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Importance Timely identification of aortic stenosis (AS) is essential for appropriate clinical management, yet screening remains limited by dependence on comprehensive echocardiography and trained imaging personnel. Objective To develop and validate a deep learning algorithm for detection of moderate or greater AS and prospectively evaluate its performance using artificial intelligence (AI)–guided focused cardiac ultrasound (FoCUS) acquired by novice operators. Design, Setting, and Participants This diagnostic study included retrospective algorithm development and validation and prospective evaluation of AI-guided FoCUS across Mayo Clinic sites in the Midwest, Arizona, and Florida. The model was developed using 6753 patients and evaluated in internal validation (n = 852), internal test (n = 844), and validation (n = 1912) cohorts. Performance was assessed on FoCUS acquired by experienced sonographers (n = 602) and prospectively by novice operators (n = 1302). The retrospective model development and validation cohorts comprised studies performed from January 2005 through September 2022. Prospective study was conducted in 2 enrollment periods from June to August 2024 and from June to September 2025. Participants from both periods were combined to comprise the final prospective cohort. Exposure AI-guided FoCUS acquisition and automated deep learning–based assessment for detection of moderate or greater AS. Main Outcomes and Measures Detection of moderate or greater AS. Performance was assessed using area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, and positive predictive value. Results The model demonstrated excellent discrimination in the internal test cohort (AUROC, 0.99; 95% CI, 0.98-1.00) and geographically distinct validation cohorts in Arizona (AUROC, 0.99; 95% CI, 0.97-1.00) and Florida (AUROC, 0.99; 95% CI, 0.96-1.00). Among FoCUS examinations acquired by experienced sonographers, sensitivity was 95% (95% CI, 82-99) and specificity was 97% (95% CI, 95-98). In the prospective novice-operator cohort, 1258 of 1302 examinations (96.6%) were suitable for automated analysis. Sensitivity was 93% (95% CI, 82-99) and specificity was 96% (95% CI, 95-97). Expert review of AI-positive and uninterpretable examinations increased the positive predictive value from 49.4% to 91.1%, with sensitivity of 85.4%. Conclusions and Relevance A deep learning algorithm accurately detected moderate or greater AS across validation cohorts in this study. In prospective evaluation, novice operators were able to acquire AI-guided FoCUS examinations that enabled accurate detection of moderate or greater AS. These findings support a scalable strategy that may expand access to AS detection in settings with limited echocardiography resources.

Abstract Background Lipomatous atrial septal hypertrophy (LASH) is generally viewed as a benign anatomical finding. Although extracardiac fat depots have been linked to adverse cardiometabolic outcomes, the prognostic implications of LASH have not been well defined. Purpose To evaluate whether LASH thickness is independently associated with long-term all-cause mortality. Methods We reviewed adult patients who underwent diagnostic transesophageal echocardiography at our Clinic sites from 2010–2017. LASH was identified by a dumbbell-shaped interatrial septum with maximal thickening ≥5 mm in the bicaval midesophageal view. All-cause mortality through January 1, 2024, served as the primary endpoint. Septal thickness was evaluated using restricted cubic splines and Cox proportional hazards models adjusted for age, sex, body mass index, Charlson Comorbidity Index, and left ventricular ejection fraction. Results Among 1,397 patients (mean age 69±12 years; 60% male), median LASH thickness was 14.2±4.4 mm. LASH severity was classified as mild (<15 mm; n=763), moderate (15-20 mm; n=522), or severe (>20 mm; n=112). During 7.5±4.0 years of follow-up, 586 deaths (42%) occurred. Mortality was higher with increasing septal thickness (37%, 46%, and 56%; p < 0.001). Each 1-mm increase in LASH-thickness was associated with higher adjusted mortality (adjusted-hazard-ratio 1.03; 95%-confidence-interval 1.01-1.05). Spline modeling suggested a risk increase beginning near 16 mm; thickness >16 mm carried a 26% higher mortality risk (adjusted-hazard-ratio 1.26; 95%-confidence-interval 1.06-1.50). Conclusions LASH was associated with long-term mortality independent of BMI and other clinical factors. Septal fat accumulation may provide a useful, noninvasive marker that complements broader cardiac risk assessment.Risk for all-cause mortality across LASHFor image description, please refer to the figure legend and surrounding text.

