Citations

Abstract Background The extravascular implantable cardioverter defibrillator (EV ICD) has recently emerged as an option for guideline indicated ICD patients. Results from the EV ICD Pilot and Pivotal studies demonstrated that the device can provide safe and effective therapy through chronic follow up. Lead placement outside the vasculature has the potential to reduce systemic infections. Infection occurrence and management details from the Pivotal are reported. Purpose To report on details of infection occurrence and management from the EV ICD Pivotal study. Methods The EV ICD Pivotal study was an international, prospective, single-arm, premarket clinical study. Patients with a class I or IIa indication for a single-chamber ICD per ESC or ACC/AHA/HRS guidelines were enrolled. Results In the EV ICD Pivotal study, 316 patients had an implant attempt, (74.7% male, age 53.8±13.1 years, 82% primary prevention, LVEF of 38.9%±15.4%, and NYHA Class I [23.7%] or II/III [65.5%]), of which 15 had a system or procedure-related infection (4.7%, 9 - 737 days post-implant) through an average 16.2 months follow-up. Of these infections, 8 were classified as major complications, 3 as minor, and 4 as observations. Most (n=9) infections were addressed through medication with or without wound care (2.8% of patients) while 6 (1.9% of patients) resulted in system removal. There was a single lead-related infection addressed through system explant. However, there were no reports of mediastinitis, sepsis, or endocarditis. No system removal resulted in sequelae. Conclusions Infection rates observed in the EV ICD Pivotal study were similar to those reported in the subcutaneous implantable cardioverter defibrillator. The results from this study support that EV ICD related infections are treatable with antibiotic therapy or system removal. No system or procedure-related systemic infections such as mediastinitis, sepsis, or endocarditis have been reported in the Pivotal study.

Abstract Background Cardiovascular disease remains the leading cause of death and burden globally, warranting the need to explore novel predictors of major adverse cardiovascular events (MACE) using new technologies. Artificial Intelligence (AI)-enhanced ECG (AI-ECG) algorithms can provide information on cardiovascular health of individuals, independent of conventional risk factors. Purpose The current study aimed to understand whether a previously described AI-ECG sex estimation algorithm could be applied for risk stratification of major adverse cardiovascular events (MACE), with a focus on a population without previous history of MACE. Methods Patients having 12-lead ECGs between 2018-2019 were included and followed up until August 2023. Exclusion criterion was a prior history of MACE. AI-ECG sex estimation was determined using a previously described algorithm predicting sex based on 12-lead ECGs. The algorithm generates outputs ranging from 0 to 1, representing the estimated probability of being male. Outputs above 0.5 were considered as AI-ECG-estimated male, and below as AI-ECG-estimated female. This was compared with biological sex to determine whether the two matched. Inconsistency between AI-ECG estimated sex and biological sex was defined as discordance. MACE was defined as myocardial infarction, coronary revascularization, non-fatal stroke, and all-cause death. Results Among 80,578 subjects (mean age 59.1 ± 16.1 years, 52.4% female), 9.7% exhibited sex discordance in AI-ECG estimation. Discordance was associated with a 56% higher risk of MACE (HR, 1.56, 95% CI 1.46-1.65, p <0.001), and remained significant in multivariable analysis with traditional risk factors (HR 1.35, 95% CI 1.27-1.43, p <0.001) (Figure 1). Sex-specific analysis showed increased MACE risk in both discordant males and females (Figure 2). Conclusion In patients with no history of MACE, discordance in AI-ECG sex estimation is associated with increased risk of MACE. These findings suggest the potential of AI-ECG sex estimation algorithm for risk assessment, especially in patients with no history of MACE.Figure 1.Cox proportional hazard modelFigure 2.Kaplan-Meier of MACE by sex

Abstract Background The determination of left ventricular diastolic dysfunction (LVDD) and filling pressure in patients with significant (>moderate) mitral regurgitation (MR) poses a complex challenge. We recently validated an artificial intelligence-enabled electrocardiogram (AI-ECG) algorithm to detect LVDD and estimate LV filling pressures. Methods This retrospective study sought to examine the risk of all-cause mortality across AI-ECG LVDD grades. Patients from the AI-ECG LVDD study test cohort who underwent transthoracic echocardiography confirming significant MR and an ECG within fourteen days of each other at Mayo Clinic between 09/2001 and 06/2022 were included. LVDD status was determined based on the index ECG, and patients were categorized into groups representing normal diastolic function (NDF) or LVDD grades 1 (G1), 2 (G2), or 3 (G3). Results Of 4,019 patients with significant MR (mean age 69.8, 49.0% women), 1,175 (29.2%), 1,881 (46.8%), and 963 (24.0%) were classified by AI-ECG as NDF/G1-LVDD, G2-LVDD, and G3-LVDD, respectively. The median mitral effective regurgitant orifice area was 26 mm² (interquartile range 20-36 mm²). Over a median follow-up of 3.5 years, 1,636 (40.7%) patients died. In multivariable survival analysis adjusted for multiple risk factors, a worse AI-ECG LVDD grade was independently associated with an increased death risk [G2DD: adjusted hazard ratio (aHR) 1.98 (95% confidence interval (95% CI) 1.63-2.40), G3DD: aHR 2.59 (95% CI 2.09-3.20)]. These findings were consistent when accounting for mitral valve intervention and among patients with >moderate MR or after mitral valve intervention. Conclusion In patients with significant MR, the grading of LVDD by AI-ECG is independently associated with all-cause mortality.

