McCully, Robert B.

Abstract Rationale Sotatercept is a novel activin signaling inhibitor approved for the treatment of pulmonary arterial hypertension (PAH). Sotatercept has been studied in randomized clinical trials, but little is known regarding the real-world use of sotatercept. We sought to describe the safety, tolerability and efficacy of sotatercept in our clinical practice. Methods We performed a combined prospective and retrospective cohort study of all patients treated with sotatercept at Mayo Clinic Rochester since its approval in March 2024. Patients enrolled in a separate clinical trial of sotatercept were excluded from this analysis as their data will be reported separately. Clinical data was abstracted from the electronic health record. Follow-up data was collected at 6 ± 3 months after initiation of sotatercept. Data is summarized as n (%) and mean ± SD. We used paired t-tests to compare baseline and follow-up characteristics within individuals. Results Fifty-eight PAH patients treated with sotatercept were included. Patients were 48.4 ± 37.6 years old at treatment initiation and were predominantly female with a mean duration of PAH of 6.7 years and a range of PAH etiologies including portopulmonary hypertension, congenital heart disease, and pulmonary veno-occlusive disease (Table 1). Twenty-one (36%) of patients had mixed etiologies of PH. Six months after initiation of sotatercept, patients had a significant improvement in NTproBNP (1922 ± 4464 to 363 ± 596 pg/ml, p = 0.04), 6 minute walk distance (378.8 ±144.6 to 395.7±129.5 meters, p = 0.02) and right ventricular systolic pressure (74.6 ± 20.5 to 60.6 ± 20.3 mm Hg, p < 0.001). No patients permanently discontinued due to abnormal laboratory values. Mean highest hemoglobin achieved over the initial 6 months of treatment was 15.5 ± 2.1 g/dL and mean lowest platelet count was 181 ± 74K. Seven patients discontinued sotatercept at a range of 170-275 days after starting. Four patients discontinued due to possible treatment-related adverse effects: 1 each of gastrointestinal hemorrhage, retinal hemorrhage, hypoxia/intrapulmonary vasodilatation and spontaneous bilateral subdural hematomas. Three patients discontinued due to other reasons: newly diagnosed metastatic cancer leading to transition to hospice and two due to lack of improvement. Conclusions In this real-world registry of sotatercept in PAH, sotatercept was well-tolerated among patients with PAH and associated with improvements in exercise capacity, echocardiographic parameters, and NTproBNP. Overall discontinuation due to possible treatment-related adverse effects was low and reasons for discontinuation included bleeding and hypoxia attributed to intrapulmonary vasodilatation. This abstract is funded by: None

Background: We previously developed deep-learning algorithms that identify coronary artery disease (CAD) risk based on (i) coronary artery calcium (CAC), (ii) obstructive CAD by angiography, and (iii) left ventricular akinesis in ≥1 segment by echocardiogram, using a 12-lead electrocardiogram (CAD ECG-AI). We tested the hypothesis that those with increased probability of CAD by CAD ECG-AI algorithms will have an increased risk of atherosclerotic cardiovascular disease (ASCVD) events and would refine the AHA Predicting Risk of Cardiovascular Disease Events (PREVENT TM ) equations’ predictive capabilities. Methods: We assessed a group of consecutive patients who sought primary care in Olmsted County, MN, between 1997 and 2003. Passive follow-up was conducted using the Rochester Epidemiology Project's record linkage system. Patients included met the same criteria of the original PREVENT TM equations. The probability output of each CAD ECG-AI algorithm was used to predict ASCVD (fatal and non-fatal myocardial infarction and ischemic stroke) and ASCVD-Plus [further including percutaneous coronary intervention (PCI), coronary artery bypass grafting (CABG), and all-cause mortality]. Events were validated in duplicate. Cox proportional hazard models adjusted for age, sex, and risk factors while modeling each CAD ECG-AI algorithm as an individual and additive predictor (see footnote). PREVENT TM risk categories stratified analysis to evaluate the effect of the CAD ECG-AI models on predicted ASCVD risk. Results: We included 21,193 patients, with mean ± SD age 51.6 ± 12.0, 54% women and 95% white. After 14.2±12.4 years follow-up, 1,351 (6.4%) developed ASCVD, and 2,808 (13.2%) had ASCVD-Plus. The risk of ASCVD and ASCVD-Plus increased with positivity for the CAD ECG-AI algorithms after adjustments for age, sex, and PREVENT TM equations factors, all p for trend <0.001 (data not shown). In additive analyses, the risk of ASCVD and ASCVD-Plus increased with increased CAD ECG-AI factors, all p for trend <0.001 ( Fig. A-B ). Furthermore, the CAD ECG-AI algorithm enhanced the predictive capabilities of PREVENT TM equations across most risk subgroups ( Fig. C-E ). Conclusions: The deep-learning CAD ECG-AI algorithms displayed an independent and additive association with long-term ASCVD and ASCVD-Plus events in the community and improved the PREVENT TM predicted ASCVD risk. The CAD ECG-AI algorithms could help identify individuals at risk in primary prevention of cardiovascular events.

