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BACKGROUND: Myocardial infarction with nonobstructive coronary arteries (MINOCAs) and angina with nonobstructive coronary arteries (ANOCAs) present diagnostic and therapeutic challenges. We assessed the frequency of coronary microvascular disease (CMD) and its subtypes in MINOCA compared with ANOCA and evaluated long-term cardiovascular outcomes. METHODS: Patients with nonobstructive coronary arteries undergoing invasive coronary function testing from 2012 to 2022 were prospectively enrolled and retrospectively classified as MINOCA or ANOCA (reference group). Myocardial infarction due to atherothrombosis, myocarditis, and Takotsubo syndrome were excluded. Invasive provocation testing was performed. CMD was classified as endothelium-independent (coronary flow reserve <2.5 with adenosine) and endothelium-dependent (<50% increase in coronary blood flow with acetylcholine). Major adverse cardiovascular events (cardiac death, nonfatal myocardial infarction, heart failure hospitalization, urgent revascularization, stroke) were recorded over 8 years. RESULTS: Of 622 patients, 73 (12%) had MINOCA (69% female, median age 56 years) and 549 (88%) ANOCA (72% female, median age 53 years). CMD was common in both groups (71% versus 64%; P =0.271), while endothelium-independent CMD was more frequent in MINOCA (41% versus 21%; P <0.001). Over a median follow-up of 4.5 years, major adverse cardiovascular events occurred more often in MINOCA (40% versus 11%; adjusted hazard ratio, 2.4; P =0.001). CMD predicted higher major adverse cardiovascular events in both MINOCA (hazard ratio, 3.6 [95% CI, 1.1–12.0]; P =0.038) and ANOCA (hazard ratio, 7.9 [95% CI, 2.9–21.8]; P <0.001). CONCLUSIONS: CMD is common in both MINOCA and ANOCA. The higher prevalence of endothelium-independent CMD in MINOCA suggests a potential mechanistic role in infarction. Patients with MINOCA have a worse prognosis than ANOCA, particularly when CMD is present.

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 Vagally mediated syncope and functional bradyarrhythmias can cause recurrent symptoms, injury and a need for permanent pacing, yet standard therapies do not address the underlying autonomic reflex. Cardioneuroablation (CNA) targets this mechanism, but adoption has been limited by uncertainty about patient selection, procedural endpoints and durable outcomes. Methods We studied 95 consecutive patients with vagally mediated syncope (n=78) or functional bradyarrhythmia (n=17) who underwent CNA at a single center by a single lead operator under a uniform, prospectively maintained protocol. Extracardiac vagal stimulation (ECVS) was performed before and after ablation to confirm vagally mediated sinus or atrioventricular (AV) nodal responses and to verify their attenuation. Outcomes through 1 year included clinical recurrence, quality of life, pacing burden and pacemaker extraction. Results Before ablation, ECVS provoked sinus pauses in 95.8% of patients and AV block in 91.6%; after ablation, sinus pauses were abolished in every patient and residual AV block was present in 4.2%. One-year freedom from recurrent syncope and from bradyarrhythmia-related events was 93.8% and 94.1%. Syncope burden fell from a median of 2.7 episodes per year to none, and disease-specific quality of life improved substantially (P<0.001). Among patients with pre-existing devices, atrial pacing burden fell from a median of 22% to 0%. There were no strokes, deaths or myocardial infarctions. Conclusions In this single-center cohort, physiology-guided CNA was associated with durable symptom control, improved quality of life and reduced pacing across vagally mediated bradyarrhythmia syndromes. Multicenter controlled studies are needed to confirm these findings.

BACKGROUND: Randomized trials comparing radial and femoral access report access site crossover rates of 3% to 9% for radial access and 1% to 3% for femoral access. However, contemporary real-world crossover rates are not well characterized. The aim was to evaluate contemporary trends and predictors of vascular access site crossover during percutaneous coronary intervention (PCI). METHODS: We analyzed index PCI procedures recorded in the National Cardiovascular Data Registry CathPCI Registry from 2018 to 2024. Access site crossover was identified using documented access data, with radial-to-femoral crossover inferred when the final successful access site was femoral. Temporal trends and clinical predictors of crossover were examined. Operator experience was assessed using cumulative total and radial PCI volumes from 2011 to 2024 as captured in CathPCI Registry versions 4 and 5. RESULTS: Among 3 861 790 index PCIs, 192 902 (5.0%) involved access site crossover, with a modest increase over time. Radial-to-femoral crossover accounted for 81.2% of crossover events. Factors independently associated with crossover included older age, female sex, higher body mass index, and peripheral vascular disease. Operators with the lowest total PCI volume and the lowest radial PCI volume experienced the highest crossover rates. Notably, operators with low radial PCI volume demonstrated a substantial decline in radial-to-femoral crossover over the study period. CONCLUSIONS: In contemporary clinical practice, access site crossover rates are consistent with those reported in randomized trials. Although crossover occurs more frequently among lower-volume operators, rates decline with increasing operator experience, supporting the presence of a radial access learning curve and improvement in procedural performance over time.

