Tsaban, Gal

Background Bicuspid aortic valve (BAV) is the most common congenital heart defect, associated with a lifetime morbidity burden exceeding 80% and an estimated 7% probability of transmission to first-degree relatives. Hence, echocardiographic screening of relatives is recommended. The aim of our study is to develop an artificial intelligence-enabled electrocardiogram (AI-ECG) model for the detection of BAV. Methods Between January 1, 1990, and June 30, 2023, the Mayo Clinic healthcare system database was searched for patients aged ≥18 years with echocardiograms performed for family history of BAV, abnormal auscultation, a BAV-related diagnosis, or to rule out the condition, and with an electrocardiogram (ECG) obtained within 6 months. Cases were patients with a confirmed BAV diagnosis, and controls were those with normal aortic valves. ECGs were randomly assigned into training (80%), internal validation (10%), and test (10%) groups. The AI-ECG model was built using Keras under TensorFlow. Results A total of 13,065 valid ECG–echocardiogram pairs were included (mean [SD] age, 58 [17] years; 44% women), with a median time of 1 day (interquartile range, 0–3 days) between studies. With a 45% BAV prevalence in the test group ( n = 1301), the area under the curve was 0.704, positive predictive value (PPV) 60.1%, and negative predictive value (NPV) 69.4%. At a prevalence of 7%, the NPV increased to 96.1%, but the PPV decreased to 12.2%. Conclusions In this retrospective cohort and using the current AI-ECG model, BAV detection showed modest diagnostic performance and limited standalone clinical utility, suggesting that ECG-only screening for BAV may require additional modalities or more refined phenotyping.

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 Aims Significant secondary tricuspid regurgitation (STR) is associated with worse morbidity and mortality. To-date, evidence on isolated tricuspid valve surgery in STR remains controversial with uncertainty regarding management strategies. We aimed to investigate sex differences in the association of isolated tricuspid valve surgical repair/replacement (TVR) with outcomes of mortality and heart failure (HF) hospitalizations in STR. Methods and results Patients with severe STR without significant aortic valve or organic mitral valve disease, congenital heart disease, pacemakers, previous valve intervention, malignancy, or end-stage kidney/liver disease were identified. Isolated TVR was studied as a time-dependent covariate in association with mortality and HF hospitalizations. Among 2,857 patients (median age 76 years; 62% women), 143 underwent isolated TVR (58% repair; 42% replacement; 64% with low TRI-SCORE risk category) at median 56 days. Over median 1.6 (IQR 0.2-5.2) years, 1,771 patients died and 1,259 had HF hospitalization. When adjusting to age, comorbidities, left/right ventricular function, pulmonary hypertension, hemoglobin level, and dyspnea, isolated TVR was associated with lower mortality in females (HR 0.54 95% CI 0.35-0.83) but not males (HR 1.29 95% CI 0.90-1.83), p-interaction<0.01; this remained the case when restricting to groups with normal left/right ventricular function. Isolated TVR was associated with HF hospitalization without interaction with sex (HR 0.68 95% CI 0.46-0.998), p-interaction=0.51. Conclusion Isolated TVR for STR was associated with decreased HF hospitalizations in both females and males but improved survival only in females. Future studies are needed to understand differences in the natural history of TR between sexes and stratify outcomes after TR intervention by sex.

Abstract Inflammation is a key driver of coronary syndromes across the continuum from chronic coronary syndromes to acute coronary syndromes. It contributes to atherogenesis, plaque destabilization, and ischaemic events and influences myocardial injury, repair, and remodelling. Inflammatory activity is further amplified by metabolic and autoimmune comorbidities such as diabetes, obesity, and connective tissue diseases, with obesity representing a major driver of chronic low-grade inflammation through adipose tissue dysfunction and cytokine activation, while psychosocial stress and environmental exposures including air pollution serve as additional triggers. Genetic variations, including human leucocyte antigen genotypes, modulate individual susceptibility to vascular inflammation and myocardial injury. This expert consensus from the European Society of Cardiology’s Association for Acute CardioVascular Care summarizes the current understanding of inflammation across the full spectrum of coronary syndromes, including underlying mechanisms, relevant biomarkers, and imaging approaches that characterize vascular inflammation, and integrates insights from experimental and clinical studies. Importantly, this document provides explicit consensus-based guidance on when, how, and in whom inflammation should be assessed and treated in routine clinical practice. While the causal association between inflammation and atherosclerotic cardiovascular disease is well established, pharmacological modification of inflammation has produced conflicting evidence. Some agents, such as low-dose colchicine, have demonstrated modest reductions in cardiovascular events, although results across trials have been inconsistent. Other anti-inflammatory therapies have not shown clinical benefit, whereas selective cytokine inhibition with canakinumab has reduced cardiovascular risk at the cost of increased infection rates. The present manuscript also focuses on the evaluation of inflammatory activity in cardiovascular patients in routine clinical practice. It further discusses criteria for patient selection and clinical decision-making regarding the introduction of anti-inflammatory therapy in individuals with established atherosclerotic cardiovascular disease.

