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

Abstract

We aimed to evaluate the performance of fast and straightforward μQFR computation in cerebrovascular stenosis lesions. A total of 30 patients with symptomatic stenosis lesions of 50-70% luminal stenosis and underwent FPR assessment at our hospital were included in this 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 fractional pressure ratios (FPRs) as a reference standard. Pearson's correlation coefficient (r) was used to assess the correlation strength between μQFR and FPR, while Bland-Altman plots were used to evaluate the agreement between μQFR and FPR. An analysis of receiver-operating characteristics (ROC) was used to evaluate the performance of μQFR. Our result displayed strong positive correlations (r = 0.92; p<0.001) between μQFR and pressure wire FPR. An excellent agreement was observed between μQFR and FPR with a mean difference of 0.01 + 0.08; p=0.263; range from -0.16 to 0.14. The overall accuracy for identifying an FPR of <0.7 was 92% (95% CI: 85% to 100%). The area under the receiver-operating characteristic curve was higher for μQFR (0.92 [95% CI: 0.81 to 0.98]) than diameter stenosis (DS) (0.88 [95% CI: 0.75 to 0.95]. The positive likelihood ratio was 3.9 for μQFR with a negative likelihood ratio of 0, respectively. The μQFR computation has a strong correlation and agrees with the FPR calculated from the pressure wire. Therefore, μQFR may provide an essential therapeutic aid in patients with symptomatic stenosis lesions.