Arruda‐Olson, Adelaide M.

A 34-year-old man was referred for progressive angina and exertional dyspnoea refractory to medical therapy, with a presumptive diagnosis of hypertrophic cardiomyopathy (HCM). Transthoracic echocardiography (TTE) revealed asymmetric septal hypertrophy without systolic anterior motion of the mitral valve leaflet and with no dynamic left ventricular outflow tract (LVOT) obstruction. However, the LVOT velocity was elevated at rest as well as with provocation, without the characteristic late peaking obstruction seen in HCM. Focused TTE to evaluate for suspected fixed obstruction demonstrated a subaortic membrane 2.2 cm below the aortic valve. Coronary CT angiography confirmed the presence of the subaortic membrane and was negative for concomitant coronary artery disease. Surgical resection of the subaortic membrane and septal myectomy resulted in significant symptomatic relief and lower LVOT velocities on postoperative TTE. This case reminds the clinician to carefully evaluate for alternative causes of LVOT obstruction, especially subaortic membrane, as a cause of symptoms mimicking HCM.

Background Symptomatic limitations in apical hypertrophic cardiomyopathy may occur because of diastolic dysfunction with resultant elevated left ventricular filling pressures, cardiac output limitation to exercise, pulmonary hypertension (PH), valvular abnormalities, and/or arrhythmias. In this study, the authors aimed to describe invasive cardiac hemodynamics in a cohort of patients with apical hypertrophic cardiomyopathy. Methods and Results Patients presenting to a comprehensive hypertrophic cardiomyopathy center with apical hypertrophic cardiomyopathy were identified (n=542) and those who underwent invasive hemodynamic catheterization (n=47) were included in the study. Of these, 10 were excluded due to postmyectomy status or incomplete hemodynamic data. The mean age was 56±18 years, 16 (43%) were women, and ejection fraction was preserved (≥50%) in 32 (91%) patients. The most common indication for catheterization was dyspnea (48%) followed by suspected PH (13%), and preheart transplant evaluation (10%). Elevated left ventricular filling pressures at rest or exercise were present in 32 (86%) patients. PH was present in 30 (81%) patients, with 6 (20%) also having right‐sided heart failure. Cardiac index was available in 25 (86%) patients with elevated resting filling pressures. Of these, 19 (76%) had reduced cardiac index and all 6 with right‐sided heart failure had reduced cardiac index. Resting hemodynamics were normal in 8 of 37 (22%) patients, with 5 during exercise; 3 of 5 (60%) patients had exercise‐induced elevation in left ventricular filling pressures. Conclusions In patients with apical hypertrophic cardiomyopathy undergoing invasive hemodynamic cardiac catheterization, 86% had elevated left ventricular filling pressures at rest or with exercise, 81% had PH, and 20% of those with PH had concomitant right‐sided heart failure.

Background: Atherosclerotic cardiovascular disease (ASCVD) often affects > 1 arterial location (polyvascular disease, PVD). The risk of adverse outcomes increases with the number of arterial territories involved. The genetic susceptibility to PVD is unknown. Hypothesis: We hypothesized that a genetic risk score representing cumulative effects of susceptibility variants to ASCVD is associated with presence of PVD. Methods: We searched genome-wide association study (GWAS) catalog to identify genetic variants associated with coronary heart disease (CHD), ischemic stroke, peripheral arterial disease (PAD) or abdominal aortic aneurysm (AAA). No variants were reported to be associated with PVD in ≥ 3 arterial territories at GWAS significance. 10 loci were associated with PVD in 2 arterial territories. We calculated a multi-locus GRS using 10 lead single nuclear polymorphisms (SNP) from these distinct loci (1 per each loci) in 7486 participants in Mayo Vascular Disease Biorepository (age 68±10 years, 61% men, 31% with CHD; 27% carotid artery stenosis; 39% PAD; 14% AAA; 38% without any of these vascular diseases). Results: Of all participants 34% had PVD (2%, 10% and 22% with exact 4, 3 and 2 arterial territories involved respectively). A higher GRS was associated with greater number of arterial territories involved (P for trend of ANOVA <0.001). Patients with PVD in ≥ 3 arterial locations had higher GRS compared to those with 2 arterial locations involved after adjustment for age, sex and cardiovascular risk factors (P =0.008). One unit increase in GRS was associated with 9% higher likelihood for each additional arterial territory involved after adjustment for all covariates (odds ratio from ordinal regression model= 1.09, P <0.001). The result remained significant after further adjustment for combinations of AAA, CHD, carotid arterial stenosis and PAD (all P ≤ 0.01). 5 Of the 10 SNPs were each associated with presence of PVD after adjustment for all covariates (all P <0.01). None of them remained significant after further adjustment for combinations of PVD in different arterial territories (all P ≥ 0.4). Conclusions: A GRS derived from 10 SNPs of ASCVD is associated with PVD,suggesting genetic contribution to the dimension of PVD that may lead to adverse outcome.