ABSTRACT Ischemic events after contemporary percutaneous coronary intervention (PCI) are uncommon but carry high morbidity/mortality. Identifying patients at highest risk is critical to guide dual antiplatelet therapy (DAPT) intensity while minimizing bleeding. Current machine learning (ML) model‐developed risk scores do not incorporate pharmacogenetic data. To develop and externally validate ML models that integrate clinical, demographic, and CYP2C19 genetic information to predict 1‐year ischemic outcomes post‐PCI to refine clinical decision making. We analyzed 8317 patients from TAILOR‐PCI trial ( n = 4572) and the Precision PCI registry ( n = 3745). The outcome was a composite of cardiovascular death, myocardial infarction, stroke, and stent thrombosis at 1 year. Boruta feature selection identified 11 predictors. Multiple ML algorithms were trained in TAILOR‐PCI and validated in Precision PCI using cross‐validation and synthetic minority oversampling (SMOTE). Model performance was assessed by area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. The best external performance was achieved with a support vector machine (SVM, polynomial kernel) (AUC 0.667; sensitivity 0.871; specificity 0.282), while XGBoost provided a more balanced profile (AUC 0.619; sensitivity 0.442; specificity 0.688). Variable importance for the SVM polynomial model demonstrated that all 11 included Boruta feature selected predictors had relatively high importance (> 75). ML models trained on large clinical trial and real‐world registry datasets can help identify the small subset of patients at high ischemic risk after PCI. This distinction is of relevance because ischemic events are rare, and most patients may safely de‐escalate DAPT to reduce bleeding risk while maintaining ischemic protection through a multimodal approach to risk stratification. Trial Registration: TAILOR‐PCI URL: https://clinicaltrials.gov/ct2/show/NCT01742117

Abstract Asthma is a chronic inflammatory condition marked by episodic airway narrowing, with exercise-induced bronchoconstriction (EIB) a common trigger. Up to 70% of athletes experience EIB-related symptoms, yet current management relies on reactive treatment rather than early physiological monitoring. This study presents a proof-of-concept multimodal wearable platform integrating impedance pneumography (IPG), electrocardiography (ECG), and inertial measurement unit (IMU) sensing to detect early physiological signatures of EIB. Candidate biomarkers include elevated heart rate, increased respiratory rate, and reduced thoracic impedance amplitude. Real-time embedded algorithms perform on-device feature extraction and threshold-based decision logic without cloud dependence, while IMU data provide posture context for signal interpretation. The system was evaluated through subsystem validation and a resistive-breathing protocol simulating bronchoconstriction. Results demonstrate reliable detection of characteristic cardiorespiratory changes, establishing the feasibility of real-time wearable detection of early EIB markers and supporting future large-cohort validation and form-factor refinement.

