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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| CCTA | Patients who are scheduled for clinically mandated elective invasive coronary angiography (ICA) at Buffalo General Hospital. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| CCTA | Diagnostic Test | Diagnostic Test |
|
| Measure | Description | Time Frame |
|---|---|---|
| Comparison of CT Based FFR With Invasive FFR, ROC Analysis | 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. | 24 hours |
| Comparison of CT Based FFR With Invasive FFR, Correlation Analysis | 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 | 24 hours |
| Comparison of CT Based FFR With Invasive FFR, Sensitivity | 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 |
| Measure | Description | Time Frame |
|---|---|---|
| Comparison of CT Based FFR With Bench-top FFR Using 3D Printed Patient Specific Phantoms | 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. |
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Inclusion Criteria:
Exclusion Criteria:
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Patients who are (1) scheduled for clinically mandated elective invasive coronary angiography (ICA) at Buffalo General Hospital or Juntendo Hospital Japan (2) clinically mandated CTA will be screened.
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Clinical and Translational Research Center Room 8052 | Buffalo | New York | 14021 | United States |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 28663663 | Background | Sommer K, Izzo RL, Shepard L, Podgorsak AR, Rudin S, Siddiqui AH, Wilson MF, Angel E, Said Z, Springer M, Ionita CN. Design Optimization for Accurate Flow Simulations in 3D Printed Vascular Phantoms Derived from Computed Tomography Angiography. Proc SPIE Int Soc Opt Eng. 2017 Feb 11;10138:101380R. doi: 10.1117/12.2253711. Epub 2017 Mar 13. | |
| Background | Ionita, C., Angel, E., Mitsouras, D., Rudin, S., Bednarek, D., Zaid, S., Wilson, M. and Rybicki, F. (2016), TU-H-CAMPUS-IeP2-03: Development of 3D Printed Coronary Phantoms for In-Vitro CT-FFR Validation Using Data from 320- Detector Row Coronary CT Angiography. Med. Phys., 43: 3781. doi:10.1118/1.4957681 | ||
| Background | Kelsey N. Sommer, Lauren M. Shepard, Vijay Iyer, Erin Angel, Michael F. Wilson, Frank J. Rybicki, Dimitrios Mitsouras, Kanako Kunishima Kumamaru, Stephen Rudin, and Ciprian N. Ionita. Comparison of benchtop pressure gradient measurements in 3D printed patient specific cardiac phantoms with CT-FFR and computational fluid dynamic simulations, Proc. SPIE 10953, Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging, 109531P (15 March 2019); |
| Label | URL |
|---|---|
| results | View source |
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| ID | Title | Description |
|---|---|---|
| FG000 | CCTA | Patients who are scheduled for clinically mandated elective invasive coronary angiography (ICA) at Buffalo General Hospital. CCTA (Coronary CT angiography) Diagnostic Test |
| Title | Milestones | Reasons Not Completed | ||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Overall Study |
|
|
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| ID | Title | Description |
|---|---|---|
| BG000 | CCTA | Patients who are scheduled for clinically mandated elective invasive coronary angiography (ICA) at Buffalo General Hospital. CCTA Coronary: Diagnostic Test |
| Units | Counts |
|---|---|
| Participants |
|
| Title | Description | Population Description | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Denominator Units Selected | Denominators | Classes |
|---|---|---|---|---|---|---|---|---|---|
| Age, Categorical | Count of Participants |
| Type | Title | Description | Population Description | Reporting Status | Anticipated Posting Date | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Time Frame | Units Analyzed | Denominator Units Selected | Arm/Group Information | Denominators | Classes | Analyses |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Primary | Comparison of CT Based FFR With Invasive FFR, ROC Analysis | 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. | From the total number of patients, nine patients had multi-vessel disease and I-FFR was measured in two vessels. In total I-FFR was measured in: 42 LADs, 11 LCXs and 8 RCAs. Invasive -FFR measurement location was from the ostium to two lesion lengths below the distal throat of the lesion . | Posted | Number | 95% Confidence Interval | probability of accurate diagnosis | 24 hours | Coronary Arteries | Coronary Arteries |
The participant was observed for 24 hours post CCTA acquisition.