Abstract Aims Prognosis in tricuspid regurgitation (TR) is shaped by complex clinical and haemodynamic interactions, complicating risk stratification. We aimed to use explainable artificial intelligence (EAI) to model these relationships and identify key predictors of outcomes. Methods and results We analysed long-term all-cause mortality in a large prospective registry of patients with native TR across the full severity spectrum. Patients underwent detailed clinical evaluation and quantitative Doppler-echocardiography. Supervised EAI models were applied to identify and rank predictors of mortality under medical therapy, capturing non-linear associations and interactions among baseline characteristics. Among 9761 patients [mean age 73 ± 15 years; 56.9% women; effective regurgitant orifice area (EROA) 40 (30–56) mm2; regurgitant volume 39 (29–53) mL/beat; 42% severe TR], 2676 deaths occurred during 3.8 ± 4.6 years of follow-up. EAI identified and ranked 20 prognostic determinants of survival. Haemodynamic measures, led by elevated pulmonary-artery systolic and right-atrial pressures, were strongest, followed by age and comorbidity. TR-related variables ranked next, led by right ventricular dysfunction and EROA, which outperformed integrative TR grade and TRIScore. Left-ventricular parameters were less influential. Non-linear effects, including those involving ejection fraction, and interactions such as higher EROA-related risk with elevated pulmonary pressures or lower TRIScore (both P < 0.03) were observed. Proportional hazards models confirmed EROA as the strongest TR severity marker (P < 0.0001), with excess mortality beginning at 40 mm2 and increasing further at 60 mm2. Conclusion EAI ranked mortality predictors in TR, highlighting patient factors, haemodynamics, TR-specific measures, and left-ventricular function. Among TR-specific measures, EROA and TRIScore were most prognostic. Recognizing their impact can refine risk stratification, guide intervention timing, and inform long-term management.

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 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 Background Acute heart failure (AHF) exhibits marked heterogeneity in diastolic hemodynamics, yet comprehensive echocardiographic assessment of diastolic function (DF) and filling pressure (FP) is often infeasible. We evaluated whether artificial intelligence–enabled electrocardiography (AI-ECG) could provide scalable DF grading and FP estimation in hospitalized AHF patients. Methods We retrospectively studied adults hospitalized for AHF across Mayo Clinic sites (2013–2023) who received ≥1 dose of intravenous loop diuretic and had paired 12-lead ECG and TTE. The previously validated AI-ECG DF model was applied without retraining to generate four DF grades and a continuous FP probability. Clinical outcomes were all-cause mortality and heart failure rehospitalization. Associations with clinical severity markers and echocardiographic indices were examined. Kaplan–Meier survival analysis and adjusted multivariable Cox proportional hazards models were performed. Exploratory analyses examine the kinetics of change in FP probability and impact on mortality. Results Among 11,513 patients (median age 75 years, 39% female), AI-ECG DF grading was feasible in 100%, whereas echocardiographic DF was indeterminate in 44% of clinically eligible patients. In 2,582 patients with determinate echocardiographic DF, AI-ECG FP probability discriminated TTE Grade 2–3 dysfunction with AUC 0.85 (95% CI 0.83 – 0.86). Higher AI-ECG DF grades were associated with higher comorbidity burden, worse NYHA class, elevated NT-proBNP, higher MAGGIC scores, elevated PCWP, and more advanced structural remodeling. After multivariable adjustment, AI-ECG DF remained independently associated with mortality (hazard ratio [HR] 1.25, 95% CI 1.16–1.35 for Grade 2; HR 1.44, 95% CI 1.33–1.56 for Grade 3 versus Normal/Grade 1). Combining AI-ECG DF with MAGGIC scores yielded ordered risk gradients, with highest mortality in patients with both high MAGGIC and Grade 2–3 DF. Among patients with serial ECGs, improvement in FP probability was independently associated with lower mortality (HR 0.85, 95% CI 0.79–0.91), whereas worsening did not show a consistent adverse gradient beyond baseline DF. Conclusions In a large, geographically diverse AHF cohort, AI-ECG DF grading was universally feasible, correlated with established hemodynamic severity markers, and provided independent prognostic information beyond established risk factors, supporting its role as a pragmatic, scalable diastolic biomarker in AHF. CLINICAL PERSPECTIVE What Is New? In 11,513 hospitalized acute heart failure (HF) patients, artificial intelligence-enabled electrocardiography provided diastolic function grading in 100% of patients from a single 12-lead ECG without requiring additional clinical variables, compared with 56% feasibility for guideline-based echocardiography grading. AI-ECG diastolic function grades correlated with established marker of severity (NYHA functional class, NT-proBNP, MAGGIC risk scores, and pulmonary capillary wedge pressure) and remained independently associated with both mortality and HF rehospitalization after multivariable adjustment. Serial AI-ECG measurements identified post-discharge filling pressure trajectories, with improvement independently associated with 15% lower mortality, a first demonstration that longitudinal ECG assessment can track post-discharge hemodynamic recovery. What Are the Clinical Implications? AI-ECG transforms the universally obtained 12-lead ECG into an actionable hemodynamic biomarker that addresses the critical gap when echocardiographic diastolic function assessment is indeterminate or unavailable in acute HF patients. Despite markedly different hemodynamic severity and long-term outcomes across AI-ECG diastolic function grades, hospitalization length of stay did not differ, suggesting advanced diastolic dysfunction represents occult risk not easily recognized during routine acute care and highlighting the need for improved post-discharge risk stratification. The continuous filling pressure probability metric enables longitudinal monitoring of post-discharge hemodynamic status using serial routine ECGs, potentially identifying patients requiring intensified follow-up or specialist referral.