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.

BACKGROUND:The natural history of asymptomatic moderate or severe aortic regurgitation (AR) remains uncertain, with conflicting reports about its progression and surgical timing. We aimed to quantify adverse outcomes under conservative management and evaluate the association of aortic valve replacement/repair (AVR) with mortality. METHODS:Systematic searches (inception-July 2025) identified cohort studies of asymptomatic moderate/severe AR. Random-effects models estimated pooled incidence rates of adverse events; fixed-effects models were used for hazard ratios (HRs) of AVR vs conservative management. RESULTS:Twenty-seven studies (4720 patients; mean age 49 years; mean follow-up 3.9 years) were included. Pooled incidence rates per 100 person-years were 1.75 (95% CI 1.27 to 2.41) for all-cause mortality, 1.29 for cardiac death, 0.29 for sudden death, 4.30 for new symptoms and 7.01 for AVR. Asymptomatic low left ventricular ejection fraction occurred in only 0.9 per 100 person-years. Mortality rates were more than double those of the general population across age groups-2.45 (1.90 to 3.18) per 100 person-years for cohorts with mean age ≥50 years versus 0.59 (0.29 to 1.21) for younger cohorts. Early AVR was associated with lower mortality (pooled HR 0.33; 95% CI 0.30 to 0.37). CONCLUSION:Asymptomatic moderate/severe AR carries significant excess mortality irrespective of age, contradicting its historically benign reputation. Given the rarity of asymptomatic LV dysfunction, earlier intervention guided by more sensitive markers of LV damage may improve outcomes, although heterogeneity and study quality warrant cautious interpretation. PROSPERO REGISTRATION NUMBER:CRD42024522683.

Randomized controlled trials (RCTs) provide high internal validity but often rely on restrictive eligibility criteria that limit generalizability and complicate real-world trial emulation. We propose AERO (AI Agent for Adaptive Eligibility Refinement and Optimization), an agentic framework that systematically adapts clinical trial eligibility criteria for application to electronic health record data. AERO integrates external clinical knowledge sources and large language model-based reasoning to classify criteria as strict inclusion, safety exclusion, confounder, or operational artifact. We evaluated AERO by emulating the WARCEF trial using Mayo Clinic Platform data restricted to the pre-trial completion period. Emulation with optimized criteria yielded a hazard ratio of 1.561 (p = 0.0605), consistent with the original neutral trial finding (HR = 1.01, p = 0.91). An ablation analysis demonstrated that eligibility handling decisions materially influence observed treatment effects. These results highlight the importance of systematic, knowledge-informed eligibility refinement in real-world evidence generation. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement The funding information is not available at this stage. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethics committee/IRB of Mayo Clinic gave ethical approval for this work I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are not available publicly. They are Mayo's internal data.

BACKGROUND:Guidelines acknowledge that discordant low-gradient (LG) aortic stenosis (AS) may be severe, but verifying this can be challenging. Right heart catheterization during exercise is considered the gold standard for evaluating ventricular hemodynamics. No invasive studies have compared the hemodynamic response of discordant LG and severe AS during exercise. The aim of this observational study was to describe exercise hemodynamics in patients with asymptomatic discordant AS and left ventricular ejection fraction ≥50%. METHODS:Patients with aortic valve area ≤1.5 cm2 underwent right heart catheterization at rest and during maximal exercise, measuring pulmonary capillary wedge pressure (PCWP), cardiac output (CO), and the PCWP/CO-slope. Patients were stratified into 3 groups: discordant LG AS (aortic valve area ≤1.0 cm2 and mean gradient <40 mm Hg); moderate AS (aortic valve area >1.0 cm2); and high-gradient (HG) severe AS (aortic valve area ≤1.0 cm2 and mean gradient ≥40 mm Hg). RESULTS:Among 86 patients, 17 (20%) had discordant LG, 49 (57%) moderate, and 20 (23%) HG severe AS. The median PCWP/CO-slope was significantly steeper in discordant LG (3.3 [interquartile range, 2.1-4.3] mm Hg/L/min) and HG severe AS (2.7 [1.9-3.4] mm Hg/L per minute) compared with moderate AS (1.9 [0.7-2.8] mm Hg/L per minute), P=0.004. In a regression model adjusted for age, sex, and rest PCWP, systemic arterial compliance and AS severity were significantly associated with the PCWP/CO-slope. Furthermore, patients with discordant LG AS had a leftward-upward shift in the PCWP/CO-curve. CONCLUSIONS:Discordant LG and HG severe AS had similar hemodynamic responses to exercise with steeper PCWP/CO-slope than in moderate AS, suggesting that discordant LG AS is a severe form of AS. In addition, the left upwards shift in PCWP/CO-curve for discordant LG compared with HG severe AS indicates that this group also has heart failure with preserved ejection fraction physiology. REGISTRATION:URL: https://www.clinicaltrials.gov; Unique identifier: NCT04913870 and NCT02395107.

