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| Name | Class |
|---|---|
| Centre Hospitalier Universitaire de Bordeaux, FRANCE | UNKNOWN |
| Materialise | INDUSTRY |
| Pie Medical Imaging | UNKNOWN |
| Clinique Pasteur Toulouse |
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This non-interventional study aims to use artificial intelligence to improve the prediction of transcatheter heart valve interventions and optimize patient outcomes. It is based on the analysis of retrospective data from various specialized centers worldwide.
The ENVISAGE study is a non-interventional, retrospective research study designed to validate an artificial intelligence (AI)-based framework for the automated analysis of cardiac imaging data, including multi-slice cardiac computed tomography (CT) and transesophageal echocardiography (TEE). The primary objective is to predict the success of transcatheter heart valve interventions, including aortic, mitral, and tricuspid valve interventions (TAVI, TMVI, M-TEER, T-TEER). The AI framework developed in this study will rely on deep learning algorithms, particularly convolutional neural networks (CNNs) and other advanced models, to automatically segment critical anatomical structures and perform accurate measurements of these structures from CT and TEE images. These measurements will then be combined with pre-interventional clinical data to optimize patient selection and intervention planning, as well as to predict surgical outcomes with high accuracy. AI will also aim to reduce human error and inter-observer variability in the interpretation of cardiac images, which could significantly improve clinical outcomes.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| TAVI | All patients who have had TAVI with a third generation transcatheter heart valve (THV). Medical imaging data (CT, TEE) and preoperative clinical data will be collected for analysis. |
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| TMVI | Patients who have had a TMVI with a dedicated transeptal device and screen failures. Medical imaging data (CT, TEE) and preoperative clinical data will be collected for analysis. |
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| TTVI | Patients who have had a TTVI with a dedicated device and screen failures. Medical imaging data (CT, TEE) and preoperative clinical data will be collected for analysis. |
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| M-TEER | All patients who have had a M-TEER with 1) G4 or newer iteration of MitraClip or 2) G2 or newer iteration of Pascal. Medical imaging data (CT, TEE) and preoperative clinical data will be collected for analysis. |
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| T-TEER | All patients who have had a T-TEER with G4 or newer iteration of TriClip or 2) G2 or newer iteration of Pascal. Medical imaging data (CT, TEE) and preoperative clinical data will be collected for analysis. |
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Medical imaging analysis via artificial intelligence algorithms | Diagnostic Test | Development of AI algorithms based on pre-procedural imaging annotations and clinical informations to predict the transcatheter procedural outcomes |
| Measure | Description | Time Frame |
|---|---|---|
| Accuracy of transcatheter AI predictions | Validation of artificial intelligence algorithms for automatic segmentation of anatomic structures and imaging measurements, and prediction of the success of transcatheter interventions. Output of AI algorithm:
Key success indicators:
| Preoperative phase: automated segmentation and measurements compared with manual assessments; Postoperative phase at day 30: comparison of predicted results with actual clinical patient outcomes. |
| Measure | Description | Time Frame |
|---|---|---|
| Performance of AI algorithms in CT and TEE image analysis | Development and evaluation of AI algorithm training platform for data analysis of patients undergoing transcatheter valve procedures. Comparison of AI model performance with existing benchmarks and manual analyses | Through study completion, an average of 2 years (retrospective analysis and validation of algorithms). |
| Measure | Description | Time Frame |
|---|---|---|
| AI-based discovery of clinical knowledge for patient selection | Comparison of AI's ability to standardize patient selection, treatment planning and clinical outcomes with traditional methods. Assessment of accuracy in predicting patient outcomes and reducing procedure-specific complications. | Baseline (pre-procedural) and post-procedural (day 90) analysis |
Inclusion Criteria:
Patients who have reached the age of legal majority under local laws.
