Madhavan, Malini

Introduction: Ebstein's anomaly (EA) is associated with ECG signature of right bundle branch block (RBBB). Patients with EA also have high incidence of accessory pathways (AP) and it has been hypothesized that the absence of RBBB on ECG may be associated with a higher prevalence of AP. This study aims to assess the prevalence of RBBB in patients with EA and examine the correlation between its absence and the presence of AP. Methods: Adults (age>18 years) with EA, identified using the Mayo Adult Congenital Heart Disease registry, were divided into 3 groups based on the QRS morphology of their first ECG. Those with prior AP ablation were excluded. The demographic characteristics, baseline ECG data, echocardiographic parameters and EP study data were retrospectively collected and compared between the three groups using Chi-square or ANOVA test as appropriate. Results: Of 596 patients with EA, 358(60.06%) patients had a RBBB, bifascicular block or an incomplete RBBB (group1) ; 18 (3.02%) patients had either a LBBB , ventricular pre-excitation, isolated left anterior or posterior fascicular block (group 2); and 220 (36.92%) patients had normal QRS (group 3). The characteristics of these groups are compared in the table. Patients with RBBB had larger RV size, reduced RV systolic function and higher likelihood of tricuspid valve surgery. EP study was performed in 51 (14%), 3 (17%) and 51 (23%) patients in groups 1, 2,and 3 respectively. One or more APs were noted in 11/51 (22%) and 14/51 (30%) patients with RBBB and normal QRS respectively (p=0.18). Additionally, in patients who did not undergo an EP study, supraventricular tachycardia was documented in 39/307 (13%), 2/15(13%) and 13/169 patients (8%) in group 1,2 and 3 respectively during followup . Conclusion: In a large cohort of adults with EA in a tertiary referral center, greater than one-third of patients had a normal QRS complex without RBBB. However, EP study did not reveal a significant difference in the prevalence of accessory pathways between patients with and without RBBB. In EA, the absence of RBBB may not increase the risk of an underlying accessory pathway.

Background Early detection of left and right ventricular systolic dysfunction (LVSD and RVSD respectively) in children can lead to intervention to reduce morbidity and death. Existing artificial intelligence algorithms can identify LVSD and RVSD in adults using a 12‐lead ECG; however, its efficacy in children is uncertain. We aimed to develop novel artificial intelligence–enabled ECG algorithms for LVSD and RVSD detection in pediatric patients. Methods and Results We identified 10 142 unique pediatric patients (age≤18) with a 10‐second, 12‐lead surface ECG within 14 days of a transthoracic echocardiogram, performed between 2002 and 2022. LVSD was defined quantitatively by left ventricular ejection fraction (LVEF). RVSD was defined semiquantitatively. Novel pediatric models for LVEF ≤35% and LVEF <50% achieved excellent test areas under the curve of 0.93 (95% CI, 0.89–0.98) and 0.88 (95% CI, 0.83–0.94) respectively. The model to detect LVEF <50% had a sensitivity of 0.85, specificity of 0.80, positive predictive value of 0.095, and negative predictive value of 0.995. In comparison, the previously validated adult data‐derived model for LVEF <35% achieved an area under the curve of 0.87 (95% CI, 0.84–0.90) for LVEF ≤35% in children. A novel pediatric model for any RVSD detection reached a test area under the curve of 0.90 (0.87–0.94). Conclusions An artificial intelligence–enabled ECG demonstrates accurate detection of both LVSD and RVSD in pediatric patients. While adult‐trained models offer good performance, improvements are seen when training pediatric‐specific models.

Abstract Cardiac implantable electronic device infection in the context of corynebacterial bloodstream infection (BSI) remains poorly understood. From 2012 to 2023 at Mayo Clinic, 4 of 12 patients with corynebacterial BSI had cardiac implantable electronic device infection: 1 patient was diagnosed during a relapsing BSI episode. Undefined source, persistent BSI, and the presence of a prosthetic cardiac valve were common characteristics.

