Citations

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.

INTRODUCTION: Building on prior results, we hypothesized that an electrocardiogram (ECG)-enabled machine learning (ML) model could be used to detect advanced chronic liver disease (CLD). METHODS: A cohort with CLD and 12-lead ECGs was matched with controls from electronic health records. A ML model was trained as a binary classifier. RESULTS: There are 12,930 patients with CLD and 64,577 controls in the cohort. The model's discriminative ability to classify CLD showed an area under the receiver-operating characteristic curve 0.858 (95% confidence interval: 0.850–0.866), and at the chosen threshold, CLD ECGs had 12 times higher odds of being classified as CLD (diagnostic odds ratio 12.33, 95% confidence interval: 11.16–13.63). DISCUSSION: An ECG-enabled ML model affords great promise in identifying advanced CLD in low resource areas.

Hypertension presents the largest modifiable public health challenge due to its high prevalence, its intimate relationship to cardiovascular diseases, and its complex pathogenesis and pathophysiology. Low awareness of blood pressure elevation and suboptimal hypertension diagnosis serve as the major hurdles in effective hypertension management. Advances in artificial intelligence in hypertension have permitted the integrative analysis of large data sets including omics, clinical (with novel sensor and wearable technologies), health-related, social, behavioral, and environmental sources, and hold transformative potential in achieving large-scale, data-driven approaches toward personalized diagnosis, treatment, and long-term management. However, although the emerging artificial intelligence science may advance the concept of precision hypertension in discovery, drug targeting and development, patient care, and management, its clinical adoption at scale today is lacking. Recognizing that clinical implementation of artificial intelligence–based solutions need evidence generation, this opinion statement examines a clinician-centric perspective of the state-of-art in using artificial intelligence in the management of hypertension and puts forward recommendations toward equitable precision hypertension care.

Background We tested whether biologic age, as estimated by deficits, functional impairments, or Age‐Gap or their combination, provide improved estimation of cause‐specific death as compared with chronological age. Methods Cardiovascular and noncardiovascular deficits, functional impairments, and Age‐Gap were prospectively collected in 535 patients aged ≥55 years undergoing percutaneous coronary interventions between August 1, 2014, and March 31, 2018. Age‐Gap was calculated as the difference between chronological age and age estimated by artificial intelligence ECG using a convolutional neural network. The full biological age model included deficits, functional impairments, and Age‐Gap >2 SD. A multivariable reduced model with the least number of variables was also created to provide a comparable C index to the full model. Results The average chronological age was 72.1±9.5 years, and there were 68% of men. During a median follow‐up of 2.61 years, 124 (23%) patients died. There was a modest correlation between Age‐Gap and biological age ( r =0.28 [95% CI, 0.20–0.35]; P <0.001). When modeled with chronologic age as a covariate, Age‐Gap predicted all‐cause (hazard ratio [HR], 1.07 [95% CI, 1.04–1.10]; P <0.001) and cardiovascular (HR, 1.07 [95% CI, 1.04–1.11]; P <0.001) mortality. As compared with chronological age, the full biological age model noted significant improvement in the prediction of long‐term overall (95% CI, 0.65–0.78), cardiovascular (95% CI, 0.69–0.77), and noncardiovascular (95% CI, 0.55–0.86) mortality. In the reduced models, most prognostic information for noncardiovascular mortality (C index: 0.79) was obtained by subjective difficulty in performing tasks, whereas the deficit‐based estimation predicted cardiovascular mortality (C index: 0.72). Conclusions Estimated biological age from deficits and functional impairments was superior to chronological age in predicting long‐term cause‐specific mortality following percutaneous coronary interventions.

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.

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.