Medina‐Inojosa, José R.

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 Cardiac rehabilitation (CR) leads to increased exercise capacity (VO 2 peak) and risk reduction of major adverse cardiovascular events (MACEs). Women exhibit blunted VO 2 peak improvements following CR. The purpose was to examine sex differences in the clinical predictors of VO 2 peak improvement and odds of MACEs following CR. Methods From 1999 to 2017, all consecutive patients undergoing cardiopulmonary exercise testing before and after CR were included. Sex differences in peak exercise metrics in response to CR were compared. Multiple logistic regression models were fit to assess clinical predictors of VO 2 peak improvement (defined as >0% from pre‐CR) and MACEs following CR for men and women. Results A total of 513 men and 158 women were included. Men had greater increases in VO 2 peak than women following CR ( P =0.001). For men, independent predictors of VO 2 peak improvement following CR included higher CR session attendance (odds ratio [OR], 1.04 [95% CI, 1.02–1.06]), peripheral artery disease (OR, 0.47 [95% CI, 0.25–0.86]), and pre‐CR VO 2 peak (OR, 0.98 [95% CI, 0.97–0.99]) (all, P <0.02). Independent predictors of lower odds of MACEs following CR in men included higher number of CR sessions (OR, 0.97 [95% CI, 0.95–0.99]), pre‐CR VO 2 peak (OR, 0.98 [95% CI, 0.97–0.99]), surgical CR indication (OR, 0.34 [95% CI, 0.18–0.68]), and no history of peripheral artery disease (OR, 2.94 [95% CI, 1.60–5.38]) (all, P <0.02). For women, the selected clinical characteristics were not statistically significant independent predictors of VO 2 peak improvement or odds of MACEs (except for diabetes) following CR. Conclusions These findings suggest that clinical predictors of VO 2 peak improvement and odds of MACEs following CR are sex specific.

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 Introduction Atrial fibrillation (AF) is a major contributor to stroke risk, yet many cases remain undiagnosed due to its silent nature. Deep learning-based electrocardiogram (DL-ECG) analysis can streamline AF risk stratification, providing a more reliable and accessible method to stratify risk using only a standard resting ECG. However, this tool has been validated only on AF detected clinically or with short ECG event monitors. Purpose This study aimed to validate the predictive accuracy of a DL-ECG model for identifying AF in normal sinus rhythm (NSR) ECGs among patients undergoing continuous cardiac monitoring with implantable loop recorders (ILRs). Methods We conducted a historical cohort study across three major U.S. academic medical centers, enrolling patients with an ILR and no prior diagnosis or documented ECG evidence of AF. Pre-implantation 10-second, 12-lead ECGs in NSR were analyzed using the DL-ECG model. Performance was evaluated using the area under the receiver operating characteristic curve (AUC) and compared with the CHARGE-AF score. Kaplan-Meier survival analysis and Cox proportional hazards models assessed the association between DL-ECG risk categories and incident AF, defined as any episode lasting ≥6 minutes. Results We included 2,523 patients (mean age 66 ± 17 years, 52% male). AF was detected in 658 (26%) over a median follow-up of 19 months (IQR 8-37), Table 1. The DL-ECG model classified 1,634 patients (65%) as at low-risk and 889 (35%) as at high-risk. AF developed in 242 (14.8%) of the low-risk group and 416 (46.8%) of the high-risk group, yielding an AUC of 0.73 (95% CI 0.70-0.75), surpassing the CHARGE-AF AUC of 0.69 (95% CI 0.66-0.71). A combined DL-ECG and CHARGE-AF model yielded an AUC of 0.74 (95% CI 0.72-0.76), Figure 1 presents the AUC, Net Reclassification Index, and DeLong test comparisons among the three models. High-risk patients had a significantly higher incidence of AF, which remained significant after adjusting for CHARGE-AF variables, Figure 2. AF incidence increased as DL-ECG quartiles increased, with 61% of patients at the top quartile developing AF during follow-up, Figure 3. False negatives (patients initially classified as low-risk but in whom later AF was detected) had a longer median time to AF detection (127 days [IQR 28-425] vs. 101 days [IQR 20-318] for true positives, p = 0.01), and where found to have at least one positive ECG during follow-up in 36%, compared to 32% of true negatives, indicating eventual risk reclassification. Conclusions Among patients undergoing ILR monitoring, the DL-ECG model demonstrated superior predictive performance compared to CHARGE-AF, offering a promising and accessible alternative for early detection of silent AF. This approach could aid early AF detection, particularly in resource-limited settings where access to specialized rhythm monitoring services is limited.Table 1 and Figure 1 Figure 2 and 3

