ABSTRACT
Background: Until recently, Russia did not utilize noninvasive fractional flow reserve (FFR) assessment. We developed an automated algorithm for noninvasive assessment of FFR based on a one-dimensional (1D) mathematical modeling. Objective: The research aims to evaluate the diagnostic accuracy of this algorithm. Methods: The study enrolled 80 patients: 16 of them underwent 64-slice computed tomography - included retrospectively, 64 - prospectively, with a 640-slice CT scan. Specialists processed CT images and evaluated noninvasive FFR. Ischemia was confirmed if FFR < 0.80 and disproved if FFR ≥ 0.80. The prospective group of patients was hospitalized for invasive FFR assessment as a reference standard. If ischemic, patients underwent stent implantation. In the retrospective group, patients already had invasive FFR values.Statistical analysis was performed using GraphPad Prism 8. We compared two methods using a Bland-Altman plot and per-vessel ROC curve analysis. Considering the abnormality of distribution by the Kolmogorov-Smirnov test, we have used Spearman's rank correlation coefficient. Results: During data processing, three patients of the retrospective and 46 patients of the prospective group were excluded. The sensitivity of our method was 66.67% (95% CI: 46.71-82.03); the specificity was 78.95% (95% CI: 56.67-91.49), p = 0.0052, in the per-vessel analysis. In per-patient analysis, the sensitivity was 69.57% (95% CI: 49.13-84.40); the specificity was 87.50% (95% CI: 52.91-99.36), p = 0.0109. The area under the ROC curve in the per-vessel analysis was 77.52% (95% CI: 66.97-88.08), p < 0.0001. Conclusion: The obtained indices of sensitivity, specificity, PPV, and NPV are, in general, comparable to those in other studies. Moreover, the noninvasive values of FFR yielded a high correlation coefficient with the invasive values. However, the AUC was not high enough, 77.52 (95% CI: 66.97-88.08), p < 0.0001. The discrepancy is probably attributed to the initial data heterogeneity and low statistical power.
