Citations

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.

Background Diastolic dysfunction is common in patients with aortic stenosis and may influence outcomes following surgical aortic valve replacement. We aimed to examine the association of preoperative artificial intelligence (AI)‐generated diastolic function grades with early and late outcomes following aortic valve replacement and how postoperative progression influence prognosis. Methods We identified 5503 patients undergoing aortic valve replacement between 2000 and 2023. Diastolic function was assessed using a validated deep‐learning AI model applied to 12‐lead ECGs done preoperatively and on postoperative follow‐up. Diastolic grades were classified by AI into Grades 1 to 3. Longitudinal trend analyses and multivariable regression models were used to assess study end points. Results Among 5503 patients (mean age 72.4±10.8 years; 39% female), higher AI ECG diastolic grades were associated with greater comorbidity burden, including diabetes, renal disease, and heart failure. AI ECG diastolic Grade 3 was independently associated with higher in‐hospital mortality (odds ratio, 2.5; P =0.007) and other complications. At 5‐year follow‐up, patients with Grade 3 showed the least improvement in diastolic function by both ECG and echocardiography. Grades 2 and 3 diastolic function at baseline were independently associated with increased late mortality (hazard ratio, 1.3 and 2.45, respectively; both P <0.001). Additionally, lack of improvement in AI ECG diastolic grade by 1 year was also independently associated with late mortality. Conclusions AI ECG–derived diastolic function grades strongly correlates with early complications and long‐term mortality and diastolic progression after aortic valve replacement. AI ECG provides a powerful, noninvasive tool for risk stratification and longitudinal monitoring of patients with aortic stenosis.

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.

Cardiovascular disease remains the world's leading cause of death, with a disproportionate burden falling on low- and middle-income countries. Emerging precision medicine approaches, such as artificial intelligence, multiomics, and digital diagnostics, offer transformational potential for prevention and management. Yet, without equity-centered policies, these innovations risk amplifying disparities. The World Heart Federation (WHF) proposes a framework premised on innovation as a public good and equity as a universal right. Through the World Heart Observatory, Digital Health Roadmap, and Emerging Leaders Program, the WHF operationalizes equity across governance, workforce, and care delivery. Drawing on exemplars from Africa, Latin America, and South/Southeast Asia, we outline implementation strategies and policy levers to translate science into population-level impact. Precision cardiovascular medicine can become the standard for all—but only when designed with equity from inception.

Abstract Introduction Atrial fibrillation (AF) is a major contributor to stroke risk, yet many cases remain undiagnosed due to its silent and paroxysmal nature. To address this, multiple risk scores have been developed to predict incident AF. This study aimed to evaluate the predictive accuracy of five widely used clinical AF prediction tools (i.e., CHARGE-AF, CHA2DS2-VASc, mC2HEST, HATCH, and HAVOC) among patients undergoing long-term continuous monitoring with implantable loop recorders (ILRs). Methods We conducted a retrospective cohort study across three major US academic medical centers, including patients with ILRs and no prior diagnosis or ECG evidence of AF. Clinical variables used to calculate each risk score were extracted from electronic health records. Discrimination was assessed using the area under the receiver operating characteristic curve (AUC). Kaplan-Meier survival analysis and Cox proportional hazards models were used to evaluate the association between each score’s risk categories and incident AF (defined as any episode lasting ≥6 minutes). Calibration was assessed using calibration plots and Brier scores. Results A total of 3,895 patients were included (mean age 61 ± 17 yrs.; 51% male). Incident AF was detected in 879 patients (22%) over a median follow up of 17 months (IQR 8-35). Table 1 presents baseline characteristics and device detected AF event data during follow up. CHARGE-AF had the highest AUC (0.68) but also the highest Brier score (Fig. 1). Failure curves showed clear risk stratification across all tools (Fig. 2). Calibration plots (Fig. 3) demonstrated modest agreement between predicted and observed risk across all models. Using CHARGE-AF as the reference, pairwise comparisons of Brier scores via the Wilcoxon signed-rank test showed lower calibration accuracy than CHA2DS2-VASc (p = 0.03), mC2HEST (p = 0.001), and HAVOC (p = 0.001), with no significant difference compared to HATCH (p = 0.15). Conclusions In patients undergoing continuous rhythm monitoring with ILRs, commonly used AF prediction scores demonstrated modest discriminatory ability and similar calibration performance. These findings suggest that existing risk models, which were largely developed in cohorts with clinically diagnosed AF, may not optimally translate to continuously monitored populations. Future work is needed to develop and validate AF prediction models specifically in cohorts undergoing long-term continuous cardiac monitoring.Figure 1Figure 2-3

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 (&lt;15 mm; n=763), moderate (15-20 mm; n=522), or severe (&gt;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 &lt; 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 &gt;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 Background The implications of poor sleep for cardiovascular disease risk are increasingly recognised. Although previous research has focused on clinical sleep disorders, emerging evidence highlights the relevance of subclinical sleep aspects for cardiovascular health. Sleep midpoint, an index of sleep timing and a proxy of circadian phase, has been shown to be associated with markers of cardiometabolic risk, including obesity, in the general population. Purpose We aimed to assess whether sleep midpoint was related to body composition measures in patients with coronary heart disease enrolled in cardiac rehabilitation (CR). Methods Study participants were recruited from the patient population referred to our CR center for acute coronary syndrome or coronary revascularisation. Shift work was exclusionary. Participants underwent sleep testing prior to entering the CR program, including completion of one week of wrist actigraphy and sleep log. Using actigraphy-derived sleep metrics, sleep midpoint was calculated as the middle time (hh:mm) between bedtime and wake up time for each individual night, and averaged across the monitoring week. Based on the median sleep midpoint of the sample, participants were categorised as having early (before 2:42) or late (after 2:42) sleep midpoint. Body composition and regional fat distribution were determined by whole-body dual-energy absorptiometry (DEXA). Between-group characteristics were compared using independent t-test, Mann-Whitney U test and Chi-square statistics as appropriate. Linear regression was used to test the association between sleep midpoint and body fat measures. Results Thirty-one participants (26 males, 64.7±12.5 years) completed both actigraphy and DEXA assessments. Demographics, lifestyle, and clinical characteristics were similar between patients with early sleep midpoint (n=15; sleep midpoint, hh:mm: 2:00, range 0:57 to 2:41) and patients with late sleep midpoint (n=16; sleep midpoint 3:18, range 2:42 to 4:17). Despite comparable body mass index between groups (p=0.21), patients with late sleep midpoint had significantly higher total body fat percentage (p=0.017), android fat percentage (p=0.015), and gynoid fat percentage (p=0.045). Late sleep midpoint remained significantly associated with adiposity measures in age- and sex-adjusted analysis (total body fat percentage, beta = 0.371, 95% CI 0.063 to 0.68; android fat percentage, beta = 0.394, 95% CI 0.078 to 0.71; gynoid fat percentage, beta = 0.301, 95% CI 0.007 to 0.596). Conclusions Late sleep midpoint was associated with excess adiposity in patients with coronary heart disease enrolled in CR. These findings support the predictive value of sleep timing for cardiometabolic risk and emphasise the importance of objective sleep assessment for secondary prevention.