Citations

Introduction: Cardiac rehabilitation (CR) is a cornerstone of secondary prevention for cardiovascular disease (CVD). CR participation is associated with improvements in secondary CVD risk factor management. However, it is unclear if CR participation is associated with maintained improvements in CVD risk factor management following CR completion. Purpose: The aim of this study was to assess the impact of CR participation on CVD risk factor management at 1 and 3 years following CR completion. Methods: This retrospective study analyzed 911 patients (73% male) enrolled in the CR program at Mayo Clinic from 2013 to 2020. Patient demographics, clinical characteristics, and CVD risk factor management were examined pre and post CR as part of clinical practice and assessed passively at 1 and 3 years following CR. Secondary prevention CVD risk factor goals included total cholesterol (<200 mg/dL), HDL cholesterol (>40 mg/dL), LDL cholesterol (<100 mg/dL), triglycerides (<150 mg/dL), systolic and diastolic blood pressure (<130 and <80 mmHg), and body mass index (BMI) (18.5-24.9 kg/m 2 ). Results: The mean age was 66±12 years and on average 33±9 CR sessions were attended. Table 1 shows the percentage (%) of patients meeting the secondary prevention CVD risk factor goals. The % of patients meeting the goals for total cholesterol, LDL, triglycerides, and diastolic blood pressure were higher post than pre CR. There were no changes in the % of patients meeting the goals for total cholesterol or LDL at 1 and 3 year follow up compared to post CR. At 1 and 3 year follow up, there was a lower % of patients meeting the goals for systolic and diastolic blood pressure and triglycerides (at 1 year follow up only) than post CR. No changes were present in the % of patients meeting the goals for HDL and BMI at any timepoint (all, p>0.09). Conclusions: CR participation was associated with maintained improvements in lipid management, but not in triglycerides or blood pressure control at 1 and 3 years following CR.

Introduction: Clonal hematopoiesis of indeterminate potential (CHIP) is associated with an increased risk of heart failure (HF) and stroke, particularly HF with preserved ejection fraction and hemorrhagic stroke (HS). Hypothesis: We hypothesized that CHIP is associated with incident hypertension and this association mediates the effect of CHIP on HF and HS. Methods: In 200K UK-Biobank participants with exome sequencing, we ascertained CHIP as variant allele fraction (VAF) ≥2% and large CHIP as VAF ≥10%. Hypertension, HF, and HS diagnosis were determined based on the ICD-9 and ICD-10 codes. Cox proportional hazards models were used to test the association of both CHIP and the CHIP subtypes based on the most implicated driver genes (i.e., DNMT3A , TET2 , and ASXL1 ) with incident hypertension. Logistic regression models were used for mediation analyses, quantifying the extent to which hypertension mediates the effects of CHIP. Results: After excluding those with hematological malignancy (n=1,025) and pre-existing hypertension (n=14,754), 184,760 participants (56.14±8.12 years, 44.25% male, and 95.33% white ethnicity) were included. During a median follow-up of 12.2 years, 42,049 (22.76%) participants developed hypertension. CHIP mutations were significantly associated with incident hypertension (Table). Mutations in DNMT3A and TET2 but not ASXL1 drove this association. Mediation analysis revealed 17.9% (9.4% - 38.2%, P<0.001) of the effect of CHIP on HF and 5.7% (2.4% - 22.4%, P<0.001) of the effect on HS were mediated through the increased risk of hypertension. Conclusion: CHIP is associated with an increased risk of hypertension. The association of CHIP with HF and HS is partially mediated by hypertension. These findings suggest a new mechanistic pathway for the association of CHIP with cardiovascular diseases.

Introduction: Left ventricular assist devices (LVAD) with magnetically levitated impellers have become standard due to hemocompatibility and survival profile. To optimize LVAD speeds, we previously introduced simultaneous right and left heart (RH/LH) catheterization during ramp studies. Here, we report pairing of this approach with pressure-volume (PV) assessment in a patient with a HeartMate 3 LVAD with mitral regurgitation (MR). The combined approach evaluated left ventricular (LV) hemodynamics and right ventricular (RV) PV-relationships during ramp testing. Methods: A 68-year-old male with ischemic cardiomyopathy, two years following HeartMate3 implantation, presented with NYHA class III symptoms. To evaluate for dyspnea, he underwent a RH/LH catheterization ramp study. RV PV-loops were created by use of a conductance catheter system, allowing creation of PV loops. Results: At the baseline LVAD speed of 5300 rotations per minute (RPM), right atrial pressure was elevated at 16 mmHg and pulmonary artery (PA) pressure of 26 mmHg. Left-end diastolic pressure (LVEDP) was elevated (16mmHg). Baseline cardiac index (CI) was 2.01 L/min/m 2 . Ramp testing was performed, increasing speeds from 5300 to 5800 RPM. Progressive LV unloading was observed with a 5 mmHg drop in LVEDP and a 3 mmHg drop in PA pressure. However, RV PV loop monitoring revealed a negative impact on RV contractility with the maximal elastance (Emax) slope demonstrating a 22% reduction. Furthermore, a 17% reduction in RV stroke volume was documented, while diastolic function remained unchanged. Thus, despite excellent LV offloading and diminishment of RV afterload, the significant negative impact on Emax resulted in no net increase in CI (+0.04 L/min/m 2 ) during the ramp evaluation. Inotrope therapy was initiated for RV function with increased LVAD RPM to outcompete MR. Conclusions: While RH/LH catheterization provides a real-time perspective of the interventricular relationship during ramp, the impact of increasing LVAD speeds on RV function has yet to be directly measured. Here, the RV PV loop relationship during LVAD ramp showed that although LVAD speed increases in the setting of MR may facilitate LV unloading, a drop in RV afterload did not compensate for reduction in RV contractility.

