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.

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.

Electrocardiography-based Artificial Intelligence Algorithms Aid in Prediction of Long-term Mortality After Kidney Transplantation

BACKGROUND

Predicting long-term mortality postkidney transplantation (KT) using baseline clinical data presents significant challenges. This study aims to evaluate the predictive power of artificial intelligence (AI)-enabled analysis of preoperative electrocardiograms (ECGs) in forecasting long-term mortality following KT.

METHODS

We analyzed preoperative ECGs from KT recipients at three Mayo Clinic sites (Minnesota, Florida, and Arizona) between January 1, 2006, and July 30, 2021. The study involved 6 validated AI algorithms, each trained to predict future development of atrial fibrillation, aortic stenosis, low ejection fraction, hypertrophic cardiomyopathy, amyloid heart disease, and biological age. These algorithms' outputs based on a single preoperative ECG were correlated with patient mortality data.

RESULTS

Among 6504 KT recipients included in the study, 1764 (27.1%) died within a median follow-up of 5.7 y (interquartile range: 3.00-9.29 y). All AI-ECG algorithms were independently associated with long-term all-cause mortality (P < 0.001). Notably, few patients had a clinical cardiac diagnosis at the time of transplant, indicating that AI-ECG scores were predictive even in asymptomatic patients. When adjusted for multiple clinical factors such as recipient age, diabetes, and pretransplant dialysis, AI algorithms for atrial fibrillation and aortic stenosis remained independently associated with long-term mortality. These algorithms also improved the C-statistic for predicting overall (C = 0.74) and cardiac-related deaths (C = 0.751).

CONCLUSIONS

The findings suggest that AI-enabled preoperative ECG analysis can be a valuable tool in predicting long-term mortality following KT and could aid in identifying patients who may benefit from enhanced cardiac monitoring because of increased risk.

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.

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-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.

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.

Incidence and Outcomes of New-Onset Right Bundle Branch Block Following Transcatheter Aortic Valve Replacement

BACKGROUND

The incidence and prognosis of right bundle branch block (RBBB) following transcatheter aortic valve replacement (TAVR) are unknown. Hence, we sought to characterize the incidence of post-TAVR RBBB and determine associated risks of permanent pacemaker (PPM) implantation and mortality.

METHODS

All patients 18 years and above without preexisting RBBB or PPM who underwent TAVR at US Mayo Clinic sites and Mayo Clinic Health Systems from June 2010 to May 2021 were evaluated. Post-TAVR RBBB was defined as new-onset RBBB in the postimplantation period. The risks of PPM implantation (within 90 days) and mortality following TAVR were compared for patients with and without post-TAVR RBBB using Kaplan-Meier analysis and Cox proportional hazards modeling. The risks of PPM implantation (within 90 days) and mortality following TAVR were compared for patients with and without post-TAVR RBBB using Kaplan-Meier analysis and Cox proportional hazards modeling.

RESULTS

Of 1992 patients, 15 (0.75%) experienced new RBBB post-TAVR. There was a higher degree of valve oversizing among patients with new RBBB post-TAVR versus those without (17.9% versus 10.0%; P=0.034). Ten patients (66.7%) with post-TAVR RBBB experienced high-grade atrioventricular block and underwent PPM implantation (median 1 day; Q1, 0.2 and Q3, 4), compared with 268/1977 (13.6%) without RBBB. Following propensity score adjustment for covariates (age, sex, balloon-expandable valve, annulus diameter, and valve oversizing), post-TAVR RBBB was significantly associated with PPM implantation (hazard ratio, 8.36 [95% CI, 4.19-16.7]; P<0.001). No statistically significant increase in mortality was seen with post-TAVR RBBB (hazard ratio, 0.83 [95% CI, 0.33-2.11]; P=0.69), adjusting for age and sex.

CONCLUSIONS

Although infrequent, post-TAVR RBBB was associated with elevated PPM implantation risk. The mechanisms for its development and its clinical prognosis require further study.

Artificial intelligence-enabled ECG for left ventricular diastolic function and filling pressure

Assessment of left ventricular diastolic function plays a major role in the diagnosis and prognosis of cardiac diseases, including heart failure with preserved ejection fraction. We aimed to develop an artificial intelligence (AI)-enabled electrocardiogram (ECG) model to identify echocardiographically determined diastolic dysfunction and increased filling pressure. We trained, validated, and tested an AI-enabled ECG in 98,736, 21,963, and 98,763 patients, respectively, who had an ECG and echocardiographic diastolic function assessment within 14 days with no exclusion criteria. It was also tested in 55,248 patients with indeterminate diastolic function by echocardiography. The model was evaluated using the area under the curve (AUC) of the receiver operating characteristic curve, and its prognostic performance was compared to echocardiography. The AUC for detecting increased filling pressure was 0.911. The AUCs to identify diastolic dysfunction grades ≥1, ≥2, and 3 were 0.847, 0.911, and 0.943, respectively. During a median follow-up of 5.9 years, 20,223 (20.5%) died. Patients with increased filling pressure predicted by AI-ECG had higher mortality than those with normal filling pressure, after adjusting for age, sex, and comorbidities in the test group (hazard ratio (HR) 1.7, 95% CI 1.645-1.757) similar to echocardiography and in the indeterminate group (HR 1.34, 95% CI 1.298-1.383). An AI-enabled ECG identifies increased filling pressure and diastolic function grades with a good prognostic value similar to echocardiography. AI-ECG is a simple and promising tool to enhance the detection of diseases associated with diastolic dysfunction and increased diastolic filling pressure.

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.

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

BACKGROUND

There has been an increasing uptake of transcatheter left atrial appendage occlusion (LAAO) for stroke reduction in atrial fibrillation.

OBJECTIVES

To investigate the perceptions and approaches among a nationally representative sample of physicians.

