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Abstract Background Cardiopulmonary exercise testing (CPET) is a widely used tool for risk stratification and prognosis in patients with chronic heart failure. In amyloid patients, cardiac amyloid deposits are associated with poor prognosis, increased risk for arrhythmias, sudden death, and subsequent exercise limitation due to restrictive cardiomyopathy. The type of amyloidosis and its affectation of the heart are different, and little is known about the CPET parameters according to the type of cardiac amyloidosis. Purpose In this study, we aim to compare CPET data in different types of amyloidosis to determine if CPET variables evaluated could have a significant difference that could impact the follow-up or prognosis of the patients based on the type of amyloidosis. Methods We conducted a retrospective cohort study of 194 patients with a diagnosis of cardiac amyloidosis referred for CPET from February 2010 to February 2024. Patients with at least one CPET data were included in our analysis. The type of amyloidosis, comorbidities, and echocardiogram data were retrieved from medical records. Patients were divided into 3 different groups according to the type of amyloidosis: AL Amyloidosis(AL), Variant TTR Amyloidosis (vTRR), and Wild Type TTR Amyloidosis (wtTTR). Individuals with CPET studies after heart transplant were excluded from the analysis. Fisher’s exact test or chi-square was used to compare categorical variables. ANOVA and Tukey-Kramer (TK) were used to compare continuous variables. Adjustment using General Linear Models (GLM) procedure was used to identify any confounder that could affect the results. A p-value <0.05 was considered significant. Results Out of the 194 patients, 66 had AL Amyloidosis, 40 had vTTR Amyloidosis, and 88 had wtTTR Amyloidosis. Most of the patients were male, and the male predominance was higher in the wtTRR group. There were 41 (62.1%) males in the AL group, 35 (87.5%) in the vTRR group, and 83 (94.3%) in the wtTTR group (p<0.0001). Comorbidities were balanced among the groups. Additional demographic and comorbidities data are shown in Table 1. From the evaluated CPET variables, VE/VCO2 nadir and slope were significantly higher in the wtTTR group when compared to the other groups, as well as Peak VE and Peak TV(Figure A). However, after adjusting for sex, BMI, and age, VE/VCO2 nadir was still significantly higher in the wtTRR group (p=0.003) compared to the other groups, as well as Peak VE, which was higher again in vTTR patients (p=0.001), and Peak TV (Table 2). Conclusion Ventilation variables evaluated in CPET showed alterations in the wtTTR group. These findings suggest that wtTRR amyloid patients could have a severe problem in ventilation that could be of significant relevance in future prognosis.

Abstract Background Evidence suggests an association between obesity and the risk for cardiomyopathy development, however robust evidence is still lacking. In this study we sought to explore the relationship of obesity with hypertrophic (HCM) and dilated cardiomyopathy (DCM) and possible interactions with sex using large scale epidemiological real-world data. Methods and Results We analyzed data from the Nationwide Inpatient Sample (NIS) of U.S. hospitalizations for the years 2015-2019. There were a total of n=46,934 admissions with a diagnosis of HCM and n=170,924 with DCM. There was a significant interaction between cardiomyopathies’ diagnosis with sex and age subgroups; the rates of both DCM and HCM increased with age (p<0.001 for both); DCM diagnosis was significantly higher in males compared with females (0.85% vs. 0.35%, p<0.001). After adjustment for age, sex, race, and presence of arterial hypertension there was a significant stepwise positive association between obesity and the population rates of both cardiomyopathy subtypes. For hospitalized patients with a body mass index (BMI) ≥30kg/m2 there was an odds ratio (OR) of 1.68 (95%CI: 1.55-1.81, p<0.001) for HCM andOR=1.82 (95%CI: 1.79-1.84, p<0.001) for DCM. More importantly, the positive relationship between a cardiomyopathy diagnosis (HCM or DCM) with increasing BMI was driven by the male sex (p<0.001 for both) and it was non-significant in females. Conclusions The findings from this nationwide observational analysis support a sexual dimorphism in the relationship between obesity and hypertrophic or dilated cardiomyopathy, which should be further investigated.Cardiomyopathy-obesity