No risk of serious adverse events, mortality, or other adverse events during CCTA scan.
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| ID | Title | Description | Deaths (Affected) | Deaths (At Risk) | Serious Events (Affected) | Serious Events (At Risk) | Other Events (Affected) | Other Events (At Risk) |
|---|---|---|---|---|---|---|---|---|
| EG000 | CCTA Coronary | Patients who are scheduled for clinically mandated elective invasive coronary angiography (ICA) at Buffalo General Hospital and Juntendo University, Japan. CCTA Coronary: Diagnostic Test |
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Operators were not blinded to the invasive-FFR results at the time of calculating the CT based FFR and Bench-top measurements
| Title | Organization | Phone | Extension | |
|---|---|---|---|---|
| Dr. Ciprian Ionita | University at Buffalo | 7164004283 | cnionita@buffalo.edu |
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| Type | Includes Protocol | Includes SAP | Includes ICF | Document Label | Document Date | Document Uploaded Date | Document File Name |
|---|---|---|---|---|---|---|---|
| ICF | No | No | Yes | Informed Consent Form | Jan 15, 2019 | Sep 24, 2019 | ICF_000.pdf |
| Prot_SAP | Yes | Yes | No | Study Protocol and Statistical Analysis Plan | Sep 24, 2019 | Oct 1, 2019 | Prot_SAP_001.pdf |
Not provided
| ID | Term |
|---|---|
| D003324 | Coronary Artery Disease |
| D050197 | Atherosclerosis |
| D003251 | Constriction, Pathologic |
| ID | Term |
|---|---|
| D003327 | Coronary Disease |
| D017202 | Myocardial Ischemia |
| D006331 | Heart Diseases |
| D002318 | Cardiovascular Diseases |
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| 24 hours |
| Comparison of CT Based FFR With Invasive FFR, Specificity | 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 | 24 hours |
| 4 weeks from baseline |
| Comparison of Bench-top FFR Using 3D Printed Patient Specific Phantoms With Invasive FFR, ROC Analysis | 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. | 4 weeks from baseline |
| Comparison of Bench-top FFR Using 3D Printed Patient Specific Phantoms With Invasive FFR, Pearson Correlation | 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 weeks from baseline |
| Comparison of Bench-top FFR Using 3D Printed Patient Specific Phantoms With Invasive FFR, Sensitivity | 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 | 4 weeks from baseline |
| Comparison of Bench-top FFR Using 3D Printed Patient Specific Phantoms With Invasive FFR, Specificity | 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 | 4 weeks from baseline |
| 30891468 | Background | Shepard LM, Sommer KN, Angel E, Iyer V, Wilson MF, Rybicki FJ, Mitsouras D, Molloi S, Ionita CN. Initial evaluation of three-dimensionally printed patient-specific coronary phantoms for CT-FFR software validation. J Med Imaging (Bellingham). 2019 Apr;6(2):021603. doi: 10.1117/1.JMI.6.2.021603. Epub 2019 Mar 12. |
| 29899591 | Background | Sommer KN, Shepard L, Karkhanis NV, Iyer V, Angel E, Wilson MF, Rybicki FJ, Mitsouras D, Rudin S, Ionita CN. 3D Printed Cardiovascular Patient Specific Phantoms Used for Clinical Validation of a CT-derived FFR Diagnostic Software. Proc SPIE Int Soc Opt Eng. 2018 Feb;10578:105780J. doi: 10.1117/12.2292736. Epub 2018 Mar 12. |
| 28649159 | Background | Shepard L, Sommer K, Izzo R, Podgorsak A, Wilson M, Said Z, Rybicki FJ, Mitsouras D, Rudin S, Angel E, Ionita CN. Initial Simulated FFR Investigation Using Flow Measurements in Patient-specific 3D Printed Coronary Phantoms. Proc SPIE Int Soc Opt Eng. 2017 Feb 11;10138:101380S. doi: 10.1117/12.2253889. Epub 2017 Mar 13. |
| Background | Kelsey N. Sommer, Lauren M. Shepard, Vijay Iyer, Erin Angel, Michael F. Wilson, Frank J. Rybicki, Dimitrios Mitsouras, Ciprian Ionita. Study of the effect of boundary conditions on fractional flow reserve using patient specific coronary phantoms. Proceedings Volume 11317, Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging; 113171J (2020) https://doi.org/10.1117/12.2548472 |
| 33444268 | Result | Sommer KN, Shepard LM, Mitsouras D, Iyer V, Angel E, Wilson MF, Rybicki FJ, Kumamaru KK, Sharma UC, Reddy A, Fujimoto S, Ionita CN. Patient-specific 3D-printed coronary models based on coronary computed tomography angiography volumes to investigate flow conditions in coronary artery disease. Biomed Phys Eng Express. 2020 May 14;6(4):045007. doi: 10.1088/2057-1976/ab8f6e. |
| 33778507 | Result | Kumamaru KK, Angel E, Sommer KN, Iyer V, Wilson MF, Agrawal N, Bhardwaj A, Kattel SB, Kondziela S, Malhotra S, Manion C, Pogorzelski K, Ramanan T, Sawant AC, Suplicki MM, Waheed S, Fujimoto S, Sharma UC, Rybicki FJ, Ionita CN. Inter- and Intraoperator Variability in Measurement of On-Site CT-derived Fractional Flow Reserve Based on Structural and Fluid Analysis: A Comprehensive Analysis. Radiol Cardiothorac Imaging. 2019 Aug 29;1(3):e180012. doi: 10.1148/ryct.2019180012. eCollection 2019 Aug. |
| Participants |
|
| Age, Continuous | Mean | Standard Deviation | years |
|
| Sex: Female, Male | Count of Participants | Participants |
|
| Ethnicity (NIH/OMB) | Count of Participants | Participants |
|
| Region of Enrollment | Count of Participants | Participants |
|
| BMI | Mean | Standard Deviation | kg/m^2 |
|
| Coronary Calcium Score | The Coronary Calcium Score also referred as Agatston score is calculated using a non-contrast computed tomography (CT) scan to measure for the presence and severity of coronary artery disease through identification of calcification in the coronary arteries. Scores can range from 0 to several thousands. The measure is without units. Score categories are as follows: 0 = no coronary disease; 1-100 = low amount of coronary artery disease; 101-400 = moderately elevated score / moderate coronary artery disease; 401-1000 = severely elevated score; >1000 very severely elevated score | Mean | Standard Deviation | Coronary Calcium Score |
|
| FFR | Count of Participants | Participants |
|
| Diabetes Mellitus | Count of Participants | Participants |
|
| Hypertension | Count of Participants | Participants |
|
| Hyperlipidemia | Count of Participants | Participants |
|
| Smoking | Count of Participants | Participants |
|
| Prior Myocardial Infarction | Count of Participants | Participants |
|
| Creatinine | Mean | Standard Deviation | mg/ dl |
|
| ID | Title | Description |
|---|---|---|
| OG000 | CCTA | Patients who are scheduled for clinically mandated elective invasive coronary angiography (ICA) at Buffalo General Hospital. CCTA Coronary: Diagnostic Test |
|
|
| Primary | Comparison of CT Based FFR With Invasive FFR, Correlation Analysis | 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 | From the total number of patients, nine patients had multi-vessel disease and I-FFR was measured in two vessels. In total I-FFR was measured in: 42 LADs, 11 LCXs and 8 RCAs. Invasive -FFR measurement location was from the ostium to two lesion lengths below the distal throat of the lesion . | Posted | Number | 95% Confidence Interval | correlation coefficient | 24 hours | Coronary Arteries | Coronary Arteries |
|
|
|
| Primary | Comparison of CT Based FFR With Invasive FFR, Sensitivity | 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 | From the total number of patients, nine patients had multi-vessel disease and I-FFR was measured in two vessels. In total I-FFR was measured in: 42 LADs, 11 LCXs and 8 RCAs. Invasive -FFR measurement location was from the ostium to two lesion lengths below the distal throat of the lesion . | Posted | Number | percentage of true positive cases | 24 hours | Coronary Arteries | Coronary Arteries |
|
|
|