Background: Patients with normal resting left ventricular ejection fraction (LVEF) without either coronary artery disease (CAD) or a hypertensive response can paradoxically develop reduced LVEF during exercise stress echocardiography (ESE). The clinical phenotype and outcomes of these patients is unknown. Hypothesis: Patients who paradoxically develop reduced LVEF during exercise may represent a sub-phenotype of heart failure with preserved LVEF (HFpEF). Aims: To describe the baseline characteristics and clinical outcomes of this unique patient population. Methods: Among all ESE performed between January 2003 and December 2022, patients without a hypertensive response to exercise and without CAD by angiogram within 90 days of ESE, who had a resting LVEF ≥50% with a ≥5% LVEF decrease during ESE were identified. All-cause mortality, HF hospitalization, and atrial fibrillation (AF) outcomes were assessed. Kaplan-Meier and Cox regression methods were used to analyze time-to-event outcomes. Results: Among 213,643 stress echocardiograms performed, 134 patients met eligibility criteria (Table 1). The mean age of the population was 66±10 years, 76% were women, and 16% had AF at baseline. Mean LVEF was 58±4% at rest and 43±4% at peak stress. Stress electrocardiogram met criteria for ischemia in 14%. The 10-year all-cause mortality risk was 12.9% (95% CI 5.5-20.3) with 10 (37%) of 27 deaths due to cancer (Figure 1, Panel A). The 10-year estimated risk of HF hospitalization was 17.6% (95% CI 9.0-26.2) (Figure 1, Panel B). Among 112 patients without AF at baseline, the 10-year risk of developing AF was 23.4% (95% CI 13.4-33.4). Conclusions: Patients with exercise-induced reduced LVEF in the absence of obstructive CAD have a high incidence of HF hospitalizations and AF. Given the preponderance of women, risk for AF, HF hospitalizations, and cancer-related deaths, the possibility that this condition is an early sub-phenotype of HFpEF should be investigated.

Background: Electrocardiography-based artificial intelligence (AI-ECG) validated models that detect cardiac disease are increasingly being applied in clinical practice. The utility of such tools to detect insidious cardiac disease in patients with normal baseline left ventricular ejection fraction (LVEF) who paradoxically develop reduced LVEF during exercise stress echocardiography (ESE), and do not have coronary artery disease (CAD) or hypertension, is unknown. Hypothesis: AI-ECG is useful to diagnose insidious cardiac disease and predict clinical outcomes in patients with exercise-induced cardiomyopathy. Aims: To assess the utility of AI ECG in patients with exercise-induced cardiomyopathy. Methods: Among all ESE performed between January 2003 and December 2022, patients without a hypertensive response to exercise and without CAD (confirmed by coronary angiography within 90 days after ESE), with resting LVEF ≥50% and a paradoxical ≥5% LVEF decrease during ESE were identified. A previously validated AI-ECG algorithm that predicts atrial fibrillation (AF), reduced LVEF, and cardiac amyloidosis (CA) was applied to the baseline ECG closest to the time of ESE. The predicted probability of AF, reduced LVEF, and CA if above the published thresholds, was determined. Results: There were 134 patients with exercise-induced cardiomyopathy who were identified. The mean age of this cohort was 66±10 years, 76% were women and 16% had AF at baseline. Mean LVEF was 58±4% at rest and 43±4% at peak stress. The median follow-up period was 6.8 years (IQR 3.0-12.2). Among patients without a baseline history of AF (n=112), AI-ECG identified 29% with a significant probability of AF, which was associated with a subsequent AF diagnosis at univariable analysis (HR 2.505, 95%CI 1.016-6.177, p=0.046). There were 10 patients with an AI-ECG prediction of reduced LVEF among whom 3 were subsequently hospitalized with HF. AI-ECG was positive for CA in 8 and 3 amongst these had subsequent HF hospitalizations (Table). Conclusions: Baseline AI-ECG may help predict subsequent AF and diagnose preclinical amyloid cardiomyopathy in patients who by resting echocardiography do not seem to have cardiac disease but with exercise develop reduced LVEF.