TPS577 Background: Pembrolizumab (pembro) for one year following resection of localized high risk clear cell renal cell carcinoma (ccRCC) in a phase 3 trial resulted in improvement in relapse-free survival and overall survival (Choueiri, NEJM 2024). However, 20% of patients still experienced a relapse within 2 years with no biomarker yet identified to predict who will respond to or needs adjuvant therapy. Addition of a tyrosine kinase inhibitor (TKI) to immunotherapy (IO) is a standard of care in metastatic ccRCC, but to date no studies have addressed the addition of a TKI to IO in the adjuvant setting. Subsequently, phase 3 STRIKE! was developed to explore the benefit of 6 months of tivozanib (tivo) added to pembro vs pembro alone in the adjuvant setting for resected high risk ccRCC. Methods: Eligible patients have an ECOG performance status ≤2 and a histologically confirmed diagnosis of RCC with clear cell component with or without sarcomatoid features after complete resection of the primary tumor, with pathology revealing pT2 grade 4 disease or any grade ≥ T3 or TxN1. Patients are also eligible if they developed metastasis within a year after resection and then underwent resection, definitive radiation or ablation of solid, isolated, soft tissue metastases (excluding brain and bone lesions) with no evidence of active disease (M1NED). Patients will be randomized 1:1 to intravenous pembro for 48 weeks with or without the addition of tivo 1.34 mg by mouth daily D1-21 q28D for 6 months stratified by stage (T2T3 vs T4/N1 vs M1NED) . With no limit on dose holds or interruptions of tivo, dose reductions of tivo to 0.89 mg D1-21 q28D or tivo 0.89 mg every other day are allowed. No dose reductions of pembro are permitted. Following baseline imaging to confirm no disease, imaging is performed every 12 weeks for first year, every 16 weeks for second year, every 24 weeks for third year and then annually until 5 years after registration or progression. The primary endpoint will be disease-free survival (DFS) as assessed by investigator, with secondary endpoints being overall survival, safety and tolerability. The study will enroll 1040 patients to detect a minimum detectable hazard ratio of 0.67 (24-month DFS 84% in experimental arm) with 90% power. Quality of life analysis will compare global quality of life and fatigue between the two arms. Imaging and specimens will be banked for future research. The study opened to accrual in April 2025 and at this time 106 patients have enrolled. Clinical trial information: NCT06661720 .

Abstract Background Cardiovascular disease (CVD) is the leading cause of death globally for both men and women, yet women remain historically underrepresented in cardiovascular clinical trials (CV trials), despite facing a disproportionately high burden of morbidity and mortality in many forms of CVD. Purpose We aim to determine the representation of women across a broad range of CV trials. Methods The participation of women in CV trials registered on ClinicalTrials.gov from 2017 to 2023 was systematically determined through the extraction of publicly available information. Data was extracted to identify the country of study, disease type, trial size, clinical intervention and age of the participants. We calculated the proportion of women and the ratio of number of females to male participants (F:M ratio) for each trial. The female participation to prevalence ratio (PPR) was estimated for each trial based on the relative prevalence of the disease by sex in the specified region. Results We identified 1,079 registered CV trials, which included 1,396,104 participants, of whom 571,641 (41.0%) were women. The median F:M ratio was significantly lower for studies on arrhythmia (0.5), coronary heart disease (CHD) (0.39), acute coronary syndrome (ACS) (0.32) and heart failure (HF) (0.51), but was higher for obesity (1.44) and pulmonary hypertension (PH) (2.86) CV trials. F:M ratio was higher for trials on lifestyle intervention (1.51) than for drug trials. PPRs were low for clinical trials on CHD (0.66), ACS (0.79), and stroke (0.74). There was a trend to increasing PPR from 2017 to 2023, p=0.056. Conclusion Representation of women in CV trials varies by disease state, region, intervention, and sponsor type. Better understanding of reasons for these differences may enable more tailored interventions to improve representation of women globally and across therapeutic areas.Figure 1 Figure 2

Abstract Aims Coronary angiography might contain clinically relevant information, beyond its traditional role in delineating coronary artery disease. We sought to explore the role of deep learning (DL) in detecting right ventricular dysfunction from routinely acquired cine images during coronary angiography. Methods and results 3D-convolutional neural networks (CNN) were trained to identify right ventricle (RV) dysfunction from diagnostic angiography of the right coronary artery (RCA). The model’s input was two cine angiograms from LAO and RAO projections. The model’s output was normal vs. abnormal RV function. Ground truth on RV function was obtained from the nearest transthoracic echocardiogram to the coronary angiogram. Two analyses were performed to identify (i) any RV dysfunction (≥mild) and (ii) significant RV dysfunction (≥mild–moderate). A total of 10 336 coronary angiograms from 9849 patients (mean age 66, 36% women) were included. The cohort was split into training (70%), validation (15%), and testing (15%) sets. In the testing sets, the models attained an area under the ROC curve (AUC), sensitivity, and specificity of 0.82 (95% CI: 0.80–0.84), 0.75, and 0.74, for detecting any RV dysfunction, and 0.83 (95% CI: 0.80–0.86), 0.82, and 0.70 for detecting significant (≥mild–moderate) RV dysfunction. Combining an ECG-driven AI model with the angio-driven model improved the AUC values to 0.83 and 0.87 for detecting any or advanced RV dysfunction, respectively. Conclusion A novel deep learning algorithm yielded acceptable classification accuracy in detecting RV dysfunction from routine cine angiography of the RCA. The model’s performance improved further by adding ECG to its inputs.