 V at a 0.4-msec pulse width (performance goal, 80%). RESULTS: We enrolled 293 patients, 162 of whom were in the 6-month end-point cohort and 151 of whom completed the 6-month follow-up period. The mean age of the patients was 60 years, 16.7% were women, and the mean (±SD) left ventricular ejection fraction was 33.1±12.6%. The percentage of patients who were free from leadless pacemaker-related major complications was 97.5%, which exceeded the prespecified performance goal. Wireless-device communication was successful in 98.8% of communication tests, which exceeded the prespecified goal. Of 151 patients, 147 (97.4%) had pacing thresholds of 2.0 V or less, which exceeded the prespecified goal. The percentage of episodes of arrhythmia that were successfully terminated by antitachycardia pacing was 61.3%, and there were no episodes for which antitachycardia pacing was not delivered owing to communication failure. Of 162 patients, 8 died (4.9%); none of the deaths were deemed to be related to arrhythmias or the implantation procedure. CONCLUSIONS: The leadless pacemaker in wireless communication with a subcutaneous ICD exceeded performance goals for freedom from major complications related to the leadless pacemaker, for communication between the leadless pacemaker and subcutaneous ICD, and for the percentage of patients with a pacing threshold up to 2.0 V at a 0.4-msec pulse width at 6 months. (Funded by Boston Scientific; MODULAR ATP ClinicalTrials.gov NCT04798768.).

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.

Artificial intelligence in cardiovascular medicine: clinical applications.

Clinical medicine requires the integration of various forms of patient data including demographics, symptom characteristics, electrocardiogram findings, laboratory values, biomarker levels, and imaging studies. Decision-making on the optimal management should be based on a high probability that the envisaged treatment is appropriate, provides benefit, and bears no or little potential harm. To that end, personalized risk-benefit considerations should guide the management of individual patients to achieve optimal results. These basic clinical tasks have become more and more challenging with the massively growing data now available; artificial intelligence and machine learning (AI/ML) can provide assistance for clinicians by obtaining and comprehensively preparing the history of patients, analysing face and voice and other clinical features, by integrating laboratory results, biomarkers, and imaging. Furthermore, AI/ML can provide a comprehensive risk assessment as a basis of optimal acute and chronic care. The clinical usefulness of AI/ML algorithms should be carefully assessed, validated with confirmation datasets before clinical use, and repeatedly re-evaluated as patient phenotypes change. This review provides an overview of the current data revolution that has changed and will continue to change the face of clinical medicine radically, if properly used, to the benefit of physicians and patients alike.

An electrocardiogram-based AI algorithm for early detection of pulmonary hypertension.

BACKGROUND: Early diagnosis of pulmonary hypertension (PH) is critical for effective treatment and management. We aimed to develop and externally validate an artificial intelligence algorithm that could serve as a PH screening tool, based on analysis of a standard 12-lead ECG. METHODS: The PH Early Detection Algorithm (PH-EDA) is a convolutional neural network developed using retrospective ECG voltage-time data, with patients classified as "PH-likely" or "PH-unlikely" (controls) based on right heart catheterisation or echocardiography. In total, 39 823 PH-likely patients and 219 404 control patients from Mayo Clinic were randomly split into training (48%), validation (12%) and test (40%) sets. ECGs taken within 1 month of PH diagnosis (diagnostic dataset) were used to train the PH-EDA at Mayo Clinic. Performance was tested on diagnostic ECGs within the test sets from Mayo Clinic (n=16 175/87 998 PH-likely/controls) and Vanderbilt University Medical Center (VUMC; n=6045/24 256 PH-likely/controls). In addition, performance was tested on ECGs taken 6-18 months (pre-emptive dataset), and up to 5 years prior to a PH diagnosis at both sites. RESULTS: Performance testing yielded an area under the receiver operating characteristic curve (AUC) of 0.92 and 0.88 in the diagnostic test sets at Mayo Clinic and VUMC, respectively, and 0.86 and 0.81, respectively, in the pre-emptive test sets. The AUC remained a minimum of 0.79 at Mayo Clinic and 0.73 at VUMC up to 5 years before diagnosis. CONCLUSION: The PH-EDA can detect PH at diagnosis and 6-18 months prior, demonstrating the potential to accelerate diagnosis and management of this debilitating disease.

