Medina-Inojosa, J R

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 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

Abstract Background The concept of ideal cardiovascular health (CVH) was developed by the American Heart Association (AHA) as a composite of seven health factors and behaviors labeled as the Life’s Simple 7 (LS7). Ideal CVH has been associated with complex measures of cardiovascular aging. We hypothesized that physiologic aging, as determined by a simple, routinely used tool such as the electrocardiogram (ECG) enabled by an artificial intelligence algorithm to predict an individual’s physiologic age (ECG age), will be associated with poor CVH as defined by an adapted version of the AHA LS7. Methods We conducted an observational retrospective study of consecutive patients aged ≥18 years who sought care between June 2019 and March 2022, self-reported behavioral history through a validated intake questionnaire in clinical outpatient visits, had a clinically indicated ECG within one year, and provided research authorization. We applied a previously developed AI-ECG convolutional neural network to derive the ECG-Age. We excluded patients with missing data, those with body mass index (BMI) <18.5 kg/m2, previous history of left-bundle branch block, atherosclerotic cardiovascular disease events, atrial fibrillation, heart failure, and non-skin cancer. An adapted version of the LS7 was used to define CVH, which includes anthropometrics (e.g., BMI), behavioral history (e.g., smoking, physical activity, and diet), vital signs (e.g., blood pressure), and laboratory values (e.g., total cholesterol, and fasting glucose). CVH was stratified as ideal, intermediate, and poor if the LS7 score had values of 9-14, 7-8, and 0-6, respectively, constructed by adding 2,1 or 0 points to each metric that met each category criteria, respectively. Accelerated aging was defined as having a Delta-Age ≥1SD above the mean, whereas Delta-age was defined as ECG minus chronological age. Logistic regression models tested the association between CVH categories and positive Delta-Age while adjusting for chronological age and sex. Results We included 114,214 individuals (53.3% females, 86.9% Caucasian). Mean chronological age was 57.7±14 years and mean AI-ECG- Age was 58 ± 12.44 years (R2 0.80, p-value <0.001). Mean Delta-Age was 0.28 ±8.8 years. LS7 average score was 8.3 ± 2.23; 46.8% had ideal, 31.5% intermediate, and 21.7% had poor CVH (Figure A). Compared to those with ideal CVH, those with poor CVH were more than 50% more likely to have Delta-Age ≥1SD (OR 1.61, CI 95% 1.54-1.69, p-value: <0.001), independent of chronological age and sex. (Figure B). Conclusion Poor CVH, defined by the AHA LS7, is linked with biological aging as determined by AI-enabled ECG, highlighting the importance of promoting CVH in adults as a strategy to decelerate biological aging and reduce its associated mortality.