AbstractIntroductionThe impact of mRNA‐based coronavirus disease‐2019 (COVID‐19) vaccines on atrial arrhythmias (AA) and ventricular arrhythmias incidence is unknown.MethodsBIOTRONIK Home Monitoring data and Medicare Claims data were utilized to identify individuals implanted with a cardiac implantable electronic device (CIED) between 2010 and 2020 who received one or more doses of COVID‐19 vaccine in 2021. The burden of AA (%) in the 3 months postvaccination was compared to those noted in the preceding 3 months using the Wilcoxon signed rank test. Sub‐analyses comparing the effects of the influenza vaccine against the COVID‐19 vaccine were also evaluated for individuals who received the influenza vaccine in 2020. A 1:1 propensity score match comparison between COVID‐19 vaccine and non‐vaccinated patients was also performed.ResultsFirst and second doses of the COVID‐19 vaccine were administered to 7757 and 6579 individuals with a CIED (age 76.2 ± 9.0 years, 49% males), respectively. While a small but statistically significant increase in the burden of AA was noted in the 3 months postvaccination compared to the preceding 3 months after the first dose of the COVID‐19 vaccine (0.43 ± 9.04%, p = .028) a similar rise in AA was found following the influenza vaccine and for matched patients who did not receive the COVID‐19 vaccine. No significant difference in device therapies was seen pre‐ and postvaccination.ConclusionsThough we report a small but significant increase in the number of CIED‐detected AAs following vaccination for COVID‐19 over a 3‐month window, we believe these results correlate more with time and the progressive nature of AF rather than the vaccine itself. While these data should not dissuade from the use of these vaccines, increased vigilance and prompt treatment of AF is required for high‐risk groups, specifically males over 70 years of age, following vaccination.

Background Shock‐reduction implantable cardioverter‐defibrillator programming (SRP) was associated with fewer therapies and improved survival in randomized controlled trials, but real‐world studies investigating SRP and associated outcomes are limited. Methods and Results The BIOTRONIK CERTITUDE registry was linked with the Medicare database. We included all patients with an implantable cardioverter‐defibrillator implanted between August 22, 2012 and September 30, 2021 in the United States. SRP was defined as programming to either a therapy rate cutoff ≥188 beats per minute or number of intervals to detection ≥30/40 for treatment. Among 6781 patients (mean 74±9 years; 27% women), 3393 (50%) had SRP. Older age, secondary prevention indication, and device implantation in the southern or western United States were associated with lower use of SRP. The cumulative incidence rate of implantable cardioverter‐defibrillator shocks was lower in the SRP group (5.1% shocks/patient year) compared with the non‐SRP group (7.2% shocks/patient year) (adjusted hazard ratio [HR], 0.83 [95% CI, 0.73–0.96]; P =0.005). Over a median follow‐up of 2.9 years, 739 deaths occurred in the SRP group and 822 deaths occurred in the non‐SRP group (adjusted HR, 0.97 [95% CI, 0.88–1.07]; P =0.569). SRP was associated with a lower all‐cause mortality among patients without ischemic heart disease compared with patients with ischemic heart disease (adjusted HR, 0.64 [95% CI, 0.48–0.87] versus adjusted HR, 1.02 [95% CI, 0.92–1.14]; P interaction =0.004). Conclusions Adoption of SRP is low in real‐world clinical practice. Age, clinical variables, and geographic factors are associated with use of SRP. In this study, SRP‐associated decrease in mortality was limited to patients without ischemic heart disease.

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.

Background: Atrial septal defects (ASD) and ventricular septal defects (VSD) are among the most common forms of congenital heart disease (CHD). Early detection and intervention for these defects are crucial to prevent complications. Given advancements in AI technology, the development of AI-enabled ECG models for detecting these defects is of significant interest. Aims: This study aimed to create AI models using pediatric ECG data for the detection of ASD, VSD, and a combined model detecting either ASD or VSD. Methods: We analyzed a dataset of 7,795 pediatric patients from Mayo Clinic who had undergone echocardiography between 2002 and 2022, with corresponding 10-second, 12-lead surface ECGs taken within 14 days of echocardiography. This dataset included 259 patients with ASDs and 139 patients with VSDs (single or multiple). The control group was comprised of 7,397 patients without significant or complex CHD. For cases and controls, we included patients with all forms of electrocardiographic abnormalities and those with valvular lesions who had less than moderate stenosis or regurgitation. The dataset was divided into training, validation, and testing subsets to develop convolutional neural network (CNN) models for ASD, VSD, and either ASD or VSD detection. Results: Our ASD and VSD detection models achieved test AUCs of 0.82 (95% CI 0.76, 0.88) and 0.80 (95% CI 0.70, 0.90), respectively. Meanwhile, a combined model that detected either ASD or VSD demonstrated a test AUC of 0.81 (95% CI, 0.76, 0.86), sensitivity of 0.73, specificity of 0.76, PPV of 0.14, and NPV of 0.98 (Figure 1). Conclusions: AI-enabled ECG models demonstrate promising performance in detecting ASD and VSD in pediatric patients. A combined model that detects either ASD or VSD also shows robust results. Future research should focus on multi-center validation of these models, refining their diagnostic capacities, and investigating their potential for early intervention in CHDs.

