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Background: Invasive fractional flow reserve (FFR) is a standard tool for identifying ischemia-producing coronary stenosis. However, in clinical practice, over 70% of treatment decisions still rely on visual estimation of angiographic stenosis, which has limited accuracy (about 60%-65%) for the prediction of FFR < 0.80. One of the reasons for the visual-functional mismatch is that myocardial ischemia can be affected by the supplied myocardial size, which is not always evident by coronary angiography. The aims of this study were to develop an angiography-based machine learning (ML) algorithm for predicting the supplied myocardial volume for a stenosis, as measured using coronary computed tomography angiography (CCTA), and then to build an angiography-based classifier for the lesions with an FFR < 0.80 versus ≥ 0.80.Methods and Findings: A retrospective study was conducted using data from 1,132 stable and unstable angina patients with 1,132 intermediate lesions who underwent invasive coronary angiography, FFR, and CCTA at the Asan Medical Center, Seoul, Korea, between 1 May 2012 and 30 November 2015. The mean age was 63 ± 10 years, 76% were men, and 72% of the patients presented with stable angina. Of these, 932 patients (assessed before 31 January 2015) constituted the training set for the algorithm, and 200 patients (assessed after 1 February 2015) served as a test cohort to validate its diagnostic performance. Additionally, external validation with 79 patients from two centers (CHA University, Seongnam, Korea, and Ajou University, Suwon, Korea) was conducted. After automatic contour calibration using the caliber of guiding catheter, quantitative coronary angiography was performed using the edge-detection algorithms (CAAS-5, Pie-Medical). Clinical information was provided by the Asan BiomedicaL Research Environment (ABLE) system. The CCTA-based myocardial segmentation (CAMS)-derived myocardial volume supplied by each vessel (right coronary artery [RCA], left anterior descending [LAD], left circumflex [LCX]) and the myocardial volume subtended to a stenotic segment (CAMS-%Vsub) were measured for labeling. The ML for (1) predicting vessel territories (CAMS-%LAD, CAMS-%LCX, and CAMS-%RCA) and CAMS-%Vsub and (2) identifying the lesions with an FFR < 0.80 was constructed. Angiography-based ML, employing a light gradient boosting machine (GBM), showed mean absolute errors (MAEs) of 5.42%, 8.57%, and 4.54% for predicting CAMS-%LAD, CAMS-%LCX, and CAMS-%RCA, respectively. The percent myocardial volumes predicted by ML were used to predict the CAMS-%Vsub. With 5-fold cross validation, the MAEs between ML-predicted percent myocardial volume subtended to a stenotic segment (ML-%Vsub) and CAMS-%Vsub were minimized by the elastic net (6.26% ± 0.55% for LAD, 5.79% ± 0.68% for LCX, and 2.95% ± 0.14% for RCA lesions). Using all attributes (age, sex, involved vessel segment, and angiographic features affecting the myocardial territory and stenosis degree), the ML classifiers (L2 penalized logistic regression, support vector machine, and random forest) predicted an FFR < 0.80 with an accuracy of approximately 80% (area under the curve [AUC] = 0.84-0.87, 95% confidence intervals 0.71-0.94) in the test set, which was greater than that of diameter stenosis (DS) > 53% (66%, AUC = 0.71, 95% confidence intervals 0.65-0.78). The external validation showed 84% accuracy (AUC = 0.89, 95% confidence intervals 0.83-0.95). The retrospective design, single ethnicity, and the lack of clinical outcomes may limit this prediction model's generalized application.Conclusion: We found that angiography-based ML is useful to predict subtended myocardial territories and ischemia-producing lesions by mitigating the visual-functional mismatch between angiographic and FFR. Assessment of clinical utility requires further validation in a large, prospective cohort study. [ABSTRACT FROM AUTHOR]

Background: Although the measurement of turbulence kinetic energy (TKE) by using magnetic resonance imaging (MRI) has been introduced as an alternative index for quantifying energy loss through the cardiac valve, experimental verification and clinical application of this parameter are still required. Objectives: The goal of this study is to verify MRI measurements of TKE by using a phantom stenosis with particle image velocimetry (PIV) as the reference standard. In addition, the feasibility of measuring TKE with MRI is explored. Methods: MRI measurements of TKE through a phantom stenosis was performed by using clinical 3T MRI scanner. The MRI measurements were verified experimentally by using PIV as the reference standard. In vivo application of MRI-driven TKE was explored in seven patients with aortic valve disease and one healthy volunteer. Transvalvular gradients measured by MRI and echocardiography were compared. Results: MRI and PIV measurements of TKE are consistent for turbulent flow (0.666 < R2 < 0.738) with a mean difference of −11.13 J/m3 (SD = 4.34 J/m3). Results of MRI and PIV measurements differ by 2.76 ± 0.82 cm/s (velocity) and −11.13 ± 4.34 J/m3 (TKE) for turbulent flow (Re > 400). The turbulence pressure drop correlates strongly with total TKE (R2 = 0.986). However, in vivo measurements of TKE are not consistent with the transvalvular pressure gradient estimated by echocardiography. Conclusions: These results suggest that TKE measurement via MRI may provide a potential benefit as an energy-loss index to characterize blood flow through the aortic valve. However, further clinical studies are necessary to reach definitive conclusions regarding this technique. [ABSTRACT FROM AUTHOR]