Fast Computational Approaches to Derive Fractional Pressure Ratio in Patients with Extracranial or Intracranial Symptomatic Stenosis

We aimed to evaluate the performance of fast and straightforward Murray law-based quantitative flow ratio (μQFR) computation in cerebrovascular stenosis. A total of 30 patients with symptomatic stenosis of 50%–70% luminal stenosis and underwent fractional pressure ratio (FPR) assessment at our hospital were included in the present study. μQFR was applied to the interrogated vessel. An artificial intelligence algorithm was proposed for automatic delineation of lumen contours of cerebrovascular stenosis. We used invasive FPRs as a reference standard. Pearson's correlation coefficient (r) was used to assess the correlation strength between the μQFR and FPR, and Bland-Altman plots were used to evaluate the agreement between the μQFR and FPR. An analysis of the receiver operating characteristic was used to evaluate the performance of μQFR. Our results displayed a strong positive correlations (r = 0.92; P < 0.001) between the μQFR and pressure wire FPR. Excellent agreement was observed between the μQFR and FPR with a mean difference of 0.01 ± 0.08 (range, −0.16 to 0.14; P = 0.263). The overall accuracy for identifying an FPR of ≤0.7 was 92% (95% confidence interval [CI], 85%–100%). The area under the receiver operating characteristic curve was higher for the μQFR (0.92; 95% CI, 0.81–0.98) than for diameter stenosis (0.88; 95% CI, 0.75–0.95). The positive likelihood ratio was 3.9 for the μQFR with a negative likelihood ratio of 0. The μQFR computation has a strong correlation and agrees with the FPR calculated from the pressure wire. Therefore, the μQFR might provide an essential therapeutic aid for patients with symptomatic stenosis.