Morales-Lara, Andrea Carolina

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.

Abstract Background Cardiomyopathy is a leading cause of pregnancy-related mortality and represents the number one cause of death in the late postpartum period. Purpose The goal of this study was to prospectively evaluate the effectiveness of an artificial intelligence-based electrocardiogram (AI-ECG) and a point-of-care artificial intelligence-enhanced digital stethoscope for cardiomyopathy screening among pregnant and postpartum women. Methods We conducted a single-arm prospective study of pregnant and postpartum women (up to 12 months following delivery) enrolled at three sites between October 2021 and October 2022. Study participants completed a standard 12-lead ECG, ECG + phonocardiogram recordings with a digital stethoscope, a demographic questionnaire, and a transthoracic echocardiogram (TTE) within a 24-hour period. Diagnostic performance of the AI-ECG and digital stethoscope for cardiomyopathy detection, defined as left ventricular ejection fraction (LVEF) &lt; 45%, was evaluated using the area under the curve (AUC) and standard measures of diagnostic performance. Results We included 100 consecutive pregnant and postpartum women who provided informed consent and had at least one ECG recorded, completed the questionnaire, and had an echocardiogram performed. All participants self-identified as female, and 41% were recruited from a federally qualified health center. The median age of participants was 31 years (Q1: 27, Q3: 34). Thirty-eight percent identified as non-Hispanic White, 32% as non-Hispanic Black, 21% as Hispanic or Latino, 6% as Asian, and 3% as Other (1 Native Hawaiian and 2 multiracial). Five percent (5/100) had LVEF &lt;45%. The 12-lead AI-ECG model demonstrated close to perfect discrimination (accuracy 99%; AUC 1.00, sensitivity 100%) for detection of cardiomyopathy with 1 false positive screen (LVEF 49%), Figure 1A. The digital stethoscope (angled position) had an AUC of 0.99 (95% CI: 0.97, 1.00) for detection of LVEF &lt;45%. Sensitivity, specificity, positive predictive value, and negative predictive value for the digital stethoscope were 60%, 98%, 60% and 98%, respectively for detection of LVEF &lt;45% (Figure 1B & 1C). Conclusions In this small prospective study, we found that 5% of pregnant and postpartum women had cardiomyopathy, a potentially life-threatening condition, and that a rapid, inexpensive, ubiquitous, massively scalable point-of-care test - the ECG - showed promising results, identifying it with an AUC &gt; 0.98 with the application of artificial intelligence.Figure 1

AbstractPatients with viral myocarditis are at risk of sudden death and may progress to dilated cardiomyopathy (DCM). Currently, no disease‐specific therapies exist to treat viral myocarditis. Here it is examined whether reconstituted, lyophilized extracellular vesicles (EVs) from platelets from healthy men and women reduce acute or chronic myocarditis in male mice. Human‐platelet‐derived EVs (PEV) do not cause toxicity, damage, or inflammation in naïve mice. PEV administered during the innate immune response significantly reduces myocarditis with fewer epidermal growth factor (EGF)‐like module‐containing mucin‐like hormone receptor‐like 1 (F4/80) macrophages, T cells (cluster of differentiation molecules 4 and 8, CD4 and CD8), and mast cells, and improved cardiac function. Innate immune mediators known to increase myocarditis are decreased by innate PEV treatment including Toll‐like receptor (TLR)4 and complement. PEV also significantly reduces perivascular fibrosis and remodeling including interleukin 1 beta (IL‐1β), transforming growth factor‐beta 1, matrix metalloproteinase, collagen genes, and mast cell degranulation. PEV given at days 7–9 after infection reduces myocarditis and improves cardiac function. MicroRNA (miR) sequencing reveals that PEV contains miRs that decrease viral replication, TLR4 signaling, and T‐cell activation. These data show that EVs from the platelets of healthy individuals can significantly reduce myocarditis and improve cardiac function.

