Noninvasive Assessment of the Fractional Flow Reserve with the CT FFRc 1D Method: Final Results of a Pilot Study.

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.