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Background: CD19 CAR T-cell therapy is a novel cancer treatment for B-cell malignancies. Systemic inflammation due to Cytokine Release Syndrome (CRS) can occur. Elevated levels of cytokines have been linked to valve damage. Hypothesis: Evaluate changes in valve morphology or progression of valvular heart disease in adult patients after CD19 CAR T-cell therapy and by clinical CRS grade. Methods: Patients that underwent CD19 CAR T-cell therapy with both a baseline and a post-treatment transthoracic echocardiogram (TTE) were included. Patient characteristics and outcomes were abstracted from the electronic medical record. CRS was categorized as CRS<2 and CRS ≥2 for all patients using the ASTCT 2019 criteria. Valve thickening was graded as a dichotomous variable. Valve disease (regurgitation or stenosis) was ranked on a 1-to-6-point numeric scale (none, mild, mild-moderate, moderate, moderate-severe, severe) and grade point value difference was calculated. Variables were compared by Chi Squared and Student’s t tests. Results: 114 patients were included with a median TTE follow-up of 44 days, 54% developed CRS ≥2 (Table 1a). Overall, no statistically significant changes in valve morphology and valve disease progression were observed in the cohort (Figure 1a, 1b). Among CRS groups, valve morphology changes and valve disease progression were similarly distributed (21% in CRS <2 vs 19% in CRS ≥2, p=0.623 and 28% in CRS <2 vs 24% in CRS ≥2, p=0.62 respectively) (Fig. 1a). In the cases that progression was observed, it was mostly mild, and regurgitation was the most common pattern (Fig. 1c). Conclusions: This is the first study to evaluate the incidence of cardiac valve disease progression or thickening in patients treated with CD19 CAR T therapies. We can conclude that no progression of valve disease or changes in valve morphology were observed in its entirety or by CRS groups. Longer follow up is needed to validate these findings.

Objective: This meta-analysis aims to compare the effectiveness of intravascular ultrasound (IVUS) versus angiography alone in percutaneous coronary intervention (PCI) procedures. Methods: A comprehensive literature search was conducted to identify relevant studies comparing IVUS-guided PCI with angiography-guided PCI. The primary outcome measure was major adverse cardiac events (MACE) including cardiac death, myocardial infarction, and target vessel revascularization. Secondary outcomes included all-cause mortality, stent thrombosis, and angiographic outcomes. Results: A total of 41 eligible studies involving 712635 patients were included in the meta-analysis. IVUS-guided PCI was associated with a significantly lower risk of MACE compared to angiography-guided PCI (pooled odds ratio [OR] 1.38, 95% confidence interval [CI] 1.24-1.53, p<0.00001). The use of IVUS also resulted in a lower risk of all-cause mortality (pooled OR 1.61, 95% CI 1.41-1.83, p<0.00001) and reduced rates of stent thrombosis (pooled OR 2.28, 95% CI 1.82-2.86, p<0.00001) and TVR (pooled OR 1.31, 95% CI 1.21-1.42, p<0.00001). Conclusion: This meta-analysis provides evidence supporting the use of IVUS-guided PCI over angiography-guided PCI. IVUS-guided procedures were associated with reduced MACE rates, improved procedural success, decreased stent thrombosis, and superior angiographic outcomes. The findings suggest that incorporating IVUS into PCI procedures may lead to improved clinical outcomes and more optimal stent deployment. However, further studies are warranted to explore potential subgroup differences and to evaluate the cost-effectiveness of IVUS-guided PCI.