Abstract Background Atrial fibrillation (AF) is the most prevalent atrial tachyarrhythmia, affecting an estimated 2-4% of adults worldwide. AF is a well-recognized and treatable risk factor for stroke, but it often remains asymptomatic or subclinical, leading to underdiagnosis. This study aimed to identify distinguishing signs during exercise stress echocardiography (ESE) among patients in sinus rhythm (SR), with and without history of paroxysmal/persistent AF (PAF). Methods An international prospective cohort comprising 1035 consecutive patients was initially considered from 12 cardiology institutions. These patients were referred for clinically-driven ESE as part of the Stress Echo 2020-2030 study network. All patients underwent comprehensive ESE with the ABCDE protocol, which included assessment of B-lines, left ventricle contractile reserve (LVCR), ECG-based heart rate reserve (HRR), LV ejection fraction (EF), LV end-diastolic volume, E/e’, pulmonary artery systolic pressure estimation, tricuspid annular plane sytolic excursion (TAPSE), left atrium volume index (LAVi) and left atrium reservoir phase strain (LASr) at rest and with exercise. Results After excluding 89 patients who were in AF during the ESE, 946 patients in SR were included as the main study cohort (101 with and 845 without a history of PAF). A scoring system was then constructed using all independent variables that differentiate the two groups (those with and without history of PAF). Presence of systemic hypertension, rest LAVI>31ml/m2, rest |LASr|< 21%, stress LAVI >29 ml/m2, stress |LASr| <24%, stress E/e’ >7, ∆TAPSE <4.5 mm, HRR<1.87, and LVCR<1.6 were identified as the discriminators between patients with and without PAF. The composite score, summing all 9 items (with a total score range from 0 to 9), yielded a score of >4 as the best balance between sensitivity (87%) and specificity (60%) in predicting those with PAF. The receiver operating characteristic curve analysis of the scoring model showed an AUC of 0.79 (p-value <0.0001), Figure. Conclusion ESE evaluation can be a valuable adjunct for identifying patients in SR with history of PAF. This multifaceted approach enhances the potential for more accurate detection and clinical management of individuals in SR at risk for AF, who could be considered for aggressive monitoring programs to prevent the development of new onset AF and subsequent increase in cardiovascular risk.

Abstract Background Short sleep is linked to increased acute myocardial infarction (MI) risk.However, the association of weekend-to-weekday sleep patterns with MI in a nationally representative population remains unknown. The goal is to investigate how weekday and weekend sleep durations affect myocardial infarction (MI) in the general population, and if longer weekend sleep can offset insufficient weekday sleep's effects. Methods A cross-sectional study utilized data from the 2017-2020 NHANES to examine the relationship between sleep patterns and MI. Participants self-reported their average sleep durations on weekdays (1) and weekends (2), along with the calculated weekend-to-weekday sleep duration ratio (SDR) (3). Logistic regression models, adjusting for various factors, including demographics and comorbidities, were employed to analyze the association. Those with SDR > 1 had more weekend sleep, while an SDR = 1 indicated equal sleep durations. Results The study involved 9,205 adult participants, with a mean ± SD age of 68.8 ± 10.8 years and 30.7% being female. Among them, 4.65% (428 patients) had a history of MI. The mean ± SD of weekday and weekend sleep hours was 7.6 ± 1.7 and 8.3 ± 1.8 hours, respectively. For weekday sleep duration, after univariate analysis, individuals compared to those in Q1 were statistically significant 0.72 times less likely in Q2 and 1.4 times more likely in Q4 to experience MI . While MI was 0.76 times less likely to occur among participants in Q3, it was not statistically significant (95% CI Q2: 0.54, 0.96; Q4: 1.05, 1.76; Q3:0.57, 1.00). After adjusting, participants only in Q3 were statistically significant 0.71 times less likely to develop MI.