METHODS

Using the American Medical Association Physician Masterfile, we selected a random sample of 500 physicians from each of the specialties: general cardiologists, interventional cardiologists, electrophysiologists, and vascular neurologists. The participants received the survey by mail up to three times from November 9, 2021 to January 14, 2022. In addition to the questions about experiences, perceptions, and approaches, physicians were randomly assigned to 1 of the 4 versions of a patient vignette: white man, white woman, black man, and black woman, to investigate potential bias in decision-making.

RESULTS

The top three reasons for considering LAAO were: a history of intracranial bleeding (94.3%), a history of major extracranial bleeding (91.8%), and gastrointestinal lesions (59.0%), whereas the top three reasons for withholding LAAO were: other indications for long-term oral anticoagulation (87.7%), a low bleeding risk (77.0%), and a low stroke risk (65.6%). For the reasons limiting recommendations for LAAO, 59.8% mentioned procedural risks, 42.6% mentioned "limiting efficacy data comparing LAAO to NOAC" and 32.8% mentioned "limited safety data comparing LAAO to NOAC." There was no difference in physicians' decision-making by patients' race, gender, or the concordance between patients' and physicians' race or gender.

CONCLUSIONS

In the first U.S. national physician survey of LAAO, individual physicians' perspectives varied greatly, which provided information that will help customize future educational activities for different audiences.

CONDENSED ABSTRACT

Although diverse practice patterns of LAAO have been documented, little is known about the reasoning or perceptions that drive these variations. Unlike prior surveys that were directed to Centers that performed LAAO, the current survey obtained insights from individual physicians, not only those who perform the procedures (interventional cardiologists and electrophysiologists) but also those who are closely involved in the decision-making and referral process (general cardiologists and vascular neurologists). The findings identify key evidence gaps and help prioritize future studies to establish a consistent and evidence-based best practice for AF stroke prevention.

Identification and risk stratification of coronary disease by artificial intelligence-enabled ECG

Background

Atherosclerotic cardiovascular disease (ASCVD) is the leading cause of death worldwide, driven primarily by coronary artery disease (CAD). ASCVD risk estimators such as the pooled cohort equations (PCE) facilitate risk stratification and primary prevention of ASCVD but their accuracy is still suboptimal.

Methods

Using deep electronic health record data from 7,116,209 patients seen at 70+ hospitals and clinics across 5 states in the USA, we developed an artificial intelligence-based electrocardiogram analysis tool (ECG-AI) to detect CAD and assessed the additive value of ECG-AI-based ASCVD risk stratification to the PCE. We created independent ECG-AI models using separate neural networks including subjects without known history of ASCVD, to identify coronary artery calcium (CAC) score ≥300 Agatston units by computed tomography, obstructive CAD by angiography or procedural intervention, and regional left ventricular akinesis in ≥1 segment by echocardiogram, as a reflection of possible prior myocardial infarction (MI). These were used to assess the utility of ECG-AI-based ASCVD risk stratification in a retrospective observational study consisting of patients with PCE scores and no prior ASCVD. The study period covered all available digitized EHR data, with the first available ECG in 1987 and the last in February 2023.

Findings

ECG-AI for identifying CAC ≥300, obstructive CAD, and regional akinesis achieved area under the receiver operating characteristic (AUROC) values of 0.88, 0.85, and 0.94, respectively. An ensembled ECG-AI identified 3, 5, and 10-year risk for acute coronary events and mortality independently and additively to PCE. Hazard ratios for acute coronary events over 3-years in patients without ASCVD that tested positive on 1, 2, or 3 versus 0 disease-specific ECG-AI models at cohort entry were 2.41 (2.14-2.71), 4.23 (3.74-4.78), and 11.75 (10.2-13.52), respectively. Similar stratification was observed in cohorts stratified by PCE or age.

Interpretation

ECG-AI has potential to address unmet need for accessible risk stratification in patients in whom PCE under, over, or insufficiently estimates ASCVD risk, and in whom risk assessment over time periods shorter than 10 years is desired.

Funding

Anumana.

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

INTRODUCTION

A 12‑lead electrocardiography (ECG)-based convolutional neural network (CNN) model can detect hypertrophic cardiomyopathy (HCM). However, since these models do not rely on discrete measurements as inputs, it is not apparent what drives their performance. We hypothesized that saliency maps could be used to visually identify ECG segments that contribute to a CNN's robust classification of HCM.

METHODS

We derived a new one‑lead (lead I) CNN model based on median beats using the same methodology and cohort used for the original 12‑lead CNN model (3047 patients with HCM, and 63,926 sex- and age-matched non-HCM controls). One‑lead, median-beat saliency maps were generated and visually evaluated in an independent cohort of 100 patients with a diagnosis of HCM and a high artificial intelligence (AI)-ECG-HCM probability score to determine which ECG segments contributed to the model's detection of HCM.

RESULTS

The one‑lead, median-beat CNN had an AUC of 0.90 (95% CI 0.89-0.92) for HCM detection, similar to the original 12‑lead ECG model. In the independent HCM cohort (n = 100), saliency maps highlighted the ST-T segment in 92 ECGs, the atrial depolarization segment in 12 ECGs, and the QRS complex in 5 ECGs.

CONCLUSIONS

Saliency maps of a one‑lead, median-beat-based CNN model identified perturbations in ventricular repolarization as the main region of interest in detecting HCM.

Electrocardiogram with Artificial Intelligence Assessment as a Predictor of Cardiac Events and Overall Survival in Patients Receiving Radiotherapy for Esophageal Cancer

PURPOSE/OBJECTIVE(S)

Neoadjuvant (chemo)radiotherapy (RT) has demonstrated an overall survival (OS) benefit in esophageal cancer and constitutes part of the standard of care trimodality therapy. Unfortunately, subsequent cardiac toxicity can reduce the benefit of treatment. Our group aimed to study whether data from electrocardiograms (ECGs) could predict clinical outcomes and cardiac events after RT for esophageal cancer, allowing for identification of and early intervention for patients at high risk for cardiac toxicity.

