López-Jiménez, Francisco

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.

Abstract Aims To develop a deep learning model that: (i) utilizes transthoracic echocardiography (TTE) clips to detect left ventricular (LV) enlargement without being provided information regarding a patient’s sex and body size; and (ii) can be accurately applied to clips acquired using either standard comprehensive TTE or handheld cardiac ultrasound (HCU). Methods and results Using retrospective TTE data (training: 8722 patients; internal validation: 468 patients), we developed a deep learning model that estimates a patient’s end-diastolic LV volume (indexed to body surface area and normalized across the sexes), and then thresholds this estimate to perform the following classifications: (1) normally sized LV vs. ≥ mild LV enlargement; (2) normal/mildly enlarged LV vs. ≥ moderate LV enlargement. For retrospective datasets, the model showed strong performance in TTE across three geographically distinct locations (Minnesota and Wisconsin: 1082 patients, AUC = 0.925 and 0.953 for classifications 1 and 2, respectively; Arizona: 1475 patients, AUC = 0.935 and 0.969; and Florida: 1481 patients, AUC = 0.934 and 0.970). Additionally, performance was strong for both TTE and HCU clips collected from a prospective cohort of 410 patients who underwent HCU immediately following TTE (TTE: AUC = 0.925 and 0.971; HCU: AUC = 0.874 and 0.902, for classifications 1 and 2, respectively). Conclusion An automated deep learning model applied to TTE or HCU images accurately categorizes LV volumes. These results lay a foundation for future work aimed at optimizing clinical outcomes for heart failure patients by enabling early detection of LV enlargement across various point-of-care settings.

Background: The gap between artificial intelligence-derived age (ECG-Age) and chronological age has been validated as a strong predictor of increased cardiovascular morbidity and mortality, independent of age, and is a potential biomarker of biological aging. Acute stress may cause real-time, transient changes in autonomic function via electrophysiological signals that, when repeated over time, may contribute to biological aging and represent mechanisms linking stress and Major Adverse Cardiovascular Events (MACE). We hypothesized that acute mental stress induced in a laboratory is associated with increased ECG and that this increase is associated with a higher risk of MACE among individuals with stable coronary heart disease (CHD). Methods: We pooled two prospective cohorts of participants with stable CHD from a university-based hospital network in Atlanta. Stress was induced using a standardized mental stress test (public speaking task) and measured 12-lead ECGs during rest, mental stress, and recovery periods. To measure ECG-age, we utilized a published algorithm using a neural network on >500,000 independent patients. The study endpoint was MACE including nonfatal myocardial infarction, hospitalization for heart failure, or cardiovascular death. The difference in ECG-Age between resting and acute mental stress was used to define the stress Age-Gap. Cox proportional hazard regression modeled the association of stress Age-Gap operationalized as tertiles adjusting for chronological age, sex, race, cardiovascular risk factors, and baseline Cohen perceived stress scale. Results: 442 participants in the total sample pool (38% women). Over a 5-year median follow-up, 95 participants (21%) experienced MACE. At rest, the mean chronologic age was 55.7±9.9 years, and ECG-Age was 57.1 ±10.7 years, with rest Age-Gap of 0. 9±9.6 (p=0.16). During stress, the stress Age-Gap of 1.95±9.8 (p<0.0001), while recovery Age-Gap was 1.1±10.3 (p=0.13). Those that demonstrated the largest ECG-Age positive change between rest and stress by ECG (top tertile) had a higher risk for MACE when compared to those with the lowest or negative ECG age change (bottom tertile), see Figure. Conclusion: Among patients with stable CHD, acute mental stress caused changes in biological age as measured by AI-ECG consistent with accelerated biological aging. Such changes were associated with MACE. These results uncover accelerated biological aging as a mechanism linking acute stress to CVD outcomes.

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.

Nonspecific symptoms and other diagnostic challenges lead to underdiagnosis of cardiac amyloidosis (CA). Artificial intelligence (AI) could help address these challenges, but a summary of the performance of these tools is lacking. This narrative review of published literature describes the performance of AI tools that use data from ECGs and echocardiography to improve identification of CA and challenges that hinder adoption of these tools. Thirteen studies met inclusion criteria with sample sizes ranging from 50 to 2451 patients. Four studies used ECG data, 8 used echocardiography data, and 1 used both. The CA gold standard was typically defined as a CA diagnosis in an institutional or other database but the requirements for these diagnoses were heterogenous across studies, and many did not distinguish among CA subtypes. AI model development varied considerably, and only 4 studies included external validation. The ability of models to predict CA ranged from 0.71 to 1.00, sensitivity ranged from 16% to 100%, and specificity from 75% to 100%. Only 1 study reported model performance across strata of sex, age, race, and CA type. Persistent challenges to AI adoption include usability, cost, value added, electronic health record/information technology interoperability, patient‐related factors, regulation, and privacy and liability. Published studies on AI for improved identification of CA show favorable performance measures but numerous methodologic and other challenges must be addressed before these tools are more widely adopted.