(Q3: 95% CI 0.51, 0.99). For weekend sleep duration, the univariate analysis showed that individuals in Q2, Q3, and Q4 were significantly less likely to experience MI (OR = 0.63, 0.67, and 0.69; 95% CIs: 0.48-0.82, 0.51-0.87, 0.53-0.90, respectively) After adjusting, only participants in Q2 were statistically significant less likely to develop MI (OR = 0.71; 95% CI 0.52 - 0.96). For the weekend-to-weekday sleep duration ratio (SDR), after univariate analysis, individuals were statistically significant 0.54 times less likely, and 1.5 times more likely to experience MI in those whose SDR is more than 1, and those whose SDR equals 1, respectively (95% CI SDR > 1: 1.14, 1.98; SDR = 1: 0.39, 0.72). After adjusting, participants with SDR more than 1 were statistically significant 0.69 times less likely to develop MI. Conclusions Moderate sleep durations on weekdays (in Q3), weekends (in Q2), along with SDR more than 1, were negatively linked with MI in a general population. This implies that sleep durations on both weekends and weekdays have comparable associations with MI. Additionally, full recovery sleep on weekends following sleep-deprived weekdays, might reduce MI risk. Further studies are needed to explore impact of weekend and weekday sleep patterns on MI.Histogram and Margins plotTable of Odd ratios

Abstract Background The concept of ideal cardiovascular health (CVH) was developed by the American Heart Association (AHA) as a composite of seven health factors and behaviors labeled as the Life’s Simple 7 (LS7). Ideal CVH has been associated with complex measures of cardiovascular aging. We hypothesized that physiologic aging, as determined by a simple, routinely used tool such as the electrocardiogram (ECG) enabled by an artificial intelligence algorithm to predict an individual’s physiologic age (ECG age), will be associated with poor CVH as defined by an adapted version of the AHA LS7. Methods We conducted an observational retrospective study of consecutive patients aged ≥18 years who sought care between June 2019 and March 2022, self-reported behavioral history through a validated intake questionnaire in clinical outpatient visits, had a clinically indicated ECG within one year, and provided research authorization. We applied a previously developed AI-ECG convolutional neural network to derive the ECG-Age. We excluded patients with missing data, those with body mass index (BMI) <18.5 kg/m2, previous history of left-bundle branch block, atherosclerotic cardiovascular disease events, atrial fibrillation, heart failure, and non-skin cancer. An adapted version of the LS7 was used to define CVH, which includes anthropometrics (e.g., BMI), behavioral history (e.g., smoking, physical activity, and diet), vital signs (e.g., blood pressure), and laboratory values (e.g., total cholesterol, and fasting glucose). CVH was stratified as ideal, intermediate, and poor if the LS7 score had values of 9-14, 7-8, and 0-6, respectively, constructed by adding 2,1 or 0 points to each metric that met each category criteria, respectively. Accelerated aging was defined as having a Delta-Age ≥1SD above the mean, whereas Delta-age was defined as ECG minus chronological age. Logistic regression models tested the association between CVH categories and positive Delta-Age while adjusting for chronological age and sex. Results We included 114,214 individuals (53.3% females, 86.9% Caucasian). Mean chronological age was 57.7±14 years and mean AI-ECG- Age was 58 ± 12.44 years (R2 0.80, p-value <0.001). Mean Delta-Age was 0.28 ±8.8 years. LS7 average score was 8.3 ± 2.23; 46.8% had ideal, 31.5% intermediate, and 21.7% had poor CVH (Figure A). Compared to those with ideal CVH, those with poor CVH were more than 50% more likely to have Delta-Age ≥1SD (OR 1.61, CI 95% 1.54-1.69, p-value: <0.001), independent of chronological age and sex. (Figure B). Conclusion Poor CVH, defined by the AHA LS7, is linked with biological aging as determined by AI-enabled ECG, highlighting the importance of promoting CVH in adults as a strategy to decelerate biological aging and reduce its associated mortality.