MATERIALS/METHODS

Included patients received at least 41.4 Gy of pre-operative or definitive photon or proton RT for esophageal cancer from 2015 through July 2022. All ECGs were assessed using a previously validated artificial intelligence assessment for atrial fibrillation (AF) and reduced ejection fraction (rEF) (Noseworthy et al. Lancet 2022). The model determined propensities for the development of multiple cardiac events, including AF and heart failure (HF). Medical records were reviewed for cardiac events and conditions prior to and after RT.

RESULTS

A cohort of 491 patients was assembled, with 301, 121, and 364 patients having an ECG prior to, during, and after RT, respectively. Of these, 84% had malignancy in the lower third of the esophagus and 48% underwent esophagectomy. At last follow-up relative to baseline assessment, patients had increased propensity for rEF (median 0.013, interquartile range (IQR): 0.001-0.038 vs. median 0.022, IQR: 0.011-0.074, p < 0.0001) and AF (median 0.16, IQR: 0.04-0.40 vs. median 0.048, IQR: 0.01-0.19, p < 0.0001). Increases in AF propensity were associated with reduced OS (hazard ratio (HR) = 1.10 per 0.1 increase, 95% confidence interval (CI): 1.03-1.17, p = 0.0071). Baseline rEF propensity was predictive of future HF events (HR = 1.14, 95% CI: 1.07-1.22, p < 0.001) for all patients or after excluding the 172 (35%) patients with baseline HF (HR = 1.45, 95% CI: 1.19-1.76, p < 0.001). Among patients who did not have HF prior to radiotherapy, the development of HF was associated with reduced OS (HR = 1.60, 95% CI: 1.10-2.32, p = 0.014). Currently available cardiac dosimetric parameters, including heart mean/max doses, did not significantly correlate with cardiac outcomes. Patients who underwent esophagectomy had improved OS (HR = 0.62, 95% CI: 0.47-0.82, p = 0.0008) and were not more likely to develop cardiac toxicity.

CONCLUSION

This analysis suggests that chemoradiotherapy for esophageal cancer can have significant impacts on a patient's propensity for cardiac events, which are associated with reduced OS. ECGs carry the potential to identify patients at greater risk for such events, and baseline ECGs with artificial intelligence assessment could select patients for increased surveillance or early intervention to further optimize the therapeutic ratio of RT.

Patient-Level Artificial Intelligence-Enhanced Electrocardiography in Hypertrophic Cardiomyopathy: Longitudinal Treatment and Clinical Biomarker Correlations

BACKGROUND

Artificial intelligence (AI) applied to 12-lead electrocardiographs (ECGs) can detect hypertrophic cardiomyopathy (HCM).

OBJECTIVES

The purpose of this study was to determine if AI-enhanced ECG (AI-ECG) can track longitudinal therapeutic response and changes in cardiac structure, function, or hemodynamics in obstructive HCM during mavacamten treatment.

METHODS

We applied 2 independently developed AI-ECG algorithms (University of California-San Francisco and Mayo Clinic) to serial ECGs (n = 216) from the phase 2 PIONEER-OLE trial of mavacamten for symptomatic obstructive HCM (n = 13 patients, mean age 57.8 years, 69.2% male). Control ECGs from 2,600 age- and sex-matched individuals without HCM were obtained. AI-ECG output was correlated longitudinally to echocardiographic and laboratory metrics of mavacamten treatment response.

RESULTS

In the validation cohorts, both algorithms exhibited similar performance for HCM diagnosis, and exhibited mean HCM score decreases during mavacamten treatment: patient-level score reduction ranged from approximately 0.80 to 0.45 for Mayo and 0.70 to 0.35 for USCF algorithms; 11 of 13 patients demonstrated absolute score reduction from start to end of follow-up for both algorithms. HCM scores were significantly associated with other HCM-relevant parameters, including left ventricular outflow tract gradient at rest, postexercise, and with Valsalva, and NT-proBNP level, independent of age and sex (all P < 0.01). For both algorithms, the strongest longitudinal correlation was between AI-ECG HCM score and left ventricular outflow tract gradient postexercise (slope estimate: University of California-San Francisco 0.70 [95% CI: 0.45-0.96], P < 0.0001; Mayo 0.40 [95% CI: 0.11-0.68], P = 0.007).

CONCLUSIONS

AI-ECG analysis longitudinally correlated with changes in echocardiographic and laboratory markers during mavacamten treatment in obstructive HCM. These results provide early evidence for a potential paradigm for monitoring HCM therapeutic response.

Postdevelopment Performance and Validation of the Artificial Intelligence-Enhanced Electrocardiogram for Detection of Cardiac Amyloidosis

BACKGROUND

We have previously applied artificial intelligence (AI) to an electrocardiogram (ECG) to detect cardiac amyloidosis (CA).

OBJECTIVES

In this validation study, the authors observe the postdevelopment performance of the AI-enhanced ECG to detect CA with respect to multiple potential confounders.

METHODS

Amyloid patients diagnosed after algorithm development (June 2019-January 2022) with a 12-lead ECG were identified (n = 440) and were required to have CA. A 15:1 age- and sex-matched control group was identified (n = 6,600). Area under the receiver operating characteristic (AUC) was determined for the cohort and subgroups.

RESULTS

The average age was 70.4 ± 10.3 years, 25.0% were female, and most patients were White (91.3%). In this validation, the AI-ECG for amyloidosis had an AUC of 0.84 (95% CI: 0.82-0.86) for the overall cohort and between amyloid subtypes, which is a slight decrease from the original study (AUC 0.91). White, Black, and patients of "other" races had similar algorithm performance (AUC >0.81) with a decreased performance for Hispanic patients (AUC 0.66). Algorithm performance shift over time was not observed. Low ECG voltage and infarct pattern exhibited high AUC (>0.90), while left ventricular hypertrophy and left bundle branch block demonstrated lesser performance (AUC 0.75 and 0.76, respectively).

