Enhanced Valves Interventions and Safe AI Generated End Results

Обзор

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.

Подробное описание

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.

Вмешательства

• Диагностический тест Medical imaging analysis via artificial intelligence algorithms
  Development of AI algorithms based on pre-procedural imaging annotations and clinical informations to predict the transcatheter procedural outcomes

Первичные конечные точки

• Accuracy of transcatheter AI predictions [Срок оценки: Preoperative phase: automated segmentation and measurements compared with manual assessments; Postoperative phase at day 30: comparison of predicted results with actual clinical patient outcomes.]

Вторичные конечные точки (1)

• Performance of AI algorithms in CT and TEE image analysis [Срок оценки: Through study completion, an average of 2 years (retrospective analysis and validation of algorithms).]

Критерии участия

Критерии включения

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:

• five to ten image volumes at cardiac phases from 5% to 95% R-R
• 0.625 mm slice thickness
• 0.625 mm spacing between slices
• 0.88 mm in-plane pixel spacing
• 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.

Критерии исключения

• For TAVI group: Valve-in-valve procedures
• For TMVI group: Valve-in-valve and valve-in-ring procedures
• For TTVI: Valve-in-valve and valve-in-ring procedures
• For M-TEER: G3 or older MitraClip, G1 Pascal
• For T-TEER: G3 Triclip, G1 Pascal

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Нет

Дизайн исследования

Модель наблюдения

Когортное

Центры проведения

Франция · 7 центров

• Centre Hospitalier Universitaire (CHU) de Bordeaux, 12 rue Dubernat 33404 Talence cedex — Bourdeaux
• CHU Lille — Lille
• CHU Marseille — Marseille
• Centre Cardiologique du Nord Paris — Paris
• Institut Cardiovasculaire Paris-Sud Paris — Paris
• Centre Hospitalier Universitaire Rennes — Rennes
• Clinque Pasteur Toulouse - France — Toulouse

Канада · 3 центра

• Montreal Heart Institute, 5000 Rue Bélanger, Montréal — Montreal
• St Michael's Hospital Toronto — Toronto
• St Paul's Hospital Vancouver — Vancouver

Германия · 2 центра

• University Medical Center Hamburg-Eppendorf — Hamburg
• Heart Valve Center Mainz — Mainz

Италия · 2 центра

• Istituto Clinico Città di Brescia — Brescia
• San Raffaele Heart Valve Center Milan — Milan

США · 1 центр

• Montefiore Medical Center New York — New York

Идентификаторы

NCT: NCT07213531 · CHUBX2024/50

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

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