Introduction: Over 100 FDA-approved medications, electrolyte perturbations, and many disease states can prolong the QT interval in up to 10% of patients. In contrast, approximately 1 in 2,000 people have congenital long QT syndrome (LQTS) hallmarked by pathological QT prolongation secondary to LQTS-causative mutations. Hypothesis: an artificial intelligence (AI) deep neural network (DNN) analysis of the 12-lead ECG can distinguish patients with LQTS from those with acquired QT prolongation. Methods: The study cohort included 1599 patients with genetically confirmed LQTS and over 2.5 million controls from Mayo Clinic’s ECG data vault. Every patient/control with ≥ 1 ECG above age- and sex- specific 99 th percentile values for QTc [> 460 ms for all patients (male/female) < 13 years of age, or > 470 ms for men and > 480 ms for women above this age] was included. An AI-DNN involving a multi-layer convolutional neural network was developed. To simulate screening conditions, patients were matched at a ratio of 1:2,000 (incidence of LQTS) or 1:200 (balance of LQTS vs acquired QT prolongation in a tertiary referral center). Results: Among the 1,599 patients with LQTS, 808 (> 50%) had ≥ 1 ECG with QTc above the aforementioned QTc thresholds (2,987 ECGs) compared to 361,069/2.5M controls (14% of Mayo Clinic patients getting an ECG, 989,313 ECGs). Following age- and sex- matching and splitting, the model successfully distinguished LQTS from those with acquired QT prolongation at 1:2,000 matching with an AUC of 0.937 (accuracy 89%, sensitivity 81%, specificity 90%, PPV 0.05, NPV 0.99). Furthermore, when matching at a rate that genetically-mediated QT prolongation would be encountered in clinic (1:200), the model still successfully separated the two groups (AUC 0.912, accuracy 88%, sensitivity 78%, specificity 89%, PPV 0.1, NPV 0.99). Conclusion: For patients with a QTc exceeding its 99 th percentile values seen in health, this novel AI-DNN 12-lead ECG distinguishes abnormal QT prolongation stemming from LQTS versus acquired QT prolongation with high performance characteristics (AUC > 0.93). Even when scaled to referral center and LQTS incidence ratios, a negative AI-DNN signal rules out the presence of a LQTS disease-causative mutation with 99% negative predictive value.

Introduction: Disease-causative variants in LMNA -encoded lamin A/C cause a penetrant dilated cardiomyopathy associated with conduction disease (CD) and ventricular arrhythmias (VA). Prior studies show conflicting results regarding the effect of sex, variant type, and CD on severity and sudden death risk in cardiac laminopathy. Methods: In this retrospective study, we utilized a single-center genetic arrhythmogenic cardiomyopathy registry (N=432) to identify patients with ultra-rare LMNA variants and ascertain the effect of sex, mutation type, and clinical presentation on disease severity. Advanced heart failure (AHF) events were defined as ventricular assist device placement or cardiac transplantation whereas VA events were defined as sudden cardiac arrest, sustained ventricular arrhythmia, or appropriate implantable cardioverter-defibrillator shock. Results: The LMNA variant-positive cohort (N=83) had an age of diagnosis of 36 ± 10 years with 80% showing a cardiac phenotype. No difference in the age at diagnosis (37 vs. 34 years; p=0.3) or prevalence of AHF events (14% vs. 18%; p=0.8) and VA events (34% vs. 36%; p=0.9) was observed between females (N=50) and males (N=33). Furthermore, there was no difference in age of diagnosis (37 vs. 33 years; p=0.1) or prevalence of AHF (14% vs. 12%; p=0.9) and VA events (33% vs. 34%; p=0.9) between individuals with missense (N=42) or frameshift (N=32) variants. Interestingly, in comparison to individuals that presented with isolated CD (N=11) those that first presented with left ventricular dysfunction or arrhythmias (atrial or ventricular) (N=41) were more likely to have VA events (9% vs. 53%; p=0.01) and AHF events (0% vs 29%; p=0.05). In addition, those with structural/arrhythmic presentation had earlier time to VA events (15 years vs. 22 years; p=0.04) and AHF events (23 vs undefined; p=0.09) than those with CD. Conclusions: Contrary to prior studies, in this large single-center cardiac laminopathy study, sex and mutation type were not associated with increased risk of AHF or VA events. However, patients presenting with isolated CD had a lower risk of AHF and VA events. Contemporary multi-center studies are needed to better ascertain risk factors associated with AHF and VA events in cardiac laminopathy.

