Background: Computed tomography–derived fractional flow reserve (CT-FFR) using on-site machine learning enables identification of both the presence of coronary artery disease and vessel-specific ischemia. However, it is unclear whether on-site CT-FFR improves clinical or economic outcomes when compared with the standard of care in patients with stable coronary artery disease. Methods: In total, 1216 patients with stable coronary artery disease and an intermediate stenosis of 30% to 90% on coronary computed tomographic angiography were randomized to an on-site CT-FFR care pathway using machine learning or to standard care in 6 Chinese medical centers. The primary end point was the proportion of patients undergoing invasive coronary angiography without obstructive coronary artery disease or with obstructive disease who did not undergo intervention within 90 days. Secondary end points included major adverse cardiovascular events, quality of life, symptoms of angina, and medical expenditure at 1 year. Results: Baseline characteristics were similar in both groups, with 72.4% (881/1216) having either typical or atypical anginal symptoms. A total of 421 of 608 patients (69.2%) in the CT-FFR care group and 483 of 608 patients (79.4%) in the standard care group underwent invasive coronary angiography. Compared with standard care, the proportion of patients undergoing invasive coronary angiography without obstructive coronary artery disease or with obstructive disease not undergoing intervention was significantly reduced in the CT-FFR care group (28.3% [119/421] versus 46.2% [223/483]; P<0.001). Overall, more patients underwent revascularization in the CT-FFR care group than in the standard care group (49.7% [302/608] versus 42.8% [260/608]; P=0.02), but major adverse cardiovascular events at 1 year did not differ (hazard ratio, 0.88 [95% CI, 0.59–1.30]). Quality of life and symptoms improved similarly during follow-up in both groups, and there was a trend towards lower costs in the CT-FFR care group (difference, –¥4233 [95% CI, –¥8165 to ¥973]; P=0.07). Conclusions: On-site CT-FFR using machine learning reduced the proportion of patients with stable coronary artery disease undergoing invasive coronary angiography without obstructive disease or requiring intervention within 90 days, but increased revascularization overall without improving symptoms or quality of life, or reducing major adverse cardiovascular events. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT03901326.

Objective To analyze the correlation between calcification factors and fractional flow reserve derived from CT (CT?FFR). And to evaluate the diagnostic efficacy of CT?FFR in coronary artery lesions with calcification compared with that of invasive FFR. Methods Sixty?five patients (74 coronary artery vessels) who were admitted to Beijing Anzhen Hospital from July 2014 to December 2016 were included in this study retrospectively. All patients had completed CCTA (coronary CT angiography), coronary angiography and invasive FFR measurements, and had coronary lesions contain calcifications. The evaluation of CCTA data included quantitative analyses of plaque components, coronary artery stenosis, and CT?FFR measurements. The patients′basic data were grouped and compared according to the FFR values. The measurement data was tested by independent?samples t tests, and the categorical data were analyzed by χ2 tests. Quantitative measurements of plaques were compared between groups using independent?sample t tests or rank sum tests based on FFR and CT?FFR values. The reproducibility of CT?FFR measurement software was evaluated by inter?class correlation coefficient (ICC) and the Youden index was calculated to determine the threshold for CT?FFR diagnosis of ischemia. Pearson or Spearman correlation analyses were used to assess the correlations between CT plaque quantitative indicators, CT?FFR and invasive FFR. Multivariate logistic regression analysis was used to analyze the predictors of ischemia by FFR and CT?FFR. In contrast to invasive FFR results, the sensitivity, specificity, negative predictive value, positive predictive value (PPV) of CT?FFR in the diagnosis of coronary ischemic lesions were evaluated, and the diagnostic consistency was evaluated by the Bland?Altman method. Results Compared with invasive FFR, CT?FFR had a more significant correlation with calcification volume and ratio of calcification in plaques (r=-0.519 and－0.547, respectively, both P=0.001). Multivariate logistic regression analysis showed that plaque length was a predictor of invasive FFR in the diagnosis of pathological ischemia ( OR=1.13, 95%CI : 1.05—1.23, P=0.002), and was associated with CT?FFR to determine pathological ischemia. In addition to plaque length ( OR=1.10, 95%CI : 1.02—1.18, P=0.010), the predictor also included ratio of calcification in plaque ( OR=1.09, 95%CI: 1.03—1.15, P=0.003). Compared with invasive FFR results, the diagnostic sensitivity of CT?FFR was 79.1%, the specificity was 80.6%, the PPV was 85.0%, and the area under the ROC curve was 0.78. The result for the diagnosis of ischemia lesion by using CT?FFR had significant statistical differences with the results by according coronary artery stenosis (χ2=10.05, P=0.002; χ2=34.71, P=0.001; χ2=7.65, P=0.006; Z=2.10, P=0.029). The Bland?Altman analysis showed a mean difference of －0.01 (-0.26—0.25) between the CT?FFR and the invasive FFR. Conclusions There is no significant correlation between the proportion of calcification components of coronary plaque and the presence or absence of myocardial ischemia, but the proportion of calcification in plaque will affect the result that is evaluated by CT?FFR. However, compared with CT?based stenosis evaluation, CT?FFR can still significantly improve the ability of CCTA to diagnose ischemia lesion with calcification.