Introduction: Sepsis and its associated systemic inflammatory response syndrome in the heart can lead to septic cardiomyopathy. We aim to explore new onset cardiac dysfunction in septic shock patients and its association of right heart dysfunction with hospital outcomes. Hypothesis: Early development of correct ventricular abnormalities due to septic shock may lead to worse hospital outcomes. Methods: We conducted a retrospective study of 102 patients who were hospitalized at the Mayo Clinic between August 2018 and September 2022 with septic shock and had at least one pre-sepsis echo (within one year before admission) and one post-sepsis echo (within seven days of access). Information on patient demographics, comorbidities, echo parameters, and outcomes was collected from our sepsis database and retrospective chart review. Categorical variables were described as percentages and compared by Chi-square test, and continuous variables as medians and compared using an independent sample t-test. Statistical analysis was performed using BlueSky Statistics v10.3.1. Results: Our study included 102 patients with a median age of 68 (55, 78) years, the majority being men (68%) and Caucasians (80%). Between pre- and post-sepsis echos, there was a noticeable decrease in LVEF (60% vs 50.5 %; p=0.002). Moreover, there was an increase in reduced RV function (31.6% vs. 62.6%, p<0.001), reduced LV function (56.5% vs. 85.2%, p=0.048), and RV dilatation (35.8% vs. 51.5%, p=0.03). There was also a noticeable decrease in IVC collapsibility of more than 50% (65.9% vs 22.9%, p<0.001). There was no significance in terms of demographics, comorbidities, and baseline vitals and labs among patients with RV dysfunction and without RV dysfunction except for a lower LVEF (49% vs 62 %, p= 0.004) and decreased >50 % IVC collapsibility (14.5% vs 48%, p=0.005) in RV dysfunction group. We found no differences in hospital mortality, ICU and hospital LOS, or mechanical ventilation requirements amongst patients with RV abnormalities in function and size. Conclusions: Our retrospective study shows that abnormalities in RV function and RV size are commonly observed in the initial week of septic shock. However, such RV abnormalities did not translate into worse hospital outcomes.

Background: Limited research has focused on the rising incidence of major adverse cardiac and cerebrovascular events (MACCE) among young women (aged 18-44) with at least one traditional cardiovascular disease risk factor (CVD), especially in rural hospital settings. We aimed to address this research gap by providing insights into the burden of CVD in this population. Methods: Using data from the National Inpatient Sample (2019), we identified young women (aged 18-44) with at least one cardiovascular risk factor admitted to rural hospitals. Baseline demographics, comorbidities, and outcomes were assessed, including MACCE and all-cause mortality. Multivariable logistic regression was performed, adjusting for age, race, income, and comorbidities. Results: Among 179,410 young women, MACCE occurred in 2,455 individuals (1.4%), with varying rates among racial groups: whites (1.2%), blacks (2.4%), Hispanics (1.3%), Asian or Pacific Islanders (API) (2%), Native Americans (NA) (1.6%), and others (1%). All-cause mortality was observed in 385 women (0.2%), with rates of 0.2% for whites and 0.4% for blacks. Blacks had twice the event rates of MACCE, and all-cause mortality compared to whites. Comorbidities and substance abuse were associated with MACCE (P<0.001). In higher-income households, multivariable analysis adjusted for confounders revealed significantly higher odds of MACCE in blacks (OR 1.23, 95% CI 0.11-14.08), API (OR 4.48, 95% CI 1.06-18.93), and NA (OR 10.22, 95% CI 1.03-101.50) compared to whites. In contrast, mortality rates were lower for Hispanics than whites (OR 0.24, 95% CI 0.02-3.80) (all p < 0.001) (Table 1). Conclusion: In higher-income households, greater odds of MACCE were noted among blacks, API, and NA compared to whites. Future research should focus on understanding factors contributing to these disparities and developing interventions to prioritize equitable healthcare access for rural women across diverse ethnic and income classes.

