Lopez‐Jimenez, Francisco

Introduction: Semaglutide 2.4 mg has been shown to reduce the risk and severity of cardiometabolic comorbidities (CMCs) and to improve cardiometabolic biomarkers in patients with overweight or obesity in clinical trials. However, real-world evidence in these areas is limited. Research Question: This study compared the incidence rates of CMCs and 12-month changes in biomarkers between adults with overweight/obesity with and without once-weekly subcutaneous semaglutide 2.4 mg use. Methods: A retrospective cohort study using a large U.S. claims database linked with clinical data evaluated patients with semaglutide 2.4 mg use for ≥12 months (June 2021–June 2023) and those without. For each CMC, including hypertension, T2D, dyslipidemia, HF, prediabetes, ASCVD, and CVD, patients were excluded from analysis of each individual CMC if they had claims indicating the corresponding CMC during the 12-month baseline. For each biomarker, patients were required to have the corresponding biomarker measurements at both baseline and 12-month follow-up. For each CMC and biomarker, propensity score weighting was used to balance patient characteristics between semaglutide 2.4 mg users and non-users. Time to CMCs were compared using weighted Cox proportional hazards models and biomarker changes were compared using weighted linear regression. Results: Sample sizes ranged from 14,067 to 25,490 in semaglutide 2.4 mg users and 4,271,926 to 8,578,948 in non-users across CMCs. After balancing, baseline characteristics were similar between users and non-users. Mean age ranged from 44.4 to 47.2 years among users and 44.4 to 47.4 years among non-users; 77%–83% of users and 75%–82% of non-users were women. Compared to non-users, semaglutide 2.4 mg users had significantly lower risk of incident hypertension (Hazard ratio [HR]: 0.62, 95% confidence intervals [CI]: 0.57; 0.68), T2D (HR: 0.45, 95% CI: 0.39; 0.51), HF (HR: 0.48, 95% CI: 0.38; 0.61), prediabetes (HR: 0.75, 95% CI: 0.70; 0.80), ASCVD (HR: 0.65, 95% CI: 0.58; 0.72), and CVD (HR: 0.69, 95% CI: 0.64; 0.75) (all p<0.001). The risk of dyslipidemia was similar between users and non-users (HR: 1.05, 95% CI: 1.00; 1.11, p=0.05). Semaglutide 2.4 mg use was also associated with significant improvements in cardiometabolic biomarkers specified in Table 1 and slower decline in eGFR. Conclusion: In adults with overweight/obesity, semaglutide 2.4 mg was associated with lower risk of specified CMCs and greater improvements in cardiometabolic biomarkers.

ABSTRACT Aims Common genetic variants have been linked to an increased risk of non-ischaemic cardiomyopathy (NICM). The primary objective of this study was to evaluate the significance of these variants in a general population. Methods and results Thirteen NICM risk alleles, previously validated by meta-analyses of case-control genetic association studies, were used to determine genetic risk (GR) in a large, unselected cohort (n = 1897) of Olmsted County residents aged &gt;45 years. Clinical and echocardiographic data, with biomarkers, were correlated with this NICM-GR over a median of 16 years of follow-up (interquartile range [IQR] 15–17). There were no significant differences between the study population quartiles, based on NICM-GR, in terms of age (median 61.6 [IQR 53.6–70.4]), sex (48% male), and cardiovascular risk factors. A significant correlation was detected by specific linear-regression models between NICM-GR and left ventricular (LV) ejection-fraction (beta [standard error]: −2.68 [0.68], p &lt; 0.001), LV end-systolic-diameter (0.22 [0.06], p &lt; 0.001), and LV end-diastolic-diameter (0.14 [0.06], p = 0.01). During follow-up, 46 subjects developed NICM. Using a 3:1 matched-cohort of non-NICM versus NICM patients, linear-regression analysis found a significant correlation between NICM-GR and NICM development (odds ratio 2.27, 95% confidence interval 1.06–4.85, p = 0.03). There was no significant association between these variants and heart failure biomarkers (aldosterone, natriuretic peptides, ST2, galectin-3). Conclusions The NICM-GR correlates with LV function and dimension, and subsequent development of NICM in the general population. The lack of correlation of these variants with cardiovascular risk factors and the renin–angiotensin–aldosterone axis or natriuretic peptides suggest different biological mechanisms involved. These findings support the need to explore newer pathophysiologic pathways in the development of NICM.