Introduction: Recently, we demonstrated transcriptional down-regulation of hypertrophy pathways in myectomy tissue derived from patients with obstructive hypertrophic cardiomyopathy (HCM) despite translational activation of associated hypertrophy pathways. Previous studies of other forms of cardiac hypertrophy demonstrate hypertrophy pathways are activated via histone acetylation and histone deacetylase (HDAC) inhibitors prevented development of hypertrophy. Whether histone modifications regulate hypertrophy pathways in HCM remains unexplored. Methods: Chromatin immunoprecipitation sequencing (ChIP-seq) was performed on myectomy tissues from 40 patients with HCM and 23 controls to characterize differential histone marks for H3K4me3 (active promoter), H3K9ac (active promoter), H3K9me3 (heterochromatin), H3K27ac (active enhancer), H3K27me3 (repression) across the genome. The differential histone marks were integrated with RNA-sequencing (RNA-seq) data derived from the same patients. Results: Overall, we identified 2,912 (7%) differential H3K4me3 peaks impacting 5 pathways, 23,339 (21%) differential H3K9ac peaks impacting 87 pathways, 33 (0.05%) differential H3K9me3 peaks, and 58,837 (42%) differential H3K27ac peaks impacting 50 pathways. There was increased H3K27ac (q-value = 0.002) and H3K9ac (q-value = 0.009) at RAS-MAPK loci. Univariate analysis of concordance between H3K9ac with RNA-seq data showed activation of cardiac hypertrophy signaling (z-score=4.5; -log [BH p-value] = 3.1), while H3K27me showed down-regulation of cardiac hypertrophy signaling (z-score=-2.4; -log [BH p-value] = 2.4). Multivariate integration of the ChIP-seq data with RNA-seq data identified 3,189 transcripts concordant with all five histone marks impacting 254 pathways, 18 (7%) of which are involved in cardiac hypertrophy. Conclusions: Myectomy tissue from patients with obstructive HCM shows enrichment of histone modifications at cardiac hypertrophy loci with activation via H3K27 and H3K9 acetylation and repression via H3K27 methylation. Further studies are needed to understand the balance and temporal relationship between these competing regulators in HCM pathobiology and the potential for epigenetic therapies.

Background: Diagnosis of heart failure with preserved ejection fraction (HFpEF) is challenging due to non-diagnostic imaging outputs and discordant clinical features. Using only a single videoclip from a standard transthoracic echocardiogram (TTE), we developed an artificial intelligence (AI) model to differentiate patients with HFpEF from those without HFpEF. In a separate test population, we assessed the model’s ability to predict cardiac mortality. Methods: A 3-dimensional convolutional neural network was developed and trained on apical 4-chamber videoclips to classify patients with HFpEF (cases) versus without HFpEF (controls). Overall and cardiac mortality were obtained from the National Death Index. Results: The model was independently tested in 646 cases and 638 controls from 8 centers in 4 states. Sensitivity (87.8%; 84.5-90.9%) and specificity (81.9%; 78.2-85.6%) were high and sustained in various subgroups, including increased left atrial volume index, average E/e’ ≥15, and pulmonary artery systolic pressure >35 mm Hg. During follow-up (median [IQR] 3.4 [1.7-6.5] years), 540 patients died. There were 135 cardiac deaths attributable to heart failure (47%), coronary artery disease (41%), valve disease (4%), arrhythmia (4%), and others (5%). Compared to a negative output, all-cause mortality was higher in patients with a positive (HR 2.9 [2.4-3.5]) or uncertain output (HR 1.8 [1.2-2.5]), and cardiac mortality (Figure) was also higher in patients with a positive (HR 7.7 [4.5-13.3]) or uncertain output (HR 3.4 [1.4-8.1]). Cardiac mortality differences according to AI model prediction remained after age adjustment (positive: HR 5.9 [3.3-10.4], p<0.001; uncertain: HR 2.8 [1.2-6.8], p=0.02). Conclusion: A novel AI model to detect HFpEF based on a single routinely acquired TTE videoclip also identifies those at increased risk of cardiac mortality.

