Non-Invasive Quantification of Fraction Flow Reserve Based on Steady-State Geometric Multiscale Models

Background: The underuse of invasive fraction flow reserve (FFR) in clinical practice has motivated research towards its non-invasive prediction. The early attempts relied on solving the incompressible three-dimensional Navier–Stokes equations in segmented coronary arteries. However, transient boundary condition has a high resource intensity in terms of computational time. Herein, a method for calculating FFR based on steady-state geometric multiscale (FFR(SS)) is proposed. Methods: A total of 154 moderately stenotic vessels (40–80% diameter stenosis) from 136 patients with stable angina were included in this study to validate the clinical diagnostic performance of FFR(SS). The method was based on the coronary artery model segmented from the patient’s coronary CTA image. The average pressure was used as the boundary condition for the inlet, and the microcirculation resistance calculated by the coronary flow was used as the boundary condition for the outlet to calculate the patient-specific coronary hyperemia. Then, the flow velocity and pressure distribution and the FFRss of each coronary artery branch were calculated to evaluate the degree of myocardial ischemia caused by coronary stenosis. Also, the FFR(SS) and FFR(CT) of all patients were calculated, and the clinically measured FFR was used as the “gold standard” to verify the diagnostic performance of FFR(SS) and to compare the correlation between FFR(SS) and FFR(CT). Results: According to the FFR(SS) calculation results of all patients, FFR(SS) and FFR have a good correlation (r = 0.68, p < 0.001). Similarly, the correlation of FFR(SS) and FFR(CT) demonstrated an r of 0.75 (95%CI: 0.67–0.72) (p < 0.001). On receiver-operating characteristic analysis, the optimal FFR(SS) cut point for FFR≤0.80 was 0.80 (AUC:0.85 [95% confidence interval: 0.79 to 0.90]; overall accuracy:88.3%). The overall sensitivity, specificity, PPV, and NPV for FFR(SS) ≤0.80 versus FFR ≤0.80 was 68.18% (95% CI: 52.4–81.4), 93.64% (95% CI: 87.3–97.4), 82.9%, and 91.1%, respectively. Conclusion: FFR(SS) is a reliable diagnostic index for myocardial ischemia. This method was similar to the closed-loop geometric multiscale calculation of FFR accuracy but improved the calculation efficiency. It also improved the clinical applicability of the non-invasive computational FFR model, helped the clinicians diagnose myocardial ischemia, and guided percutaneous coronary intervention.