CONCLUSIONS

The AI-ECG for the detection of CA maintained an overall strong performance with respect to patient age, sex, race, and amyloid subtype. Lower performance was noted in left bundle branch block, left ventricular hypertrophy, and ethnically diverse populations emphasizing the need for subgroup-specific validation efforts.

Exploring Factors Influencing ECG Interpretation Proficiency of Medical Professionals

The electrocardiogram (ECG) is a crucial diagnostic tool in medicine with concerns about its interpretation proficiency across various medical disciplines. Our study aimed to explore potential causes of these issues and identify areas requiring improvement. A survey was conducted among medical professionals to understand their experiences with ECG interpretation and education. A total of 2515 participants from diverse medical backgrounds were surveyed. A total of 1989 (79%) participants reported ECG interpretation as part of their practice. However, 45% expressed discomfort with independent interpretation. A significant 73% received less than 5 hours of ECG-specific education, with 45% reporting no education at all. Also, 87% reported limited or no expert supervision. Nearly all medical professionals (2461, 98%) expressed a desire for more ECG education. These findings were consistent across all groups and did not vary between primary care physicians, cardiology FIT, resident physicians, medical students, APPs, nurses, physicians, and nonphysicians. This study reveals substantial deficiencies in ECG interpretation training, supervision, and confidence among medical professionals, despite a strong interest in increased ECG education.

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

BACKGROUND

Artificial intelligence (AI), and more specifically deep learning, models have demonstrated the potential to augment physician diagnostic capabilities and improve cardiovascular health if incorporated into routine clinical practice. However, many of these tools are yet to be evaluated prospectively in the setting of a rigorous clinical trial-a critical step prior to implementing broadly in routine clinical practice.

OBJECTIVES

To describe the rationale and design of a proposed clinical trial aimed at evaluating an AI-enabled electrocardiogram (AI-ECG) for cardiomyopathy detection in an obstetric population in Nigeria.

DESIGN

The protocol will enroll 1,000 pregnant and postpartum women who reside in Nigeria in a prospective randomized clinical trial. Nigeria has the highest reported incidence of peripartum cardiomyopathy worldwide. Women aged 18 and older, seen for routine obstetric care at 6 sites (2 Northern and 4 Southern) in Nigeria will be included. Participants will be randomized to the study intervention or control arm in a 1:1 fashion. This study aims to enroll participants representative of the general obstetric population at each site. The primary outcome is a new diagnosis of cardiomyopathy, defined as left ventricular ejection fraction (LVEF) < 50% during pregnancy or within 12 months postpartum. Secondary outcomes will include the detection of impaired left ventricular function (at different LVEF cut-offs), and exploratory outcomes will include the effectiveness of AI-ECG tools for cardiomyopathy detection, new diagnosis of cardiovascular disease, and the development of composite adverse maternal cardiovascular outcomes.

SUMMARY

This clinical trial focuses on the emerging field of cardio-obstetrics and will serve as foundational data for the use of AI-ECG tools in an obstetric population in Nigeria. This study will gather essential data regarding the utility of the AI-ECG for cardiomyopathy detection in a predominantly Black population of women and pave the way for clinical implementation of these models in routine practice.

TRIAL REGISTRATION

Clinicaltrials.gov: NCT05438576.

Design and rationale of the MODULAR ATP global clinical trial: A novel intercommunicative leadless pacing system and the subcutaneous implantable cardioverter-defibrillator

Background

The subcutaneous implantable cardioverter-defibrillator (S-ICD) has demonstrated safety and efficacy for the treatment of malignant ventricular arrhythmias. However, a limitation of the S-ICD lies in the inability to either pace-terminate ventricular tachycardia or provide prolonged bradycardia pacing support.

Objective

The rationale and design of a prospective, single-arm, multinational trial of an intercommunicative leadless pacing system integrated with the S-ICD will be presented.

Methods

A technical description of the modular cardiac rhythm management (mCRM) system (EMPOWER leadless pacemaker and EMBLEM S-ICD) and the implantation procedure is provided. MODULAR ATP (Effectiveness of the EMPOWER™ Modular Pacing System and EMBLEM™ Subcutaneous ICD to Communicate Antitachycardia Pacing) is a multicenter, international trial enrolling up to 300 patients at risk of sudden cardiac death at up to 60 centers trial design. The safety endpoint of freedom from major complications related to the mCRM system or implantation procedure at 6 months and 2 years are significantly higher than 86% and 81%, respectively, and all-cause survival is significantly >85% at 2 years.

Results

Efficacy endpoints are that at 6 months mCRM communication success is significantly higher than 88% and the percentage of subjects with low and stable thresholds is significantly higher than 80%. Substudies to evaluate rate-responsive features and performance of the pacing module are also described.

Conclusion

The MODULAR ATP global clinical trial will prospectively test the safety and efficacy of the first intercommunicating leadless pacing system with the S-ICD. This trial will allow for robust validation of device-device communication, pacing performance, rate responsiveness, and system safety.

Contemporary trends and barriers to oral anticoagulation therapy in Non-valvular atrial fibrillation during DOAC predominant era

There is a need to reassess contemporary oral anticoagulation (OAC) trends and barriers against guideline directed therapy in the United States. Most previous studies were performed before major guideline changes recommended direct oral anticoagulant (DOAC) use over warfarin or have otherwise lacked patient level data. Data on overuse of OAC in low-risk group is also limited. To address these knowledge gaps, we performed a nationwide analysis to analyze current trends. This is a retrospective cohort study assessing non-valvular AF identified using a large United States de-identified administrative claims database, including commercial and Medicare Advantage enrollees. Prescription fills were assessed within a 90-day follow-up from the patient's index AF encounter between January 1, 2016, and December 31, 2020. Among the 339,197 AF patients, 4.4%, 8.0%, and 87.6% were in the low-, moderate-, and high-risk groups (according to CHA₂DS₂-VASc score). An over (29.6%) and under (52.2%) utilization of OAC was reported in low- and high-risk AF patients. A considerably high frequency for warfarin use was also noted among high-risk group patients taking OAC (33.1%). The results suggest that anticoagulation use for stroke prevention in the United States is still comparable to the pre-DOAC era studies. About half of newly diagnosed high-risk non-valvular AF patients remain unprotected against stroke risk. Several predictors of OAC and DOAC use were also identified. Our findings may identify a population at risk of complications due to under- or over-treatment and highlight the need for future quality improvement efforts.