Introduction: Atherosclerotic cardiovascular disease (ASCVD) is the leading cause of death worldwide. The genetic contribution to mortality in patients with ASCVD is unknown. Hypothesis: Abdominal aortic aneurysm (AAA) often co-exists with ASCVD and two with shared genetic susceptibility underlying disease development. We hypothesiz that a genetic risk score (GRS) for susceptibility variants to AAA and ASCVD increases the risk for all-cause mortality in patients with ASCVD. Methods: We analyzed high-density genotyping data in 3053 participants (age 71 ±10 years, 72% men) with ASCVD in Mayo Vascular Disease Biorepository. We used 4 single nucleotide polymorphisms (SNPs) (rs2383207, rs7025486, rs599839, rs1466535) previously shown to be associated with AAA and ASCVD at GWAS significance (P ≤10^-8) in the NHGRI GWAS catalog to build the GRS. We defined ASCVD as a history of myocardial infarction / coronary revascularization / ischemic stroke / peripheral arterial disease with revascularization. Results: Patients with GRS > median level had similar age (70±10 vs. 71±10), prevalence of men (72% vs. 73%), hypertension (83% vs. 82%), diabetes (32% vs. 31%) and smokers (74% vs. 72%) compared to those with GRS ≤ median (all P>0.2), except for dyslipidemia (91% vs. 88%, P =0.02). After a median follow-up of 4.56 years (interquartile: 3.07-6.26), 620 patients died. Patients with GRS > median had poorer survival compared to those with GRS ≤ median (Figure: Kaplan-Meier curve); and with 23% higher risk for death after adjustment for age, sex and cardiovascular risk factors (hazard ratio HR, 95% CI: 1.23, 1.05-1.45). The association of GRS with all-cause mortality remained significant after excluding patients with known AAA (n=672) (HR, 95%CI: 1.22, 1.01-1.48). Conclusions: A GRS for susceptibility variants of AAA and ASCVD is a prognostic marker in patients with ASCVD.

Background Patients with peripheral artery disease are at increased risk for major adverse cardiac events, major adverse limb events, and all‐cause death. Developing tools capable of identifying those patients with peripheral artery disease at greatest risk for major adverse events is the first step for outcome prevention. This study aimed to determine whether computer‐assisted analysis of a resting Doppler waveform using deep neural networks can accurately identify patients with peripheral artery disease at greatest risk for adverse outcome events. Methods and Results Consecutive patients (April 1, 2015, to December 31, 2020) undergoing ankle–brachial index testing were included. Patients were randomly allocated to training, validation, and testing subsets (60%/20%/20%). Deep neural networks were trained on resting posterior tibial arterial Doppler waveforms to predict major adverse cardiac events, major adverse limb events, and all‐cause death at 5 years. Patients were then analyzed in groups based on the quartiles of each prediction score in the training set. Among 11 384 total patients, 10 437 patients met study inclusion criteria (mean age, 65.8±14.8 years; 40.6% women). The test subset included 2084 patients. During 5 years of follow‐up, there were 447 deaths, 585 major adverse cardiac events, and 161 MALE events. After adjusting for age, sex, and Charlson comorbidity index, deep neural network analysis of the posterior tibial artery waveform provided independent prediction of death (hazard ratio [HR], 2.44 [95% CI, 1.78–3.34]), major adverse cardiac events (HR, 1.97 [95% CI, 1.49–2.61]), and major adverse limb events (HR, 11.03 [95% CI, 5.43–22.39]) at 5 years. Conclusions An artificial intelligence–enabled analysis of Doppler arterial waveforms enables identification of major adverse outcomes among patients with peripheral artery disease, which may promote early adoption and adherence of risk factor modification.

Objective Tricuspid regurgitation (TR) is a prevalent valve disease associated with significant morbidity and mortality. We aimed to apply machine learning (ML) to assess risk stratification in patients with ≥moderate TR. Methods Patients with ≥moderate TR on echocardiogram between January 2005 and December 2016 were retrospectively included. We used 70% of data to train ML-based survival models including 27 clinical and echocardiographic features to predict mortality over a 3-year period on an independent test set (30%). To account for differences in baseline comorbidities, prediction was performed in groups stratified by increasing Charlson Comorbidity Index (CCI). Permutation feature importance was calculated using the best-performing model separately in these groups. Results Of 13 312 patients, mean age 72 ± 13 years and 7406 (55%) women, 7409 (56%) had moderate, 2646 (20%) had moderate–severe and 3257 (24%) had severe TR. The overall performance for 1-year mortality by 3 ML models was good, c-statistic 0.74–0.75. Interestingly, performance varied between CCI groups, (c-statistic = 0.774 in lowest CCI group and 0.661 in highest CCI group). The performance decreased over 3-year follow-up (average c-index 0.78). Furthermore, the top 10 features contributing to these predictions varied slightly with the CCI group, the top features included heart rate, right ventricular systolic pressure, blood pressure, diuretic use and age. Conclusions Machine learning of common clinical and echocardiographic features can evaluate mortality risk in patients with TR. Further refinement of models and validation in prospective studies are needed before incorporation into the clinical practice.

Abstract Hypertrophic Cardiomyopathy (HCM) is an inheritable heart disease with the highest rate of sudden cardiac death (SCD) in young adults. Implantable Cardioverter Defibrillator (ICD) therapy is recommended for HCM patients at high risk for SCD. Reviewing the recent clinical literature revealed the potential to improve the selection of candidates for ICD implantation. The current study uses information extracted from echocardiography reports to evaluate HCM patients with ICD and aims to provide a comparative insight into patients who benefited the most from the device therapy, including shock and anti-tachycardia pacing (ATP). The proposed interpretable machine learning approach has used the XGboost algorithm. The model’s performance was considered satisfactory, as evidenced by an accuracy score of 81% and an area under the receiver operating characteristic curve (AUC) value of 69% and SHapley Additive exPlanations (SHAP) identified common properties of HCM patients in each category and provided high-level reasoning and foundation for a clinical decision support tool.