Objective To assess the association between educational level and cardiovascular age acceleration metric derived from ECG, and to determine whether this association is mediated by established cardiovascular disease (CVD) risk factors. Design Prospective population-based cohort study (the Tromsø Study). Setting General population of the Tromsø municipality, Norway. Participants The study sample consisted of 4367 participants of the Tromsø Study, who took part in both Tromsø6 (2007–2008) and Tromsø7 (2015–2016), had a 12-lead ECG obtained at Tromsø7 and did not report a history of heart attack, stroke or atrial fibrillation. Primary outcome measures δ-age, a biomarker of cardiovascular ageing, is defined as the difference (in years) between an individual’s ECG-predicted heart age and their chronological age. ECG-predicted heart age was estimated using a previously validated deep neural network. Results Our findings indicate an inverse association between education and δ-age, with a regression coefficient per increment increase in education of −0.24 (95% CI −0.41 to –0.07) in the overall sample, −0.38 (95% CI −0.59 to –0.16) for women and −0.04 (95% CI −0.31 to 0.23) for men. Participants with the highest level of education (university/college for 4 or more years) had the lowest estimated δ-age with a regression coefficient of −0.69 years (95% CI −1.23 to –0.16) compared with the group with primary education for the overall sample, −1.05 years (95% CI −1.73 to –0.37) for women and −0.15 years (95% CI −1.03 to 0.73) for men. CVD risk factors mediated up to 75% of the association between overall education and δ-age, and 80% of the association among those with the highest education level (university/college for 4 or more years). Among women, 50% of the effect of overall education on δ-age was mediated by CVD risk factors, rising to 53% in the category with the highest level of education. However, in the subsample of men, there was no significant association between education and δ-age, and the mediation analysis produced natural direct and indirect effects pointing in opposite directions. Conclusions Cardiovascular ageing is inversely associated with educational level, an effect that appears to be largely mediated through established risk factors.

Methods: From a sample cohort of 274,539 consecutive adults who sought care at Mayo Clinic between 2018-2022, we ascertained demographic and clinical information, including clinically indicated TTE within 15 days and up to a year after the index clinical visit. The Area Deprivation Index (ADI), a tool that captures neighborhood-level disadvantages that are linked to cardiovascular morbidity and outcomes, was extracted from the Neighborhood Atlas and ranked accordingly as state percentiles stratified in quintiles, with the highest values representing “most disadvantaged” areas. We performed non-parametric multivariate regression models that adjusted for cardiovascular risk factors expressed in odds ratio (OR) and 95% confidence interval (CI). Results: A total of 45,823 (16.9%) subjects had a TTE performed, with a mean age of 63±16 years, 48% were women, and 86% were non-Hispanic white. According to the TTE, 4% of subjects had LVSD, while 11% had a high probability of LVSD by AI-ECG. Median state ADI was 4 IQR (2-7). In the whole cohort, subjects residing in the most disadvantaged areas were less likely to have a TTE [OR 0.94 CI (0.91- 0.97)] see Fig. 1-A ; conversely, they were more likely to have LVSD by TTE [OR 1.80 CI (1.52-2.13)] see Fig. 1-B , and higher probability of LVSD by AI-ECG [OR 1.43 CI (1.36-1.51)] when compared to those with the least disadvantage see Fig. 1-C , independent of age, sex, and cardiovascular risk factors, p for trend<0.001. Conclusion: Individuals living in socially disadvantaged areas were slightly less likely to have an echocardiogram despite a significantly higher likelihood of having an LVSD (suspected by AI-ECG and confirmed by TTE), independent of cardiovascular risk factors. Highlighting an important disparity in the diagnosis of LVSD.