Physiological Age by Artificial Intelligence-Enhanced Electrocardiograms as a Novel Risk Factor of Mortality in Kidney Transplant Candidates

BACKGROUND

Mortality risk assessment before kidney transplantation (KT) is imperfect. An emerging risk factor for death in nontransplant populations is physiological age as determined by the application of artificial intelligence to the electrocardiogram (ECG). The aim of this study was to examine the relationship between ECG age and KT waitlist mortality.

METHODS

We applied a previously developed convolutional neural network to the ECGs of KT candidates evaluated 2014 to 2019 to determine ECG age. We used a Cox proportional hazard model to examine whether ECG age was associated with waitlist mortality.

RESULTS

Of the 2183 patients evaluated, 59.1% were male, 81.4% were white, and 11.4% died during follow-up. Mean ECG age was 59.0 ± 12.0 y and mean chronological age at ECG was 53.3 ± 13.6 y. After adjusting for chronological age, comorbidities, and other characteristics associated with mortality, each increase in ECG age of >10 y than the average ECG age for patients of a similar chronological age was associated with an increase in mortality risk (hazard ratio 3.59 per 10-y increase; 95% confidence interval, 2.06-5.72; P  < 0.0001).

CONCLUSIONS

ECG age is a risk factor for KT waitlist mortality. Determining ECG age through artificial intelligence may help guide risk-benefit assessment when evaluating candidates for KT.

Trends in the 30-year span of Noninfectious Cardiovascular Implantable Electronic Device Complications in Olmsted County

Background

Cardiovascular 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

Describe trends in noninfectious CIED complications over the past three 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-current. CIED implants between 1988-2018 were determined. Trends in noninfectious complications within 30 days of implant were analyzed.

Results

175 out of 2536 (6.9%) patients who received CIED experienced device complications. 3.8% of the implants had major complications requiring intervention. Lead dislodgement was the most common (2.9%), followed by hematoma (2.1%). Complications went up from 1988 to 2005, then showed a downtrend until 2018, driven by a decline in hematomas in the last decade (p<0.01). 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 score has trended up over the 30 years.

Conclusions

Our study describes a real-world trend of CIED complications over three 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.

Correlation between artificial intelligence-enabled electrocardiogram and echocardiographic features in aortic stenosis

Aims

An artificial intelligence-enabled electrocardiogram (AI-ECG) is a promising tool to detect patients with aortic stenosis (AS) before developing symptoms. However, functional, structural, or haemodynamic components reflected in AI-ECG responsible for its detection are unknown.

Methods and results

The AI-ECG model that was developed at Mayo Clinic using a convolutional neural network to identify patients with moderate-severe AS was applied. In patients used as the testing group, the correlation between the AI-ECG probability of AS and echocardiographic parameters was investigated. This study included 102 926 patients (63.0 ± 16.3 years, 52% male), and 28 464 (27.7%) were identified as AS positive by AI-ECG. Older age, atrial fibrillation, hypertension, diabetes, coronary artery disease, and heart failure were more common in the positive AI-ECG group than in the negative group (P < 0.001). The AI-ECG was correlated with aortic valve area (ρ = -0.48, R² = 0.20), peak velocity (ρ = 0.22, R² = 0.08), and mean pressure gradient (ρ = 0.35, R² = 0.08). The AI-ECG also correlated with left ventricular (LV) mass index (ρ = 0.36, R² = 0.13), E/e' (ρ = 0.36, R² = 0.12), and left atrium volume index (ρ = 0.42, R² = 0.12). Neither LV ejection fraction nor stroke volume index had a significant correlation with the AI-ECG. Age correlated with the AI-ECG (ρ = 0.46, R² = 0.22) and its correlation with echocardiography parameters was similar to that of the AI-ECG.

Conclusion

A combination of AS severity, diastolic dysfunction, and LV hypertrophy is reflected in the AI-ECG to detect AS. There seems to be a gradation of the cardiac anatomical/functional features in the model and its identification process of AS is multifactorial.

Safety of Magnetic Resonance Imaging in Patients with Surgically Implanted Permanent Epicardial Leads

BACKGROUND

Magnetic resonance imaging (MRI) safety in patients with an epicardial cardiac implantable electronic device (CIED) is uncertain.

OBJECTIVE

To assess the safety and adverse effects of MRI in patients who had surgically implanted epicardial CIED.

METHODS

Patients with surgically implanted CIEDs who underwent MRI with an appropriate Cardiology-Radiology collaborative protocol between January 2008 and January 2021 were prospectively studied in two clinical centers. All patients underwent close cardiac monitoring through MRI procedures. Outcomes were compared between the epicardial CIED group and matched the non-MRI-conditional transvenous CIED group.

RESULTS

Twenty-nine consecutive patients with epicardial CIED (male 41.4%, mean age of 43 years) underwent 52 MRIs in the 57 anatomic regions. Sixteen patients had pacemakers, 9 had cardiac defibrillators or cardiac resynchronization therapy defibrillators, and 4 had no device generators. There were no significant adverse events in epicardial or transvenous CIED groups. The battery life, pacing, sensing thresholds, lead impedance and cardiac biomarkers were not significantly changed, except one patient had a transient decrease in atrial lead sensing function.

CONCLUSION

MRI of CIEDs with epicardially implanted leads does not represent a greater risk than the transvenous CIEDs when performed with a multidisciplinary collaborative protocol centered on patient safety.