Abstract Background Data on racial/ethnic and sex disparities in the utilization and outcomes of tricuspid valve surgery (TVS) in the United States are scarce. Purpose We aimed to evaluate the impact of race/ethnicity and sex on the utilization and outcomes of TVS. Methods We analyzed the National Inpatient Sample database from 2016 to 2020 to identify hospitalizations for tricuspid valve surgery. Racial/ethnic and sex disparities in tricuspid valve surgery outcomes were determined using logistic regression models. Results Between 2016 and 2020, 19,395 hospitalizations for tricuspid valve surgery were identified. The utilization rates (defined as the number of tricuspid valve surgery performed per 100,000 US hospitalizations) were lower in Black and Hispanic patients compared with White patients for surgical tricuspid valve repair (331 versus 493 versus 634, p<0.01) and surgical tricuspid valve replacement (312 versus 601 versus 728, p<0.01). Similarly, the utilization rates were lower for women compared with men for surgical tricuspid valve repair (1,021 versus 1,364, p<0.01) and surgical tricuspid valve replacement (930 versus 1,316, p<0.01). Compared to White men undergoing tricuspid valve surgery, all women had lower odds of acute kidney injury (aOR 0.65, 95% CI 0.55-0.78) and higher odds of blood transfusion (aOR 1.30, 95% CI 1.07-1.59), and Black men had higher odds of blood transfusion (aOR 1.59, 95% CI 1.08-2.35). In-hospital mortality and other surgical complications were similar between all groups (all p>0.05). Conclusions Significant racial/ethnic and sex disparities exist in the utilization of tricuspid valve surgery in the United States. Further studies are needed to understand the reasons for these disparities and to identify effective strategies for their mitigation.

Abstract Background Transcatheter aortic valve replacement (TAVR) continues to be increasingly performed. While machine learning has demonstrated utility in predicting adverse outcomes in various cardiovascular procedures, its effectiveness in predicting in-hospital mortality in TAVR remains uncertain. Purpose We aimed to examine the accuracy of machine learning in predicting in-hospital mortality in TAVR. Methods We searched PubMed and Cochrane databases up to February 2024 for observational studies assessing the diagnostic accuracy of machine learning for predicting in-hospital mortality in TAVR. Extracted data included study characteristics, patient demographics, type of predictive model used, and area under the receiver operating characteristic curves (AUC). Predictive accuracy was assessed by pooling AUC of machine-learning models across studies. AUC values ranging from 0.80 to 0.90, 0.70 to 0.80, 0.60 to 0.70, and 0.50 to 0.60 corresponded to excellent, good, fair, and poor discrimination ability, respectively (1). We used a random effects model to estimate the overall effect size with a 95% confidence interval (CI). Heterogeneity was evaluated with the I2 test.We conducted subgroup analyses based on the type of machine-learning model utilized to assess for any potential differences in predictive accuracy. Results A total of three studies were identified, comprising a study population of 70,714 patients. Across studies, eight machine learning models were utilized, consisting of three artificial neural network models, one logistic regression model, one naive Bayes model, two random forest models, and one support vector machine model. Pooled AUC showed excellent accuracy of machine learning in predicting in-hospital mortality in TAVR (0.92; 95% CI 0.90 - 0.95). Subgroup analyses revealed similar yet excellent accuracy across all types of machine learning models utilized: artificial neural network (0.92; 95% CI 0.86-0.99), logistic regression (0.92; 95% CI 0.89-0.95), naive Bayes (0.90; 95% CI 0.88-0.92), random forest (0.94; 95% CI 0.87-1.00), and support vector machine (0.94; 95% CI 0.91-0.96). Conclusion Our study suggests that utilizing machine learning can accurately predict in-hospital mortality in TAVR. Operators of TAVR procedures may benefit from incorporating machine learning prediction models in their clinical practice. Further exploration of machine learning on outcomes in TAVR is needed to verify our findings.

