Diagnostic Performance of CT FFR With a New Parameter Optimized Computational Fluid Dynamics Algorithm From the CT-FFR-CHINA Trial: Characteristic Analysis of Gray Zone Lesions and Misdiagnosed Lesions

Agreement of Fractional Flow Reserve Estimated by Computed Tomography With Invasively Measured Fractional Flow Reserve: A Systematic Review and Meta-Analysis

BACKGROUND

Fractional flow reserve (FFR) is the ratio of blood pressure measured distal to a stenosis and pressure proximal to a stenosis. FFR can be estimated noninvasively using computed tomography (CT) although the usefulness of this technique remains controversial. This meta-analysis evaluated the agreement of FFR estimated by CT (FFR-CT) with invasively measured FFR. The study also evaluated the diagnostic accuracy of FFR-CT, defined as the ability of FFR-CT to classify lesions as hemodynamically significant (invasive FFR ≤0.8) or insignificant (invasive FFR >0.8).

METHODS AND RESULTS

Forty-three studies reporting on 7291 blood vessels from 5236 patients were included. A moderate positive linear relationship between FFR-CT and invasively measured FFR was observed (Spearman correlation coefficient: 0.67). Agreement between the 2 measures increased as invasively measured FFR values approached 1. The overall diagnostic accuracy, sensitivity and specificity of FFR-CT were 82.2%, 80.9%, and 83.1%, respectively. Diagnostic accuracy of 90% could be demonstrated for FFR-CT values >0.90 and <0.49. The diagnostic accuracy of off-site tools was 79.4% and the diagnostic accuracy of on-site tools was 84.1%.

CONCLUSIONS

The agreement between FFR-CT and invasive FFR is moderate although agreement is highest in vessels with FFR-CT >0.9. Diagnostic accuracy varies widely with FFR-CT value but is above 90% for FFR-CT values >0.90 and <0.49. Furthermore, on-site and off-site tools have similar performance. Ultimately, FFR-CT may be a useful adjunct to CT coronary angiography as a gatekeeper for invasive coronary angiogram.

Coronary Artery Disease Assessment via On-Site CT Fractional Flow Reserve in Patients Undergoing Transcatheter Aortic Valve Replacement

Purpose To examine the clinical feasibility of workstation-based CT fractional flow reserve (CT-FFR) for coronary artery disease (CAD) evaluation during preprocedural planning in patients undergoing transcatheter aortic valve replacement (TAVR). Materials and Methods In this retrospective single-center study, 434 patients scheduled for TAVR between 2018 and 2020 were screened for study inclusion; a relevant proportion of patients (35.0% [152 of 434]) was not suitable for evaluation due to insufficient imaging properties. A total of 112 patients (mean age, 82.1 years ± 6.7 [SD]; 58 [52%] men) were included in the study. Invasive angiography findings, coronary CT angiography results, and Agatston score were acquired and compared with on-site CT-FFR computation for evaluation of CAD and prediction of major adverse cardiovascular events (MACE) within a 24-month follow-up. Results Hemodynamic relevant CAD, as suggested by CT-FFR of 0.80 or less, was found in 41 of 70 (59%) patients with stenosis of 50% or more. MACE occurred in 23 of 112 (20.5%) patients, from which 14 of 23 had stenoses with CT-FFR of 0.80 or less (hazard ratio [HR], 3.33; 95% CI: 1.56, 7.10; P = .002). CT-FFR remained a significant predictor of MACE after inclusion in a multivariable model with relevant covariables (HR, 2.89; 95% CI: 1.22, 6.86; P = .02). An Agatston score of 1000 Agatston units or more (HR, 2.25; 95% CI: 0.98, 5.21; P = .06) and stenoses of 50% or more determined via invasive angiography (HR, 0.94; 95% CI: 0.41, 2.17; P = .88) were not significant predictors of MACE. Conclusion Compared with conventional CAD markers, CT-FFR better predicted adverse outcomes after TAVR. A relevant portion of the screened cohort, however, was not suitable for CT-based CAD evaluation. Keywords: CT, Transcatheter Aortic Valve Implantation/Replacement (TAVI/TAVR), Cardiac, Coronary Arteries, Outcomes Analysis © RSNA, 2024 See also the commentary by Weir-McCall and Pugliese in this issue.

On-site CT-derived cFFR in patients with suspected coronary artery disease: Feasibility on a 128-row CT scanner in everyday clinical practice

PURPOSE

 Technical feasibility of CT-based calculation of fractional flow reserve (cFFR) using a 128-row computed tomography scanner in an everyday routine setting. Post-processing and everyday practicability should be analyzed on the scanner on-site in connection with clinical parameters.

MATERIALS AND METHODS

 This single-center retrospective analysis included 230 patients (74 female; mean age 63.8 years) with CCTA within 21 months between 01/2018 and 09/2019 without non-pathological examinations. cFFR values were obtained using a deep learning-based non-commercial research prototype (cFFR Version3.5.0; Siemens Healthineers GmbH, Erlangen). cFFR values were evaluated at two points: at the maximum point of the stenosis and 1.0 cm distal to the stenosis. Comparison with invasive coronary angiography in 57/230 patients (24.7 %) was performed. CT parameters and quality were evaluated. Further subgroup classification concerning criteria of technical postprocessing was performed: no changes necessary, minor corrections necessary, major corrections necessary, and no evaluation was possible. The required time from starting the software to the final result was evaluated.

RESULTS

 A total of 116/448 (25.9 %) mild, 223/448 (49.8 %) moderate, and 109/448 (24.3 %) obstructive stenoses was found. The mean cFFR at the maximum point of the stenosis was 0.92 ± 0.09 and significantly higher than the cFRR value of 0.89 ± 0.13 distal to the stenosis (p < 0.001*). The mean degree of stenosis was 44.02 ± 26.99 % (range: 1-99 %) with an area of 5.39 ± 3.30 mm2. In a total of 45 patients (19.1 %), a relevant reduction in cFFR below 0.80 was determined. Overall, in 57/230 patients (24.8 %), catheter angiography was performed. No significant difference in the degree of maximal stenosis (CAD-RADS 0-2/3/4) was detected between the classification of CCTA and ICA (p = 0.171). The mean post-processing time varied significantly with 8.34 ± 4.66 min. in single-vessel CAD vs. 12.91 ± 3.92 min. in two-vessel CAD vs. 21.80 ± 5.94 min. in three-vessel CAD (each p < 0.001).

CONCLUSION

 Noninvasive onsite quantification of cFFR is feasible with minimal observer interaction in a routine real-world setting on a 128-row scanner. Deep learning-based algorithms allow a robust and semi-automatic on-site determination of cFFR based on data from standard CT scanners.

KEY POINTS

· Non-invasive on-site quantification of cFFR is feasible with minimal observer interaction.. · Deep-learning based algorithms allow robust and semi-automatic on-site determination of cFFR.. · The mean follow-up time varied significantly with the extent of vascular CAD..