NCT03149042

Validation of a Computed Tomography (CT) Based Fractional Flow Reserve (FFR) Software Using the 320 Detector Aquilion ONE CT Scanner.

Completed Results posted Last updated 17 November 2020

What this trial tests

trial testing CCTA in Atherosclerosis, Coronary in 75 participants. Completed in 21 April 2019.

Timeline

28 May 2016

Primary endpoint
31 December 2018

21 April 2019

Quick facts

Lead sponsor	State University of New York at Buffalo
Status	Completed
Study type	OBSERVATIONAL
Enrollment	75
Start date	28 May 2016
Primary completion	31 December 2018
Estimated completion	21 April 2019
Sites	1 location across United States

Drugs / interventions tested

• CCTA

Conditions studied

• Atherosclerosis, Coronary — all drugs for Atherosclerosis, Coronary →

Sponsor

State University of New York at Buffalo

Who can join

18 and older, any sex, with Atherosclerosis, Coronary. Patients with the condition only — healthy volunteers not accepted.

Results — posted to ClinicalTrials.gov

Per-arm endpoint measurements with 95% confidence intervals where reported. Source: trial results section.

Comparison of CT Based FFR With Invasive FFR, ROC Analysis Primary · 24 hours

Patient CCTA images were imported into Vitrea segmentation software (Vital Images, Minnetonka, MN) for use in the research-based CT based FFR algorithm. The software analyzes four data volumes acquired a 70%, 80%, 90% and 99% of the R-R interval and computes the FFR based on the changes in vessel diameter and computational fluid dynamics. Within the algorithm, the aortic root and three main coronary arteries (LAD, LCX, and RCA) were automatically segmented, and then manually adjusted to obtain accurate centerline and contours. The CT based FFR was calculated and the user adjusted the location

Group	Value	95% CI
CCTA	0.8	0.7 – 0.87

Comparison of CT Based FFR With Invasive FFR, Correlation Analysis Primary · 24 hours

Patient CCTA images were imported into Vitrea segmentation software (Vital Images, Minnetonka, MN) for use in the research-based CT based FFR algorithm. The software analyzes four data volumes acquired a 70%, 80%, 90% and 99% of the R-R interval and computes the FFR based on the changes in vessel diameter and computational fluid dynamics. Within the algorithm, the aortic root and three main coronary arteries (LAD, LCX, and RCA) were automatically segmented, and then manually adjusted to obtain accurate centerline and contours. The CT based FFR was calculated and the user adjusted the location

Group	Value	95% CI
CCTA	0.75	0.62 – 0.85

Comparison of CT Based FFR With Invasive FFR, Sensitivity Primary · 24 hours

Patient CCTA images were imported into Vitrea segmentation software (Vital Images, Minnetonka, MN) for use in the research-based CT based FFR algorithm. The software analyzes four data volumes acquired a 70%, 80%, 90% and 99% of the R-R interval and computes the FFR based on the changes in vessel diameter and computational fluid dynamics. Within the algorithm, the aortic root and three main coronary arteries (LAD, LCX, and RCA) were automatically segmented, and then manually adjusted to obtain accurate centerline and contours. The CT based FFR was calculated and the user adjusted the location

Group	Value	95% CI
CCTA	82.61

Comparison of CT Based FFR With Invasive FFR, Specificity Primary · 24 hours

Patient CCTA images were imported into Vitrea segmentation software (Vital Images, Minnetonka, MN) for use in the research-based CT based FFR algorithm. The software analyzes four data volumes acquired a 70%, 80%, 90% and 99% of the R-R interval and computes the FFR based on the changes in vessel diameter and computational fluid dynamics. Within the algorithm, the aortic root and three main coronary arteries (LAD, LCX, and RCA) were automatically segmented, and then manually adjusted to obtain accurate centerline and contours. The CT based FFR was calculated and the user adjusted the location

Group	Value	95% CI
CCTA	76.32

Comparison of CT Based FFR With Bench-top FFR Using 3D Printed Patient Specific Phantoms Secondary · 4 weeks from baseline

CT images were used to measure CT-FFR and to generate patient-specific 3D printed models of the aortic root and three main coronary arteries. Each patient-specific 3D printed model was connected to a programmable pulsatile pump and bench-top FFR (B-FFR) was derived from pressures measured proximal and distal to coronary stenosis using pressure transducers. B-FFR was measured for hyperemic", 500 mL/min by adjusting the model's distal coronary resistance. Linear regression and Pearson correlation was calculated.