AbstractChronic lung disease (CLD) is the second leading cause of pulmonary hypertension (PH) and is associated with significant morbidity and mortality. Although PH associated with CLD (PH‐CLD) leads to impaired health‐related quality of life (HRQOL), there are no validated tools to assess HRQOL in PH‐CLD. The Pulmonary Arterial Hypertension–Symptoms and Impact Questionnaire (PAH‐SYMPACT) is an HRQOL instrument aimed at assessing the symptoms and impact of PH on overall function and well‐being. We performed a single‐center prospective cohort study using PAH‐SYMPACT scores to compare symptoms, exercise capacity and HRQOL in patients with PAH and PH‐CLD. One hundred and twenty‐five patients (99 patients with idiopathic/heritable PAH and 26 with PH‐CLD) completed the PAH‐SYMPACT questionnaire which consists of 22 questions that assess HRQOL across four domains: cardiopulmonary (CP) symptoms, cardiovascular (CV) symptoms, physical impact (PI), and cognitive/emotional (CE) impact. Higher scores indicate worse HRQOL. We compared patients with PAH and PH‐CLD using a Wilcoxon rank sum or chi‐squared test as appropriate. Multivariate linear regression analysis was used to assess the relationship between PH classification and SYMPACT scores. Compared to PAH, patients with PH‐CLD were older, more likely to use oxygen and had worse functional class and exercise capacity. While there was no significant difference between the two groups in CP, CV, or CE domain scores, patients with PH‐CLD had significantly worse PI scores by univariate (1.79 vs. 1.13, p < 0.001) and multivariate analysis (1.61 vs. 1.17, p = 0.02) and overall worse SYMPACT scores (1.19 vs. 0.91, p = 0.03). In conclusion, patients with PH‐CLD have worse HRQOL as assessed by the PAH‐SYMPACT questionnaire versus patients with PAH. Although PAH‐SYMPACT has not been validated in PH‐CLD, the results of this study can guide clinicians in understanding the symptoms and impact of PH‐CLD relative to PAH.

Background: Atrial fibrillation (a-fib) occurs in up to 40% of patients after noncardiac surgery and is associated with increased risk of adverse outcomes. Practice guidelines recommend stress testing, including stress echocardiography (SE) to assess for coronary artery disease in selected high-risk patients prior to noncardiac surgery. The utility of using SE findings for predicting postoperative a-fib is unknown. Hypothesis: Myocardial ischemia can contribute to arrhythmogenic potential; other echocardiographic variables are likely associated with a-fib. Findings on SE may identify patients at risk for a-fib after noncardiac surgery. Aims: To determine if certain SE findings predict a-fib following noncardiac surgery. Methods: The study included patients undergoing pharmacological and exercise SE before noncardiac surgery. The primary outcome was a-fib/flutter within 30 days after surgery. Multivariate analysis was adjusted for age, cardiovascular comorbidities, and markers of surgical risk. Results: This analysis included 2,376 patients (mean age 68 +/- 7 years; 60% male); 1,986 patients had no known history of a-fib/flutter. Of these, 48 (2.4%) developed postoperative a-fib/flutter. In multivariate analysis, SE variables associated with development of new a-fib/flutter included left ventricular dilation (OR 2.11, p=0.049), increased left atrial volume (OR 1.19, p=0.045), and increased left ventricular mass index (OR 1.13, p=0.043) on rest images. The presence of ischemia or infarct on stress echocardiogram was not associated with postoperative a-fib/flutter. A total of 390 patients had a history of paroxysmal a-fib/flutter and were in sinus rhythm at the time of surgery. Of these, 90 (23%) experienced recurrent a-fib/flutter within 30 days of surgery. Multivariate analysis did not reveal any SE variables significantly associated with recurrence, including presence of myocardial ischemia or infarct at the time of SE. Conclusions: All SE variables associated with postoperative a-fib following noncardiac surgery are available from resting images alone. Therefore, SE that includes comprehensive resting images may offer benefit in the pre-operative evaluation of selected patients.

