[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"trial:NCT07213531":3,"trial-entities:NCT07213531":260,"trial-summary:NCT07213531":264},{"id":4,"nct_id":4,"org_study_id":5,"brief_title":6,"official_title":6,"overall_status":7,"completion_date":8,"status_verified_date":9,"last_update_date":10,"start_date":11,"sponsor_name":12,"lead_sponsor_class":13,"has_dmc":14,"brief_summary":15,"detailed_description":16,"conditions":17,"keywords":26,"study_type":32,"primary_purpose":33,"phases":34,"enrollment_info":35,"interventions":38,"primary_outcomes":44,"secondary_outcomes":49,"sex":54,"minimum_age":55,"maximum_age":33,"healthy_volunteers":14,"eligibility_criteria":56,"std_ages":70,"locations":73,"central_contacts":250,"overall_officials":254,"references":258,"see_also_links":259},"NCT07213531","CHUBX2024\u002F50","Enhanced Valves Interventions and Safe AI Generated End Results","RECRUITING","2029-05","2025-10","2025-10-09","2024-05-01","Montreal Heart Institute","OTHER",false,"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.",[18,19,20,21,22,23,24,25],"Heart Valve Disease","TAVI","M-TEER","TTVI","TMVI","T-TEER","Mitraclip","TriClip",[27,28,29,20,23,19,22,30,31],"Medical imaging","CT","Artificial intelligence","valvulopathy","predictive algorithms","OBSERVATIONAL",null,[],{"count":36,"type":37},21000,"ESTIMATED",[39],{"type":40,"name":41,"description":42,"armGroupLabels":43},"DIAGNOSTIC_TEST","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",[20,23,19,22,21],[45],{"measure":46,"description":47,"timeFrame":48},"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.\n\nOutput of AI algorithm:\n\n* Sizes, types, and number of devices to be implanted\n* Device success\n* Percentage risk of permanent pacemaker implantation (for TAVI and TTVI)\n* Percentage risk of 30-day (para)valvular regurgitation for TAVI, and residual regurgitation for M-TEER and T-TEER\n* Single leaflet detachment for M-TEER and T-TEER\n* Left ventricular outflow tract obstruction for TMVI.\n\nKey success indicators:\n\n* First, independent retrospective validation dataset AI algorithms predict procedural outcome with \\>90% accuracy and low inter-reader observer variability when compared to measured procedural outcome.\n* Second independent retrospective dataset, perform a study to validate AI algorithms with \\>90% accuracy and low inter-reader observer variability when compared to measured procedural outcome.","Preoperative phase: automated segmentation and measurements compared with manual assessments; Postoperative phase at day 30: comparison of predicted results with actual clinical patient outcomes.",[50],{"measure":51,"description":52,"timeFrame":53},"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).","ALL","18 Years",{"inclusion":57,"exclusion":63,"raw_text":69},[58,59,60,61,62],"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.",[64,65,66,67,68],"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","Inclusion Criteria:\n\nPatients who have reached the age of legal majority under local laws.\n\n* 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:\n\n  1. five to ten image volumes at cardiac phases from 5% to 95% R-R\n  2. 0.625 mm slice thickness\n  3. 0.625 mm spacing between slices\n  4. 0.88 mm in-plane pixel spacing\n* For TMVI group: Patients who have had a TMVI with a dedicated device and screen failures, with an available optimal quality CT scan.\n* For TTVI group: Patients who have had a TTVI with a dedicated device and screen failures, with an available optimal quality CT scan.