Introduction: The performance of automated left ventricular ejection fraction (LVEF) assessment in patients with altered cardiac chamber geometry, such as those with obstructive hypertrophic cardiomyopathy (HCM), is not well described. Aims: Accurate assessment of LVEF is required for dose titration and continuation of myosin inhibitors in obstructive HCM. From the VALOR-HCM trial (NCT04349072), the agreement of two commercially available software packages was compared with blinded core lab assessment of LVEF. Methods: From baseline and week 16, among 112 patients randomized to either mavacamten or placebo, 178 echocardiograms were analyzed. Representative clips were manually selected, and automated LVEFs were calculated. Comparisons of LVEFs were made using Pearson’s correlation coefficients and Bland-Altman analysis. Results: With core lab assessment, the overall median LVEF was 68% (Q1 66%, Q3 70%). The correlation with vendor 1 was moderate (r = 0.47, 95% CI 0.35-0.58, p <0.001) and weak with vendor 2 (r =0.21, 95% CI 0.07-0.35, p =0.004). Correlation was also weak between vendor 1 and 2 (r = 0.14, 95% CI 0.00-0.28, p = 0.06). Vendors 1 and 2 had an underestimation bias of LVEF of 8% (95% CI 6-9%) and 8% (95% CI 7-9%), respectively, compared to core lab LVEF (Figure 1). Conclusions: In VALOR-HCM, the concordance of LVEF between different commercially available software packages with each other and with a core lab assessment was limited. Further study is needed to evaluate and improve the performance of automated LVEF tools in obstructive HCM patients who have altered cardiac chamber geometry.

Introduction: Atherosclerotic peripheral artery disease (PAD) leads to a great burden of cardiovascular and limb-related morbidity and mortality in African Americans (AA) compared to White Americans. Medical center partnerships with churches may be an effective method to improve PAD awareness and detection in the AA community. Hypotheses: We hypothesized that implementing a medical educational program in partnership with churches would increase PAD familiarity and identify participants with undiagnosed PAD. Aims: To determine (1) PAD familiarity before and after a one-time educational intervention and (2) the prevalence of PAD in older, church-going Nashville residents. Methods: Using a community-engaged research (CER) model, we partnered with 5 churches to enroll participants ≥ 50 years of age. Participants were given a 16-point pre- and post-educational program assessment to determine baseline and post-intervention PAD knowledge. Pre- and post-intervention scores were compared using Wilcoxon signed-rank test. Ankle-brachial index (ABI) testing was used to screen for PAD (ABI < 0.9). Results: We enrolled 120 participants (mean age [SD]: 64.67 [8.33] years, 74% women, 80% AA). The prevalence of PAD was 10.83%. Pre-intervention PAD awareness was low (mean score (MS): 10.89 [4.24]) and improved significantly post-intervention in all participants (MS: 13.88 [2.76; p=2 x 10 -12 ). Participants without PAD (ABI ≥ 0.9-1.39) (MS: 10.81 [4.28] vs. 13.91 [2.79]; p= p=4 x 10 -11 ) and with indeterminate status (ABI>1.4) (MS [SD]: 10.00 [5.03] vs. 14.10 [1.73; p=0.01) showed significant improvement in PAD awareness pre vs post intervention. Participants with PAD (ABI <0.9) (MS: 12.15 [3.26] vs. 13.46 [3.26; p=0.40) did not have significant improvement in their awareness scores. Conclusions: The PRAISE study demonstrates an effective CER partnership with churches as a method to increase PAD awareness and early detection of PAD, specifically among AAs.