For TAVI group: All patients who have had TAVI with a third generation transcatheter heart valve (THV), with an available pre-procedural optimal quality CT scan as defined by an ECG- gating CT with:
For TMVI group: Patients who have had a TMVI with a dedicated device and screen failures, with an available optimal quality CT scan.
For TTVI group: Patients who have had a TTVI with a dedicated device and screen failures, with an available optimal quality CT scan.
For M-TEER: All patient who have had a M-TEER with 1) G4 or newer iteration of MitraClip or 2) G2 or newer iteration of Pascal, with available pre-procedural TEE videos images from one of two vendors: Phillips or GE, with clear identifiable views of the Mitral valve, frame per second equal or higher than 40 frames per second, acceptable 3D reconstructions.
For T-TEER: All patient who have had a T-TEER with G4 or newer iteration of TriClip or 2) G2 or newer iteration of Pascal, with available pre-procedural TEE videos images from one of two vendors: Phillips or GE, with clear identifiable views of the Tricuspid valve, frame per second equal or higher than 40 frames per second, acceptable transgastric image with acceptable 3D reconstructions.
Exclusion Criteria:
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Patients with heart valve disease eligible for transcatheter interventions (TAVI, M-TEER, TMVI, TTVI).
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Thomas Modine, MD, PhD | Contact | +33(0)5 25 377541 | thomasmodine@gmail.com | |
| Walid Ben Ali, MD, PhD | Contact | +1 5145611037 | dr.walidbenali@gmail.com |
| Name | Affiliation | Role |
|---|---|---|
| Thomas Modine, MD, PhD | University Hospital Bordeaux, France | Principal Investigator |
| Walid Ben Ali, MD, PhD | Montreal Heart Institute | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Montefiore Medical Center New York | Recruiting | New York | New York | 10467 | United States |
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| OTHER |
| Centre Cardiologique du Nord | OTHER |
| Rennes University Hospital | OTHER |
| San Raffaele University Hospital, Italy | OTHER |
| Istituto clinico Città di Brescia | UNKNOWN |
| Unity Health Toronto | OTHER |
| University Hospital, Marseille | OTHER |
| University Hospital, Lille | OTHER |
| Hospitaux Universitaires Paris Sud | UNKNOWN |
| Montefiore Medical Center | OTHER |
| University Medical Center Mainz | OTHER |
| Universitätsklinikum Hamburg-Eppendorf | OTHER |
| Vancouver Hospital | UNKNOWN |
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| Montreal Heart Institute, 5000 Rue Bélanger, Montréal | Recruiting | Montreal | Quebec | H1T 1C8 | Canada |
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| St Michael's Hospital Toronto | Recruiting | Toronto | Canada |
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| St Paul's Hospital Vancouver | Recruiting | Vancouver | Canada |
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| Centre Hospitalier Universitaire (CHU) de Bordeaux, 12 rue Dubernat 33404 Talence cedex | Recruiting | Bourdeaux | 33404 | France |
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| CHU Lille | Recruiting | Lille | France |
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| CHU Marseille | Recruiting | Marseille | France |
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| Centre Cardiologique du Nord Paris | Recruiting | Paris | France |
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| Institut Cardiovasculaire Paris-Sud Paris | Recruiting | Paris | France |
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| Centre Hospitalier Universitaire Rennes | Recruiting | Rennes | France |
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| Clinque Pasteur Toulouse - France | Recruiting | Toulouse | France |
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| University Medical Center Hamburg-Eppendorf | Recruiting | Hamburg | Germany |
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| Heart Valve Center Mainz | Recruiting | Mainz | Germany |
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| Istituto Clinico Città di Brescia | Recruiting | Brescia | Italy |
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| San Raffaele Heart Valve Center Milan | Recruiting | Milan | Italy |
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| ID | Term |
|---|---|
| D006349 | Heart Valve Diseases |
| ID | Term |
|---|---|
| D006331 | Heart Diseases |
| D002318 | Cardiovascular Diseases |
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