BACKGROUND

Fractional flow reserve with computed tomography (FFR-CT) is influenced by calcified plaque artifacts, which can reduce its accuracy in predicting myocardial ischemia. Subtraction techniques can mitigate these artifacts, thereby enhancing diagnostic precision. However, the potential of subtraction FFR-CT and the pericoronary fat attenuation index (FAI) to improve the prediction of revascularization in patients with coronary artery disease (CAD) remains unclear. We aimed to evaluate the diagnostic performance of FFR-CT and pericoronary FAI in identifying the need for revascularization in patients with CAD.

METHODS

We retrospectively analyzed coronary computed tomography angiography (CCTA) data from 76 patients with CAD (142 branches) using both conventional and subtraction CCTA images. The diagnostic performance of FFR-CT and FAI in identifying revascularization needs was assessed using receiver operating characteristic curves.

RESULTS

Among the 76 patients, 54 underwent revascularization. Patients who underwent revascularization had higher pericoronary FAI and lower FFR-CT values. Subtraction FFR-CT values were higher than those obtained using conventional methods. Models 4 (subtraction CCTA), 5 (Model 4 + subtraction FFR-CT), and 6 (Model 5 + subtraction FAI) showed significantly better diagnostic efficacy for revascularization needs than compared to the Models 1 (conventional CCTA), 2 (Model 1 + conventional FFR-CT), and 3 (Model 2 + conventional FAI) (all  < 0.05). In the subtraction models, Model 6 and 5 were significantly more effective than Models 4 (all  < 0.05). Additionally, when clinical variables (male, age, body mass index, hypertension, dyslipidemia, diabetes mellitus, and smoking) were incorporated into Models 3 and 6, the resulting Models 7 and 8 performed significantly better than Model 3 (all  < 0.05).

CONCLUSION

Subtraction techniques have significantly improved the efficacy of CCTA with FFR-CT in assessing the need for revascularization in patients with CAD. By integrating clinical variables, CCTA, FFR-CT, and pericoronary FAI, individualized therapeutic decisions for CAD patients can be further optimized.