\n* 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.\n* 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.\n\nExclusion Criteria:\n\n* For TAVI group: Valve-in-valve procedures\n* For TMVI group: Valve-in-valve and valve-in-ring procedures\n* For TTVI: Valve-in-valve and valve-in-ring procedures\n* For M-TEER: G3 or older MitraClip, G1 Pascal\n* For T-TEER: G3 Triclip, G1 Pascal",[71,72],"ADULT","OLDER_ADULT",[74,88,104,115,126,141,152,163,174,182,193,204,216,227,239],{"facility":75,"status":7,"city":76,"state":76,"zip":77,"country":78,"contacts":79,"geoPoint":85},"Montefiore Medical Center New York","New York","10467","United States",[80],{"name":81,"role":82,"phone":83,"email":84},"Andrea Scotti, MD, PhD","CONTACT","+1 718-920-4321","a.scotti@hotmail.com",{"lat":86,"lon":87},40.71427,-74.00597,{"facility":89,"status":7,"city":90,"state":91,"zip":92,"country":93,"contacts":94,"geoPoint":101},"Montreal Heart Institute, 5000 Rue Bélanger, Montréal","Montreal","Quebec","H1T 1C8","Canada",[95,99],{"name":96,"role":82,"phone":97,"email":98},"Walid Ben Ali, MD, PhD","+1 5145611037","dr.walidbenali@gmail.com",{"name":96,"role":100},"PRINCIPAL_INVESTIGATOR",{"lat":102,"lon":103},45.50884,-73.58781,{"facility":105,"status":7,"city":106,"country":93,"contacts":107,"geoPoint":112},"St Michael's Hospital Toronto","Toronto",[108],{"name":109,"role":82,"phone":110,"email":111},"Neil Fam, MD","+1 416-864-5939","neil.fam@unityhealth.to",{"lat":113,"lon":114},43.70643,-79.39864,{"facility":116,"status":7,"city":117,"country":93,"contacts":118,"geoPoint":123},"St Paul's Hospital Vancouver","Vancouver",[119],{"name":120,"role":82,"phone":121,"email":122},"John Webb, MD","+1-604-806-8804","johngraydonwebb@gmail.com",{"lat":124,"lon":125},49.24966,-123.11934,{"facility":127,"status":7,"city":128,"zip":129,"country":130,"contacts":131,"geoPoint":138},"Centre Hospitalier Universitaire (CHU) de Bordeaux, 12 rue Dubernat 33404 Talence cedex","Bourdeaux","33404","France",[132,136],{"name":133,"role":82,"phone":134,"email":135},"Lionel Leroux, MD","+33(0)5 25 377541","lionel.leroux@chu-bordeaux.fr",{"name":137,"role":100},"Thomas Modine, MD, PhD",{"lat":139,"lon":140},44.58582,5.13611,{"facility":142,"status":7,"city":143,"country":130,"contacts":144,"geoPoint":149},"CHU Lille","Lille",[145],{"name":146,"role":82,"phone":147,"email":148},"Augustin Coisne, MD, PhD","+33 3 20 44 59 62","augustincoisne@hotmail.com",{"lat":150,"lon":151},50.63391,3.05512,{"facility":153,"status":7,"city":154,"country":130,"contacts":155,"geoPoint":160},"CHU Marseille","Marseille",[156],{"name":157,"role":82,"phone":158,"email":159},"Thomas Cuisset, MD, PhD","+33 04 91 38 59 75","thomas.cuisset@ap-hm.fr",{"lat":161,"lon":162},43.29695,5.38107,{"facility":164,"status":7,"city":165,"country":130,"contacts":166,"geoPoint":171},"Centre Cardiologique du Nord Paris","Paris",[167],{"name":168,"role":82,"phone":169,"email":170},"Julien Dreyfus, MD, PhD","+33 1 49 33 41 41","dreyfusjulien@yahoo.fr",{"lat":172,"lon":173},48.85341,2.3488,{"facility":175,"status":7,"city":165,"country":130,"contacts":176,"geoPoint":181},"Institut Cardiovasculaire Paris-Sud Paris",[177],{"name":178,"role":82,"phone":179,"email":180},"Myriam Akodad, MD","+33 1 60 13 46 02","akodadmyriam@gmail.com",{"lat":172,"lon":173},{"facility":183,"status":7,"city":184,"country":130,"contacts":185,"geoPoint":190},"Centre Hospitalier Universitaire Rennes","Rennes",[186],{"name":187,"role":82,"phone":188,"email":189},"Erwan