Prediction of coronary artery calcium scoring from surface electrocardiogram in atherosclerotic cardiovascular disease: a pilot study

AimsCoronary artery calcium (CAC) scoring is an established tool for cardiovascular risk stratification. However, the lack of widespread availability and concerns about radiation exposure have limited the universal clinical utilization of CAC. In this study, we sought to explore whether machine learning (ML) approaches can aid cardiovascular risk stratification by predicting guideline recommended CAC score categories from clinical features and surface electrocardiograms.Methods and resultsIn this substudy of a prospective, multicentre trial, a total of 534 subjects referred for CAC scores and electrocardiographic data were split into 80% training and 20% testing sets. Two binary outcome ML logistic regression models were developed for prediction of CAC scores equal to 0 and ≥400. Both CAC = 0 and CAC ≥400 models yielded values for the area under the curve, sensitivity, specificity, and accuracy of 84%, 92%, 70%, and 75%, and 87%, 91%, 75%, and 81%, respectively. We further tested the CAC ≥400 model to risk stratify a cohort of 87 subjects referred for invasive coronary angiography. Using an intermediate or higher pretest probability (≥15%) to predict CAC ≥400, the model predicted the presence of significant coronary artery stenosis (P = 0.025), the need for revascularization (P < 0.001), notably bypass surgery (P = 0.021), and major adverse cardiovascular events (P = 0.023) during a median follow-up period of 2 years.ConclusionML techniques can extract information from electrocardiographic data and clinical variables to predict CAC score categories and similarly risk-stratify patients with suspected coronary artery disease

Electrocardiogram-Based Machine Learning Emulator Model for Predicting Novel Echocardiography-Derived Phenogroups for Cardiac Risk-Stratification: A Prospective Multicenter Cohort Study

Purpose: Electrocardiography (ECG)-derived machine learning models can predict echocardiography (echo)-derived indices of systolic or diastolic function. However, systolic and diastolic dysfunction frequently coexists, which necessitates an integrated assessment for optimal risk-stratification. We explored an ECG-derived model that emulates an echo-derived model that combines multiple parameters for identifying patient phenogroups at risk for major adverse cardiac events (MACE). Methods: In this substudy of a prospective, multicenter study, patients from 3 institutions (n = 727) formed an internal cohort, and the fourth institution was reserved as an external test set (n = 518). A previously validated patient similarity analysis model was used for labeling the patients as low-/high-risk phenogroups. These labels were utilized for training an ECG-derived deep neural network model to predict MACE risk per phenogroup. After 5-fold cross-validation training, the model was tested on the reserved external dataset. Results: Our ECG-derived model showed robust classification of patients, with area under the receiver operating characteristic curve of 0.86 (95% CI: 0.79–0.91) and 0.84 (95% CI: 0.80–0.87), sensitivity of 80% and 76%, and specificity of 88% and 75% for the internal and external test sets, respectively. The ECG-derived model demonstrated an increased probability for MACE in high-risk vs low-risk patients (21% vs 3%; P \u3c 0.001), which was similar to the echo-trained model (21% vs 5%; P \u3c 0.001), suggesting comparable utility. Conclusions: This novel ECG-derived machine learning model provides a cost-effective strategy for predicting patient subgroups in whom an integrated milieu of systolic and diastolic dysfunction is associated with a high risk of MACE

Left atrial reservoir function as a potent marker for first atrial fibrillation or flutter in persons > or = 65 years of age

The aim of this prospective study was to evaluate the incremental value of left atrial (LA) function for the prediction of risk for first atrial fibrillation (AF) or atrial flutter. Maximum and minimum LA volumes were quantitated by echocardiography in 574 adults (mean age 74 ± 6 years, 52% men) without a history or evidence of atrial arrhythmia. During a mean follow-up period of 1.9 ± 1.2 years, 30 subjects (5.2%) developed electrocardiographically confirmed AF or atrial flutter. Subjects with new AF or atrial flutter had lower LA reservoir function, as measured by total LA emptying fraction (38% vs 49%, p <0.0001) and higher maximum LA volumes (47 vs 40 ml/m2, p = 0.005). An increase in age-adjusted risk for AF or atrial flutter was evident when the cohort was stratified according to medians of LA emptying fraction (≤49%: hazard ratio 6.5, p = 0.001) and LA volume (≥38 ml/m2: hazard ratio 2.0, p = 0.07), with the risk being highest for subjects with concomitant LA emptying fractions ≤49% and LA volume ≥38 ml/m2 (hazard ratio 9.3, p = 0.003). LA emptying fraction (p = 0.002) was associated with risk for first AF or atrial flutter after adjusting for baseline clinical risk factors for AF or atrial flutter, left ventricular ejection fraction, diastolic function grade, and LA volume. In conclusion, reduced LA reservoir function markedly increases the propensity for first AF or atrial flutter, independent of LA volume, left ventricular function, and clinical risk factors

17α-estradiol alleviates age-related metabolic and inflammatory dysfunction in male mice without inducing feminization

Aging is associated with visceral adiposity, metabolic disorders, and chronic low-grade inflammation. 17α-estradiol (17α-E2), a naturally occurring enantiomer of 17α-estradiol (17α-E2), extends life span in male mice through unresolved mechanisms. We tested whether 17α-E2 could alleviate age-related metabolic dysfunction and inflammation. 17α-E2 reduced body mass, visceral adiposity, and ectopic lipid deposition without decreasing lean mass. These declines were associated with reductions in energy intake due to the activation of hypothalamic anorexigenic pathways and direct effects of 17α-E2 on nutrient-sensing pathways in visceral adipose tissue. 17α-E2 did not alter energy expenditure or excretion. Fasting glucose, insulin, and glycosylated hemoglobin were also reduced by 17α-E2, and hyperinsulinemic-euglycemic clamps revealed improvements in peripheral glucose disposal and hepatic glucose production. Inflammatory mediators in visceral adipose tissue and the circulation were reduced by 17α-E2. 17α-E2 increased AMPK? and reduced mTOR complex 1 activity in visceral adipose tissue but not in liver or quadriceps muscle, which is in contrast to the generalized systemic effects of caloric restriction. These beneficial phenotypic changes occurred in the absence of feminization or cardiac dysfunction, two commonly observed deleterious effects of exogenous estrogen administration. Thus, 17α-E2 holds potential as a novel therapeutic for alleviating age-related metabolic dysfunction through tissue-specific effects