Increasing evidence has suggested that plaque characteristics are closely associated with ischemia, and coronary computed tomography (CT) angiography-derived fractional flow reserve (FFR CT ) based on deep machine learning algorithms has also been used to identify lesion-specific ischemia. Therefore, the aim of the present study was to explore the predictive ability of plaque characteristics in combination with deep learning-based FFR CT for lesion-specific ischemia. To meet this end, invasive FFR was used as a reference standard, with the joint aims of the early prediction of ischemic lesions and guiding clinical treatment. In the present study, the plaque characteristics, including non-calcified plaque (NCP), low-density NCP (LD-NCP), plaque length, total plaque volume (TPV), remodeling index, calcified plaque, fibrous plaque and plaque burden, were obtained using a semi-automated program. The FFR CT values were derived based on a deep machine learning algorithm. On the basis of the data obtained, differences among the values between the atopic ischemia and the non-significant lesions groups were analyzed to further determine the predictive value of independent predictors for atopic ischemia. Of the plaque features, FFR CT , LD-NCP, NCP, TPV and plaque length differed significantly when comparing between the lesion-specific ischemia and no hemodynamic abnormality groups, and LD-NCP and FFR CT were both independent predictors for ischemia. Additionally, FFR CT combined with LD-NCP showed a greater ability at discriminating ischemia compared with FFR CT or LD-NCP alone. Taken together, the findings of the present study suggest that the combination of FFR CT and LD-NCP has a synergistic effect in terms of predicting ischemia, thereby facilitating the identification of specific ischemia in patients with coronary artery disease.

Background Coronary distensibility index (CDI), as an early predictor of cardiovascular diseases, has the potential to complement coronary computed tomography angiography (cCTA)-derived fractional flow reserve (CT-FFR) for predicting major adverse cardiac events (MACEs). Thus, the prognostic value of CT-FFR combined with CDI for MACEs is worth exploring. Methods Patients with a moderate or severe single left anterior descending coronary artery stenosis were included and underwent FFR and CDI analysis based on cCTA, followed up at least 1 year, and recorded MACEs. Multivariate logistic regression analysis was performed to determine independent predictors of MACEs. The area under of receiver operating characteristic (ROC) curve was used to evaluated evaluate the diagnostic performance of CT-FFR, CDI, and a combination of the two. Results All the vessel-specific data were from LAD. 150 patients were analysed. 55 (37%) patients experienced MACEs during follow-up. Patients with CT-FFR ≤ 0.8 had higher percentage of MACEs compared with CT-FFR > 0.8 (56.3% vs.7.3%, p < 0.05). Patients’ CDI was significantly decreased in MACEs group compared with non-MACEs group (p < 0.05). Multivariate analysis revealed that diabetes (p = 0.025), triglyceride (p = 0.015), CT-FFR ≤ 0.80 (p = 0.038), and CDI (p < 0.001) are independent predictors of MACEs. According to ROC curve analysis, CT-FFR combined CDI showed incremental diagnostic performance over CT-FFR alone for prediction of MACEs (AUC = 0.831 vs. 0.656, p = 0.0002). Conclusion Our study provides initial evidence that combining CDI with CT-FFR shows incremental discriminatory power for MACEs over CT-FFR alone, independent of clinical risk factors. Diabetes and triglyceride are also associated with MACEs.