Abstract Background/Introduction MANP is a novel atrial natriuretic peptide (ANP) analog bioengineered as a particulate guanylyl cyclase A receptor (GC-A) activator with superior receptor binding, resistance to neprilysin degradation, natriuretic, aldosterone suppressing and blood pressure (BP) lowering properties than native ANP. MANP is now under clinical development for resistant hypertension (RH). The rationale for its design was the fundamental role of the ANP/GC-A system in BP homeostasis through which GC-A activation with MANP leads to BP reduction through renal, vascular, and hormonal mechanisms mediated by the second messenger cGMP. Purpose We investigated the safety, tolerability, cGMP activation and BP reductions of MANP compared to placebo in subjects with RH. Methods We performed a double-blind, placebo controlled randomized trial in 36 RH patients comprised of approximately 50% non-African Americans (NAA) and 50% AA. MANP was administered in 6 cohorts receiving ascending subcutaneous (SQ) doses of MANP once daily from 3 ug/kg to a maximal dose of 7 ug/kg or placebo for 5 days and then followed for 21 days after dosing. Endpoints included safety, cGMP activation and changes in systolic BP (SBP), aldosterone (Aldo) levels and glomerular filtration rate (GFR). Results MANP was safe and well tolerated. One patient in the 7 ug/kg cohort was discontinued due to symptomatic hypotension. Compared to baseline, plasma cGMP increased in all MANP groups on Day 1 and Day 5 with no change in the placebo group. The maximal SBP reduction in the MANP group on Days 1 (D1) and 5 (D5) was -17.5 (D1) and -16 mmHg (D5) with 3 ug/kg (lowest dose) and -39.5 (D1) and -36.3 mmHg (D5) with 7 ug/kg (highest dose); Changes in the Placebo group were -6.3 (D1) and -16.7 mmHg (D5). Notably, the SBP reductions in AA were greater than NAA in the 4 and 7 ug/kg cohorts. After the 1-hour timepoint on both the first and fifth days, Aldo and percentage of Aldo reduction generally remained suppressed compared to baseline. Twenty-one days after MANP dosing, SBP remained lower in three groups receiving SQ MANP (4, 4.5 and 7 ug/kg) compared to placebo in association with a preservation of GFR. There were no reported drug related serious adverse events. Conclusions This clinical trial demonstrates that once a day SQ administration of MANP in RH subjects activates cGMP, without attenuation over 5 days, and reduces SBP and preserves GFR. At higher doses of MANP, SBP reductions were greater in AA compared to NAA. MANP also demonstrated Aldo inhibiting actions compared to placebo. Importantly, MANP was overall safe and well-tolerated. Our findings support the continued clinical development of MANP as a novel GC-A activating peptide for the treatment of RH.