Group	Value	95% CI
CCTA	0.64	0.46 – 0.76

Comparison of Bench-top FFR Using 3D Printed Patient Specific Phantoms With Invasive FFR, ROC Analysis Secondary · 4 weeks from baseline

CT images were used to create patient specific 3d-printed phantom. Each patient-specific 3D printed model was connected to a programmable pulsatile pump and benchtop FFR (B-FFR) was derived from pressures measured proximal and distal to coronary stenosis using pressure transducers. B-FFR was measured for hyperemic", 500 mL/min by adjusting the model's distal coronary resistance. Benchtop-FFR was compared with Invasive-FFR. Area under the Receiver Operator Characteristic were measured where an Invasive FFR\<=0.8 was considered positive.

Group	Value	95% CI
CCTA	0.81	0.64 – 0.91

Comparison of Bench-top FFR Using 3D Printed Patient Specific Phantoms With Invasive FFR, Pearson Correlation Secondary · 4 weeks from baseline

CT images were used to create patient specific 3d-printed phantom. Each patient-specific 3D printed model was connected to a programmable pulsatile pump and benchtop FFR (B-FFR) was derived from pressures measured proximal and distal to coronary stenosis using pressure transducers. B-FFR was measured for hyperemic", 500 mL/min by adjusting the model's distal coronary resistance. Benchtop-FFR was compared with Invasive-FFR. Pearson Correlation factor was calculated.

Group	Value	95% CI
CCTA	0.71	0.56 – 0.81

Comparison of Bench-top FFR Using 3D Printed Patient Specific Phantoms With Invasive FFR, Sensitivity Secondary · 4 weeks from baseline

CT images were used to create patient specific 3d-printed phantom. Each patient-specific 3D printed model was connected to a programmable pulsatile pump and benchtop FFR (B-FFR) was derived from pressures measured proximal and distal to coronary stenosis using pressure transducers. B-FFR was measured for hyperemic", 500 mL/min by adjusting the model's distal coronary resistance. Benchtop-FFR was compared with Invasive-FFR. Sensitivity was measure, where an Invasive FFR\<=0.8 was considered positive.Sensitivity reflects the percentage of true positive cases identified by B-FFR compared to I-FFR

Group	Value	95% CI
CCTA	86.96

Comparison of Bench-top FFR Using 3D Printed Patient Specific Phantoms With Invasive FFR, Specificity Secondary · 4 weeks from baseline

CT images were used to create patient specific 3d-printed phantom. Each patient-specific 3D printed model was connected to a programmable pulsatile pump and benchtop FFR (B-FFR) was derived from pressures measured proximal and distal to coronary stenosis using pressure transducers. B-FFR was measured for hyperemic", 500 mL/min by adjusting the model's distal coronary resistance. Benchtop-FFR was compared with Invasive-FFR. Specificity was calculated, where an Invasive FFR\<=0.8 was considered positive. Specificity reflects the percentage of true negative cases identified by B-FFR compared to I

Group	Value	95% CI
CCTA	97.37

Sponsor's own description

Coronary Computed Tomography Angiography (CCTA) contrast opacification gradients and FFR-CT estimation can aid in the severity estimation of significant atherosclerotic lesions. Currently, FFR-CT algorithms can only be optimized using theoretical models and can only be validated in large multi-center clinical trials. Using patient specific 3D printed coronary phantoms would allow optimization of FFR-CT algorithms with a measured validation technique without the need for large clinical trials. Thus the investigators believe that this study will result in a FFR-CT algorithm/method with a better predictability for arterial lesion severity than those existing on the market today. Flow measurements will be compared with: CT-FFR for both patients and phantoms, angio lab FFR measurements and 30 days follow-up. This pilot clinical study includes \~50 patients over a year and half at GVI.

Publications & conference data

2 peer-reviewed publications reference this trial (live from Europe PMC):

1. 3D Printed Cardiovascular Patient Specific Phantoms Used for Clinical Validation of a CT-derived FFR Diagnostic Software.
   Sommer KN, Shepard L, Karkhanis NV, Iyer V, et al · · 2018 · cited 13× · PMID 29899591 · DOI 10.1117/12.2292736
2. Patient-specific 3D-printed coronary models based on coronary computed tomography angiography volumes to investigate flow conditions in coronary artery disease.
   Sommer KN, Shepard LM, Mitsouras D, Iyer V, et al · · 2020 · cited 7× · PMID 33444268 · DOI 10.1088/2057-1976/ab8f6e