ELUCIDATE FFRct Study

NCT ID: NCT06018194

Last Updated: 2024-02-20

Study Results

Results pending

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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Recruitment Status

RECRUITING

Total Enrollment

350 participants

Study Classification

OBSERVATIONAL

Study Start Date

2023-09-01

Study Completion Date

2024-07-31

Brief Summary

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The goal of this study is to evaluate the diagnostic accuracy of a novel plaque-based coronary CT angiography (CCTA) fractional flow reserve (FFRct) software device for the estimation of invasive fractional flow reserve (FFR).

Researchers will compare the Elucid plaque-based FFRct analysis to invasively measured FFR in patients who have previously undergone CCTA and invasively assessed FFR.

Detailed Description

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Invasive fractional flow reserve is a clinically validated measure of lesion-specific ischemia and is preferred over visual estimation of diameter stenosis for clinical decision-making regarding coronary revascularization in patients with stable clinical presentations. Fractional flow reserve derived from coronary computed tomography angiography (FFRct) using computational fluid dynamic (CFD)-based software has been shown to be a reasonably accurate estimate of invasive FFR and is included in contemporary guidelines as a decision-tool for management of patients with intermediate stenosis on CCTA. However, CFD-based FFRct is calculated based predominately on detailed coronary lumen geometry. It is understood that the burden and type of coronary atherosclerosis, in addition to lumen geometry, significantly impacts the vasodilatory capacity of the coronary endothelium.

Preliminary studies suggest that invasive FFR can be accurately estimated based on the quantification of coronary plaque burden and the assessment of plaque composition. Previously, the investigators have demonstrated that a novel plaque-based FFRct approach, using a histologically validated software (ElucidVivoTM) for the measurement of plaque morphology (volume, plaque risk characteristics, and stenosis) to train a deep-learning model, was shown to be accurate and superior to lumen stenosis for predicting invasive FFR in a single-site feasibility study.

In this study, the investigators seek to assess the diagnostic accuracy of the Elucid plaque-based FFRct software to estimate invasive FFR in patients at multiple centers.

Conditions

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Coronary Artery Disease

Study Design

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Observational Model Type

COHORT

Study Time Perspective

RETROSPECTIVE

Study Groups

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Low FFR

Invasive FFR \<= 0.80

ElucidVivo

Intervention Type DEVICE

Plaque morphology characterization on coronary CTA for predicting fractional flow reserve (FFRct)

High FFR

Invasive FFR \> 0.80

ElucidVivo

Intervention Type DEVICE

Plaque morphology characterization on coronary CTA for predicting fractional flow reserve (FFRct)

Interventions

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ElucidVivo

Plaque morphology characterization on coronary CTA for predicting fractional flow reserve (FFRct)

Intervention Type DEVICE

Eligibility Criteria

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Inclusion Criteria

\- Adults \>=18 years with at least one 30-90% stenosis on CCTA (\>64- detector row) performed within 60 days of undergoing invasive FFR.

Exclusion Criteria

* History of coronary revascularization with coronary artery bypass graft (CABG) surgery and/or percutaneous coronary intervention (PCI) (stent or balloon angioplasty) prior to CCTA and invasive FFR.
* History of myocardial infarction prior to CCTA and invasive FFR.
* Known complex congenital heart disease.
* Existing pacemaker or internal defibrillator lead implantation(s).
* Existing prosthetic heart valve.
* Absence of CCTA Digital Imaging and Communications in Medicine (DICOM) data for analysis.
* Absence of invasive coronary angiography imaging or adequate description defining the position of the invasive FFR wire location.
* Poor CCTA image quality that does not meet Elucid image processing requirements for plaque and FFRct.
* Absence of study date for CCTA or invasive FFR.
* Interval clinical myocardial infarction, diagnosis of decompensated heart failure, stroke, or coronary revascularization procedure between CCTA and invasive FFR.
* Non-diagnostic CCTA or invasive FFR. Studies where the results of the study were deemed non-diagnostic for clinical decision-making will be excluded.
* CCTA or invasive FFR studies performed prior to 1 January 2016.
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Lundquist Institute at Harbor-UCLA Medical Center

UNKNOWN

Sponsor Role collaborator

Elucid Bioimaging Inc.

INDUSTRY

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Principal Investigators

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Ziad Ali, MD

Role: PRINCIPAL_INVESTIGATOR

Catholic Health - St. Francis Hospital

Todd C Villines, MD

Role: PRINCIPAL_INVESTIGATOR

Elucid Bioimaging Inc.