Abstract Aims Gender-affirming hormone therapy (GAHT) is used by some transgender individuals (TG), who comprise 1.4% of US population. However, the effects of GAHT on electrocardiogram (ECG) remain unknown. The objective is to assess the effects of GAHT on ECG changes in TG. Methods and results Twelve-lead ECGs of TG on GAHT at the Mayo Clinic were inspected using a validated artificial intelligence (AI) algorithm. The algorithm assigns a patient’s ECG male pattern probability on a scale of 0 (female) to 1 (male). In the primary analysis, done separately for transgender women (TGW) and transgender men (TGM), 12-lead ECGs were used to estimate the male pattern probability before and after GAHT. In a subanalysis, only patients with both pre- and post-GAHT EGCs were included. Further, the autopopulated PR, QRS, and QTc intervals were compared before and after GAHT. Among TGW (n = 86), the probability (mean ± SD) of an ECG male pattern was 0.84 ± 0.25 in the pre-GAHT group, and it was lowered to 0.59 ± 0.36 in the post-GAHT group (n = 173, P < 7.8 × 10−10). Conversely, among TGM, male pattern probability was 0.16 ± 0.28 (n = 47) in the pre-GAHT group, and it was higher at 0.41 ± 0.38 in the post-GAHT group (n = 53, P < 2.4×10−4). The trend persisted in the subanalysis. Furthermore, both the PR (P = 5.68 × 10−4) and QTc intervals (P = 6.65×10−6) prolonged among TGW. Among TGM, the QTc interval shortened (P = 4.8 × 10−2). Conclusion Among TG, GAHT is associated with ECG changes trending towards gender congruence, as determined by the AI algorithm and ECG intervals. Prospective studies are warranted to understand GAHT effects on cardiac structure and function.

BACKGROUND: Substernal lead placement of the extravascular implantable cardioverter defibrillator (EV ICD) permits both defibrillation at thresholds similar to those seen with transvenous implantable cardioverter defibrillators and effective anti-tachycardia pacing (ATP) while avoiding the vasculature and associated complications. The global Pivotal study has shown the EV ICD system to be safe and effective through 6 months, but long-term experience has yet to be published. Our aim was to report the performance and safety of the EV ICD system throughout the study. METHODS: The EV ICD Pivotal study was a prospective, global, single-arm, premarket clinical study. Individuals with a Class I or IIa indication for a single-chamber implantable cardioverter defibrillator per guidelines were enrolled. Freedom from major system- or procedure-related complications and appropriate and inappropriate therapy rates were assessed through 3 years with the Kaplan-Meier method. ATP success was calculated from simple proportions. RESULTS: An implantation was attempted in 316 patients (25.3% female; 53.8±13.1 years of age; 81.6% primary prevention; left ventricular ejection fraction, 38.9±15.4%). Of 299 patients with a successful implantation, 24 experienced 82 spontaneous arrhythmic episodes that were appropriately treated with ATP only (38, 46.3%), shock only (34, 41.5%), or both (10, 12.2%) for a Kaplan-Meier–estimated rate of first any appropriate therapy of 9.2% at 3 years. ATP was successful in 77.1% (37/48) of episodes, and ATP use significantly increased from discharge to last follow-up visit ( P <0.0001). Shock therapy was successful in 100% (27/27) of discrete, spontaneous ventricular arrhythmias. The inappropriate shock rates at 1 and 3 years were 9.8% and 17.5%, respectively, with P-wave oversensing the predominant cause. No major intraprocedural complications were reported, and the estimated freedom from system- or procedure-related major complications was 91.9% at 1 year and 89.0% at 3 years. The most common major complications were lead dislodgement (10 events; n=9 patients, 2.8%), postoperative wound or device pocket infection (n=8, 2.5%), and device inappropriate shock delivery (n=4, 1.3%). Twenty-four system revisions were performed as a result of major complications related to the EV ICD system or procedure. CONCLUSIONS: From implantation to study completion, the EV ICD Pivotal study demonstrated that a single integrated system with an extravascular lead placed in the substernal space maintains high ATP success, effective defibrillation, and a consistent safety profile. REGISTRATION: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT04060680.