Background: Heart failure (HF) is a complex trait influenced by environmental and genetic factors. Genome-wide association studies (GWAS) have found common genetic variants associated with HF risk, but the extent that common and rare variants explain risk and implicate similar genes and pathways in HF remains less understood. Methods: For common variants, we meta-analyze existing GWAS of all-cause HF with data from the Million Veteran Program, including 207,346 individuals with HF and 2,151,210 without in diverse populations. We examine rare coding variants in the Penn Medicine BioBank (PMBB) using gene-collapsing association studies and burden heritability regression. We test for an interaction between a polygenic risk score (PRS) based on our GWAS meta-analysis and truncating variants in TTN (TTNtv). Results: GWAS meta-analysis identified 176 risk loci (146 previously unreported), implicating known risk factors for HF and genes involved in cardiac development. New signals at GIPR/ GLP1R highlight potential contributions of dysregulated glucose homeostasis and metabolism in HF. Gene collapsing studies identified associations between HF and rare loss-of-function variants in TTN (OR 2.94, 95% CI 2.43-3.56) and MYBPC3 (OR 7.71, 95% CI 3.97-14.97) at exome-wide significance. HF heritability mediated through common variants (4.5%) was significantly higher than for rare variants (1.8%), but rare variant heritability was further enriched in cardiomyopathy genes compared to common variants (27-fold vs. 3.4-fold). We found a significant interaction between a new multi-ancestry HF-associated PRS (AUC 0.72, 95% CI 0.72-0.73) and TTNtv carrier status (interaction OR 1.32, 95% CI 1.06-1.64). Conclusions: Common and rare variants contribute to HF heritability. Common variant HF heritability is highly polygenic, while rare variant burden heritability appears more concentrated in core cardiomyopathy genes. HF risk conferred by TTNtv carrier status is further increased by an individual’s polygenic contribution. Our results support the continued inclusion of cardiomyopathy genes in rare-variant genetic testing panels for HF but suggest a meaningful polygenic component to HF exists that is not currently captured by clinical genetic testing.

Introduction: Predictive algorithms using multiple clinical variables can identify patients at higher risk for abdominal aortic aneurysms (AAA), however they are cumbersome and cannot used directly by patients. A simple, cheap, noninvasive, and readily available (virtual) screening tool for identification of AAA could aid in screening algorithms. Aims: We sought to determine if a machine learning algorithm could predict the presence of an AAA by using a image of the face Methods: Diagnostic imaging studies were extracted from the electronic health record (EHR) from 5/7/2018 through 1/1/2023 and analyzed by a natural language processing algorithm previously validated to identify abdominal aortic aneurysms. Patient EHR profile pictures were extracted and matched to imaging studies. Various deep neural network (DNN) architectures were explored, all trained as classifiers on the face images along with clinical variables to predict clinical outcomes. All models used were CNNs with an EfficientNet architecture. Model performance was evaluated by standard metrics such as the area under the curve (AUC), specificities (Sp), and sensitivities (Sn). All performance metrics reported results from the test subset of the primary dataset and did not contain any observations considered during training. Results: A total of 5522 patients with facial images and AAA diagnostic studies were analyzed, among whom 1314 (23.8%) had a AAA. The optimal model had an AUC of 0.72 (Sn 0.44, Sp 0.83) for predicting AAA and remained similar in women (AUC 0.7), men (AUC 0.65), age >65 (AUC 0.63), and age <=65 (AUC 0.72) (Table). Conclusions: Using only an image of a patients face, the presence of an AAA could be identified with moderate performance in men, women, and in those less than 65. The model’s performance in demographics not typically screened for AAA may allow for targeted screening programs in these groups.