Abstract Background Common genetic variation has been linked to an increased risk of developing dilated cardiomyopathy (DCM) in case-control studies. However, the phenotypic profile and biological mechanisms by which genetic background may predispose individuals to DCM and outcomes in the general population remain unclear. Aim The primary objectives of this study were twofold: (1) to validate a DCM polygenic score (PRS-1) based on a prior meta-analysis of case–control genetic association studies [Tadros R et al. Nat Genet (2021)]; and (2) To apply this PRS, along with two recently developed PRSs for DCM [PRS-2 from Zheng SL et al. Nat Genet (2024) and PRS-3 from Jurgens SJ et al. Nat Genet (2024)] to a general population that underwent comprehensive longitudinal phenotypic assessments and had long-term outcomes available. Methods While the PRS-1 was derived from only 13 SNPs, PRS-2 and PRS-3 were generated from 709,534 and 1,072,247 SNPs, respectively with significant overlap between them. These DCM-PRSs were applied to an unselected community-based cohort (n=1897) aged &gt;45 years, who underwent a physical examination, echocardiogram, and blood sample at baseline. Subsequent clinical outcomes were collected over a median 16 year follow-up (IQR: 15-17). Results No significant differences between the quartiles were present in terms of age, sex (48% male), and cardiovascular risk factors (CV-RF) (such as hypertension, diabetes, and coronary artery disease). However, linear regression models demonstrated a significant correlation between all DCM-PRSs and left ventricular (LV) parameters, including LV ejection fraction, LV end-systolic diameter, and LV end-diastolic diameter (Table). Multivariable regression analysis confirmed that these correlations were independent of age, sex, CV-RF, and population stratification (adjusted for the first 10 principal components [PCs]). During follow-up, 47 individuals developed DCM. A logistic regression analysis, using a 3:1 matched cohort of non-DCM vs. DCM individuals based on age and sex and adjusted for the same 10 PCs, found a significant correlation between DCM development and both PRS-1 [OR=2.27 (1.06–4.85), p=0.03] and PRS-3 [OR=2.63 (1.20–5.75), p=0.02], while PRS-2 did not show a significant association [OR=1.85 (0.84–4.06), p=0.12]. No association was found between the DCM-PRSs and heart failure (HF) biomarkers (aldosterone, natriuretic peptides, ST2, Galectin-3) or HF outcomes (all-cause mortality/HF hospitalization). Conclusion PRS-1 for DCM despite being derived from only 13 risk alleles, performs at least as well as the newer and more extensive PRSs, in its correlation with LV function, dimensions, and DCM development (except for PRS-2) in a general population. The absence of correlation between these DCM-PRSs and the renin-angiotensin-aldosterone system or natriuretic peptides suggests that genetic background influences LV traits through mechanisms independent of these biological pathways.

