Digital Twin and Ml-basEd MOdel of TEVAR Interventions

NCT07640828 | Recruiting

• 1 other identifier: 6492
• Study Type: observational
• Target: 5,000
• Locations: 1 country
• Sites: 1

Brief Summary

The study aims to collect clinical data and pseudonymized CT images of patients undergoing TEVAR in order to create an anatomical digital twin capable of simulating procedural outcomes and training machine learning (ML) algorithms. This approach will support predictive models that may assist physicians in selecting the optimal medical device, improving pre-TEVAR planning, and predicting post-TEVAR complications.

Conditions

Aorta Disease; Aorta, Thoracic Pathologies

Keywords

machine learning; thoracic aorta; Finite Element Analysis

Outcome Measures

Primary Outcomes (1)

• Determine the accuracy of patient-specific numerical simulations in replicating TEVAR deployment outcomes

  Accuracy of the simulations, expressed in terms of the match between simulated and post-operative device-vessel interaction (e.g., configuration, sealing quality, apposition), as assessed via comparison of post-operative CT image with the simulation results

  up to 1 year

Secondary Outcomes (1)

• Assess the predictive performance of the ML model in forecasting clinical complications

  up to 1 year

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

patients undergoing TEVAR

You may qualify if:

• ≥18 Years and older (Adult, Older Adult)
• Female and male
• Received TEVAR for: Chronic or acute dissection, Aneurysm, Penetrating aortic ulcer, aortic thrombus, intramural hematoma or traumatic injury

You may not qualify if:

• Younger than 18 years old
• Received TEVAR in surgical graft that replaced native aorta
• Poor CT image quality that leads to failure in generating a high-fidelity 3D FE model of patient anatomy (no preoperative multidetector contrast-enhanced CT-scan available, preoperative CTscan slice thickness greater than 1mm, preoperative CT-scan with artifacts, motion artifacts due to the presence of other implanted devices affecting the region of interest)

Sponsors & Collaborators

• Fondazione IRCCS Ca' Granda, Ospedale Maggiore Policlinico: lead

Study Sites (1)

Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico

Milan, Italy

RECRUITING

MeSH Terms

Conditions

Aortic Diseases

Condition Hierarchy (Ancestors)

Vascular Diseases; Cardiovascular Diseases

Central Study Contacts

SANTI TRIMARCHI, MD, PHD

CONTACT

+390255032438; santi.trimarchi@policlinico.mi.it

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: May 4, 2026
• First Posted: June 11, 2026
• Study Start: February 11, 2026
• Primary Completion (Estimated): September 30, 2026
• Study Completion (Estimated): September 30, 2026
• Last Updated: June 11, 2026

Record last verified: 2026-04

Locations

IT (1)