Clinical trial NCT02805621: Machine leArning Based CT angiograpHy derIved FFR: a Multi-ceNtEr, Registry
Official title: Machine leArning Based CT angiograpHy derIved FFR a Multi-ceNtEr, Registry
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Coronary Heart Disease
Coronary Artery Disease
Lead sponsor: Erasmus Medical Center
Sponsor class: OTHER
Collaborators: name: Asan Medical Center
name: University Hospital, Linkoeping
name: National Institute of Cardiology, Warsaw, Poland
name: Medical University of South Carolina
name: Siemens Healthcare Diagnostics Inc
First posted: 2016-06-20
Enrollment: 352
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: 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

Brief summary: 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.

Detailed description: 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.

Eligibility criteria: Inclusion Criteria:

* Know or suspect coronary artery disease followed within 6 months by an invasive FFR measurement.

Exclusion Criteria:

* Cardiac event between coronary CT angiography and the invasive FFR procedure, noninterpretable coronary CT angiography image quality, or incomplete coronary CT angiography coverage.

Primary outcomes:
measure: 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.
timeFrame: 6 months
Secondary outcomes:
measure: Influence of calcium on diagnostic accuracy of CT angiography derived FFR.
timeFrame: 6 months
measure: Confidence intervals of CT angiography derived FFR
timeFrame: 6 months
measure: Direct vessel based comparison between CT angiography derived FFR and QCT stenosis measurements
timeFrame: 6 months
measure: Analysis of anatomically mild stenosis (<50% lumen diameter reduction) but functionally significant (invasive FFR ≤ 0.80)
timeFrame: 6 months
measure: Long term clinical outcome of CT angiography derived FFR
timeFrame: 12 months
Other outcomes:
Locations:
city: Rotterdam
country: Netherlands
facility: ErasmusMC
state: Zuid Holland
Study officials:
affiliation: Erasmus Medical Center
name: Koen Nieman, MD PHD
role: PRINCIPAL_INVESTIGATOR
References:
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.
citation: 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.