Donal, MD, PhD","+33 (0)2 99 28 71 58","erwan.donal@chu-rennes.fr",{"lat":191,"lon":192},48.11109,-1.67431,{"facility":194,"status":7,"city":195,"country":130,"contacts":196,"geoPoint":201},"Clinque Pasteur Toulouse - France","Toulouse",[197],{"name":198,"role":82,"phone":199,"email":200},"Didier Tchétché, MD","+33 5 62 21 31 31","d.tchetche@clinique-pasteur.com",{"lat":202,"lon":203},43.60426,1.44367,{"facility":205,"status":7,"city":206,"country":207,"contacts":208,"geoPoint":213},"University Medical Center Hamburg-Eppendorf","Hamburg","Germany",[209],{"name":210,"role":82,"phone":211,"email":212},"Lenard Conradi, MD, PhD","+49 (0) 40 7410 - 58949","l.conradi@uke.de",{"lat":214,"lon":215},53.55073,9.99302,{"facility":217,"status":7,"city":218,"country":207,"contacts":219,"geoPoint":224},"Heart Valve Center Mainz","Mainz",[220],{"name":221,"role":82,"phone":222,"email":223},"Ralph Stephan von Bardeleben, MD, PhD","06131 17-7342","stephan.von_bardeleben@unimedizin-mainz.de",{"lat":225,"lon":226},49.98185,8.28008,{"facility":228,"status":7,"city":229,"country":230,"contacts":231,"geoPoint":236},"Istituto Clinico Città di Brescia","Brescia","Italy",[232],{"name":233,"role":82,"phone":234,"email":235},"Marianna Adamo, MD, PhD","+39 030 2988","mariannaadamo@hotmail.com",{"lat":237,"lon":238},45.53558,10.21472,{"facility":240,"status":7,"city":241,"country":230,"contacts":242,"geoPoint":247},"San Raffaele Heart Valve Center Milan","Milan",[243],{"name":244,"role":82,"phone":245,"email":246},"Francesco Maisano, MD, PhD","+39 340 357 3125","maisano.francesco@hsr.it",{"lat":248,"lon":249},45.46427,9.18951,[251,253],{"name":137,"role":82,"phone":134,"email":252},"thomasmodine@gmail.com",{"name":96,"role":82,"phone":97,"email":98},[255,257],{"name":137,"affiliation":256,"role":100},"University Hospital Bordeaux, France",{"name":96,"affiliation":12,"role":100},[],[],{"nct_id":4,"conditions":261,"biomarkers":263},[262],"Valvular Heart Disorder",[],{"nct_id":4,"found":265,"summary":266,"prompt_version":276},true,{"design":267,"status":268,"heading":269,"summary":270,"follow_up":271,"word_count":272,"commitments":273,"compensation":274,"drugs_mentioned":275},"This is an observational study, meaning researchers will analyze existing data rather than giving new treatments. It aims to include 21,000 participants.","completed","AI for Heart Valve Interventions (ENVISAGE Study)","This study, called ENVISAGE, is looking at how artificial intelligence (AI) can help doctors better predict the results of heart valve procedures. These procedures include TAVI (Transcatheter Aortic Valve Implantation), M-TEER (Mitral Transcatheter Edge-to-Edge Repair), TTVI (Transcatheter Tricuspid Valve Implantation), and TMVI (Transcatheter Mitral Valve Implantation). Researchers will use AI to analyze medical images and patient information to see if it can accurately predict how well these procedures will work. The goal is to improve patient selection and planning for these important heart valve treatments. This study is for adults aged 18 and older who have had a TAVI procedure and have specific CT scans available. Success will be measured by how accurate the AI predictions are compared to actual patient outcomes 30 days after the procedure. The study status is unclear.","The study will compare predicted results with actual patient outcomes 30 days after the procedure.",132,"Not specified in the trial record.","Not stated in the trial record.",[],"v2"]