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.

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.

Successful prediction of left bundle branch block-induced cardiomyopathy and treatment effect by artificial intelligence-enabled electrocardiogram.

BACKGROUND: Left bundle branch block (LBBB) induced cardiomyopathy is an increasingly recognized disease entity.  However, no clinical testing has been shown to be able to predict such an occurrence. CASE REPORT: A 70-year-old male with a prior history of LBBB with preserved ejection fraction (EF) and no other known cardiovascular conditions presented with presyncope, high-grade AV block, and heart failure with reduced EF (36%). His coronary angiogram was negative for any obstructive disease. No other known etiologies for cardiomyopathy were identified. Artificial intelligence-enabled ECGs performed 6 years prior to clinical presentation consistently predicted a high probability (up to 91%) of low EF. The patient successfully underwent left bundle branch area (LBBA) pacing with correction of the underlying LBBB. Subsequent AI ECGs showed a large drop in the probability of low EF immediately after LBBA pacing to 47% and then to 3% 2 months post procedure. His heart failure symptoms markedly improved and EF normalized to 54% at the same time. CONCLUSIONS: Artificial intelligence-enabled ECGS may help identify patients who are at risk of developing LBBB-induced cardiomyopathy and predict the response to LBBA pacing.

Assessing Biological Age: The Potential of ECG Evaluation Using Artificial Intelligence: JACC Family Series.

Biological age may be a more valuable predictor of morbidity and mortality than a person's chronological age. Mathematical models have been used for decades to predict biological age, but recent developments in artificial intelligence (AI) have led to new capabilities in age estimation. Using deep learning methods to train AI models on hundreds of thousands of electrocardiograms (ECGs) to predict age results in a good, but imperfect, age prediction. The error predicting age using ECG, or the difference between AI-ECG-derived age and chronological age (delta age), may be a surrogate measurement of biological age, as the delta age relates to survival, even after adjusting for chronological age and other covariates associated with total and cardiovascular mortality. The relative affordability, noninvasiveness, and ubiquity of ECGs, combined with ease of access and potential to be integrated with smartphone or wearable technology, presents a potential paradigm shift in assessment of biological age.

Unlocking the potential of artificial intelligence in electrocardiogram biometrics: age-related changes, anomaly detection, and data authenticity in mobile health platforms.

Aims: Mobile devices such as smartphones and watches can now record single-lead electrocardiograms (ECGs), making wearables a potential screening tool for cardiac and wellness monitoring outside of healthcare settings. Because friends and family often share their smart phones and devices, confirmation that a sample is from a given patient is important before it is added to the electronic health record. Methods and results: We sought to determine whether the application of Siamese neural network would permit the diagnostic ECG sample to serve as both a medical test and biometric identifier. When using similarity scores to discriminate whether a pair of ECGs came from the same patient or different patients, inputs of single-lead and 12-lead medians produced an area under the curve of 0.94 and 0.97, respectively. Conclusion: The similar performance of the single-lead and 12-lead configurations underscores the potential use of mobile devices to monitor cardiac health.

Ten-Year Trend of Oral Anticoagulation Use in Postoperative and Nonpostoperative Atrial Fibrillation in Routine Clinical Practice.

BACKGROUND: The study aimed to describe the patterns and trends of initiation, discontinuation, and adherence of oral anticoagulation (OAC) in patients with new-onset postoperative atrial fibrillation (POAF), and compare with patients newly diagnosed with non-POAF. METHODS AND RESULTS: This retrospective cohort study identified patients newly diagnosed with atrial fibrillation or flutter between 2012 and 2021 using administrative claims data from OptumLabs Data Warehouse. The POAF cohort included 118 366 patients newly diagnosed with atrial fibrillation or flutter within 30 days after surgery. The non-POAF cohort included the remaining 315 832 patients who were newly diagnosed with atrial fibrillation or flutter but not within 30 days after a surgery. OAC initiation increased from 28.9% to 44.0% from 2012 to 2021 in POAF, and 37.8% to 59.9% in non-POAF; 12-month medication adherence increased from 47.0% to 61.8% in POAF, and 59.7% to 70.4% in non-POAF. The median time to OAC discontinuation was 177 days for POAF, and 242 days for non-POAF. Patients who saw a cardiologist within 90 days of the first atrial fibrillation or flutter diagnosis, regardless of POAF or non-POAF, were more likely to initiate OAC (odds ratio, 2.92 [95% CI, 2.87-2.98]; P <0.0001), adhere to OAC (odds ratio, 1.08 [95% CI, 1.04-1.13]; P <0.0001), and less likely to discontinue (odds ratio, 0.83 [95% CI, 0.82-0.85]; P <0.0001) than patients who saw a surgeon or other specialties. CONCLUSIONS: The use of and adherence to OAC were higher in non-POAF patients than in POAF patients, but they increased over time in both groups. Patients managed by cardiologists were more likely to use and adhere to OAC, regardless of POAF or non-POAF.