Introduction: Warfarin is a commonly prescribed anticoagulant for treating atrial fibrillation, mechanical valves, and venous thromboembolism. Warfarin dose management remains challenging due to dosing variability between patients and warfarin’s narrow therapeutic window. Time in therapeutic range (TTR) is critical to warfarin’s safety and efficacy, but TTR typically remains low (40-50%) in community practices. Specialized anticoagulation clinics and protocolized approaches can increase TTR but have great administrative burdens and health care costs. Aim: To develop standardized optimal warfarin dose decision support using a machine learning model based on time series anticoagulation data and patient demographic characteristics Methods: The dataset included 12,497 warfarin patients monitored in the anticoagulation tracker of electronic medical records across the Mayo Clinic Enterprise. The dataset contained time series anticoagulation data (warfarin dose, INR, INR target range) and patient demographic characteristics. We implemented an offline deep reinforcement learning model (DRL) to predict the cumulative warfarin dose for the days until the next INR test based on a patient’s historical anticoagulation data. Prior approaches utilized traditional supervised learning methods such as regression or Long Short Term Memory (LSTM) neural networks to approximate a function mimicking the behavior of the training data. On the other hand, DRL learned an optimal dosing policy through continuous interaction and feedback from the training data. The key advantage of DRL is the model can learn to behave differently (and potentially better) for suboptimal clinical states in the data such as overdosing or underdosing. To evaluate the DRL model we compared the predicted warfarin doses with the physician-prescribed doses Results: DRL model’s prediction accuracy was 96.96%, outperforming our implementation of a baseline LSTM model with a prediction accuracy of 70.58%. Further evaluation of the DRL model indicated that the model correctly adjusted the warfarin dose at time steps when patients had out-of-range INRs. Conclusions: Offline deep reinforcement learning demonstrates potential in supporting warfarin dose management to maximize TTR.

Introduction: Recently, an artificial intelligence (AI) algorithm was validated for identifying ECG signatures of atrial fibrillation (AF) risk during normal sinus rhythm. Traditionally, socioeconomically disadvantaged populations are less likely to receive novel or guideline-recommended treatments, and have high chronic disease burden, including cardiovascular diseases. There have also been concerns about new technologies creating a digital divide, worsening existing health disparities. Hypothesis: An AI algorithm will detect AF risk unequally across patients with different levels of social determinants of health (SDOH). Methods: The initial prospective, non-randomized, interventional study leveraged EHR and digital technologies to prospectively enroll 1004 patients with a broad range of AI-ECG risks of AF, who received continuous cardiac rhythm monitoring for up to 30 days. The Area Deprivation Index (ADI) was used as a composite measure of SDOH which is comprised of indicators spanning four domains: income, education, employment, and house quality. 904 intervention patients linked to the ADI data were matched to 904 real-world control patients derived from eligible but unenrolled patients. Those with ADI under 33% were defined as having better SODH; and patients with ADI above 33% were defined as having worse SODH. Results: Median age was 74 years and 38% were women. After matching, the intervention and control arms were balanced in all baseline characteristics, including HF, HTN, diabetes, stroke, CAD and ADI. A hazard ratio of 3.11 (P<0.001) was computed when comparing intervention to control for primary AF diagnosis outcome. In patients with worse SDOH, the intervention increased AF detection from 2.6% to 8.8% (HR 3.63 [2.09, 6.32]); in the better SDOH, the intervention increased AF detection from 3.9% to 9.3% (2.41 [1.23, 4.74]). Conclusions: An AI-guided AF screening program increased the detection of AF in both patients with better or worse SDOH. The current study validates the use of AI in historically disadvantaged populations displaying the possibility for broad implementation with decreased possibility of perpetuating disparities.

Introduction: Hyperkalemia has been associated with increased mortality in cardiac intensive care unit (CICU) patients. An artificial intelligence (AI) augmented electrocardiogram (ECG) can predict hyperkalemia, and other AI-ECG algorithms have predicted mortality in CICU patients. Hypothesis: We hypothesized that the AI-ECG hyperkalemia algorithm could stratify mortality risk beyond the serum potassium measurement. Methods: We included 11 234 unique Mayo Clinic CICU patients admitted from 2007 to 2018 who had both a 12 lead ECG and blood potassium (K) level obtained at admission with K>=5 mEq/L defining hyperkalemia. ECGs underwent AI evaluation for probability of hyperkalemia (probability >=0.5 defined as positive). Hospital mortality was analyzed using logistic regression and survival to one year was estimated using Kaplan-Meier and Cox analysis. Results: The mean age was 70 years, 37.8% were females, and 92.4% were white. Chronic kidney disease was present in 20%. The mean K value was 4.2 mEq/L, and 13% (n=1460) had hyperkalemia. AI-ECG predicted hyperkalemia in 34% (n=3820). In-hospital mortality was higher in patients with laboratory and/or AI-ECG hyperkalemia, and these patients had lower survival out to 1 year. Mortality was lowest in patients with normal K and negative AI-ECG and highest in patients with both laboratory-confirmed hyperkalemia and AI-ECG predicted hyperkalemia. Patients with isolated laboratory hyperkalemia (false-negative AI-ECG) had higher mortality than those with isolated AI-ECG hyperkalemia (false-positive AI-ECG). Conclusions: AI-ECG probability of hyperkalemia, even with a normal laboratory K, was associated with higher short-term and long-term mortality in CICU patients. The laboratory K value and AI-ECG probability of hyperkalemia are complementary risk factors. The AI algorithm can detect prognostically important ECG abnormalities which may reflect the effects of K levels on the myocardium.