CMR-AI and Outcomes in AS

Condition(s) studied

Study 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.

Eligibility

Primary outcome measure(s)

• 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. — 2 years
  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.

Trial sites (7)

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