These results suggest that EAT may reflect the amount of visceral fat, which is associated with insulin resistance and inflammation. The echocardiographic measurement of EAT may provide additional information for assessing CAD risk and predicting the extent and activity of CAD.

Index of microcirculatory resistance, a new index representing microvascular integrity, is a reliable early on-site determinant of myocardial viability and LV recovery after primary stenting for AMI.

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BackgroundIn a previous study, we demonstrated that the thickness of epicardial adipose tissue (EAT), measured by echocardiography, was increased in patients with metabolic syndrome (MS) and coronary artery disease (CAD). Several studies on obese patients, however, failed to demonstrate any relationship between EAT and CAD. We hypothesized that body mass index (BMI) affected the link between EAT and MS and CAD.MethodsWe consecutively enrolled 643 patients (302 males, 341 females; 59 ± 11 years), who underwent echocardiography and coronary angiography. The EAT thickness was measured on the free wall of the right ventricle at the end of diastole. All patients were divided into two groups: high BMI group, ≥27 kg/m2 (n = 165), and non-high BMI group, < 27 kg/m2 (n = 478).ResultsThe median and mean EAT thickness of 643 patients were 3.0 mm and 3.1 ± 2.4 mm, respectively. In the non-high BMI group, the median EAT thickness was significantly increased in patients with MS compared to those without MS (3.5 vs. 1.9 mm, p < 0.001). In the high BMI group, however, there was no significant difference in the median EAT thickness between patients with and without MS (3.0 vs. 2.5 mm, p = 0.813). A receiver operating characteristic (ROC) curve analysis predicting MS revealed that the area under the curve (AUC) of the non-high BMI group was significantly larger than that of the high BMI group (0.659 vs. 0.506, p = 0.007). When compared to patients without CAD, patients with CAD in both the non-high and high BMI groups had a significantly higher median EAT thickness (3.5 vs. 1.5 mm, p < 0.001 and 4.0 vs. 2.5 mm, p = 0.001, respectively). However, an ROC curve analysis predicting CAD revealed that the AUC of the non-high BMI group tended to be larger than that of the high BMI group (0.735 vs. 0.657, p = 0.055).ConclusionsWhile EAT thickness was significantly increased in patients with MS and CAD, the power of EAT thickness to predict MS and CAD was stronger in patients with BMI < 27 kg/m2. These findings showed that the measurement of EAT thickness by echocardiography might be especially useful in an Asian population with a non-high BMI, less than 27 kg/m2.

Background An angiography‐based supervised machine learning ( ML ) algorithm was developed to classify lesions as having fractional flow reserve ≤0.80 versus >0.80. Methods and Results With a 4:1 ratio, 1501 patients with 1501 intermediate lesions were randomized into training versus test sets. Between the ostium and 10 mm distal to the target lesion, a series of angiographic lumen diameter measurements along the centerline was plotted. The 24 computed angiographic features based on the diameter plot and 4 clinical features (age, sex, body surface area, and involve segment) were used for ML by XGBoost. The model was independently trained and tested by 2000 bootstrap iterations. External validation with 79 patients was conducted. Including all 28 features, the ML model with 5‐fold cross‐validation in the 1204 training samples predicted fractional flow reserve ≤0.80 with overall diagnostic accuracy of 78±4% (averaged area under the curve: 0.84±0.03). The 12 high‐ranking features selected by scatter search were involved segment; body surface area; distal lumen diameter; minimal lumen diameter; length of a lumen diameter <2.0 mm, <1.5 mm, and <1.25 mm; mean lumen diameter within the worst segment; sex; diameter stenosis; distal 5‐mm reference lumen diameter; and length of diameter stenosis >70%. Using those 12 features, the ML predicted fractional flow reserve ≤0.80 in the test set with sensitivity of 84%, specificity of 80%, and overall accuracy of 82% (area under the curve: 0.87). The averaged diagnostic accuracy in bootstrap replicates was 81±1% (averaged area under the curve: 0.87±0.01). External validation showed accuracy of 85% (area under the curve: 0.87). Conclusions Angiography‐based ML showed good diagnostic performance in identifying ischemia‐producing lesions and reduced the need for pressure wires.