Validation of Noninvasive Detection of Hyperkalemia by Artificial Intelligence-Enhanced Electrocardiography in High Acuity Settings.

Background: Artificial intelligence (AI) electrocardiogram (ECG) analysis can enable detection of hyperkalemia. In this validation, we assessed the algorithm's performance in two high acuity settings. Methods: An emergency department (ED) cohort (February to August 2021) and a mixed intensive care unit (ICU) cohort (August 2017 to February 2018) were identified and analyzed separately. For each group, pairs of laboratory-collected potassium and 12 lead ECGs obtained within 4 hours of each other were identified. The previously developed AI ECG algorithm was subsequently applied to leads 1 and 2 of the 12 lead ECGs to screen for hyperkalemia (potassium >6.0 mEq/L). Results: The ED cohort (N=40,128) had a mean age of 60 years, 48% were male, and 1% (N=351) had hyperkalemia. The area under the curve (AUC) of the AI-enhanced ECG (AI-ECG) to detect hyperkalemia was 0.88, with sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and positive likelihood ratio (LR+) of 80%, 80%, 3%, 99.8%, and 4.0, respectively, in the ED cohort. Low-eGFR (<30 ml/min) subanalysis yielded AUC, sensitivity, specificity, PPV, NPV, and LR+ of 0.83, 86%, 60%, 15%, 98%, and 2.2, respectively, in the ED cohort. The ICU cohort (N=2636) had a mean age of 65 years, 60% were male, and 3% (N=87) had hyperkalemia. The AUC for the AI-ECG was 0.88 and yielded sensitivity, specificity, PPV, NPV, and LR+ of 82%, 82%, 14%, 99%, and 4.6, respectively in the ICU cohort. Low-eGFR subanalysis yielded AUC, sensitivity, specificity, PPV, NPV, and LR+ of 0.85, 88%, 67%, 29%, 97%, and 2.7, respectively in the ICU cohort. Conclusions: The AI-ECG algorithm demonstrated a high NPV, suggesting that it is useful for ruling out hyperkalemia, but a low PPV, suggesting that it is insufficient for treating hyperkalemia.

Thirty-Day High-Grade Aortic Valve Block Post-Transcatheter Aortic Valve Replacement in Patients Discharged on Heart Rhythm Monitor.

Background: Conduction disease is an important and common complication post-transcatheter aortic valve replacement (TAVR). Previously, we developed a conduction disease risk stratification and management protocol post-TAVR. This study aims to evaluate high-grade aortic valve block (HAVB) incidence and risk factors in a large cohort undergoing ambulatory cardiac monitoring post-TAVR according to conduction risk grouping. Methods: This single-center, retrospective study evaluated all patients discharged on ambulatory cardiac monitoring between 2016 and 2021 and stratified them into 3 groups based on electrocardiogram predictors of HAVB risk (group 1 [low], group 2 [intermediate], and group 3 [high]). HAVB was defined as ≥2 consecutive nonconducted P waves in sinus rhythm or bradycardia <50 beats/minute with a fixed rate for atrial fibrillation/flutter. Descriptive statistics were used to show the incidence and timeline, while logistic regression was utilized to evaluate predictors of HAVB. Results: Five hundred twenty-eight patients were included (median age 80 years [74-85]; 43.8% female). Forty-one patients (7.8%) developed HAVB during ambulatory monitoring (68% were asymptomatic). Over a median follow-up of 2 years (1.3-2.7), the overall mortality rate was 15.0% (30-day mortality rate of 0.57%, n = 3). Risk factors for HAVB were male sex (odds ratio [OR] = 2.46, p = 0.02, 95% CI = 1.21-5.43), baseline right bundle branch block (OR = 2.80, p = 0.01, 95% CI = 1.17-6.19), and post-TAVR QRS >150 â€‹ms (OR = 2.16, p = 0.03, 95% CI = 1.01-4.40). The negative predictive value for patients in groups 1 and 2 for 30-day HAVB was 95.0 and 93.8%, respectively. Conclusions: The risk of 30-day HAVB post-TAVR on ambulatory monitoring post-TAVR varies according to post-TAVR electrocardiogram findings, and a 3-group algorithm effectively identifies groups with a low negative predictive value for HAVB.

