CMR-AI and Outcomes in AS

NCT06128876 | Active Not Recruiting

• 1 other identifier: VIE_CMR-AI
• Study Type: observational
• Target: 1,500
• Locations: 6 countries
• Sites: 7

Brief Summary

Background \& Aims: Artificial Intelligence (AI) in cardiac magnetic resonance (CMR) imaging has previously been shown to provide highly reproducible and accurate measures of myocardial structure and function, outperforming clinical experts. The prognostic value of more sensitive markers of early left (LV) and right ventricular (RV) function, such as global longitudinal shortening (GLS), mitral annular plane systolic excursion (MAPSE), and tricuspid annular plane systolic excursion (TAPSE) has not been established due to the lack of automated analysis. Thus, our aim is to evaluate whether AI-based measurements of these early markers of adverse cardiac remodeling convey relevant prognostic information in patients with severe aortic stenosis (AS) beyond LV and RV ejection fraction (EF). Materials \& Methods: In a current large-scale international, prospective, multi-center study \~1500 patients with severe AS underwent CMR imaging prior to aortic valve replacement (AVR). An AI-based algorithm, developed in the UK, was used for fully automated assessment of parameters of cardiac structure (end-diastolic volume, end-systolic volume, LV mass, maximum wall thickness) and function (EF, GLS, MAPSE, TAPSE). In this proposed follow-up project, we aim to associate these AI-based CMR parameters at baseline with mid-term clinical outcomes at 24-months post-AVR. A composite of all-cause mortality and heart failure hospitalization will serve as the primary endpoint. CMR-AI will be repeated at 24-months follow-up and trajectories from pre- to post-AVR will be assessed as a secondary endpoint. Future Outlook: In severe AS, a novel AI-based algorithm allows immediate and precise measurements of ventricular structure and function on CMR imaging. Our goal is to identify early markers of cardiac dysfunction indicating adverse mid-term prognosis post-AVR. This has guideline-forming potential as the optimal timepoint for AVR in patients with AS is currently a matter of debate.

Conditions

Aortic Stenosis

Keywords

Aortic Stenosis; Artificial Intelligence; Cardiac Magnetic Resonance; Imaging; Outcomes

Outcome Measures

Primary Outcomes (1)

• Number of patients with AI-measured parameters of impaired left and right ventricular structure and function on cardiac magnetic resonance imaging and association with the composite of all-cause death and heart failure hospitalization.

  Association of AI-based parameters (end-diastolic volume \[ml\], end-systolic volume \[ml\], left ventricular mass \[gram\], maximum wall thickness \[mm\], ejection fraction \[%\], global longitudinal shortening \[%\], mitral/tricuspid annular plane systolic excursion \[mm\]) on cardiac magnetic resonance imaging with the composite of all-cause death and heart failure hospitalization. Captured clinical endpoints will include all-cause death, cardiovascular mortality, and heart failure hospitalization. Data will be ascertained by follow-up visits, state-wide electronic hospital charts, and phone calls. In addition, mortality data will be obtained via National Death Registries of the participating countries.

  2 years

Secondary Outcomes (2)

• Number of patients with AI-measured parameters of impaired left and right ventricular structure and function on cardiac magnetic resonance imaging and association with components of the primary endpoint analyzed individually.

  2 years

• Number of patients with AI-measured parameters of impaired left and right ventricular structure and function on cardiac magnetic resonance imaging and association with cardiovascular mortality.

  2 years

Other Outcomes (1)

• Number of patients with AI-measured parameters of impaired left and right ventricular structure and function on cardiac magnetic resonance imaging at baseline and changes at 2-year follow-up after aortic valve replacement (AVR).

  2 years

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 with severe degenerative AS scheduled for valvular treatment undergoing pre-procedural CMR imaging were previously enrolled from seven university-affiliated tertiary care centers in Continental Europe, the UK, and Asia between January 2020 and August 2024.

You may qualify if:

• Written informed consent
• Severe AS scheduled for Heart Team decision

You may not qualify if:

• Inability or unwillingness to perform any of the diagnostic tests
• Inability or unwillingness to participate in follow-up visits
• Metal implants, e.g. cochlear implants and pacemakers
• Chronic kidney failure (GFR \< 30 ml/min/1.73m2)

Sponsors & Collaborators

• Medical University of Vienna: lead

Study Sites (7)

Medical University of Vienna

Vienna, Austria

Location

Université Catholique de Louvain

Brussels, Belgium

Location

University of Goettingen Medical Center

Göttingen, Germany

Location

Vilnius University

Vilnius, Lithuania

Location

Samsung Medical Center

Seoul, South Korea

Location

Seoul National University College

Seoul, South Korea

Location

Barts Heart Centre

London, United Kingdom

Location

MeSH Terms

Conditions

Aortic Valve Stenosis

Condition Hierarchy (Ancestors)

Aortic Valve Disease; Heart Valve Diseases; Heart Diseases; Cardiovascular Diseases; Ventricular Outflow Obstruction

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator

Study Record Dates

• First Submitted: October 26, 2023
• First Posted: November 13, 2023
• Study Start: January 1, 2020
• Primary Completion (Estimated): September 1, 2026
• Study Completion (Estimated): February 1, 2027
• Last Updated: December 4, 2024

Record last verified: 2024-11

Locations

GB (1); AU (1); KR (2); DE (1); BE (1); LI (1)