Lopez-Jimenez, F

Abstract Aims Artificial intelligence electrocardiography (AI-ECG) algorithms are emerging tools for identifying individuals at risk of atrial fibrillation (AF). We evaluated the predictive performance of a validated AI-ECG algorithm for incident AF in UK Biobank participants with AF risk factors, irrespective of prevalent cardiovascular disease, and its incremental value when added to clinical predictors. Methods and results The AI-ECG tool was applied to sinus rhythm ECGs from UK-Biobank participants with risk factors for AF but no AF. Model performance was evaluated using time-dependent ROC-AUC and Harrell’s C-index. Multivariable Cox regression was used to identify clinical risk factors associated with incident AF and to quantify the contribution of AI-ECG. A total of 21 842 participants (56% male) were included. The median follow-up time was 3.7 years (IQR 0.5–5.4) The ECG-AI tool achieved a ROC-AUC of 0.73 (95% CI 0.68–0.78) at 1 and 0.69 (95% CI 0.66–0.72) at 3 years. A multivariable Cox regression model using clinical parameters achieved a ROC-AUC of 0.71 (95% CI 0.66–0.75) at 1 and 0.71 (95% CI 0.68–0.74) at 3 years. By adding ECG-AI to the clinical Cox regression model, the ROC-AUC increased to 0.75 (95%CI 0.71–0.80) at 1 and 0.74 (95% CI 0.71–0.77) at 3 years. AI-ECG showed a hazard ratio of 1.23 per decile increase (95% CI 1.18–1.27). Conclusion An AI-ECG algorithm improved the prediction of incident AF when added to a clinical parameter-based model over a median follow-up time of 3.7 years among individuals with comorbidities predisposing to AF who may benefit from targeted screening and preventive strategies.

Abstract Background Normal weight obesity (NWO) has been linked to metabolic syndrome (MetS), cardiovascular disease (CVD) and mortality, but has not been comprehensively studied in the Hispanic population. Purpose Our study aims to examine the CVD risk factors associated with NWO and to compare the development of CVD risk factors between NWO and normal weight non-obese (NWNO) Hispanic adults over 6 years. Methods This is a prospective study using data from the Hispanic Community Health Study (HCHS). Interviews and clinical assessments were collected from 2008–2011 (visit 1) to 2014–2017 (visit 2). NWO was defined as a body mass index (BMI) between 18.5–24.9 kg/m2 and a body fat percentage of ≥25 % in men and ≥35% in women, estimated using the Tanita scale. We identified 1,000 HCHS participants as having a normal weight at visit 1 and not missing data on covariates of interest including hypertension (HTN), hyperlipidemia (HLD), MetS, and insulin resistance (Homeostatic Model Assessment for Insulin Resistance (HOMA-IR) ≥2.5). Participants were categorized into NWO (n=104) or NWNO (n=896) groups. Univariate logistic regression models were used to estimate the odds ratio (OR) and 95% confidence intervals (CI) of the association between health risk factors and NWO. Multivariate logistic regression models were used to estimate the odds of developing CVD risk factors over 6 years, using propensity score matching. Results The prevalence of NWO among our sample of Hispanic adults was 10.4% and the 6-year incidence of NWO at visit 2 was 3.5% (n=31) from NWNO participants at visit 1. Higher BMI (OR=5.19; 95% CI=3.15–9.27, p<0.001) and a higher mean overnight heart rate (OR=1.34; 95% CI=1.01–1.79, p=0.043) were statistically significantly associated with NWO at visit 1. Furthermore, HTN (OR=2.88; 95% CI=1.31–6.71, p=0.011), HLD (OR=2.12; 95% CI=1.21–3.77, p=0.009), and MetS (OR=3.48; 95% CI=1.62–8.09, p=0.002) were statistically significantly associated with NWO at visit 1. Participants with NWO at visit 1 had higher odds of developing insulin resistance (OR=1.79; 95% CI=1.07–3.04, p=0.029) and MetS (OR=2.03; 95% CI=1.15–3.66, p=0.016) at visit 2 compared to NWNO participants at visit 1, after age and sex matching. Conclusion Among Hispanic adults, the prevalence of NWO was associated with a higher BMI, higher mean overnight heart rate, treated HTN, HLD, and MetS. Hispanic adults with NWO were more likely to develop insulin resistance and MetS over 6 years compared to NWNO Hispanic adults. Overall, NWO among Hispanic adults was associated with CVD risk factors which places this population at a higher risk for CVD morbidity and mortality.

