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Completed

NCT Number: NCT03149042

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

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.

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Key information

Conditions

Atherosclerosis, Coronary Arterial Occlusive Diseases Arteriosclerosis Atherosclerosis Cardiovascular Diseases Constriction, Pathologic Coronary Artery Disease Coronary Disease Heart Diseases Myocardial Ischemia Pathological Conditions, Anatomical Pathological Conditions, Signs and Symptoms Vascular Diseases

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

Primary location

Clinical and Translational Research Center Room 8052

Buffalo, New York, 14021, United States

About this study

Coronary Computed Tomography Angiography (CCTA) contrast opacification gradients and FFR-CT estimation can aid in the severity estimation of significant atherosclerotic lesions. Following this trend, the investigators recently developed a collaboration between Brigham and Women's Hospital (BWH) and Gates Vascular Institute (GVI). The investigators 3D-printed patient specific coronary phantoms at (GVI) and scanned them with a Toshiba Aquilion scanner to test several aspects of the contrast opacification gradients using a method established at BWH. The initial results showed strong correlation between the flow in the phantom and opacification gradients. The investigators believe that this approach could be further developed to test and validate FFR-CT algorithms. Currently, FFR-CT algorithms can only be optimized using theoretical models and can only be validated in large multi-center clinical trials. This phantom approach 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. The approach is to use the infrastructure at GVI to perform a detailed validation of the FFR-CT method using 3D printed patient specific phantoms. The subject enrollment criteria is: at least one CCTA, at least one lesion with >50% stenosis or 30-50% and an angio based FFR. Each patient will have a 3D phantom printed, containing the culprit lesion and used in a benchtop flow analysis. 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 will include ~50 patients over a year and half at GVI. The investigators are confident that this approach performed via 3D-phantom testing will prove the validity of FFR-CT based measurements as well as develop a new standard for validating FFR-CT algorithms.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • All the patients >18 yrs of age , who are undergoing CCTA and angio-FFR. Patients who are (1) scheduled for clinically mandated elective invasive coronary angiography (ICA) at Buffalo General Hospital or (2) clinically mandated CTA will be screened.

Exclusion criteria

  • Adults unable to consent
  • Individuals who are not yet adults (infants, children, teenagers)
  • Pregnant women
  • Prisoners
  • atrial fibrillation,
  • Renal insufficiency (estimated glomerular filtration rate (GFR) <60 ml/min/1.73 m2),
  • Active Bronchospasm prohibiting the use of beta blockers
  • Morbid obesity (body mass index 40 kg/m2)
  • Contraindications to iodinated contrast.
  • Emergencies requiring immediate intervention or patients unable to consent.
  • Patients not showing coronary calcium during Calcium Scoring procedures

Treatment and study plan

CCTA

Diagnostic Test

Diagnostic Test

Primary outcomes

  1. Comparison of CT Based FFR With Invasive FFR, ROC Analysis

    Time frame: 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 of the distal pressure measurement to calculate the CT basedFFR at the same location as Invasive-FFR, two lesion lengths below the distal end of the lesion.

    Area under the Receiver Operator Characteristic were measured where an Invasive FFR<=0.8 was considered positive.

  2. Comparison of CT Based FFR With Invasive FFR, Correlation Analysis

    Time frame: 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 of the distal pressure measurement to calculate the CT basedFFR at the same location as Invasive-FFR, two lesion lengths below the distal end of the lesion.

    Pearson Correlation between Invasive FFR and CT based FFR was measured

  3. Comparison of CT Based FFR With Invasive FFR, Sensitivity

    Time frame: 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 of the distal pressure measurement to calculate the CT basedFFR at the same location as Invasive-FFR, two lesion lengths below the distal end of the lesion.

    Sensitivity were measured where an Invasive FFR<=0.8 was considered positive. Sensitivity reflects the percentage of true positive cases identified by CT-FFR compared to I-FFR

  4. Comparison of CT Based FFR With Invasive FFR, Specificity

    Time frame: 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 of the distal pressure measurement to calculate the CT basedFFR at the same location as Invasive-FFR, two lesion lengths below the distal end of the lesion.

    Specificity was measured, where an Invasive FFR<=0.8 was considered positive. Specificity reflects the percentage of true negative cases identified by CT-FFR compared to I-FFR

Secondary outcomes

  1. Comparison of CT Based FFR With Bench-top FFR Using 3D Printed Patient Specific Phantoms

    Time frame: 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.

  2. Comparison of Bench-top FFR Using 3D Printed Patient Specific Phantoms With Invasive FFR, ROC Analysis

    Time frame: 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.

  3. Comparison of Bench-top FFR Using 3D Printed Patient Specific Phantoms With Invasive FFR, Pearson Correlation

    Time frame: 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.

  4. Comparison of Bench-top FFR Using 3D Printed Patient Specific Phantoms With Invasive FFR, Sensitivity

    Time frame: 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

  5. Comparison of Bench-top FFR Using 3D Printed Patient Specific Phantoms With Invasive FFR, Specificity

    Time frame: 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-FFR

Sponsors and collaborators

Lead sponsor

State University of New York at Buffalo

Other

Registry information

Important dates

Study start
2016
Primary completion
2018
Study completion
2019
First posted
May 11, 2017
Registry last updated
Nov 17, 2020

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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