Abstract Background Automated data extraction from echocardiography reports could facilitate large-scale registry creation and clinical surveillance of valvular heart diseases (VHD). We evaluated the performance of open-source large language models (LLMs) guided by prompt instructions and chain of thought (CoT) for this task. Methods From consecutive transthoracic echocardiographies performed in our center, we utilized 200 random reports from 2019 for prompt optimization and 1000 from 2023 for evaluation. Five instruction-tuned LLMs (Qwen2.0-72B, Llama3.0-70B, Mixtral8-46.7B, Llama3.0-8B, and Phi3.0-3.8B) were guided by prompt instructions with and without CoT to classify prosthetic valve presence and VHD severity. Performance was evaluated using classification metrics against expert-labeled ground truth. Mean squared error (MSE) was also calculated for predicted severity’s deviation from actual severity. Results With CoT prompting, Llama3.0-70B and Qwen2.0 achieved the highest performance (accuracy: 99.1% and 98.9% for VHD severity; 100% and 99.9% for prosthetic valve; MSE: 0.02 and 0.05, respectively). Smaller models showed lower accuracy for VHD severity (54.1%-85.9%) but maintained high accuracy for prosthetic valve detection (&gt;96%). Chain of thought reasoning yielded higher accuracy for larger models while increasing processing time from 2-25 to 67-154 seconds per report. Based on CoT reasonings, the wrong predictions were mainly due to model outputs being influenced by irrelevant information in the text or failure to follow the prompt instructions. Conclusions Our study demonstrates the near-perfect performance of open-source LLMs for automated echocardiography report interpretation with the purpose of registry formation and disease surveillance. While larger models achieved exceptional accuracy through prompt optimization, practical implementation requires balancing performance with computational efficiency.

Background Left ventricular diastolic dysfunction (LVDD) predicts mortality in patients in cardiac intensive care units. An artificial intelligence enhanced ECG (AIECG) algorithm can predict LVDD and mortality in general populations but has not been examined in cardiac intensive care units. Methods This historical cohort study included consecutive adults admitted to Mayo Clinic cardiac intensive care unit from 2007 to 2018 with an admission AIECG. The AIECG assigned the LVDD grade (0–3). Medial mitral E/e' ratio >15 on transthoracic echocardiogram (TTE) defined elevated filling pressures. In‐hospital and 1‐year mortality was evaluated, before and after multivariable adjustment. Results We included 11 868 patients (median age 69.5 years, 37.7% female); 48% had heart failure and 44% had acute coronary syndromes. AIECG LVDD grade was 0 (normal), 33%; 1, 7%; 2, 39%; and 3, 21%. In‐hospital and 1‐year mortality increased in each higher AIECG LVDD grade. After adjustment, each higher AIECG LVDD grade was associated with higher in‐hospital (adjusted odds ratio [OR], 1.22 [95% CI, 1.13–1.32]) and 1‐year mortality (adjusted hazard ratio [HR], 1.23 [95% CI, 1.19–1.29]); this persisted after adjustment for TTE measurements. Patients with grade 2 or 3 LVDD by AIECG and medial mitral E/e' ratio >15 by TTE had the highest in‐hospital (adjusted OR, 2.54 [95% CI, 1.69–3.88]) and 1‐year (adjusted HR, 2.03 [95% CI, 1.65–2.48]) mortality, whereas patients meeting either of these criteria had similar, elevated mortality. Conclusions The AIECG LVDD grade was strongly associated with in‐hospital and 1‐year mortality in patients in cardiac intensive care units, even after adjusting for clinical variables and TTE measurements. Patients with concordant AIECG and TTE for elevated filling pressures were at highest risk.

Migraine with aura(MwA) is associated with an increased risk of stroke and adverse vascular outcomes compared to those with migraine without aura (MwoA). AI-ECG prediction models developed at our institution can evaluate the probability of atrial fibrillation (AF) and estimate a patient’s age based on a normal sinus rhythm (NSR) ECG. Delta age, AI-ECG estimated age minus chronological age, indicates endothelial dysfunction and is associated with cardiovascular mortality. We aim to assess how AI-ECG prediction model outputs, specifically the AF probability and delta age, are associated with adverse vascular outcomes in patients with migraine. Adult patients diagnosed with MwA and MwoA from 2000-2020 with at least one digital, standard 12-lead ECG were identified. The first ECG that showed NSR is used as the index ECG. Patients with any adverse vascular outcomes before the index ECG were excluded. Adverse vascular outcomes used as endpoints include acute ischemic stroke, acute myocardial infarction, deep vein thrombosis/pulmonary embolism, AF, and carotid artery dissection. A total of 31301 patients (13783 MwA, 17518 MwoA) were included in our analysis. The mean age at the time of the index ECG was 44.3 (14.5), and the average follow-up time was 71.6 (69.8) months. Based on the index ECG, the mean AF probability was significantly higher in MwA compared to MwoA (2.39 vs 2.06, p< 0.001), while the delta age was lower in MwA compared to MwoA (3.7 vs 4.2, p <.001). The prevalences of all vascular outcomes are summarized in Table 1. Multivariate Cox regression models showed that a higher AF prediction model output (every 10% increase), as well as a higher delta age (every 10-year increase), are associated with an increased risk of composite vascular outcomes (HR1.20, 95%CI 1.17-1.23, p<.001, and HR1.19, 95%CI 1.14-1.23, p < .001) [Table 2]. The Kaplan-Meier curve of adverse vascular outcome-free survival by AF prediction model output, using 1% as the cutoff (HR 1.49, 95% CI 1.41, 1.57, p<.001), and by delta age, using 2-year as the cutoff (HR 1.14, 95% CI 1.08, 1.21, p <. 001) are presented in Figure 1. Having aura, older age, male, coronary artery disease, congestive heart failure, diabetes, hypertension, and tobacco use were also associated with a higher risk of adverse vascular outcomes. Our results demonstrated the potential of using AI-ECG AF prediction model output and delta age to identify migraine patients at risk for adverse vascular outcomes.