Ignite Creation Date:
2024-05-06 @ 7:45 PM
Last Modification Date:
2024-10-26 @ 3:13 PM
Study NCT ID:
NCT06128876
Status:
NOT_YET_RECRUITING
Last Update Posted:
2023-11-13
First Post:
2023-10-26
Brief Title:
CMR-AI and Outcomes in Aortic Stenosis
Sponsor:
Medical University of Vienna
Organization:
Medical University of Vienna
Study Overview
Official Title:
Artificial Intelligence-based Risk Stratification and Outcome in Patients With Severe Aortic Stenosis Undergoing Cardiac Magnetic Resonance Imaging
Status:
NOT_YET_RECRUITING
Status Verified Date:
2023-11
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
Background Artificial Intelligence AI in cardiac imaging has previously been shown to provide highly reproducible and accurate results outperforming clinical experts Cardiac magnetic resonance CMR imaging represents the gold standard for assessment of myocardial structure and function However measurements 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 are frequently not performed due to the lack of automated analysis
Objectives The investigators aim to evaluate whether AI-based measurements of ventricular structure and function convey important prognostic information in patients with severe aortic stenosis AS beyond LV and RV ejection fraction EF and represent early markers of adverse cardiac remodeling
Materials Methods This large-scale international multi-center observational study will recruit 1500 patients with severe AS scheduled for aortic valve replacement AVR Patients are invited to undergo CMR imaging prior to AVR and at 12-months post-AVR An AI-based algorithm developed in the UK will be 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 Application of the AI-model allows to capture these parameters for large patient cohorts within seconds as opposed to the current practice of time-consuming manual post-processing Association of AI-based CMR parameters with clinical outcomes post-AVR will be analyzed The composite of all-cause mortality and heart failure hospitalization will serve as the primary endpoint Trajectories of AI-based parameters 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 prognosis post-AVR This has guideline-forming potential as the optimal timepoint for AVR in patients with AS is currently a matter of debate
Objectives The investigators aim to evaluate whether AI-based measurements of ventricular structure and function convey important prognostic information in patients with severe aortic stenosis AS beyond LV and RV ejection fraction EF and represent early markers of adverse cardiac remodeling
Materials Methods This large-scale international multi-center observational study will recruit 1500 patients with severe AS scheduled for aortic valve replacement AVR Patients are invited to undergo CMR imaging prior to AVR and at 12-months post-AVR An AI-based algorithm developed in the UK will be 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 Application of the AI-model allows to capture these parameters for large patient cohorts within seconds as opposed to the current practice of time-consuming manual post-processing Association of AI-based CMR parameters with clinical outcomes post-AVR will be analyzed The composite of all-cause mortality and heart failure hospitalization will serve as the primary endpoint Trajectories of AI-based parameters 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 prognosis post-AVR This has guideline-forming potential as the optimal timepoint for AVR in patients with AS is currently a matter of debate
Detailed Description:
Artificial Intelligence AI and Machine Learning are reshaping our daily clinical practice which has the potential to be more efficient precise and personalized Adopting these technologies in cardiac imaging does not only affect decision making by improved accuracy and risk stratification but also significantly reduces scan times and post-imaging workup
Current guidelines acknowledge cardiac magnetic resonance CMR imaging as gold standard for assessment of myocardial structure and function Although of fundamental importance in various cardiac diseases measurements of size mass and ejection fraction EF are flawed by the inherent variability and subjectivity of human analysis Recent developments in deep learning using convolutional neural networks CNNs allow for automated segmentation of the ventricular blood pool and myocardium using pre-existing CMR datasets Importantly these tools are integrated into CMR scanners generating real-time measurements without the need of time-consuming image post-processing AI-based models have previously shown superior precision in ventricular contouring volumetry and maximum wall thickness measurements outperforming clinical experts
In patients with aortic stenosis AS changes in EF more often occur late in the disease process Longitudinal shortening represents an earlier and more sensitive marker of left ventricular LV dysfunction However these measurements remain subjective time-consuming and are therefore not routinely performed due to the lack of automated analysis Recently AI-measured global longitudinal shortening GLS and mitral annular plane systolic excursion MAPSE have been demonstrated to provide more reproducible and accurate results when compared to human experts The investigators hypothesize that AI-based GLS and MAPSE could convey important prognostic information beyond LVEF in severe AS and represent early markers of adverse cardiac remodeling