Introduction: Left and right ventricular systolic dysfunction (LVSD and RVSD respectively) contribute to considerable pediatric morbidity. Both LVSD and RVSD can occur in children before symptom onset, emphasizing the need for early detection. Existing deep-learning algorithms can identify LVSD in adults using 12-lead ECG; however, their efficacy in children is uncertain. Aims: We aimed to create a deep-learning model for LVSD and RVSD detection in pediatric patients using an AI-enabled ECG and compare novel pediatric models to a previously validated adult LVSD model. Methods: We identified 10,214 pediatric patients at the Mayo Clinic with a transthoracic echocardiogram (TTE) and a 10-second, 12-lead surface ECG within 14 days of TTE, performed from 2002 -2022. Our cohort comprised the first TTE-ECG pair from each patient, totaling 138 with EF ≤ 35%, 312 with EF ≤ 50%, 482 with RVSD, and 9,567 without any LVSD or RVSD. The cohort was split into training, validation, and testing datasets to develop neural networks for systolic dysfunction detection. Results: The cohort was 52.3% male, with an average age of 10.3 years. Novel models generated in the pediatric cohort demonstrated excellent performance in systolic dysfunction detection (Figure 1). Pediatric models for EF ≤ 35% and EF ≤ 50% achieved test AUCs of 0.92 (95% CI 0.89, 0.95) and 0.89 (95% CI 0.85, 0.94) respectively. The RVSD model reached a test AUC of 0.91 (95% CI 0.88, 0.94). A combined model for EF ≤ 35% or RVSD exhibited a test AUC of 0.90 (95% CI 0.87, 0.93), sensitivity 0.88, specificity 0.82, PPV 0.20, and NPV 0.95. A combined model for EF ≤ 50% or RVSD exhibited a test AUC of 0.88 (95% CI 0.85, 0.91). The previously validated adult data-derived model achieved an AUC of 0.86 (95% CI 0.83, 0.89) for EF ≤ 50% in children. Conclusions: AI-enabled ECGs demonstrate efficient detection of both LVSD and RVSD in pediatric patients. Future multi-site validation is needed to further establish the reliability of these models.

Introduction: Biologic sex and hormonal concentrations shape ECG parameters due to sex hormone effects on cardiac function. AI-enabled ECGs have previously been shown to accurately predict patient sex in adults and correlate with sex hormone levels. Aims: We aimed to test the ability of AI-enabled ECGs to predict pediatric patient sex and explore the influence of pubertal development. Methods: Two AI-enabled ECG models were created using a convolutional neural network trained on pediatric 10-second, 12-lead surface 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 89,063 pediatric patients (aged <19 years) recorded from 1988-2022, and divided the cohort into training, validation, and testing datasets. Subgroup analysis was performed on prepubertal (0-7 years), pubertal (8-14 years), and post-pubertal (15-18 years) patients. Results: The cohort consisted of 46.6% males, with 21,434 prepubertal, 26,300 pubertal, and 41,329 post-pubertal children. No significant performance difference was observed between the two models. Both models demonstrated 81% accuracy and an overall AUC of 0.91 in the testing cohorts, with high discriminatory ability in post-pubertal teenagers (AUC = 0.98). (Figure 1) Conclusions: AI-ECG predicts pediatric patient sex with varying performance across pubertal stages. The highest discriminatory ability is observed post-puberty, decreasing in pubertal and prepubertal children. The results suggest hormonal and physiological changes during puberty may influence cardiac electrophysiology measurably. More research is required to interpret CNN-predicted sex discordance with actual sex and its implications for future cardiovascular risk.