Abstract Aims We aim to determine if our previously validated, diagnostic artificial intelligence (AI) electrocardiogram (ECG) model is prognostic for survival among patients with cardiac amyloidosis (CA). Methods A total of 2533 patients with CA (1834 with light chain amyloidosis (AL), 530 with wild-type transthyretin amyloid protein (ATTRwt) and 169 with hereditary transthyretin amyloid (ATTRv)] were included. An amyloid AI ECG (A2E) score was calculated for each patient reflecting the likelihood of CA. CA stage was calculated using the European modification of the Mayo 2004 criteria for AL and Mayo stage for transthyretin amyloid (ATTR). Risk of death was modelled using Cox proportional hazards, and Kaplan–Meier was used to estimate survival. Results Median age of the cohort was 67 [inter-quartile ratio (IQR) 59, 74], and 71.6% were male. The median overall survival for the cohort was 35.6 months [95% confidence interval (CI) 32.3, 39.5]. For AL, ATTRwt and ATTRv, respectively, median survival was 22.9 (95% CI 19.2, 28.2), 47.2 (95% CI 43.4, 52.3) and 61.4 (95% CI 48.7, 75.9) months. On univariate analysis, an increasing A2E score was associated with more than a two-fold risk of all-cause death. On multivariable analysis, the A2E score retained its importance with a risk ratio of 2.0 (95% CI 1.58, 2.55) in the AL group and 2.7 (95% CI 1.81, 4.24) in the ATTR group. Conclusions Among patients with AL and ATTR amyloidosis, the A2E model helps to stratify risk of CA and adds another dimension of prognostication.

Abstract Aims The extravascular implantable cardioverter-defibrillator (EV ICD) has been shown to be safe and effective for patients at risk of sudden cardiac death, but little is known about EV ICD lead removal in humans. This analysis aimed to characterize the EV ICD lead removal experience thus far. Methods and results This was a retrospective analysis of lead removals from the EV ICD Pilot, Pivotal, and Continued Access Studies. Patients with a successful EV ICD implant who underwent lead removal were included. The main objective was lead removal success. Ancillary objectives included characterizing technique used, procedure complications, and reimplantation status. An EV ICD system was successfully implanted in 347 patients across the 3 studies (25.9% female; 53.4 ± 13.3 years; left ventricular ejection fraction: 39.7 ± 15.9). Of these patients, 29 (8.4%) underwent lead removal with a mean lead dwell time of 12.6 ± 14.3 months (0.2–58.4). The main reason for lead removal was lead dislodgement (n = 9, 31.0%). Lead removal was successful in 27/29 (93.1%) cases [100% (19/19) success rate &lt;1 year and 80% (8/10) success rate &gt;1 year post-implant]. Simple traction was used in 22/26 (84.6%) and extraction tools in 4/26 (15.4%) successful cases where technique was known. No complications were reported for any of the removal procedures. All 11 EV ICD reimplant attempts were successful. Conclusion Complete removal of the EV ICD lead was successful in 93.1% of cases, and simple traction was sufficient in most instances. Based on these results, lead removal from the substernal space was safe and achievable up to 3 years post-implant.

Abstract Artificial intelligence (AI)–enabled interpretation of electrocardiogram (ECG) images (AI-ECGs) can identify patterns predictive of future adverse cardiac events. We hypothesized that such an approach would provide prognostic information for the risk of cardiac complications and mortality in patients undergoing hematopoietic cell transplantation (HCT). We retrospectively subjected ECGs obtained before HCT to an externally trained, deep-learning model designed to predict the risk of atrial fibrillation (AF). Included were 1377 patients (849 autologous [auto] HCT and 528 allogeneic [allo] HCT recipients). The median follow-up was 2.9 years. The 3-year cumulative incidence of AF was 9% (95% confidence interval [CI], 7-12) in patients who underwent auto HCT and 13% (10%-16%) in patients who underwent allo HCT. In the entire cohort, pre-HCT AI-ECG estimate of AF risk correlated highly with the development of clinical AF (hazard ratio [HR], 7.37; 95% CI, 3.53-15.4; P &lt; .001), inferior survival (HR, 2.4; 95% CI, 1.3-4.5; P = .004), and greater risk of nonrelapse mortality (NRM; HR, 95% CI, 3.36; 1.39-8.13; P = .007), without increased risk of relapse. Association with mortality was only noted in allo HCT recipients, where the risk of NRM was greater. The use of cyclophosphamide after transplantation resulted in greater 90-day incidence of AF (13% vs 5%; P = .01) compared to calcineurin inhibitor–based graft-versus-host disease prophylaxis, corresponding to temporal changes in AI-ECG AF prediction after HCT. In summary, AI-ECG can inform risk of posttransplant cardiac outcomes and survival in HCT patients and represents a novel strategy for personalized risk assessment.

Abstract 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.

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.