Machine leArning Based CT angiograpHy derIved FFR: a Multi-ceNtEr, Registry
NCT ID: NCT02805621
Last Updated: 2017-01-04
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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COMPLETED
352 participants
OBSERVATIONAL
2016-04-30
2017-01-31
Brief Summary
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Detailed Description
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Conditions
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Study Design
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COHORT
RETROSPECTIVE
Study Groups
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Subject
Patients with know or suspected coronary artery disease, who underwent both CT angiography and invasive coronary angiography including invasive FFR measurements.
No interventions assigned to this group
Eligibility Criteria
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Inclusion Criteria
Exclusion Criteria
18 Years
ALL
No
Sponsors
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Asan Medical Center
OTHER
University Hospital, Linkoeping
OTHER
National Institute of Cardiology, Warsaw, Poland
OTHER
Medical University of South Carolina
OTHER
Siemens Healthcare Diagnostics Inc
INDUSTRY
Erasmus Medical Center
OTHER
Responsible Party
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Laurens Groenendijk
dr
Principal Investigators
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Koen Nieman, MD PHD
Role: PRINCIPAL_INVESTIGATOR
Erasmus Medical Center
Locations
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ErasmusMC
Rotterdam, South Holland, Netherlands
Countries
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References
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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.
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.
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.
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.
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.
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.
Other Identifiers
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Machine
Identifier Type: -
Identifier Source: org_study_id
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