Abstract Aims Current non-invasive screening methods for cardiac allograft rejection have shown limited discrimination and are yet to be broadly integrated into heart transplant care. Given electrocardiogram (ECG) changes have been reported with severe cardiac allograft rejection, this study aimed to develop a deep-learning model, a form of artificial intelligence, to detect allograft rejection using the 12-lead ECG (AI-ECG). Methods and results Heart transplant recipients were identified across three Mayo Clinic sites between 1998 and 2021. Twelve-lead digital ECG data and endomyocardial biopsy results were extracted from medical records. Allograft rejection was defined as moderate or severe acute cellular rejection (ACR) based on International Society for Heart and Lung Transplantation guidelines. The extracted data (7590 unique ECG-biopsy pairs, belonging to 1427 patients) was partitioned into training (80%), validation (10%), and test sets (10%) such that each patient was included in only one partition. Model performance metrics were based on the test set (n = 140 patients; 758 ECG-biopsy pairs). The AI-ECG detected ACR with an area under the receiver operating curve (AUC) of 0.84 [95% confidence interval (CI): 0.78–0.90] and 95% (19/20; 95% CI: 75–100%) sensitivity. A prospective proof-of-concept screening study (n = 56; 97 ECG-biopsy pairs) showed the AI-ECG detected ACR with AUC = 0.78 (95% CI: 0.61–0.96) and 100% (2/2; 95% CI: 16–100%) sensitivity. Conclusion An AI-ECG model is effective for detection of moderate-to-severe ACR in heart transplant recipients. Our findings could improve transplant care by providing a rapid, non-invasive, and potentially remote screening option for cardiac allograft function.

Abstract Background Atrial fibrillation (AF) is diagnosed in up to 50% of patients with heart failure. However, the prevalence of AF among patients with peripartum cardiomyopathy (PPCM) ranges from only 2–10%, with the lowest rates in Black women. An artificial intelligence enhanced electrocardiogram (AI-ECG) has previously been shown to be effective in detecting AF while in sinus rhythm, and for AF risk prediction in a population-based study. Purpose Our objective was to evaluate the use of an AI-ECG for AF risk stratification among women of reproductive age (18 to 49 years) with PPCM compared to other forms of cardiomyopathy. Methods We identified 59 reproductive age women with a diagnosis of PPCM between January 2007, and October 2018 and included matched controls in a 3:1 fashion. Matching was performed based on sex, age, race, and left ventricular ejection fraction. We excluded patients with a diagnosis of AF prior to cardiomyopathy diagnosis date. AI-ECG prediction probabilities were generated for ECGs performed within a 30-day window prior to the patient's first cardiomyopathy diagnosis date for the entire study cohort. Results A total of 236 patients were included in the final analysis (59 cases, 177 controls). Overall, the median age at cardiomyopathy diagnosis was 31.7 years (IQR: 18.5, 49.4), 76.3% were White, 8.5% were Black, and 15.3% represented other or unknown race. Over the period studied, 3.4% of women with PPCM developed AF compared to 5.6% of women with other cardiomyopathies. The frequency of positive AI-ECG predictions for AF was more common among women with other cardiomyopathies (40.7%) compared to women with PPCM (20.3%). The predicted odds ratio for AF development following a cardiomyopathy diagnosis based on AI-ECG results was 0.37 (95% CI: 0.18, 0.73) for PPCM compared to other cardiomyopathies (p=0.006). Conclusion We demonstrated that an AI-ECG model for AF prediction may play a potential role in arrhythmia risk stratification/prediction among young women with PPCM who have a demonstrable lower risk for AF compared to women with other cardiomyopathies. Mechanisms for lower AF risk among patients with PPCM remain unknown. Further studies evaluating mechanistic pathways will be essential. Funding Acknowledgement Type of funding sources: None.

Background: Transcatheter aortic valve replacement (TAVR) is now an approved alternative to surgical aortic valve replacement for the treatment of severe aortic stenosis. As the clinical adoption of TAVR expands, it remains important to identify predictors of mortality after TAVR. We aimed to evaluate the impact of sex differences in aortic valve calcium score (AVCS) on long-term mortality following TAVR in a large patient sample. Methods: We included consecutive patients who successfully underwent TAVR for treatment of severe native aortic valve stenosis from June 2010 to May 2021 across all US Mayo Clinic sites with follow-up through July 2021. AVCS values were obtained from preoperative computed tomography of the chest. Additional clinical data were abstracted from medical records. Kaplan-Meier curves and Cox-proportional hazard regression models were employed to evaluate the effect of AVCS on long-term mortality. Results: A total of 2543 patients were evaluated in the final analysis. Forty-one percent were women, median age was 82 years (Q1: 76, Q3: 86), 18.4% received a permanent pacemaker following TAVR, and 88.5% received a balloon expandable valve. We demonstrate an increase in mortality risk with higher AVCS after multivariable adjustment ( P <0.001). When stratified by sex, every 500-unit increase in AVCS was associated with a 7% increase in mortality risk among women (adjusted hazard ratio, 1.07 [95% CI, 1.02–1.12]) but not in men. Conclusions: We demonstrate a notable sex difference in the association between AVCS and long-term mortality in a large TAVR patient sample. This study highlights the potential value of AVCS in preprocedural risk stratification, specifically among women undergoing TAVR. Additional studies are needed to validate this finding.