Abstract Background Atherosclerotic cardiovascular disease (ASCVD) develops over a prolonged latent period, progressing from subclinical ASCVD to major cardiovascular events due to plaque accumulation. Subclinical ASCVD is a valid subrogate to assess potential risk factors or ASCVD. Dietary patterns are known to contribute to or prevent ASCVD. Dairy consumption has been associated with lower rates of obesity, hypertension, and diabetes, while saturated fats, present in most dairy products, have been associated with ASCVD. Studies assessing the association between subclinical ASCVD and dairy consumption have shown mixed results but have not been systematically analyzed Purpose This study aimed to systematically review and synthesize the existing evidence on the association between dairy product consumption and subclinical ASCVD Methods The study protocol was registered in the International Prospective Register of Systematic Reviews and adhered to the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. We conducted a comprehensive search of PubMed, EMBASE, Scopus, Cochrane Central Register, Google Scholar, and Web of Science to identify experimental and observational studies that examined the association of dairy product consumption with measures of subclinical ASCVD assessed through coronary artery calcium (CAC) scanning, intima-media thickness (IMT), pulse pressure (PP) or pulse wave velocity (PWV), from inception to July 9, 2024. Titles and abstracts were screened in duplicate, followed by a full-text review and data abstraction from articles that met the study criteria. Dairy products were classified as: a) Dairy, according to the United States Department of Agriculture, encompassing milk, yogurt, cheese, lactose-free milk, and fortified soy milk/yogurt; and b) Dairy+, which further included higher fat content products such as cream cheese, sour cream, cream, and butter Results A total of 973 articles were identified; 22 met inclusion criteria representing 20 studies; being predominantly cross-sectional (55%) conducted in North America (50%); food frequency questionnaires were the most implemented tool for dietary assessment (86%), and 41% used PWV for subclinical ASCVD assessment; Fig. 1. Total Dairy and Dairy+ exhibited predominantly neutral effects with a trend toward a beneficial effect, similar to yogurt intake. Milk and cheese demonstrated contrasting effects, with studies reporting protective, neutral, and harmful associations, particularly for PWV and IMT. Limited data on CAC suggested no strong relationship with dairy consumption; Table 1 Conclusion Available evidence on the association between dairy consumption and subclinical ASCVD is complex with mixed effects across dairy products. No consistent evidence of harm or benefit was found; future research is needed to clarify the role of different dairy components in the development of subclinical ASCVD and incidence of cardiovascular eventsFig1.Summary of studies characteristics Table 1.Summary of effects

Background Mitral valve prolapse (MVP) can be associated with ventricular arrhythmias (VA), but little is known about the relationship between VA and exercise in these patients. The aim of this study was to assess the occurrence and severity of VA during exercise tests in patients with MVP, and to explore the association between VA during exercise and the occurrence of arrhythmic events during follow-up. Methods In this multicentre study, 375 patients with MVP (58 (48–69) years, 53% male) who underwent a clinically indicated exercise test were included. Severity of VA during exercise was defined as: (1) no VA, (2) minor VA (premature ventricular contractions ≥5% or non-sustained ventricular tachycardia (nsVT) <120 beats per minute) and (3) major VA (nsVTs ≥120 beats per minute). Results During exercise test, 242 (65%) patients showed no VA, 88 (24%) minor VA and 45 (12%) major VA. Patients with minor and major VAs showed more often bileaflet prolapse and mitral annular disjunction (MAD) than patients with no VA (p<0.001). Over a median follow-up of 101 months (IQR 58–138 months), 35 patients (9%) developed a severe arrhythmic event, defined as sustained VA or ventricular fibrillation, implantation of an implantable cardioverter-defibrillator and VA ablation. At the Kaplan-Meier curve analysis, patients with major VA showed the worst arrhythmic event-free survival (log-rank p<0.001). On multivariable analysis, left ventricular end-systolic diameter, MAD and VA severity during exercise were independently associated with this outcome. Conclusions In patients with MVP, the occurrence of VA during exercise is associated with more advanced mitral valve abnormalities, including MAD, and with higher rates of severe arrhythmic events during follow-up. Performing an exercise test, combined with the clinical and echocardiographic assessments, may therefore offer important complementary information useful for patient management.