Ischemic mitral regurgitation (IMR) is a common complication of coronary artery disease (CAD), often occurring after myocardial infarction. Even when moderate in severity, IMR is associated with adverse outcomes. To compare functional vulnerability and outcomes in patients with different degrees of non-severe IMR. This prospective international multicenter study included 126 patients with CAD (94 males; mean age 66 ± 9 years) from 13 institutions. Patients were divided into two groups: Group 1: Patients with moderate IMR, Group 2: Patients with absent-to-mild IMR. All patients underwent comprehensive exercise stress echocardiography (ESE). Despite similar left ventricular ejection fraction (LVEF) at rest (I = 47 ± 11

Carcinoid heart disease (CHD) is associated with advanced neuroendocrine tumor liver metastases (NETLM) and may preclude surgical cytoreduction. We assessed perioperative and long-term outcomes of hepatectomy in patients with CHD. We retrospectively analyzed 311 patients undergoing cytoreductive hepatectomy for intestinal NETLM: non-functional (n = 163), carcinoid syndrome (CS) without CHD (n = 110), and CHD (n = 38), including patients undergoing pre-hepatectomy valve replacement. CHD patients more frequently had >10 liver metastases (78%) and larger lesions (median 9.5 cm) and required major hepatectomy more often (58%). Major morbidity was higher in CHD (up to 47%), yet 90-day mortality remained low (≤4%). Median overall survival after hepatectomy was comparable across groups (12.5 vs. 9.1 vs. 11.4 years; p = .19), including matched analyses. With optimal cardiac management, cytoreductive hepatectomy in CHD is feasible and provides long-term survival comparable to patients without CHD.

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.

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.

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.

BACKGROUND:In patients with heart failure (HF) with reduced ejection fraction (HFrEF) from acquired heart disease, guideline-directed medical therapy (GDMT) for HF is associated with improved left ventricular (LV) ejection fraction (LVEF) (ie, HF with improved EF (HFimpEF), and which in turn is associated with improved survival. Similar data are lacking in patients with congenital heart disease (CHD). OBJECTIVES:The objective of the study was to describe the prevalence, correlates, and prognostic implications of HFimpEF in adults with CHD, biventricular physiology, and systemic LV. METHODS:Retrospective study of adults with CHD, biventricular physiology, and systemic LV presenting with HFrEF (2003-2023). Echocardiogram was performed at baseline and 1-year follow-up encounters. GDMT use was assessed at baseline and 1-year follow-up encounters using GDMT score. GDMT uptitration (ΔGDMT) was calculated as the difference between GDMT scores. HFimpEF was defined as HF with baseline LVEF ≤40%, and a subsequent absolute LVEF increase ≥10%, leading to LVEF >40% at follow-up echocardiogram. HFrEF relapse was defined as decline in absolute LVEF >10% leading to LVEF ≤40% in patients with HFimpEF. Cox regression analysis was used to assess the relationship between HFimpEF, GDMT score, and outcomes (death and cardiovascular events). RESULTS:Of the 327 patients (age 46 ± 16 years; LVEF 31% ± 6%), 63 (19%) had HFimpEF. GDMT use (higher baseline and ΔGDMT) was associated with great odds of HFimpEF. HFimpEF was associated with a 26% decrease in all-cause mortality, and 28% decrease in cardiovascular events on multivariable analysis compared to patients without improvement in LVEF. Of the 63 patients with HFimpEF, 27% had HFrEF relapse, and HFrEF relapse was associated with higher risk of cardiovascular events. CONCLUSIONS:These data support the use and optimization of GDMT in this subgroup of CHD patients, and the need for ongoing clinical and imaging surveillance.

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.