Abstract Introduction Doxorubicin is an anthracycline used as a chemotherapeutic drug for the treatment of a wide range of adult and pediatric cancers. Doxorubicin is associated with an increased risk of cardiomyopathy and heart failure with up to 10% of patients developing cardiac complications. A GWAS conducted of 1,191 patients from the N9831 clinical trial identified that cardiac gene expression and genetic variants of TRPC6 were associated with a decline in left ventricular ejection fraction (LVEF) (p=0.005 and p=1.6x10–6, respectively). TRPC6 is a non-selective cation channel expressed in heart and vascular tissue. TRPC6 participates in the pathogenesis of cardiac hypertrophy as a pathological response to chronic mechanical stress. Chronic activation has been found to promote cardiac fibrosis leading to heart failure. Purpose TRPC6 variants could be associated with increased risk of doxorubicin-induced cardiotoxicity. Data/tests to determine which patients may progress to cardiomyopathy and heart failure are currently lacking and there are no targeted treatments to prevent cardiomyopathy in these patients. Methods In preliminary in vivo data, B6.129 wild-type mice or TRPC6 knockout mice were treated with either 6x intraperitoneal saline or 4mg/kg doxorubicin injections (cumulative dose of 24mg/kg). Results We found doxorubicin increased cardiac vacuolation (male, p≤0.001 and female, p≤0.05) in WT mice compared to controls. Higher HW/BW ratio was observed in male TRPC6 knock out mice compared to wild-type mice when both were treated with doxorubicin (males, p=0.005 and females, p=0.19). Additionally, we found that doxorubicin-induced cardiac injury was significantly reduced in TRPC6 knock-out mice compared to wild-type mice based on reduced vacuolation (p=0.0004 males, p=0.03 females), with the effect being greater in male mice than female mice. Furthermore, a significant decrease in stroke volume (p=0.007), diastolic volume (p=0.01) and cardiac output (p=0.004) in wild-type male mice treated with doxorubicin compared to control and TRPC6 knock-out mice. Our in vitro preliminary data show that inhibition of TRPC6 using the TRPC6 inhibitor GsMTx-4 in human iPSC-derived cardiomyocytes significantly reduced doxorubicin-induced apoptosis (p&lt;0.0001). Further we treated male mice with the TRPC6 inhibitor, GsMTx4, and found that they had less global longitudinal cardiac strain (p=0.04) and higher ejection fraction (p=0.01) compared to mice who were only treated with doxorubicin. Histologically we found that mice given the TRPC6 inhibitor had less fibrosis as measured by Trichrome stained heart sections (p=0.004) which could account for improvements in cardiac function. Conclusion TRPC6 could be a novel therapeutic target in the prevention of chemotherapy-induced cardiomyopathy and heart failure. Additionally genetic mapping of TRCP6 functional variants may provide a new screening tool to determine increased risk of developing heart failure. Funding Acknowledgement Type of funding sources: Private hospital(s). Main funding source(s): Mayo Clinic

Background: Myocarditis is an inflammatory heart disease caused by viral infections that can lead to heart failure, and occurs more often in men than women. Since animal studies have shown that myocarditis is influenced by sex hormones, we hypothesized that endocrine disruptors, which interfere with natural hormones, may play a role in the progression of the disease. The human population is exposed to the endocrine disruptor bisphenol A (BPA) from plastics, such as water bottles and plastic food containers. Methods: Male and female adult BALB/c mice were housed in plastic versus glass caging, or exposed to BPA in drinking water versus control water. Myocarditis was induced with coxsackievirus B3 on day 0, and the endpoints were assessed on day 10 post infection. Results: We found that male BALB/c mice that were exposed to plastic caging had increased myocarditis due to complement activation and elevated numbers of macrophages and neutrophils, whereas females had elevated mast cell activation and fibrosis. Conclusions: These findings show that housing mice in traditional plastic caging increases viral myocarditis in males and females, but using sex-specific immune mechanisms.