Abstract Introduction Transthoracic echocardiography (TTE) identifies a hypercontractile phenotype (HP) in chronic coronary syndromes (CCS), characterized by elevated resting left ventricular (LV) elastance (force = systolic blood pressure/end-systolic volume). To evaluate the prognostic significance and functional correlates of HP. Methods In a prospective multicentre study, 10 677 patients with CCS underwent resting TTE to assess LV ejection fraction (EF), stroke volume, and force by quantitative volumetric echocardiography. All patients were followed for the endpoint of all-cause mortality. In a subset of 5834 patients, stress echocardiography (exercise or dobutamine) was performed for LV contractile reserve and heart rate reserve. Results Patients were stratified into Force quintiles (Q1–Q5). Patients with hypercontractile phenotype exhibited lower stroke volume at rest (Q5 = 34.8 ± 12.3 vs Q1–Q4 = 57.4 ± 19.1 mL; P < .01) and higher EF at rest (Q5 = 64.8 ± 6.9% vs Q1–Q4 = 58.1 ± 8.7%, P < .01). During a median follow-up of 24 months (interquartile range = 12–40 months), 509 deaths occurred. The exposure-adjusted death rate was lowest in Q3 (3.53–4.51 mmHg/mL; 1.03 per 100 person/years) and higher in Q1 (≤2.62 mmHg/mL, 2.88), Q2 (2.63–3.52 mmHg/mL, 1.86), Q4 (4.52–6.11 mmHg/mL, 1.56), and Q5 (HP, >6.11 mmHg/mL; 1.88; P < .0001 vs Q1 and Q3). Multivariable analysis identified HP (Q5; HR 1.531 vs Q3, 95% CI 1.116–2.099; P = .006) and EF (HR 0.963, 95% CI 0.953–0.972; P < .0001) as independent predictors of death. During exercise or dobutamine stress, HP showed reduced LV contractile reserve (ΔEF: Q5 = 4.3 ± 9.4% vs Q1–Q4 = 7.0 ± 9.3%; P < .001) and blunted heart rate reserve (Q5 = 1.77 ± 0.33 vs Q1–Q4 = 1.85 ± 0.39; P < .01). All patients with force-based LV contractile reserve >4.1 (present in 185, 3.2% of the population) survived. Conclusion Patients with CCS with HP assessed by resting TTE demonstrate higher mortality and multilayered functional impairment, including reduced LV contractile and chronotropic reserves. Hypercontractile phenotype improved the prediction of mortality by EF. A ‘stronger’ heart is, in fact, functionally and prognostically weaker.

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

Abstract Aims Heart failure is prevalent; however, there is no cost-effective screening option. Against formal echocardiography, we assessed the diagnostic performance of focused cardiac ultrasound (FoCUS), performed by novice users guided and interpreted by artificial intelligence (AI) for the assessment of left ventricular ejection fraction (LVEF). Methods and results We prospectively enrolled 496 adults referred for diagnostic echocardiography. Novice operators (without clinical or imaging experience) underwent a 4-h imaging workshop, then performed FoCUS using a point-of-care device (Philips Lumify) utilizing real-time AI-image guidance (UltraSight). Images were assessed by AI-image interpretation software (Mayo Clinic), and two expert blinded echocardiologists. Median age was 67, 39% female, and body mass index range 17–56 kg/m2. Forty-one subjects (8.3%) exhibited moderate or greater LV dilatation and 28 (5.6%) had LVEF <40%. The median scan time was 4 min (IQR 3–5). Adequate views were achieved in 95.0% and 97.4% of subjects for AI interpretation and expert analysis, respectively. A two-step diagnostic screening process for low EF in which only abnormal AI reads or uninterpretable scans were reviewed by experts (15.1%) had a sensitivity 96.2%, specificity 95.4%, PPV 54.3%, and NPV 99.8%. A subsequent prospective validation study of 344 subjects demonstrated similar results (sensitivity 100%, specificity 99.4%). Conclusion In this early feasibility investigation, AI-guidance technology embedded on a handheld device, enabled novice users without clinical or imaging experience to acquire sufficient quality images to accurately assess LVEF with minimal training. The technology evaluated in this study may hold promise for AI-guidance and interpretation to facilitate low-cost screening for cardiac dysfunction.

Abstract Aims Early detection of structural heart disease (SHD) improves patient outcomes. However, population-based screening is not recommended due to the lack of accurate and cost-effective tools. We evaluated the costs of artificial intelligence–enabled electrocardiogram (AI-ECG) alone vs. AI-ECG followed by handheld cardiac ultrasound (HCU) for SHD screening. Methods and results We performed a model-based cost analysis using data from 286 adult patients who underwent ECG and same-day HCU performed by a novice operator. Transthoracic echocardiogram (TTE) was the reference standard. We compared two screening strategies: (i) AI-ECG alone and (ii) a stepwise approach (AI-ECG followed by HCU). We assessed costs per diagnosis of aortic stenosis (AS), increased left ventricular wall thickness (ILVWT), and left ventricular systolic dysfunction (LVSD). Sensitivity analyses were conducted for varying disease prevalence. The stepwise approach decreased the cost per diagnosis of AS from $6386 (AI-ECG alone) to $2746 (57.0% savings), ILVWT from $4448 to $2895 (34.9% savings), and LVSD from $1469 to $1296 (11.8% savings). Overall, the cost per diagnosis for all SHDs combined decreased from $1940 to $1570 (19.1% savings). Sensitivity analysis demonstrated that cost savings were inversely proportional to disease prevalence. Nevertheless, stepwise screening remained cost-saving compared with AI-ECG alone until prevalence exceeded ∼55.9% for AS, 28.9% for ILVWT, 20.7% for LVSD, and 40.8% for all SHDs combined. Conclusion A stepwise screening strategy incorporating HCU after a positive AI-ECG reduces the immediate costs of SHD detection by minimizing unnecessary TTEs. This approach may enhance the feasibility of population-based SHD screening, particularly in lower-prevalence settings.