Introduction: Deep neural networks are increasingly used to generate diagnostic algorithms in cardiovascular medicine. A biomarker of cardiovascular aging, derived from a deep-learning algorithm applied to digitized 12-lead electrocardiograms (ECGs), has recently been introduced. This biomarker, delta age (δ-age), is defined as the difference between predicted ECG age and chronological age. Hypthesis: We hypothesized that δ-age will improve the prediction of incident fatal and non-fatal myocardial infarction (MI) and stroke on top of what contemporary CVD prediction tools would do. Methods: In this cohort study, we included 7,111 men and women from the Norwegian Tromsø Study conducted in 2015-16, with follow-up through 2021 for incident fatal and non-fatal myocardial infarction (MI) and hemorrhagic or cerebral stroke. We used Cox proportional hazards regression models to assess the independent effect of δ-age on MI and stroke. Discrimination was evaluated using Harrell’s concordance statistic (C-index) and the net reclassification improvement (NRI). Results: During a median follow-up of 5.9 years, we observed 155 incident cases of MI, 141 cases stroke and 290 cases with either MI or stroke. δ-age was observed with a mean of 0 and standard deviation of 6.2 years. In men and women combined, hazard ratios (HRs) per standard deviation increase in δ-age, after adjustment for traditional risk factors, were 1.24 (95% confidence interval (CI) 1.09, 1.41) for the combined outcome, 1.26 (1.06, 1.49) for MI and 1.25 (1.03, 1.50) for stroke. In men, the corresponding HRs were 1.27 (1.09, 1.49), 1.46 (1.18, 1.79) and 1.02 (0.80, 1.31), respectively, and in women, 1.20 (0.97, 1.49), 0.87 (0.64, 1.20) and 1.58 (1.19, 2.11), respectively. The C-index increased modestly when δ-age was added to a model with traditional risk factors. The 95% CI for the C-index increase excluded zero for the combined outcome overall, and for MI in men and for stroke in women. The NRI was 26.0% (13.3%, 38.1%) for the combined outcome, 17.5% (0.6%, 33.5%) for MI and 37.2% (20.1%, 53.0%) for stroke. Conclusions: Incorporating δ-age into primary prevention risk prediction models significantly improved performance beyond traditional cardiovascular risk factors for the combined outcome and separately for MI and stroke.

AbstractBackgroundThere is increasing need for noninvasive biomarkers of Alzheimer’s Disease (AD) neuropathologic change for early detection and intervention through risk‐factor modification and disease‐modifying therapies. One such biomarker is the prediction of chronological age from routine clinical tests such as an electrocardiogram (EKG) to discriminate between observed biological age from chronological age for healthy aging. Deviation of true age from predicted age has been associated with heart failure, hypertension, and coronary heart disease. However, the association between EKG‐based age and underlying neuropathology has not been studied.MethodWe used a cohort of 336 participants from the Mayo Clinic Study of Aging, a population‐based prospective study of residents in Olmsted County, Minnesota, with antemortem EKG and post‐mortem brain autopsy (Table 1 & 2). Age was predicted from EKG using a validated convolutional neural network algorithm. We used ordinal logistic regression to identify associations between lower neuropathologic scores of AD (Thal phase, neuritic plaque score, Braak stage, and overall AD neuropathologic change) and the Age‐Gap, defined as EKG‐predicted age minus chronological age. Results were controlled for true age, sex, and APOE e4 carrier status. Neuropathologic scales were grouped into none, low, intermediate, and high categories. Observations were weighted by the inverse of years between last EKG and time of death to account for the time difference between acquisition of EKG and autopsy.ResultWe found that an increasing Age‐Gap (i.e. older predicted age) was inversely associated with lower overall AD neuropathologic change (OR 0.93 [0.89, 0.97], p = 0.002). Investigation of the components of AD neuropathologic change showed that an increasing Age‐Gap was also inversely associated with lower amyloid metrics such as Thal phase (OR 0.85 [0.80, 0.89], p < 0.001) and neuritic plaque scores (OR 0.91 [0.87, 0.96], p < 0.001), but not tau metrics like Braak staging (OR 1.05 [1.00, 1.10], p = 0.05).ConclusionIn this work, we show that older predicted age on EKG, a noninvasive biomarker associated with vascular and biological aging, is associated with higher amyloid burden and neuropathologic change of AD.

