Feasibility of 3D Printed Models of Aortic Stenosis in Guiding TAVI Procedure
NCT ID: NCT05484713
Last Updated: 2023-05-16
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
20 participants
OBSERVATIONAL
2022-07-08
2023-07-31
Brief Summary
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Detailed Description
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The project will include 20 subjects who present severe aortic stenosis prior to study enrollment.
Diagnosis of severe aortic stenosis (AS) as determined by echocardiography, who are deemed eligible for TAVI by the Center of Advanced Research in Multimodal Cardiac Imaging Cardiomed Heart Valve Team.
All patients will undergo coronary CT angiography, cardiac perfusion CT and peripheral CT angiography at the moment of enrollment in the study, for complex assessment of aortic valvular and perivalvular structures as well as for peripheral vascular approach.
The study will be conducted over a period of 1 year, in which patients will be examined at baseline, and during follow-up visit. At the one-year follow-up, the study subjects will undergo CT coronary angiography for re-evaluation of the aortic valve, in the prospects of analyzing the rate of valvular leak or peri-procedural complications such as embolic events or atrio-ventricular conduction block. In case of non-presentation for the follow-up visit, patients will be contacted via telephone by one of the investigators, and questioned about the general health status, occurrence of cardiovascular or cerebrovascular symptoms and interventions, for possible non-cardiovascular related hospitalizations, as well as regarding the presence of aortic valvular disease rates/repeated interventions.
All patients will sign an informed written consent prior to being enrolled in the study. After the screening process, patients that do not present exclusion criteria will be enrolled in the study.
Study objectives:
Primary: to analyze the accuracy of replicating cardiovascular anatomical structures using different techniques and to evaluate the feasibility of 3D printed models of aortic stenosis in guiding TAVI procedure
Secondary: re-evaluation of the aortic valve, in the prospects of analyzing the rate of valvular leak or peri-procedural complications such as embolic events or atrio-ventricular conduction block.
Study Timeline:
Baseline (day 0)
* Obtain and document consent from participant on study consent form.
* Verify inclusion/exclusion criteria.
* Obtain demographic information, medical history, medication history, alcohol and tobacco use history.
* Record results of physical examinations and 12-lead ECG.
* Imaging procedures: transthoracic 2-D echocardiography, 128-multislice CT angiography (CCTA)
* 3d printed model by FDM technique based on CCTA
* Evaluation of 3D printing workflow accuracy Visit 2 (month 6)
* Follow-up after TAVI procedure
* Record results of physical examinations, 12-lead ECG and medical history.
* Imaging procedures: transthoracic 2-D echocardiography Final study visit (month 12)
* Record results of physical examinations, 12-lead ECG and medical history.
* Imaging procedures: transthoracic 2-D echocardiography, 128-multisclice CCTA
* End-point evaluation.
Study procedures:
* Clinical examination, medical history
* 12-lead ECG
* 2D transthoracic echocardiography with measurement of: cardiac diameters, volumes, valvular function and regurgitation, pressure gradients, pericardial fat thickness, pericardial effusion, left ventricular global and regional function and ejection fraction.
* 128-multislice CT coronary angiography with the evaluation of: epicardial fat volume, coronary plaque burden, total and local calcium score, aortic valve, perivalvular aortic structures, peripheral iliac vessels, ascending and descending segments of aorta.
* Processing of DICOM image dataset by performing segmentation and STL adjustments
* Processing STL image dataset by performing CAD analysis and adjustments
* Processing G-code image dataset and sending to 3d printing station
* 3D printing of aortic model including perivalvular aortic structures using fused filaments method
Data collection: In a dedicated database that includes all patient information, demographics, medical history, medication, therapeutic procedures, information derived from imaging techniques (echocardiography, CT angiography, CT imaging post-processing and shear stress evaluation).
Conditions
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Study Design
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COHORT
PROSPECTIVE
Interventions
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Cardiac imaging
Baseline - 2D transthoracic echocardiography, 128-multislice CT coronary angiography, CCTA of peripheral lower limbs vessels 12-months follow-up - 128-multislice CT coronary angiography with transthoracic echocardiography
3D printing of the aortic valve
3D printing of the aortic valve based on CCTA image acquisition
Eligibility Criteria
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Inclusion Criteria
Exclusion Criteria
* patients that undergo surgical aortic valve replacement for aortic stenosis
* patients with contraindications for iodine contrast agent administration (acute renal failure, allergy, thyroid dysfunction).
* other conditions associated with suboptimal CCTA image acquisition which could interfere with CT image postprocessing for creation of 3D printed models.
60 Years
ALL
No
Sponsors
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George Emil Palade University of Medicine, Pharmacy, Sciences and Technology of Targu Mures
OTHER
University Hospital of Targu Mures, Romania
OTHER
Cardio Med Medical Center
INDUSTRY
Responsible Party
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Principal Investigators
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Daniel Cernica, MD
Role: STUDY_DIRECTOR
CardioMed Medical Center
Locations
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Cardio Med Medical Center
Târgu Mureş, , Romania
Countries
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Central Contacts
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Facility Contacts
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References
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Cernica D, Benedek I, Polexa S, Tolescu C, Benedek T. 3D Printing-A Cutting Edge Technology for Treating Post-Infarction Patients. Life (Basel). 2021 Sep 1;11(9):910. doi: 10.3390/life11090910.
Benedek A, Cernica D, Mester A, Opincariu D, Hodas R, Rodean I, Keri J, Benedek T. Modern Concepts in Regenerative Therapy for Ischemic Stroke: From Stem Cells for Promoting Angiogenesis to 3D-Bioprinted Scaffolds Customized via Carotid Shear Stress Analysis. Int J Mol Sci. 2019 May 25;20(10):2574. doi: 10.3390/ijms20102574.
Wang DD, Qian Z, Vukicevic M, Engelhardt S, Kheradvar A, Zhang C, Little SH, Verjans J, Comaniciu D, O'Neill WW, Vannan MA. 3D Printing, Computational Modeling, and Artificial Intelligence for Structural Heart Disease. JACC Cardiovasc Imaging. 2021 Jan;14(1):41-60. doi: 10.1016/j.jcmg.2019.12.022. Epub 2020 Aug 26.
Vukicevic M, Mosadegh B, Min JK, Little SH. Cardiac 3D Printing and its Future Directions. JACC Cardiovasc Imaging. 2017 Feb;10(2):171-184. doi: 10.1016/j.jcmg.2016.12.001.
Other Identifiers
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CM-G510-3DP
Identifier Type: -
Identifier Source: org_study_id
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