Locations

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Cardiovascular Medical Group of Southern California

Beverly Hills, California, United States

Site Status NOT_YET_RECRUITING

Piedmont Healthcare

Atlanta, Georgia, United States

Site Status NOT_YET_RECRUITING

Parkview Health

Fort Wayne, Indiana, United States

Site Status NOT_YET_RECRUITING

Midwest Heart and Vascular Specialists

Overland Park, Kansas, United States

Site Status RECRUITING

Valley Health System

Ridgewood, New Jersey, United States

Site Status NOT_YET_RECRUITING

Cleveland Clinic

Cleveland, Ohio, United States

Site Status NOT_YET_RECRUITING

Centennial Heart

Nashville, Tennessee, United States

Site Status NOT_YET_RECRUITING

Ascension Medical Group

Nashville, Tennessee, United States

Site Status NOT_YET_RECRUITING

University of Virginia

Charlottesville, Virginia, United States

Site Status RECRUITING

Countries

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United States

Central Contacts

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Todd C Villines, MD

Role: CONTACT

978-468-0508

John Aluru, MBBS

Role: CONTACT

978-468-0508

Facility Contacts

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Ronald P Karlsberg, MD

Role: primary

Venkateshwar Polsani, MD

Role: primary

Dustin Thomas, MD

Role: primary

Stephen A Bloom, MD

Role: primary

Himanshu Gupta, MD

Role: primary

Milind Desai, MD, MBA

Role: primary

David Huneycutt, MD

Role: primary

Andrew O Zurich, MD

Role: primary

Patricia Rodriguez-Lozano, MD

Role: primary

References

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Ahmadi A, Stone GW, Leipsic J, Serruys PW, Shaw L, Hecht H, Wong G, Norgaard BL, O'Gara PT, Chandrashekhar Y, Narula J. Association of Coronary Stenosis and Plaque Morphology With Fractional Flow Reserve and Outcomes. JAMA Cardiol. 2016 Jun 1;1(3):350-7. doi: 10.1001/jamacardio.2016.0263.

Reference Type BACKGROUND
PMID: 27438119 (View on PubMed)

Diaz-Zamudio M, Dey D, Schuhbaeck A, Nakazato R, Gransar H, Slomka PJ, Narula J, Berman DS, Achenbach S, Min JK, Doh JH, Koo BK. Automated Quantitative Plaque Burden from Coronary CT Angiography Noninvasively Predicts Hemodynamic Significance by using Fractional Flow Reserve in Intermediate Coronary Lesions. Radiology. 2015 Aug;276(2):408-15. doi: 10.1148/radiol.2015141648. Epub 2015 Apr 17.

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Varga-Szemes A, Schoepf UJ, Maurovich-Horvat P, Wang R, Xu L, Dargis DM, Emrich T, Buckler AJ. Coronary plaque assessment of Vasodilative capacity by CT angiography effectively estimates fractional flow reserve. Int J Cardiol. 2021 May 15;331:307-315. doi: 10.1016/j.ijcard.2021.01.040. Epub 2021 Jan 30.

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PMID: 33529657 (View on PubMed)

Stuijfzand WJ, van Rosendael AR, Lin FY, Chang HJ, van den Hoogen IJ, Gianni U, Choi JH, Doh JH, Her AY, Koo BK, Nam CW, Park HB, Shin SH, Cole J, Gimelli A, Khan MA, Lu B, Gao Y, Nabi F, Nakazato R, Schoepf UJ, Driessen RS, Bom MJ, Thompson R, Jang JJ, Ridner M, Rowan C, Avelar E, Genereux P, Knaapen P, de Waard GA, Pontone G, Andreini D, Al-Mallah MH, Lu Y, Berman DS, Narula J, Min JK, Bax JJ, Shaw LJ; CREDENCE Investigators. Stress Myocardial Perfusion Imaging vs Coronary Computed Tomographic Angiography for Diagnosis of Invasive Vessel-Specific Coronary Physiology: Predictive Modeling Results From the Computed Tomographic Evaluation of Atherosclerotic Determinants of Myocardial Ischemia (CREDENCE) Trial. JAMA Cardiol. 2020 Dec 1;5(12):1338-1348. doi: 10.1001/jamacardio.2020.3409.

Reference Type BACKGROUND
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Lin A, van Diemen PA, Motwani M, McElhinney P, Otaki Y, Han D, Kwan A, Tzolos E, Klein E, Kuronuma K, Grodecki K, Shou B, Rios R, Manral N, Cadet S, Danad I, Driessen RS, Berman DS, Norgaard BL, Slomka PJ, Knaapen P, Dey D. Machine Learning From Quantitative Coronary Computed Tomography Angiography Predicts Fractional Flow Reserve-Defined Ischemia and Impaired Myocardial Blood Flow. Circ Cardiovasc Imaging. 2022 Oct;15(10):e014369. doi: 10.1161/CIRCIMAGING.122.014369. Epub 2022 Oct 13.

Reference Type BACKGROUND
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Buckler AJ, Sakamoto A, Pierre SS, Virmani R, Budoff MJ. Virtual pathology: Reaching higher standards for noninvasive CTA tissue characterization capability by using histology as a truth standard. Eur J Radiol. 2023 Feb;159:110686. doi: 10.1016/j.ejrad.2022.110686. Epub 2022 Dec 31.

Reference Type BACKGROUND
PMID: 36603478 (View on PubMed)

Other Identifiers

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3-ST-03-03

Identifier Type: -

Identifier Source: org_study_id

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