Background: Risk stratification for suspected pulmonary emboli (PE) remains an area of active research. Current risk stratification algorithms have moderate discriminatory capabilities and are guideline-recommended, but they are poorly utilized in practice, with most clinicians relying predominantly on their clinical training and personal “gestalt.” Our group recently derived an Artificial Intelligence (AI) algorithm using electrocardiograms (ECG) to predict the presence of PE (AUC 0.69) on computed tomography scans of the pulmonary arteries (CTPA) in a cohort of 79K patients. Aim: Validate the AI-ECG algorithm in a contemporary dataset of patients with suspected PE. Methods: Emergency Department (ED) visits between 9/1/2022 and 12/31/2023 across the Mayo Clinic Enterprise were searched using a unified data platform. ED visits in patients over 18 years of age with a discretely documented Wells' PE or PERC Rule, a D-dimer result, or a CTPA were included. Only CTPA imaging and D-dimers performed within 48 hours of ED admission were eligible to be included. Patients were included in the final cohort if an ECG was performed within 6 hours of ED admission, D-dimer testing, or CTPA. Patients were considered negative for PE if CTPA was negative, and when this wasn't performed, a negative D-dimer (high sensitivity) or PERC rule of 0 was considered negative. A previously derived Artificial Neural Network algorithm to analyze the ECG to predict PE was applied to the dataset for validation. Results: A total of 27,028 ED visits in 24,288 unique patients meet inclusion criteria. Among this group, the first ED visit with a CTPA and an ECG within 6 hours was included, leaving 18,936 patients for the final validation cohort. The mean age of the cohort was 54.7 (SD 18.7), and 57% were male. Among the entire cohort, 13,573 underwent D-dimer testing for which 4,895 (36%) were positive at standard thresholds. A total of 10,486 patients underwent CTPA (55% of patients evaluated), of which 455 were positive for PE (4.3%). Using the AI-ECG model from the derivation cohort applied to this validation cohort resulted in an AUC of 0.69, identical to the derivation cohort. Using ECG model estimates alone, low (0.74%, n=2447, NPV 99.3%), elevated (2.27%, n=15404, NPV, 97.72%), and high-risk (8.02%, n=1085, NPV 91.98%) categories for PE can be created. A negative D-dimer in the elevated risk group increased the negative predictive value (NPV) to &gt;99.9% and could be used to reduce imaging in this group further. Using the proposed ECG to D-dimer cascade risk stratification could further reduce the need for PE imaging for an overall NPV of 99.9% and sensitivity of 95.2%. We then used additional clinical parameters to train a new algorithm with additional clinical data and the AI-ECG algorithm estimates. Multiple machine learning algorithms were then evaluated in a dataset divided into training (2/3) and testing (1/3) datasets, after downsampling (to address class imbalance). The best results were obtained with AI-ECG results and D-dimer: Ada Boost (AUC 0.93, F1 score 0.895) compared to standard logistic regression (AUC 0.79, F1 score 0.746). The addition of age, sex, and oxygen saturation did not improve model performance. Conclusion: We have now validated the previously derived AI-ANN ECG algorithm for PE in a contemporary and independent cohort and demonstrate potential for integration into clinical PE assessment algorithms. Unlike prior risk stratification processes, ECGs are invariably performed in patients with cardiopulmonary symptoms and have the potential to prevent missed or delayed PE diagnosis by proactively identifying high-risk patients. Additionally, we demonstrate that the results of this algorithm can be combined with D-dimer values, resulting in a simple, powerful, predictive algorithm (AUC 0.93) that pairs cardiovascular electrophysiology with laboratory biomarkers and could lead to a new paradigm in clinical risk stratification for PE.

Background: Artificial intelligence (AI) can be used to predict a person’s age from the electrocardiogram (ECG) (AI-ECG age) and has been proposed as a measurement of biological age. The difference between AI-ECG age and chronological age (defined as delta-age) is an independent predictor of mortality in the general population. This study assessed the relationship between AI ECG estimated age and mortality among adults living with congenital heart disease (CHD). Methods: A previously validated neural network to estimate AI-ECG age was used to analyze standard digital 12-lead ECGs in a cohort of subjects aged >18 years seen in the adult CHD (ACHD) clinic between 1992 to 2023. A single ECG, collected during the year of the first visit to the ACHD clinic was analyzed to compute the delta-age.. A positive delta-age indicates an AI ECG estimated age higher than the subject’s chronological age. The relationship between the delta-age and mortality was evaluated using Cox proportional hazard models adjusting for influential clinical factors including chronologic age, sex, ACHD severity, NYHA class and history of arrhythmia. Results: Of 5,780 subjects tested (50% females), the mean chronological age was 39.1 ± 15.0 years. AI ECG estimated age was 52.3 ± 16.6 years. Complexity of CHD was classified as mild, moderate, and severe in 7.4%, 73.9% and 18.7% of patients respectively. Patients with severe CHD had the highest delta-age of 17.1 (±18.2) years followed by moderate 12.7 (±13.6) y and simple 7.7 (±11.5) y. Patients with single ventricle had the highest deviation in AI estimated age [median delta-age 21.0 (IQR 28.6) y] followed by those with a systemic right ventricle [median delta-age of 16.3 (IQR 29.3) y]. During a median follow-up of 6.4 y (25th – 75th centile 1.58 – 13.7 y), mortality occurred in 839 (14.5%) patients. After adjusting for chronological age, CHD severity, and other clinical variables, delta-age gap was associated with increased mortality risk (HR 1.06 (1.03 – 1.09) per 5-year increment in delta-age, p<0.005), Table 1. Conclusion: The difference between AI ECG and chronological age is an independent predictor of all-cause mortality in ACHD patients.