Left bundle branch pacing vs ventricular septal pacing for cardiac resynchronization therapy.

Background: The outcomes of left bundle branch pacing (LBBP) and left ventricular septal pacing (LVSP) in patients with heart failure remain to be learned. Objective: The objective of this study was to assess the echocardiographic and clinical outcomes of LBBP, LVSP, and deep septal pacing (DSP). Methods: This retrospective study included patients who met the criteria for cardiac resynchronization therapy (CRT) and underwent attempted LBBP in 5 Mayo centers. Clinical, electrocardiographic, and echocardiographic data were collected at baseline and follow-up. Results: A total of 91 consecutive patients were included in the study. A total of 52 patients had LBBP, 25 had LVSP, and 14 had DSP. The median follow-up duration was 307 (interquartile range 208, 508) days. There was significant left ventricular ejection fraction (LVEF) improvement in the LBBP and LVSP groups (from 35.9 ± 8.5% to 46.9 ± 10.0%, P < .001 in the LBBP group; from 33.1 ± 7.5% to 41.8 ± 10.8%, P < .001 in the LVSP group) but not in the DSP group. A unipolar paced right bundle branch block morphology during the procedure in lead V1 was associated with higher odds of CRT response. There was no significant difference in heart failure hospitalization and all-cause deaths between the LBBP and LVSP groups. The rate of heart failure hospitalization and all-cause deaths were increased in the DSP group compared with the LBBP group (hazard ratio 5.10, 95% confidence interval 1.14-22.78, P = .033; and hazard ratio 7.83, 95% confidence interval 1.38-44.32, P = .020, respectively). Conclusion: In patients undergoing CRT, LVSP had comparable CRT outcomes compared with LBBP.

Artificial intelligence-enhanced electrocardiogram for arrhythmogenic right ventricular cardiomyopathy detection.

Aims: ECG abnormalities are often the first signs of arrhythmogenic right ventricular cardiomyopathy (ARVC) and we hypothesized that an artificial intelligence (AI)-enhanced ECG could help identify patients with ARVC and serve as a valuable disease-detection tool. Methods and results: We created a convolutional neural network to detect ARVC using a 12-lead ECG. All patients with ARVC who met the 2010 task force criteria and had disease-causative genetic variants were included. All case ECGs were randomly assigned in an 8:1:1 ratio into training, validation, and testing groups. The case ECGs were age- and sex-matched with control ECGs at our institution in a 1:100 ratio. Seventy-seven patients (51% male; mean age 47.2 ± 19.9), including 56 patients with PKP2, 7 with DSG2, 6 with DSC2, 6 with DSP, and 2 with JUP were included. The model was trained using 61 case ECGs and 5009 control ECGs; validated with 7 case ECGs and 678 control ECGs and tested in 22 case ECGs and 1256 control ECGs. The sensitivity, specificity, positive and negative predictive values of the model were 77.3, 62.9, 3.32, and 99.4%, respectively. The area under the curve for rhythm ECG and median beat ECG was 0.75 and 0.76, respectively. Conclusion: Our study found that the model performed well in excluding ARVC and supports the concept that the AI ECG can serve as a biomarker for ARVC if a larger cohort were available for network training. A multicentre study including patients with ARVC from other centres would be the next step in refining, testing, and validating this algorithm.

Trends in the 30-year span of noninfectious cardiovascular implantable electronic device complications in Olmsted County.