Abstract Background Data on the relative utilization and outcomes of transcatheter aortic valve replacement (TAVR) versus surgical AVR (SAVR) in patients <65 years are limited. Purpose To compare the relative utilization and outcomes of TAVR versus SAVR in patients <65 years in the United States. Methods We queried the National Inpatient Sample (2016-2020) to identify patients <65 years hospitalized for isolated AVR. In-hospital outcomes of TAVR versus SAVR were compared using multivariable regression and propensity-matching analyses. Results Of 74,155 patients aged <65 years hospitalized for isolated AVR, 17,300 (23.3%) underwent isolated TAVR, and 56,855 (76.7%) underwent isolated SAVR. From 2016-2020, the use of TAVR increased, while the use of SAVR decreased in patients <65 years (both ptrend<0.01). Factors independently associated with TAVR selection in patients <65 years included female sex, congestive heart failure, pulmonary disease, renal failure, liver disease, cancer, and prior revascularization. Factors independently associated with SAVR selection included bicuspid aortic valve. After adjustment for baseline characteristics, TAVR was associated with lower odds of in-hospital mortality (adjusted odds ratio [aOR] 0.61, 95% confidence interval [CI] 0.38-0.97), stroke (aOR 0.58, 95% CI 0.38-0.90), acute kidney injury (aOR 0.41, 95% CI 0.34-0.49), and major bleeding (aOR 0.62, 95% CI 0.38-0.96), and higher vascular complications compared with SAVR (aOR 2.39, 95% CI 1.75-3.24). Length of stay was shorter and nonhome discharges were lower with TAVR (all p<0.01). Similar findings were observed in the propensity-matched cohort. Conclusions This nationwide observational analysis showed that TAVR is a feasible treatment option for patients <65 years with lower in-hospital mortality and resource utilization compared with SAVR.Central Illustration

Abstract Introduction Vulnerable populations experience a higher burden of cardiovascular disease.(1) However, the incidence of cardiovascular risk factors, such as diabetes, within these populations, is incompletely characterized. Recently, the United States Centers for Disease Control and Prevention (US CDC) developed the Social Vulnerability Index (SVI) to categorize counties that may be vulnerable to worse health outcomes. The SVI is composed of 4 themes: socioeconomic status (Theme 1), household composition & disability (Theme 2), minority status & language (Theme 3), and housing type & transportation (Theme 4).(2) We sought to evaluate the association of the 4 SVI themes with county incidence of diagnosed diabetes. Method We used CDC data (2004-2019; reported for 94% of US counties), on county incidence of diagnosed diabetes. The SVI themes were scored from 0 to 1 (low to high vulnerability) based on national percentile ranking. We used weighted least squares regression with restricted cubic splines to model the relationship between SVI themes and incidence of diagnosed diabetes (median data 2004−2019), adjusting for median age, sex ratio, state, percent uninsured, food environment index, healthcare providers, and rurality.(3) The partial effects of SVI themes and diabetes incidence were reported as F-statistics (P-values) and diabetes incidence rate ratios (IRR [95% confidence interval]), which were plotted for all values compared with the 50th percentile (SVI 0.5). For each theme, we also present the IRR for a 0.5-point increase when moving over the lower (SVI score 0.5 vs. 0.0) and the upper (1.0 vs. 0.5) half of the SVI score distribution. Results The 4 SVI theme rank scores were significantly associated with diabetes incidence (all, P<.001). However, Theme 1 and 2 scores showed stronger associations with IRR than Theme 3 and 4 (Figure). For Theme 1, SVI score 0.5 vs. 0.0 was associated with a 15% higher incidence (IRR 1.15 [1.13–1.18]) and score 1.0 vs. 0.5 was associated with a 13% higher incidence (IRR 1.13 [1.11–1.16]). For Theme 2, SVI score 0.5 vs. 0.0 was associated with a 16% higher incidence (IRR 1.16 [1.14–1.18]), but there was no difference between rates for SVI score 1.0 vs. 0.5 (IRR 1.01 [0.99–1.03]). For Theme 3, there was no difference between SVI score 0.5 vs. 0.0 (IRR 0.98 [0.96–1.00]), but rates were 8% lower for SVI score 1.0 vs. 0.5 (IRR 0.92 [0.90–0.94]). For Theme 4, SVI score 0.5 vs. 0.0 was associated with a 3% lower incidence (IRR 0.97 [0.96–0.99]) and score 1.0 vs. 0.5 was associated with a 2% higher incidence (IRR 1.02 [1.01–1.04]). Conclusion In US counties, the strength and directionality of the association between SVI themes and diabetes incidence differed by theme and level of SVI scores. These themes, particularly socioeconomic status and household composition & disability may be leveraged to identify high-risk counties for primary prevention of diabetes, with implications for reduction in cardiovascular risk.