Furthermore the investigators could previously demonstrate that right ventricular RV dysfunction on CMR rather than conventional parameters assessed by echocardiography was independently associated with outcome in individuals with AS undergoing transcatheter aortic valve implantation The investigators aim to extend on our findings and investigate whether AI-based RV GLS and tricuspid annular plane systolic excursion TAPSE represent early markers of RV dysfunction indicating adverse prognosis
Finally the assessment of reverse cardiac remodeling by CMR requires high precision reproducibility AI has been proven to outperform humans in both precision and accuracy and therefore has great potential for the comprehensive evaluation of longitudinal structural changes in AS following valve replacement The investigators aim to analyze reverse cardiac remodeling in patients with AS using novel AI technology
Aims
With significant previous contributions in cardiac imaging and valvular heart disease being made in the past the investigators aim to expand the knowledge in this field by exploring the following
Association of AI-measured LV and RV structural and functional markers on CMR in patients with severe AS with clinical outcomes following aortic valve replacement AVR
Reverse cardiac remodeling at baseline to 12-months after AVR as determined by AI-based CMR methods
The ultimate goal is to provide automated precise and time-saving algorithms to identify patients at risk undergoing AVR
Methods
This is a large-scale international multi-center multi-cohort observational study Patients with severe degenerative AS scheduled for valvular treatment discussion in an interdisciplinary Heart Team will be invited to participate Enrollment will be performed prospectively at seven university-affiliated tertiary care centers in Continental Europe the UK and Asia
Baseline evaluation consists of medical history physical examination routine blood tests electrocardiogram echocardiography and CMR imaging Patients will be prospectively followed up by an ambulatory visit at 12 months In addition CMR will be repeated at 1 year
Current guidelines acknowledge cardiac magnetic resonance CMR imaging as gold standard for assessment of myocardial structure and function Although of fundamental importance in various cardiac diseases measurements of size mass and ejection fraction EF are flawed by the inherent variability and subjectivity of human analysis Recent developments in deep learning using convolutional neural networks CNNs allow for automated segmentation of the ventricular blood pool and myocardium using pre-existing CMR datasets Importantly these tools are integrated into CMR scanners generating real-time measurements without the need of time-consuming image post-processing AI-based models have previously shown superior precision in ventricular contouring volumetry and maximum wall thickness measurements outperforming clinical experts
In patients with aortic stenosis AS changes in EF more often occur late in the disease process Longitudinal shortening represents an earlier and more sensitive marker of left ventricular LV dysfunction However these measurements remain subjective time-consuming and are therefore not routinely performed due to the lack of automated analysis Recently AI-measured global longitudinal shortening GLS and mitral annular plane systolic excursion MAPSE have been demonstrated to provide more reproducible and accurate results when compared to human experts The investigators hypothesize that AI-based GLS and MAPSE could convey important prognostic information beyond LVEF in severe AS and represent early markers of adverse cardiac remodeling
Furthermore the investigators could previously demonstrate that right ventricular RV dysfunction on CMR rather than conventional parameters assessed by echocardiography was independently associated with outcome in individuals with AS undergoing transcatheter aortic valve implantation The investigators aim to extend on our findings and investigate whether AI-based RV GLS and tricuspid annular plane systolic excursion TAPSE represent early markers of RV dysfunction indicating adverse prognosis
Finally the assessment of reverse cardiac remodeling by CMR requires high precision reproducibility AI has been proven to outperform humans in both precision and accuracy and therefore has great potential for the comprehensive evaluation of longitudinal structural changes in AS following valve replacement The investigators aim to analyze reverse cardiac remodeling in patients with AS using novel AI technology
Aims
With significant previous contributions in cardiac imaging and valvular heart disease being made in the past the investigators aim to expand the knowledge in this field by exploring the following
Association of AI-measured LV and RV structural and functional markers on CMR in patients with severe AS with clinical outcomes following aortic valve replacement AVR
Reverse cardiac remodeling at baseline to 12-months after AVR as determined by AI-based CMR methods
The ultimate goal is to provide automated precise and time-saving algorithms to identify patients at risk undergoing AVR
Methods
This is a large-scale international multi-center multi-cohort observational study Patients with severe degenerative AS scheduled for valvular treatment discussion in an interdisciplinary Heart Team will be invited to participate Enrollment will be performed prospectively at seven university-affiliated tertiary care centers in Continental Europe the UK and Asia
Baseline evaluation consists of medical history physical examination routine blood tests electrocardiogram echocardiography and CMR imaging Patients will be prospectively followed up by an ambulatory visit at 12 months In addition CMR will be repeated at 1 year
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None