Ignite Creation Date:
2025-12-24 @ 11:14 PM
Ignite Modification Date:
2026-01-02 @ 8:34 AM
Study NCT ID:
NCT06206369
Status:
RECRUITING
Last Update Posted:
2024-01-16
First Post:
2024-01-04
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Developing Trustworthy Artificial Intelligence (AI)-Driven Tools to Predict Vascular Disease Risk and Progression
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D017544', 'term': 'Aortic Aneurysm, Abdominal'}, {'id': 'D058729', 'term': 'Peripheral Arterial Disease'}], 'ancestors': [{'id': 'D001014', 'term': 'Aortic Aneurysm'}, {'id': 'D000783', 'term': 'Aneurysm'}, {'id': 'D014652', 'term': 'Vascular Diseases'}, {'id': 'D002318', 'term': 'Cardiovascular Diseases'}, {'id': 'D001018', 'term': 'Aortic Diseases'}, {'id': 'D050197', 'term': 'Atherosclerosis'}, {'id': 'D001161', 'term': 'Arteriosclerosis'}, {'id': 'D001157', 'term': 'Arterial Occlusive Diseases'}, {'id': 'D016491', 'term': 'Peripheral Vascular Diseases'}]}, 'interventionBrowseModule': {'meshes': [{'id': 'D012189', 'term': 'Retrospective Studies'}], 'ancestors': [{'id': 'D016022', 'term': 'Case-Control Studies'}, {'id': 'D016021', 'term': 'Epidemiologic Studies'}, {'id': 'D016020', 'term': 'Epidemiologic Study Characteristics'}, {'id': 'D004812', 'term': 'Epidemiologic Methods'}, {'id': 'D008919', 'term': 'Investigative Techniques'}, {'id': 'D015331', 'term': 'Cohort Studies'}, {'id': 'D017531', 'term': 'Health Care Evaluation Mechanisms'}, {'id': 'D011787', 'term': 'Quality of Health Care'}, {'id': 'D017530', 'term': 'Health Care Quality, Access, and Evaluation'}, {'id': 'D011634', 'term': 'Public Health'}, {'id': 'D004778', 'term': 'Environment and Public Health'}]}}, 'protocolSection': {'designModule': {'bioSpec': {'retention': 'SAMPLES_WITH_DNA', 'description': 'Blood and tissue samples (from existing biobanks)'}, 'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'RETROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 11000}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'RECRUITING', 'startDateStruct': {'date': '2023-10-31', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2024-01', 'completionDateStruct': {'date': '2029-05-01', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2024-01-04', 'studyFirstSubmitDate': '2024-01-04', 'studyFirstSubmitQcDate': '2024-01-04', 'lastUpdatePostDateStruct': {'date': '2024-01-16', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2024-01-16', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2029-05-01', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Development of disease progression prediction algorithms', 'timeFrame': '3 years', 'description': 'The primary goal of this retrospective study is to develop and train algorithms to predict disease progression and risk of cardiovascular events in AAA and PAD patients by leveraging multi-parametric data from 5000 AAA (\\>1000 in AUMC) and 6000 PAD (\\>1000 in AUMC) patients from existing cohorts and biobanks.'}], 'secondaryOutcomes': [{'measure': 'Internal validation of disease progression prediction algorithms', 'timeFrame': '3 years', 'description': 'The secondary objective will be the internal validation of the developed algorithms using data from retrospective cohorts.'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Artificial Intelligence, Prediction models, Image analysis, Abdominal Aortic Aneurysms, Peripheral Arterial Disease'], 'conditions': ['Aneurysm Abdominal', 'Peripheral Arterial Disease']}, 'referencesModule': {'references': [{'pmid': '39921236', 'type': 'DERIVED', 'citation': 'Rijken L, Zwetsloot S, Smorenburg S, Wolterink J, Isgum I, Marquering H, van Duivenvoorde J, Ploem C, Jessen R, Catarinella F, Lee R, Bera K, Buisan J, Zhang P, Dias-Neto M, Raffort J, Lareyre F, Muller C, Koncar I, Tomic I, Zivkovic M, Djuric T, