Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
| Name | Class |
|---|---|
| Asan Medical Center | OTHER |
| University Hospital, Linkoeping | OTHER |
| National Institute of Cardiology, Warsaw, Poland | OTHER |
| Medical University of South Carolina |
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Demonstrate in a large multicenter population the diagnostic performance of a pre-commercial on-site, local, CT angiography derived FFR algorithm in comparison to invasive FFR.
To retrospectively evaluate the diagnostic accuracy of FFRCT, in patients with known or suspected CAD. the investigators propose to do technical assessment of the software and evaluate how different parameters effect the outcome. Validate the FFTCT outcome by comparing the FFRCT values with invasive FFR values from retrospective patient data. To analyze the potential of FFRCT on decision making and prognosis.
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Subject | Patients with know or suspected coronary artery disease, who underwent both CT angiography and invasive coronary angiography including invasive FFR measurements. |
Not provided
| Measure | Description | Time Frame |
|---|---|---|
| Diagnostic accuracy of local reduced order CFD and machine learning based CT angiography derived FFR, both validated against invasive FFR. Measured at both vessel and patient level. | 6 months |
| Measure | Description | Time Frame |
|---|---|---|
| Influence of calcium on diagnostic accuracy of CT angiography derived FFR. | 6 months | |
| Confidence intervals of CT angiography derived FFR | 6 months | |
| Direct vessel based comparison between CT angiography derived FFR and QCT stenosis measurements |
Not provided
Inclusion Criteria:
Exclusion Criteria:
Not provided
Not provided
Not provided
In general, each center used its own specific inclusion and exclusion criteria. Here the investigators describe the general criteria, please see the respected publications for a detailed description.1-4
Not provided
| Name | Affiliation | Role |
|---|---|---|
| Koen Nieman, MD PHD | Erasmus Medical Center | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| ErasmusMC | Rotterdam | South Holland | 3015CE | Netherlands |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 26691914 | Background | De Geer J, Sandstedt M, Bjorkholm A, Alfredsson J, Janzon M, Engvall J, Persson A. Software-based on-site estimation of fractional flow reserve using standard coronary CT angiography data. Acta Radiol. 2016 Oct;57(10):1186-92. doi: 10.1177/0284185115622075. Epub 2015 Dec 20. | |
| 26897667 | Background | Kruk M, Wardziak L, Demkow M, Pleban W, Pregowski J, Dzielinska Z, Witulski M, Witkowski A, Ruzyllo W, Kepka C. Workstation-Based Calculation of CTA-Based FFR for Intermediate Stenosis. JACC Cardiovasc Imaging. 2016 Jun;9(6):690-9. doi: 10.1016/j.jcmg.2015.09.019. Epub 2016 Feb 17. |
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
| ID | Term |
|---|---|
| D003327 | Coronary Disease |
| D003324 | Coronary Artery Disease |
| ID | Term |
|---|---|
| D017202 | Myocardial Ischemia |
| D006331 | Heart Diseases |
| D002318 | Cardiovascular Diseases |
| D014652 | Vascular Diseases |
Not provided
Not provided
| OTHER |
| Siemens Healthcare Diagnostics Inc | INDUSTRY |
Not provided
Not provided
Not provided
| 6 months |
| Analysis of anatomically mild stenosis (<50% lumen diameter reduction) but functionally significant (invasive FFR ≤ 0.80) | 6 months |
| Long term clinical outcome of CT angiography derived FFR | 12 months |
| 25403173 | Background | Baumann S, Wang R, Schoepf UJ, Steinberg DH, Spearman JV, Bayer RR 2nd, Hamm CW, Renker M. Coronary CT angiography-derived fractional flow reserve correlated with invasive fractional flow reserve measurements--initial experience with a novel physician-driven algorithm. Eur Radiol. 2015 Apr;25(4):1201-7. doi: 10.1007/s00330-014-3482-5. Epub 2014 Nov 18. |
| 25322342 | Background | Coenen A, Lubbers MM, Kurata A, Kono A, Dedic A, Chelu RG, Dijkshoorn ML, Gijsen FJ, Ouhlous M, van Geuns RJ, Nieman K. Fractional flow reserve computed from noninvasive CT angiography data: diagnostic performance of an on-site clinician-operated computational fluid dynamics algorithm. Radiology. 2015 Mar;274(3):674-83. doi: 10.1148/radiol.14140992. Epub 2014 Oct 13. |
| 27354345 | Background | Yang DH, Kim YH, Roh JH, Kang JW, Ahn JM, Kweon J, Lee JB, Choi SH, Shin ES, Park DW, Kang SJ, Lee SW, Lee CW, Park SW, Park SJ, Lim TH. Diagnostic performance of on-site CT-derived fractional flow reserve versus CT perfusion. Eur Heart J Cardiovasc Imaging. 2017 Apr 1;18(4):432-440. doi: 10.1093/ehjci/jew094. |
| 31422141 | Derived | Tesche C, Otani K, De Cecco CN, Coenen A, De Geer J, Kruk M, Kim YH, Albrecht MH, Baumann S, Renker M, Bayer RR, Duguay TM, Litwin SE, Varga-Szemes A, Steinberg DH, Yang DH, Kepka C, Persson A, Nieman K, Schoepf UJ. Influence of Coronary Calcium on Diagnostic Performance of Machine Learning CT-FFR: Results From MACHINE Registry. JACC Cardiovasc Imaging. 2020 Mar;13(3):760-770. doi: 10.1016/j.jcmg.2019.06.027. Epub 2019 Aug 14. |
| D001161 |
| Arteriosclerosis |
| D001157 | Arterial Occlusive Diseases |