Introduction: Calcium scoring computed tomography (CAC) is a widely available and inexpensive modality of screening for atherosclerosis. This study aims to improve the predictive value of CAC by incorporating pericardial fat characteristics in a CatBoost model. Methods: A sample of 7,000 non-contrast clinically indicated CAC scans were randomly selected from a cohort of consecutive Olmsted County MN residents. Known history of atherosclerotic cardiovascular disease or impaired images were excluded. A pericardial fat segmentation model, trained on 191 images of the same dataset with a dice similarity score of 94%, was applied to 6,194 CT scans; Volume and mean Hounsfield unit (mHU) of the output masks were added to age at imaging, radiologist-measured calcium score, male gender, diabetes, and smoking (Figure 1). A 70,20 and 10% split was used to produce the training, validation and test set, respectively. Features were inputted to CatBoost classifier, as the best model tested, to predict four outcomes: acute coronary syndrome (ACS), heart failure (HF), ischemic stroke, and all-cause mortality. Model was evaluated by area under the ROC curve (AUC) on the unseen test set. Results: Mean age was 55.2 (8.8), 55.2 (8.8) and 54.8 (8.5) years in the training, validation and test sets, respectively, and 65%, 67% and 66% of the populations were male. The mean AUC, obtained through bootstrapping with 100 iterations, was 0.81 [0.77-0.85], 0.86 [0.60-0.92], 0.80 [0.73-0.87] and 0.87 [0.81-0.91] for HF, ACS, stroke and death, respectively; The same analysis excluding pericardial fat measurements showed a 3% reduction in the AUC for all of the investigated outcomes (Figure 2). Conclusion: Our study presents a robust risk prediction model for ACS, HF, stroke, and mortality based on CAC scans. The improved performance of the model by incorporating pericardial fat characteristics, highlights the potentials of deep learning in timely inexpensive risk prediction in large populations.

Background: AI-ECG is a cost-effective tool for left ventricular dysfunction (LVD) screening. However, its cost-effectiveness for other forms of structural heart disease (SHD) is unknown. While AI-ECG is inexpensive, a drawback is low positive predictive value (PPV), which leads to high costs from unnecessary follow-up tests. Therefore, strategies to improve the yield of AI-ECG-based screening are needed. Aim: To evaluate the cost savings of a stepwise approach to SHD screening with AI-ECG followed by POCUS compared to AI-ECG alone. Methods: 286 adult outpatients undergoing AI-ECG were selected at random. Participants received same-day POCUS and had a recent TTE (our gold standard for SHD). We evaluated four SHDs: aortic stenosis (AS), cardiac amyloidosis (CA), HCM, and LVD. The costs of AI-ECG ($75) and TTE ($1,305) were obtained from Healthcare Bluebook. The cost of POCUS ($100) was estimated independently. Cost savings were analyzed for simultaneous screening for all forms of SHD and screening for individual SHDs. Results: AI-ECG identified potential SHD in 125 patients, but only 39 were true positives by TTE (31% PPV). In AI-ECG positive patients, POCUS demonstrated findings of SHD in 52/125. Compared to TTE, this stepwise approach yielded 32 true positives and 20 false positives (62% PPV). The cost per patient diagnosed with SHD was $4,733 with AI-ECG alone but decreased to $3,182 with stepwise screening (33% cost savings). Screening for individual SHDs resulted in cost reduction from $18,724 to $6,315 (66% savings) for AS, $21,023 to $12,230 (42% savings) for CA, $9,883 to $6,175 (38% savings) for HCM, and $4,019 to $3,582 (11% savings) for LVD. Conclusions: Stepwise screening for SHD with AI-ECG followed by POCUS significantly reduces costs compared to AI-ECG alone. We also suggest a model for parallel screening for multiple SHDs, which is likely more cost-effective than screening for individual SHDs.