| Primary | Comparison of CT Based FFR With Invasive FFR, Specificity | 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 | From the total number of patients, nine patients had multi-vessel disease and I-FFR was measured in two vessels. In total I-FFR was measured in: 42 LADs, 11 LCXs and 8 RCAs. Invasive -FFR measurement location was from the ostium to two lesion lengths below the distal throat of the lesion . | Posted | Number | percentage of of true negative cases | 24 hours | Coronary Arteries | Coronary Arteries |
|
|
|
| Secondary | Comparison of CT Based FFR With Bench-top FFR Using 3D Printed Patient Specific Phantoms | 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. | From the total number of patients, nine patients had multi-vessel disease and Invasive-FFR was measured in two vessels. In total I-FFR was measured in: 42 LADs, 11 LCXs and 8 RCAs. Bench-top FFR was measured at the same locations as Invasive -FFR, from the ostium to two lesion lengths below the distal throat of the lesion . | Posted | Number | 95% Confidence Interval | correlation coefficient | 4 weeks from baseline | Coronary Arteries | Coronary Arteries |
|
|
|
| Secondary | Comparison of Bench-top FFR Using 3D Printed Patient Specific Phantoms With Invasive FFR, ROC Analysis | 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. | From the total number of patients, nine patients had multi-vessel disease and Invasive-FFR was measured in two vessels. In total I-FFR was measured in: 42 LADs, 11 LCXs and 8 RCAs. Bench-top FFR was measured at the same locations as Invasive -FFR, from the ostium to two lesion lengths below the distal throat of the lesion . | Posted | Number | 95% Confidence Interval | probability of accurate diagnosis | 4 weeks from baseline | Coronary Arteries | Coronary Arteries |
|
|
|
| Secondary | Comparison of Bench-top FFR Using 3D Printed Patient Specific Phantoms With Invasive FFR, Pearson Correlation | 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. | From the total number of patients, nine patients had multi-vessel disease and Invasive-FFR was measured in two vessels. In total I-FFR was measured in: 42 LADs, 11 LCXs and 8 RCAs. Bench-top FFR was measured at the same locations as Invasive -FFR, from the ostium to two lesion lengths below the distal throat of the lesion . | Posted | Number | 95% Confidence Interval | correlation coefficient | 4 weeks from baseline | Coronary Arteries | Coronary Arteries |
|
|
|
| Secondary | Comparison of Bench-top FFR Using 3D Printed Patient Specific Phantoms With Invasive FFR, Sensitivity | 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 | From the total number of patients, nine patients had multi-vessel disease and Invasive-FFR was measured in two vessels. In total I-FFR was measured in: 42 LADs, 11 LCXs and 8 RCAs. Bench-top FFR was measured at the same locations as Invasive -FFR, from the ostium to two lesion lengths below the distal throat of the lesion . | Posted | Number | percentage of true positive cases | 4 weeks from baseline | Coronary Arteries | Coronary Arteries |
|
|
|
| Secondary | Comparison of Bench-top FFR Using 3D Printed Patient Specific Phantoms With Invasive FFR, Specificity | 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 | From the total number of patients, nine patients had multi-vessel disease and Invasive-FFR was measured in two vessels. In total I-FFR was measured in: 42 LADs, 11 LCXs and 8 RCAs. Bench-top FFR was measured at the same locations as Invasive -FFR, from the ostium to two lesion lengths below the distal throat of the lesion . | Posted | Number | percentage of true negative cases | 4 weeks from baseline | Coronary Arteries | Coronary Arteries |
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|
|
| 0 |
| 52 |
| 0 |
| 52 |
| 0 |
| 52 |
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| D001161 |
| Arteriosclerosis |
| D001157 | Arterial Occlusive Diseases |
| D014652 | Vascular Diseases |
| D020763 | Pathological Conditions, Anatomical |
| D013568 | Pathological Conditions, Signs and Symptoms |