Background: Cardiac implantable electronic devices (CIEDs), such as permanent pacemakers, implantable cardioverter-defibrillators, and cardiac resynchronization therapy devices, alleviate morbidity and mortality in various diseases. There is a paucity of real-world data on CIED complications and trends. Objectives: We sought to describe trends in noninfectious CIED complications over the past 3 decades in Olmsted County. Methods: The Rochester Epidemiology Project is a medical records linkage system comprising records of over 500,000 residents of Olmsted County from 1966 to present. CIED implantations between 1988 and 2018 were determined. Trends in noninfectious complications within 30 days of implantation were analyzed. Results: A total of 157 (6.2%) of 2536 patients who received CIED experienced device complications. A total of 2.7% of the implants had major complications requiring intervention. Lead dislodgement was the most common (2.8%), followed by hematoma (1.7%). Complications went up from 1988 to 2005, and then showed a downtrend until 2018, driven by a decline in hematomas in the last decade (P < .01). Those with complications were more likely to have prosthetic valves. Obesity appeared to have a protective effect in a multivariate regression model. The mean Charlson comorbidity index has trended up over the 30 years. Conclusion: Our study describes a real-world trend of CIED complications over 3 decades. Lead dislodgements and hematomas were the most common complications. Complications have declined over the last decade due to safer practices and a better understanding of anticoagulant management.

Artificial intelligence-based classification of echocardiographic views.

Aims: Augmenting echocardiography with artificial intelligence would allow for automated assessment of routine parameters and identification of disease patterns not easily recognized otherwise. View classification is an essential first step before deep learning can be applied to the echocardiogram. Methods and results: We trained two- and three-dimensional convolutional neural networks (CNNs) using transthoracic echocardiographic (TTE) studies obtained from 909 patients to classify nine view categories (10 269 videos). Transthoracic echocardiographic studies from 229 patients were used in internal validation (2582 videos). Convolutional neural networks were tested on 100 patients with comprehensive TTE studies (where the two examples chosen by CNNs as most likely to represent a view were evaluated) and 408 patients with five view categories obtained via point-of-care ultrasound (POCUS). The overall accuracy of the two-dimensional CNN was 96.8%, and the averaged area under the curve (AUC) was 0.997 on the comprehensive TTE testing set; these numbers were 98.4% and 0.998, respectively, on the POCUS set. For the three-dimensional CNN, the accuracy and AUC were 96.3% and 0.998 for full TTE studies and 95.0% and 0.996 on POCUS videos, respectively. The positive predictive value, which defined correctly identified predicted views, was higher with two-dimensional rather than three-dimensional networks, exceeding 93% in apical, short-axis aortic valve, and parasternal long-axis left ventricle views. Conclusion: An automated view classifier utilizing CNNs was able to classify cardiac views obtained using TTE and POCUS with high accuracy. The view classifier will facilitate the application of deep learning to echocardiography.

Machine learning for prediction of ventricular arrhythmia episodes from intracardiac electrograms of automatic implantable cardioverter-defibrillators.

BACKGROUND: Despite effectiveness of the implantable cardioverter-defibrillator (ICD) in saving patients with life-threatening ventricular arrhythmias (VAs), the temporal occurrence of VA after ICD implantation is unpredictable. OBJECTIVE: The study aimed to apply machine learning (ML) to intracardiac electrograms (IEGMs) recorded by ICDs as a unique biomarker for predicting impending VAs. METHODS: The study included 13,516 patients who received Biotronik ICDs and enrolled in the CERTITUDE registry between January 1, 2010, and December 31, 2020. Database extraction included IEGMs from standard quarterly transmissions and VA event episodes. The processed IEGM data were pulled from device transmissions stored in a centralized Home Monitoring Service Center and reformatted into an analyzable format. Long-range (baseline or first scheduled remote recording), mid-range (scheduled remote recording every 90 days), or short-range predictions (IEGM within 5 seconds before the VA onset) were used to determine whether ML-processed IEGMs predicted impending VA events. Convolutional neural network classifiers using ResNet architecture were employed. RESULTS: Of 13,516 patients (male, 72%; age, 67.5 ± 11.9 years), 301,647 IEGM recordings were collected; 27,845 episodes of sustained ventricular tachycardia or ventricular fibrillation were observed in 4467 patients (33.0%). Neural networks based on convolutional neural networks using ResNet-like architectures on far-field IEGMs yielded an area under the curve of 0.83 with a 95% confidence interval of 0.79-0.87 in the short term, whereas the long-range and mid-range analyses had minimal predictive value for VA events. CONCLUSION: In this study, applying ML to ICD-acquired IEGMs predicted impending ventricular tachycardia or ventricular fibrillation events seconds before they occurred, whereas midterm to long-term predictions were not successful. This could have important implications for future device therapies.