Introduction: Artificial Intelligence-enabled electrocardiogram (AI-ECG) can estimate the probability of having atrial fibrillation (AFib) while in sinus rhythm. Recurrent stroke in subjects without known AFib may be due to undiagnosed AFib. We evaluated the association between AI-ECG AFib and recurrent ischemic stroke and with incident AFib Methods: Consecutive adults admitted to a Mayo Clinic or Mayo Clinic Health System hospital or to Olmsted Community Hospital for ischemic stroke between January,1,2000 and June,30,2022 were identified using the Rochester Epidemiology Project. Recurrent ischemic strokes were identified after reviewing medical records and imaging studies. We excluded those with known AFib or without ECGs within 3 years prior to the first ischemic stroke. The AI-ECG network was previously developed using 649,931 ECGs from > 180,000 subjects. The primary endpoint was recurrent ischemic stroke; secondary endpoint was incident AFib. Cox model adjusted for age, sex, and CHA2DS2-VASc2 score variables. Results: We included 4,816 subjects, mean age 70±15 yr, 54% male, 92% white, median CHA2DS2-VASc2 score 4 [3-5], median follow-up 3 [1-7] yr. 280 subjects (6%) had a second stroke and 366 (8%) were diagnosed with incident AFib over follow-up. Those with a positive AI-ECG AFib had an increased risk for recurrent ischemic stroke(HR 1.54, 95% CI 1.21-1.96, P<0.001 Figure A) This association remained significant after adjustment for CHA2DS2-VASc2 score(HR 1.37 95% CI 1.07-1.76, P=0.01). These associations were not affected by anticoagulation use over follow-up. AI-ECG AFib was also associated with incident AFib(HR 2.42 95% CI 1.96-2.98, P<0.001 Figure B) Conclusions: AI-ECG AFib predicts recurrent ischemic stroke in the community, possibly reflecting undiagnosed AFib that can be detected by AI-ECG, as it also predicts incident AFib. These results underscore the potential utility of AI-ECG for risk stratification of patients with ischemic stroke.

Introduction: Lp(a) is a causal risk factor for ASCVD that is often elevated in patients with personal or family history (FHx) of ASCVD. The 2019 ESC guidelines recommend universal Lp(a) testing, while the 2019 AHA/ACC guidelines include Lp(a) as a ‘risk enhancer’. We used electronic health records (EHR) to assess utilization of Lp(a) testing in patients with ASCVD, premature ASCVD, and FHx of ASCVD at Mayo Clinic sites in Rochester MN, Phoenix AZ and Jacksonville FL and the Mayo Clinic Health System (MCHS) from 1999-2021. Methods: We ascertained Lp(a) testing using LOINC codes and ASCVD status using validated ICD/CPT-based algorithms. ASCVD included coronary heart disease (CHD), cerebrovascular disease (CVD) (ischemic stroke and/or carotid artery stenosis), peripheral artery disease, and abdominal aortic aneurysm. Premature ASCVD was defined as occurring in males < 55 years and females < 65 years. We ascertained FHx using EHR survey data from 2017-2021. We estimated the proportion of patients with ASCVD, premature ASCVD, or FHx of ASCVD who underwent Lp(a) testing. Results: Between 1999-2021, we identified 310,760 patients with at least 1 ASCVD subtype of whom 66,710 had premature disease. FHx data was available starting 2017. For the timeframe 2017-2021, we identified 267,177 patients with FHx of ASCVD and 90,269 ASCVD patients. As summarized in Table 1, Lp(a) was tested only in a small proportion of patients; 7% of ASCVD patients, 11% of premature ASCVD patients, and 6% of patients with FHx of ASCVD. Premature CHD patients had the highest testing proportion (17%). Conclusions: Our results demonstrate underutilization of Lp(a) testing in patients with personal or FHx of ASCVD. Lp(a) is one of the mediators of “ASCVD residual risk” in patients on statins, and with new Lp(a)-lowering medications in phase III clinical trials, patient and provider education will be needed to increase awareness of Lp(a) as a modulator of ASCVD risk.