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. 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-%V_sub) were measured for labeling. The ML for (1) predicting vessel territories (CAMS-%LAD, CAMS-%LCX, and CAMS-%RCA) and CAMS-%V_sub 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-%V_sub. With 5-fold cross validation, the MAEs between ML-predicted percent myocardial volume subtended to a stenotic segment (ML-%V_sub) and CAMS-%V_sub 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. 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.

Han S, et al. Korean Circ J. 2016 Sep;48:e29. https://doi.org/10.4070/kcj.2017.0359

Aims: We aimed to evaluate the contribution of the retrograde approach to real-world practice over time and its long-term outcomes in chronic total occlusion (CTO) percutaneous coronary intervention (PCI). Methods and results: We evaluated 1,635 CTO procedures conducted at our high-volume centre between 2003 and 2015. The retrograde approach has been actively adopted in practice since January 2007. The primary endpoint was target vessel failure (TVF), a composite of cardiac death, target vessel-related myocardial infarction, or target vessel revascularisation/reocclusion. The technical success rate of CTO-PCI has increased from 79.5% to 87.1% since 2007, although the complexity of the CTOs has also significantly increased in that time (J-CTO scores: from 1.8 +/- 1.2 to 2.0 +/- 1.1, p=0.03). The incidence of in-hospital MACCE using the retrograde approach was 4.5%, which was comparable to the antegrade-only approach rate of 4.1% (p= 0.58). The retrograde approach showed a higher four-year TVF rate after successful stenting compared with the antegrade-only approach (17.1% vs 9.4%, p= 0.01), but this difference was mainly driven by a higher target vessel revascularisation/reocclusion rate. Multivariable analysis revealed that renal dysfunction (hazard ratio [HR] 3.33, 95% confidence interval [CI]: 1.42-7.83), acute coronary syndrome (HR 1.99, 95% CI: 1.26-3.14), the J-CTO score (per 1, HR 1.23, 95% CI: 1.00-1.51), and the smallest stent diameter (per 1 mm, HR 0.39, 95% CI: 0.21-0.74) (all p< 0.05) were independently associated with TVF. Conclusions: A retrograde approach contributes to the increased success of more complex CTO-PCI over time with an acceptable frequency of in-hospital complications and four-year TVF rate.