Pediatric sex estimation using AI-enabled ECG analysis: influence of pubertal development.

AI-enabled ECGs have previously been shown to accurately predict patient sex in adults and correlate with sex hormone levels. We aimed to test the ability of AI-enabled ECGs to predict sex in the pediatric population and study the influence of pubertal development. AI-enabled ECG models were created using a convolutional neural network trained on pediatric 10-second, 12-lead ECGs. The first model was trained de novo using pediatric data. The second model used transfer learning from a previously validated adult data-derived algorithm. We analyzed the first ECG from 90,133 unique pediatric patients (aged ≤18 years) recorded between 1987-2022, and divided the cohort into training, validation, and testing datasets. Subgroup analysis was performed on prepubertal (0-7 years), peripubertal (8-14 years), and postpubertal (15-18 years) patients. The cohort was 46.7% male, with 21,678 prepubertal, 26,740 peripubertal, and 41,715 postpubertal children. The de novo pediatric model demonstrated 81% accuracy and an area under the curve (AUC) of 0.91. Model sensitivity was 0.79, specificity was 0.83, positive predicted value was 0.84, and the negative predicted value was 0.78, for the entire test cohort. The model's discriminatory ability was highest in postpubertal (AUC = 0.98), lower in the peripubertal age group (AUC = 0.91), and poor in the prepubertal age group (AUC = 0.67). There was no significant performance difference observed between the transfer learning and de novo models. AI-enabled interpretation of ECG can estimate sex in peripubertal and postpubertal children with high accuracy.

Atrial Fibrillation Substrate and Catheter Ablation Outcomes in MYBPC3- and MYH7-Mediated Hypertrophic Cardiomyopathy.

BACKGROUND: The effects of disease-causing MYBPC3 or MYH7 genetic variants on atrial myopathy, atrial fibrillation (AF) clinical course, and catheter ablation efficacy remain unclear. OBJECTIVES: The aim of this study was to characterize the atrial substrate of patients with MYBPC3- or MYH7-mediated hypertrophic cardiomyopathy (HCM) and its impact on catheter ablation outcomes. METHODS: A retrospective single-center study of patients with HCM who underwent genetic testing and catheter ablation for AF was performed. Patients with MYBPC3- or MYH7-mediated HCM formed the gene-positive cohort; those without disease-causative genetic variants formed the control cohort. High-density electroanatomical mapping was performed using a 3-dimensional mapping system, followed by radiofrequency ablation. RESULTS: Twelve patients were included in the gene-positive cohort (mean age 55.6 ± 9.9 years, 83% men, 50% MYBPC3, 50% MYH7, mean ejection fraction 59.3% ± 13.7%, mean left atrial [LA] volume index 51.7 ± 13.1 mL/m2, mean LA pressure 20.2 ± 5.4 mm Hg) and 15 patients in the control arm (mean age 61.5 ± 12.6 years, 60% men, mean ejection fraction 64.9% ± 5.1%, mean LA volume index 54.1 ± 12.8 mL/m2, mean LA pressure 19.6 ± 5.41 mm Hg). Electroanatomical mapping demonstrated normal voltage in 87.7% ± 5.03% of the LA in the gene-positive cohort and 94.3% ± 3.58% of the LA in the control cohort (P < 0.001). Of the abnormal regions, intermediate scar (0.1-0.5 mV) accounted for 6.33% ± 1.97% in the gene-positive cohort and 3.07% ± 2.46% in the control cohort (P < 0.01). Dense scar (<0.1 mV) accounted for 5.93% ± 3.20% in the gene-positive cohort and 2.61% ± 2.19% in the control cohort (P < 0.01). Freedom from AF at 12 months was similar between the gene-positive (75%) and control (73%) cohorts (P = 0.92), though a greater number of procedures were required in the gene-positive cohort. CONCLUSIONS: Patients with MYBPC3- or MYH7-mediated HCM undergoing AF ablation have appreciably more low-amplitude LA signals, suggestive of fibrosis. However, catheter ablation remains an effective rhythm-control strategy.