Ventricular Arrhythmias in Patients with Prior Aortic Valve Intervention: Characteristics, Ablation and Outcomes

BACKGROUND

Data regarding ventricular tachycardia (VT) or premature ventricular complex (PVC) ablation in patients with aortic valve intervention (AVI) is limited. Catheter ablation (CA) can be challenging given perivalvular substrate in the setting of prosthetic valves.

OBJECTIVE

To investigate the characteristics, safety, and outcomes of CA in patients with prior AVI and ventricular arrhythmias (VA).

METHODS

We identified consecutive patients with prior AVI (replacement or repair) who underwent CA for VT or PVC between 2013 and 2018. We investigated the mechanism of arrhythmia, ablation approach, perioperative complications, and outcomes.

RESULTS

We included 34 patients (88% men, mean age 64±10.4 years, left ventricular ejection fraction 35.2±15.0%) with prior AVI who underwent CA (22 VT; 12 PVC). LV access was obtained through trans-septal approach in all patients except one patient who had percutaneous transapical access. One patient had combined retrograde aortic and trans-septal approach. Scar-related reentry was the dominant mechanism of induced VTs. Two patients had bundle branch reentry VTs. In the VT group, substrate mapping demonstrated heterogeneous scar that involved the periaortic valve area in 95%. Despite that, the site of successful ablation included the periaortic region only in 6 (27%) patients. In the PVC group, signal abnormalities consistent with scar in the periaortic area were noted in 4 (33%) patients. In 8 (67%) patients, the successful site of ablation was unrelated to the periaortic area. No procedure-related complications occurred. The survival and recurrence-free survival rate at 1 year tended to be lower in VT group than in PVC group (P=0.06 and P=0.05, respectively) with a 1-year recurrence-free survival rate of 52.8% and 91.7%, respectively. No arrhythmia-related death was documented on long-term follow-up.

CONCLUSION

CA of VAs can be performed safely and effectively in patients with prior AVI. This article is protected by copyright. All rights reserved.

Nonthrombogenic Roles of the Left Atrial Appendage: JACC Review Topic of the Week

The atrial appendage (LAA) is a well-established source of cardioembolism in patients with atrial fibrillation. Therefore, research involving the LAA has largely focused on its thrombogenic attribute and the utility of its exclusion in stroke prevention. However, recent studies have highlighted several novel functions of the LAA that may have important therapeutic implications. In this paper, we provide a concise overview of the LAA anatomy and summarize the emerging data on its nonthrombogenic roles.

Artificial Intelligence-Derived Electrocardiogram Assessment of Cardiac Age and Molecular Markers of Senescence in Heart Failure

OBJECTIVE

To ascertain whether heart failure (HF) itself is a senescent phenomenon independent of age, and how this is reflected at a molecular level in the circulating progenitor cell niche, and at a substrate level using a novel electrocardiogram (ECG)-based artificial intelligence platform.

PATIENTS AND METHODS

Between October 14, 2016, and October 29, 2020, CD34⁺ progenitor cells were analyzed by flow cytometry and isolated by magnetic-activated cell sorting from patients of similar age with New York Heart Association functional classes IV (n = 17) and I-II (n = 10) heart failure with reduced ejection fraction and healthy controls (n = 10). CD34⁺ cellular senescence was quantitated by human telomerase reverse transcriptase expression and telomerase expression by quantitative polymerase chain reaction, and senescence-associated secretory phenotype (SASP) protein expression assayed in plasma. An ECG-based artificial intelligence (AI) algorithm was used to determine cardiac age and difference from chronological age (AI ECG age gap).

RESULTS

CD34⁺ counts and telomerase expression were significantly reduced and AI ECG age gap and SASP expression increased in all HF groups compared with healthy controls. Expression of SASP protein was closely associated with telomerase activity and severity of HF phenotype and inflammation. Telomerase activity was more closely associated with CD34⁺ cell counts and AI ECG age gap.

CONCLUSION

We conclude from this pilot study that HF may promote a senescent phenotype independent of chronological age. We show for the first time that the AI ECG in HF shows a phenotype of cardiac aging beyond chronological age, and appears to be associated with cellular and molecular evidence of senescence.

Community paramedic hospital reduction and mitigation program: study protocol for a randomized pragmatic clinical trial

BACKGROUND

New patient-centered models of care are needed to individualize care and reduce high-cost care, including emergency department (ED) visits and hospitalizations for low- and intermediate-acuity conditions that could be managed outside the hospital setting. Community paramedics (CPs) have advanced training in low- and high-acuity care and are equipped to manage a wide range of health conditions, deliver patient education, and address social determinants of health in the home setting. The objective of this trial is to evaluate the effectiveness and implementation of the Care Anywhere with Community Paramedics (CACP) program with respect to shortening and preventing acute care utilization.

METHODS

This is a pragmatic, hybrid type 1, two-group, parallel-arm, 1:1 randomized clinical trial of CACP versus usual care that includes formative evaluation methods and assessment of implementation outcomes. It is being conducted in two sites in the US Midwest, which include small metropolitan areas and rural areas. Eligible patients are ≥ 18 years old; referred from an outpatient, ED, or hospital setting; clinically appropriate for ambulatory care with CP support; and residing within CP service areas of the referral sites. Aim 1 uses formative data collection with key clinical stakeholders and rapid qualitative analysis to identify potential facilitators/barriers to implementation and refine workflows in the 3-month period before trial enrollment commences (i.e., pre-implementation). Aim 2 uses mixed methods to evaluate CACP effectiveness, compared to usual care, by the number of days spent alive outside of the ED or hospital during the first 30 days following randomization (primary outcome), as well as self-reported quality of life and treatment burden, emergency medical services use, ED visits, hospitalizations, skilled nursing facility utilization, and adverse events (secondary outcomes). Implementation outcomes will be measured using the RE-AIM framework and include an assessment of perceived sustainability and metrics on equity in implementation. Aim 3 uses qualitative methods to understand patient, CP, and health care team perceptions of the intervention and recommendations for further refinement. In an effort to conduct a rigorous evaluation but also speed translation to practice, the planned duration of the trial is 15 months from the study launch to the end of enrollment.