Abstract Echocardiography is the most widely used cardiac imaging modality, yet artificial intelligence-enabled interpretation remains limited by the inability of existing models to integrate visual assessment, quantitative measurement, and clinical reasoning within a unified framework. Here we present EchoAtlas, the first autoregressive vision-language model developed for echocardiographic interpretation. Trained on over 12.9 million question-answer pairs derived from approximately 2 million echocardiogram videos, EchoAtlas achieves 0.966 accuracy on multiple-choice questions in our internal test set and establishes a new state-of-the-art on the public MIMIC-EchoQA benchmark (0.699 vs. 0.508 previously). EchoAtlas also provides accurate quantitative measurements, segment-level regional wall motion assessment, longitudinal comparison, and diagnostic reasoning across diverse question formats — capabilities not previously demonstrated in this domain. These results highlight the potential of autoregressive vision-language models as a foundation for interactive echocardiographic interpretation, representing an early step toward scalable, auditable artificial intelligence systems in cardiology practice.

Background Tricuspid regurgitation (TR) is a common valvular disorder that can affect patients’ quality of life and survival. The impact of TR etiology on overall survival and the associated risk factors in each subgroup are not well studied. Methods Multisite, retrospective study evaluating survival based on TR etiology using Kaplan–Meier and Cox regression models. Stepwise approach was used to define TR etiology (≥moderate): primary (PTR; presence of a primary valvular pathology), lead associated (LTR; lead interfering with closure), and secondary TR (STR) in the setting of left‐sided valvular disease, left ventricular disease, other causes of pulmonary hypertension, right ventricular disease, and atrial secondary TR. Results A total of 12 899 patients were included (3% PTR, 90% STR, 7% LTR). The mean age was 68±14, 73±13, and 71±13 years, respectively (44%–55% female patients). Adjusting for age, sex, and TR severity, patients with PTR had better overall survival (PTR: reference; STR hazard ratio [HR], 1.4 [1.2–1.6], LTR HR, 1.5 [1.1–1.7]). On multivariable Cox regression, the Tricuspid Regurgitation Impact on Outcomes score was associated with mortality across all groups ( P <0.05 for all). In addition, other risk factors associated with worse outcomes included pulmonary hypertension and ≥moderate right ventricular enlargement in PTR; left‐sided valvular disease, pulmonary hypertension, and ≥moderate right ventricular dysfunction in STR; and left ventricular disease and ≥moderate right ventricular dysfunction in LTR. Conclusions Patients with PTR have better survival than STR and LTR. Tricuspid Regurgitation Impact on Outcomes score was associated with mortality across etiologies, whereas other risk factors are etiology specific. Improved risk profiling may enhance patient selection, treatment decisions, and outcomes.

Abstract Background Exercise stress echocardiography (ESE) is recommended in patients with mitral regurgitation (MR) when resting echocardiographic findings and symptoms do not align. However, the role of MR assessment in patients undergoing ESE for any indication remains uncertain. Purpose This study aimed to investigate whether the assessment of MR during ESE provides prognostic value beyond the evaluation of inducible regional wall motion abnormalities (RWMA) in patients undergoing ESE for any clinical indication, including suspected coronary artery disease (CAD). Methods Data were analyzed from a large multicenter and multinational prospective study. Current analysis included patients who underwent ESE for any indication and had MR (from 0= absent, to 3= severe) assessed at rest and peak exercise with an integrated qualitative, semi-quantitative, and (in selected cases) quantitative assessment. The primary endpoint was all-cause mortality. Results Of 1,351 included patients (age 56±16 years; 66% women; 30% with known CAD). MR score increased during exercise (rest=0.74±0.7 vs. peak=0.89±0.8, p&lt;0.001), with severe MR present in 2% of patients at rest and 7% at peak ESE. Patients with severe MR during exercise (n=96, Group 1) compared to those with non-severe MR during exercise (n=1255, Group 2) showed more B-lines, lower left ventricular contractile and chronotropic reserve, and trend (p=0.09) to more frequent decrease in ejection fraction during ESE (Table 1). After a median follow-up of 2.1 years (IQR 1.0–6.9), 99 patients (7%) had died. Severe MR at rest was associated with increased mortality (log-rank p&lt;0.001). Patients with severe MR at peak ESE had a higher risk of death compared to the other patients (log-rank p 0.035) (Figure 1). After adjusting for age, CAD, and inducible RWMA, severe MR with exercise remained strongly associated with an increased risk of death (adjusted-HR: 2.2, 95%CI 1.3-3.8, p=0.002). Similar results were seen with further adjustment for rest MR severity (adjusted-HR:1.9, 95%CI 1.02-3.5, p=0.004). Conclusion Patients, with and without CAD, with severe MR during ESE have greater functional impairment expressed by more pulmonary congestion, blunted heart rate reserve, and lower left ventricular contractile reserve. The assessment of MR during ESE provides significant prognostic information, independent of resting RWMA or inducible ischemia. Incorporating MR evaluation into routine ESE protocols may improve clinical decision-making and patient outcomes.Fig 1 Survival by MR at peak exercise Table 1