Abstract Background Atherosclerotic cardiovascular disease (ASCVD), of which coronary artery disease (CAD) is the primary manifestation, is the leading cause of death worldwide. The AHA/ACC Pooled Cohort Equation (PCE) facilitates risk stratification and initiation of primary prevention of ASCVD, however their performance is suboptimal. Purpose We develop an artificial intelligence-based electrocardiogram (ECG) analysis (AI-ECG) algorithm to detect different stages of CAD and assessed the value of AI-ECG over PCE to predict incident myocardial infarction (MI) and survival over 3-years. Methods Using electronic health record data from approximately 7 million patients seen at over 70 hospitals and clinics across 5 states in the USA, we created independent AI-ECG models to identify elevated coronary artery calcium (CAC) by computer tomography, the presence of obstructive CAD by stress test or angiography, and left ventricular regional akinesis by echocardiogram, presumed to represent prior MI. Deep structured data, AI-enabled natural language processing of unstructured data were used to ensure cohort, and physician review of labeling were used to ensure cohort accuracy. These models were ensembled into a single model that was used to assess the utility of AI-ECG for predicting new occurrence MI in PCE- and age-stratified cohorts of patient with no prior history of ASCVD, over a 3-year timeframe using Cox’s proportional hazards analysis. Results A total of 6,080 patients (15,192 ECGs) were labeled positive for CAC ≥ 300, 12,561 patients (18,915 ECGs) positive for obstructive CAD, and 10,767 patients (23,870 ECGs) positive for regional akinesis. AI-ECG for identifying elevated CACS, obstructive CAD, and regional akinesis achieved AUROCs of 0.86, 0.80, and 0.93, respectively. All AI-ECG models identified increased 3-year risk for MI and death in AI-ECG positive versus AI-ECG negative patients and subgroups with 2 or 3 positive results had stepwise increased risk (Figure A). In a 3-year survival analysis of all-comer patients, the hazard ratio for those that tested positive on 1, 2, or 3 versus 0 positive disease-specific models were 1.61 (1.53-1.69). 2.40 (2.27-2.55), 3.42 (3.21-3.65), respectively. Risk stratification was also observed in cohorts without prior ASCVD stratified by low and high PCE-derived 10-year ASCVD risk (Figure B) and in cohorts stratified by age, including under the age of 40. Conclusions AI-ECG has potential to address unmet need for accessible risk stratification in patients in whom PCE under, over, or insufficiently estimate ASCVD risk, and in whom risk assessment over periods of time shorter than 10 years is desired.

Objective. To determine the prevalence of monoclonal gammopathy of undetermined significance (MGUS) in patients with PH as well as precapillary PH. Methods. Olmsted County residents with PH, diagnosed between 1/1/1995 and 9/30/2017, were identified, and age and sex were matched to a normal control group. The PH group and normal control group were then cross-referenced with the Mayo Clinic MGUS database. Charts were reviewed to verify MGUS and PH. Heart catheterization data were then analyzed in these patients for reference to the gold standard for diagnosis. Results. There were 3419 patients diagnosed with PH by echocardiography between 1995 and 2017 in Olmsted County that met the criteria of our study. When the PH group (N = 3313) was matched to a normal control group (3313), a diagnosis of MGUS was significantly associated with PH 10.2% (OR = l.84 [95% CI 1.5–2.2], p < 0.001 ), compared with controls 5.8% based on echo diagnosis. Using heart catheterization data (484 patients), a diagnosis of MGUS was associated with PH 13.0% (OR = 3.94 [95% CI 2.28–6.82], p < 0.001 ). For pulmonary artery hypertension (N = 222), a diagnosis of MGUS was associated with PH at similar 12.2% (OR = 4.50 [95%CI 1.86–10.90], p < 0.001 . Conclusions. There is a higher prevalence of MGUS in patients with PH and precapillary PH compared with normal controls. This association cannot be explained fully by other underlying diagnoses associated with PH. Assessing for this in patients with PH of unclear etiology may be reasonable in the workup of patients found to have PH.

AbstractPulmonary arterial hypertension (PAH) is a progressive pulmonary vascular disease that negatively impacts health‐related quality of life (HRQOL). The PAH‐symptoms and impact (PAH‐SYMPACT) questionnaire is a validated disease‐specific patient‐reported outcome (PRO) instrument that assesses a patient's symptoms and the impact of PAH and its treatment on well‐being. We performed a single‐center prospective cohort study of patients with PAH to determine the feasibility of assessing PROs in clinical practice and to determine the association between PAH‐SYMPACT domains and clinical characteristics and outcomes. One hundred and ten patients completed the 1‐day version of the PAH‐SYMPACT questionnaire which consists of 22 Likert‐scale questions that assess HRQOL across four domains: cardiopulmonary (CP) symptoms, cardiovascular (CV) symptoms, physical impact (PI), and cognitive and emotional (CE) impact. Higher scores indicate worse HRQOL. Patients were predominantly female (n = 86, 78%) with a mean age of 57.8 ± 16.2 years. While several patient characteristics were associated with CP and PI domains, few were associated with CV and CE domains. PI and CE impact scores were associated with recent hospitalizations and mortality and CE impact score was independently associated with an increased risk of death after adjustment for disease severity (hazard ratio: 3.29, 95% confidence interval: 1.56–6.91, p = 0.002). In conclusion, the assessment of PROs in clinical practice using the PAH‐SYMPACT questionnaire is both feasible and valuable. PAH‐SYMPACT scores have independent prognostic value and are not adequately reflected by traditional measures of disease severity. These findings underscore the importance of assessing HRQOL in clinical practice.