DISCUSSION

This study will provide robust and timely evidence for the effectiveness of the CACP program, which may pave the way for large-scale implementation. Implementation outcomes will inform any needed refinements and best practices for scale-up and sustainability.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05232799. Registered on 10 February 2022.

Accelerated Aging in LMNA Mutations Detected by Artificial Intelligence ECG-Derived Age

OBJECTIVE

To demonstrate early aging in patients with lamin A/C (LMNA) gene mutations after hypothesizing that they have a biological age older than chronological age, as such a finding impacts care.

PATIENT AND METHODS

We applied a previously trained convolutional neural network model to predict biological age by electrocardiogram (ECG) [Artificial Intelligence (AI)-ECG age] to LMNA patients evaluated by multiple ECGs from January 1, 2003, to December 31, 2019. The age gap was the difference between chronological age and AI-ECG age. Findings were compared with age-/sex-matched controls.

RESULTS

Thirty-one LMNA patients who had a total of 271 ECGs were studied. The median age at symptom onset was 22 years (range, <1-53 years; n=23 patients); eight patients were asymptomatic family members carrying the LMNA mutation. Cardiac involvement was detected by ECG and echocardiogram in 16 patients and consisted of ventricular arrhythmias (13), atrial fibrillation (12), and cardiomyopathy (6). Four patients required cardiac transplantation. Fourteen patients had neurological manifestations, mainly muscular dystrophy. LMNA mutation carriers, including asymptomatic carriers, were 16 years older by AI-ECG than non-LMNA carriers, suggesting accelerated biological age. Most LMNA patients had an age gap of more than 10 years, compared with controls (P<.001). Consecutive AI-ECG analysis showed accelerated aging in the LMNA group compared with controls (P<.0001). There were no significant differences in age-gap among LMNA patients based on phenotype.

CONCLUSION

AI-ECG predicted that LMNA patients have a biological age older than chronological age and accelerated aging even in the absence of cardiac abnormalities by traditional methods. Such a finding could translate into early medical intervention and serve as a disease biomarker.

Artificial intelligence-augmented electrocardiography for left ventricular systolic dysfunction in patients undergoing high-sensitivity cardiac troponin T

AIMS

Our goal was to evaluate a previously validated artificial intelligence-augmented electrocardiography (AI-ECG) screening tool for left ventricular systolic dysfunction (LVSD) in patients undergoing high-sensitivity-cardiac troponin T (hs-cTnT).

METHODS AND RESULTS

Retrospective application of AI-ECG for LVSD in emergency department (ED) patients undergoing hs-cTnT. AI-ECG scores (0-1) for probability of LVSD (left ventricular ejection fraction ≤ 35%) were obtained. An AI-ECG score ≥0.256 indicates a positive screen. The primary endpoint was a composite of post-discharge major adverse cardiovascular events (MACEs) at two years follow-up. Among 1977 patients, 248 (13%) had a positive AI-ECG. When compared with patients with a negative AI-ECG, those with a positive AI-ECG had a higher risk for MACE [48 vs. 21%, P < 0.0001, adjusted hazard ratio (HR) 1.39, 95% confidence interval (CI) 1.11-1.75]. This was largely because of a higher rate of deaths (32 vs. 14%, P < 0.0001; adjusted HR 1.26, 95% 0.95-1.66) and heart failure hospitalizations (26 vs. 6.1%, P < 0.001; adjusted HR 1.75, 95% CI 1.25-2.45). Together, hs-cTnT and AI-ECG resulted in the following MACE rates and adjusted HRs: hs-cTnT < 99th percentile and negative AI-ECG: 116/1176 (11%; reference), hs-cTnT < 99th percentile and positive AI-ECG: 28/107 (26%; adjusted HR 1.54, 95% CI 1.01-2.36), hs-cTnT > 99th percentile and negative AI-ECG: 233/553 (42%; adjusted HR 2.12, 95% CI 1.66, 2.70), and hs-cTnT > 99th percentile and positive AI-ECG: 91/141 (65%; adjusted HR 2.83, 95% CI 2.06, 3.87).

CONCLUSION

Among ED patients evaluated with hs-cTnT, a positive AI-ECG for LVSD identifies patients at high risk for MACE. The conjoint use of hs-cTnT and AI-ECG facilitates risk stratification.

New atrial arrhythmia occurrence in single chamber implantable cardioverter defibrillator patients: A real-world investigation

INTRODUCTION

A current limitation of single chamber implantable cardioverter defibrillators (ICDs) is the lack of an atrial lead to reliably detect atrial fibrillation (AF) episodes. A novel ventricular based atrial fibrillation (VBAF) detection algorithm was created for single chamber ICDs to assess R-R variability for detection of AF.

METHODS

Patients implanted with Visia AF™ ICDs were prospectively enrolled in the Medtronic Product Surveillance Registry from December 15, 2015 to January 23, 2019 and followed with at least 30 days of monitoring with the algorithm. Time to device-detected daily burden of AF ≥ 6 min, ≥6 h, and ≥23 h were reported. Clinical actions after device-detected AF were recorded.

RESULTS

A total of 291 patients were enrolled with a mean follow-up of 22.5 ± 7.9 months. Of these, 212 (73%) had no prior history of AF at device implant. However, 38% of these individuals had AF detected with the VBAF algorithm with daily burden of ≥6 min within two years of implant. In these 80 patients with newly detected AF by their ICD, 23 (29%) had a confirmed clinical diagnosis of AF by their provider. Of patients with a clinical diagnosis of AF, nine (39%) were newly placed on anticoagulation, including five of five (100%) patients having a burden >23 h.

CONCLUSIONS

Continuous AF monitoring with the new VBAF algorithm permits early identification and actionable treatment for patients with undiagnosed AF that may improve patient outcomes.

