A CCTA Image Assisted Triage Software for the Assessment of Patients With Suspected Coronary Artery Disease
NCT ID: NCT06172985
Last Updated: 2023-12-15
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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UNKNOWN
1093 participants
OBSERVATIONAL
2023-05-09
2024-02-29
Brief Summary
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Detailed Description
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Experiment group: Evaluated by the software Independent expert group: Evaluated by experts (≥5 years of CCTA experience required)
Conditions
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Study Design
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CASE_CROSSOVER
RETROSPECTIVE
Study Groups
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Suspected patients with coronary heart disease.
The coronary CT angiography (CCTA) images collected by each center within a certain period of time will be desensitized after the screening is successful. The final CCTA images were sent to an independent judgment expert group for diagnosis, the results of the test group and the independent judgment expert group were compared, and the clinical application of the coronary artery CT angiography image vascular stenosis auxiliary triage software developed by Keya Medical Technology Co., Ltd. was evaluated. Validity and Accuracy
No intervention
Due to observational study
Interventions
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No intervention
Due to observational study
Eligibility Criteria
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Inclusion Criteria
* Check Modal = CT
* Number of detector rows ≥ 64 rows
* Layer thickness ≤1mm
* Layer spacing ≤1mm
* Pixel pitch ≤ 0.5mm
* Ball tube voltage ≥ 70kV
* Number of layers ≥ 100 layers
2. CCTA image quality score ≥ 3 (5-point Likert scale).
Exclusion Criteria
2. Previous percutaneous coronary intervention (PCI) or coronary artery bypass grafting (CABG);
3. Congenital anomalies of coronary artery origin or other malformations;
4. Coronary artery occlusive lesions;
5. Implantation of the pacemaker, internal defibrillator electrode, or prosthetic heart valve.
ALL
No
Sponsors
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Keya Medical
INDUSTRY
Responsible Party
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Principal Investigators
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Bin Lu
Role: PRINCIPAL_INVESTIGATOR
Chinese Academy of Medical Sciences, Fuwai Hospital
Locations
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Fuwai Hospital, Chinese Academy of Medical Sciences
Beijing, Beijing Municipality, China
The Pearl River Hospital of Southern Medical University
Guangzhou, Guangdong, China
Affiliated Hospital of Zunyi Medical University
Zunyi, Guizhou, China
The First Affiliated Hospital of Hebei Medical University
Shijiazhuang, Hebei, China
Huanggang Central Hospital
Huanggang, Hubei, China
Countries
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References
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Meijboom WB, Meijs MF, Schuijf JD, Cramer MJ, Mollet NR, van Mieghem CA, Nieman K, van Werkhoven JM, Pundziute G, Weustink AC, de Vos AM, Pugliese F, Rensing B, Jukema JW, Bax JJ, Prokop M, Doevendans PA, Hunink MG, Krestin GP, de Feyter PJ. Diagnostic accuracy of 64-slice computed tomography coronary angiography: a prospective, multicenter, multivendor study. J Am Coll Cardiol. 2008 Dec 16;52(25):2135-44. doi: 10.1016/j.jacc.2008.08.058.
Lucke C, Foldyna B, Andres C, Boehmer-Lasthaus S, Grothoff M, Nitzsche S, Gutberlet M, Lehmkuhl L. Post-processing in cardiovascular computed tomography: performance of a client server solution versus a stand-alone solution. Rofo. 2014 Dec;186(12):1111-21. doi: 10.1055/s-0034-1366726. Epub 2014 Aug 14.
Choi AD, Marques H, Kumar V, Griffin WF, Rahban H, Karlsberg RP, Zeman RK, Katz RJ, Earls JP. CT Evaluation by Artificial Intelligence for Atherosclerosis, Stenosis and Vascular Morphology (CLARIFY): A Multi-center, international study. J Cardiovasc Comput Tomogr. 2021 Nov-Dec;15(6):470-476. doi: 10.1016/j.jcct.2021.05.004. Epub 2021 Jun 12.
Paul JF, Rohnean A, Giroussens H, Pressat-Laffouilhere T, Wong T. Evaluation of a deep learning model on coronary CT angiography for automatic stenosis detection. Diagn Interv Imaging. 2022 Jun;103(6):316-323. doi: 10.1016/j.diii.2022.01.004. Epub 2022 Jan 26.
Meyer M, Schoepf UJ, Fink C, Goldenberg R, Apfaltrer P, Gruettner J, Vajcs D, Schoenberg SO, Henzler T. Diagnostic performance evaluation of a computer-aided simple triage system for coronary CT angiography in patients with intermediate risk for acute coronary syndrome. Acad Radiol. 2013 Aug;20(8):980-6. doi: 10.1016/j.acra.2013.02.014. Epub 2013 Jun 2.
Chen Y, Yu H, Fan B, Wang Y, Wen Z, Hou Z, Yu J, Wang H, Tang Z, Li N, Jiang P, Wang Y, Yin W, Lu B. Diagnostic performance of deep learning-based coronary computed tomography angiography in detecting coronary artery stenosis. Int J Cardiovasc Imaging. 2025 May;41(5):979-989. doi: 10.1007/s10554-025-03383-0. Epub 2025 Mar 29.
Related Links
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Guerbet. Xenetix® 350: Comparative Assessment of Image Quality for Coronary CT Angiography (X-ACT)\[EB/OL\]
Other Identifiers
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CT-091-2022
Identifier Type: -
Identifier Source: org_study_id