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 >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

Abstract Background Left ventricular diastolic function (LVDF) and left bundle branch block (LBBB) are both linked with increased risk of LV systolic dysfunction, heart failure, and mortality. Assessing LVDF, which is typically done through echocardiography, becomes challenging in the presence of LBBB due to the intraventricular conduction delay that disrupts the reliability of tissue Doppler imaging. Recently, we validated an artificial intelligence-enabled electrocardiogram (AI-ECG) model to assess LVDF and filling pressures. Methods We performed a retrospective study among patients with ECG-confirmed LBBB who also underwent comprehensive echocardiography within 14 days of the index ECG between September 2001 and June 2022. Using multivariable survival models, we assessed the risk of all-cause mortality across AI-ECG-derived LVDF grades. Results Of 2,554 patients with LBBB (mean age 72.0+12.6, 54.6% women), 252 (9.8%), 656 (25.7%), and 1,436 (56.2%), and 210 (8.2%) had normal LVDF, G1 LVDF, G2 LVDF, and G3 LVDF, respectively. The median LV ejection fraction (LVEF) was 54% and 49.3% of the patients had heart failure. Worse LVDF was associated with older age, lower LVEF, and higher rates of diabetes, hypertension, chronic pulmonary disease, renal failure, cardiovascular disease, and heart failure (p-of-trend<0.001 for all). During a median follow-up of 2.6 years (interquartile range 0.5-4.0 years), 847 (33.2%) patients died. In multivariable survival analysis, adjusted to multiple risk factors including baseline LVEF and heart failure, worse LVDF was independently associated with a higher risk of death [G1 LVDF: adjusted hazard ratio (aHR) 2.15 (95% confidence interval (95%CI) 0.75-1.77), G2 LVDF: aHR 2.25 (95%CI 1.50-3.38) G3 LVDF: aHR 2.96 (95%CI 1.88-4.65); p<0.001 for both)]. These findings were consistent also in a subgroup of patients with baseline LVEF > 50%. Conclusion Among patients with LBBB, worse LVDF, as derived by a dedicated AI-ECG algorithm is independently associated with all-cause mortality beyond baseline LVEF and clinical heart failure.survival across AI-ECG LVDF grades

Abstract Background Artificial intelligence (AI) models trained on 12-lead ECGs effectively detect left ventricular systolic dysfunction (LVSD; left ventricular ejection fraction [LVEF] <=40%). Continuous ECG monitoring via Holter recordings provides an opportunity for opportunistic screening for structural heart disease beyond rhythm disorders. We hypothesized that a lead-invariant version of the 12-lead AI model would enable a Holter monitor to screen for both arrhythmias and ventricular dysfunction. Methods We retrospectively analyzed continuous Holter ECGs from 17,665 patients who underwent a Holter and transthoracic echocardiogram (TTE) within 30 days of each other at Mayo Clinic. From each Holter, a random 20-minute of valid (non-flatline/lead disconnect) ECG segment was extracted and analyzed for LVSD detection using the adapted lead-invariant AI model. To evaluate stability, we examined model performance across different time points of the day, presenting results as area under the receiver operating characteristic curve (AUC) over time. Moreover, we illustrated the model’s robustness to noisy data by comparing its performance on raw ECG signals with that on bandpass-filtered inputs. Results Among 17,665 patients (mean age 59 years, 48.57% female), 4.96% had an LVEF <=40%. The AI model demonstrated strong predictive performance (20-minute segment AUC 0.90, mean prediction of 24-hour AUC 0.92). Analysis of results over time (Figure) revealed temporal patterns in predictive accuracy, with specific time periods showing greater stability. Despite modest variability, model performance remained consistently high throughout the day, confirming robustness across different physiological states. The predictions remained robust with noisy input. We did not observe performance improvement when the baseline wander and high frequency noise are removed by the bandpass filter. Conclusion Applying a 12-lead AI ECG model with a lead-invariant framework to a continuous Holter ECG enables effective screening for left ventricular dysfunction. This suggests that AI-based analysis of Holter-monitors can facilitate opportunistic screening of ventricular dysfunction and may enable assessment of an arrhythmia’s impact on LVEF, as well as the relationship between arrhythmia burden and LVEF.Figure 1.Mean prediction AUC of the day Figure 2.AUC for different time point