Artificial Intelligence-Augmented Electrocardiogram in Determining Sex: Correlation with Sex Hormone Levels

OBJECTIVE

To study the relationship between the sex probability derived from the artificial intelligence (AI)-augmented electrocardiogram (ECG) and sex hormone levels.

PATIENTS AND METHODS

Adult patients with total testosterone (TT; ng/dL) or estradiol (E2; pg/mL) levels (January 1, 2000, to December 31, 2020) with ECGs obtained within 6 months of the blood sample were identified. The closest ECG to the blood test was used. The AI-ECG model output ranges from 0.0 to 1.0, with higher numbers indicating high probability of being male. Low male probability was defined as ≤0.3, intermediate as 0.31 to 0.69, and high as ≥0.7. Continuous variables are expressed as median (interquartile range).

RESULTS

Paired TT-ECGs were available in 58,084 male subjects and 11,190 female subjects. Paired E2-ECGs were available in 2835 male patients and 18,228 female patients. TT levels had moderate positive correlation with AI-ECG male sex probability (r=0.46, P<.001). Male subjects with low AI-ECG male sex probability had lower TT and higher E2 levels compared with men with high probability (TT: 303 [129-474] vs 381 [264-523], P <.001; E2: 35 [21-49] vs 32 [22-38], P=.05). Female subjects with high AI-ECG male sex probability had higher TT and lower E2 levels compared with those who had low male probability (TT: ≤50 years of age: 31 [18-55] vs 26 [16-39], P<.001; >50 years of age: 27 [12-68] vs 20 [12-34], P<.001; E2: ≤50 years of age: 58 [30-124] vs 47 [25-87], P=.001; >50 years of age: 30 [10-55] vs 21 [10-41], P=.006).

CONCLUSION

In this study, TT levels were lower and E2 levels higher with decreasing AI-ECG male probability in both sexes. Male and female patients with discordant AI-ECG sex probability had significantly different TT or E2 levels. This suggests that the ECG could be used as a biomarker of hormone status.

Deep learning-derived cardiovascular age shares a genetic basis with other cardiac phenotypes

Artificial intelligence (AI)-based approaches can now use electrocardiograms (ECGs) to provide expert-level performance in detecting heart abnormalities and diagnosing disease. Additionally, patient age predicted from ECGs by AI models has shown great potential as a biomarker for cardiovascular age, where recent work has found its deviation from chronological age ("delta age") to be associated with mortality and co-morbidities. However, despite being crucial for understanding underlying individual risk, the genetic underpinning of delta age is unknown. In this work we performed a genome-wide association study using UK Biobank data (n=34,432) and identified eight loci associated with delta age ([Formula: see text]), including genes linked to cardiovascular disease (CVD) (e.g. SCN5A) and (heart) muscle development (e.g. TTN). Our results indicate that the genetic basis of cardiovascular ageing is predominantly determined by genes directly involved with the cardiovascular system rather than those connected to more general mechanisms of ageing. Our insights inform the epidemiology of CVD, with implications for preventative and precision medicine.

Causes of Early Mortality After Transvenous Lead Removal

BACKGROUND

Recognition of the causes of early mortality (≤30 days) after transvenous lead removal (TLR) is an essential step for the development of quality improvement programs.

OBJECTIVES

This study sought to determine the causes of early mortality after TLR and to further understand the circumstances surrounding death after TLR.

METHODS

A retrospective analysis was performed of all patients undergoing TLR from January 1, 2001, to January 1, 2021, at the Mayo Clinic (Rochester, Minnesota; Phoenix, Arizona; and Jacksonville, Florida). Causes of death were identified through a detailed chart review of the electronic health record from within the Mayo Clinic system and outside records when available. The causes of death were further characterized based on whether it was related to the TLR procedure.

RESULTS

A total of 2,319 patients were included in the study. The overall 30-day all-cause mortality rate was 3% (n = 69). Among all 30-day deaths, infection was the most common primary cause of death (42%). This was followed by decompensated heart failure (17%), procedure-related death (10%), sudden cardiac arrest (7%), and respiratory failure (6%). The 30-day mortality rate directly due to complications associated with the TLR procedure was 0.3%. One-third of deaths (33%) occurred after discharge from the index hospitalization; among these, 43% were readmitted before their death, 35% died at home or at a nursing facility, and 22% were discharged on comfort care and died in hospice. The main reasons for readmission before death were sepsis and decompensated heart failure.

CONCLUSIONS

The majority (90%) of 30-day mortality after TLR was not due to complications associated with TLR procedures. The primary causes were infection and decompensated heart failure. This highlights the importance of increased emphasis on postprocedure management of infection and heart failure to reduce postoperative mortality, including after hospital discharge.

Prospective evaluation of smartwatch-enabled detection of left ventricular dysfunction

Although artificial intelligence (AI) algorithms have been shown to be capable of identifying cardiac dysfunction, defined as ejection fraction (EF) ≤ 40%, from 12-lead electrocardiograms (ECGs), identification of cardiac dysfunction using the single-lead ECG of a smartwatch has yet to be tested. In the present study, a prospective study in which patients of Mayo Clinic were invited by email to download a Mayo Clinic iPhone application that sends watch ECGs to a secure data platform, we examined patient engagement with the study app and the diagnostic utility of the ECGs. We digitally enrolled 2,454 unique patients (mean age 53 ± 15 years, 56% female) from 46 US states and 11 countries, who sent 125,610 ECGs to the data platform between August 2021 and February 2022; 421 participants had at least one watch-classified sinus rhythm ECG within 30 d of an echocardiogram, of whom 16 (3.8%) had an EF ≤ 40%. The AI algorithm detected patients with low EF with an area under the curve of 0.885 (95% confidence interval 0.823-0.946) and 0.881 (0.815-0.947), using the mean prediction within a 30-d window or the closest ECG relative to the echocardiogram that determined the EF, respectively. These findings indicate that consumer watch ECGs, acquired in nonclinical environments, can be used to identify patients with cardiac dysfunction, a potentially life-threatening and often asymptomatic condition.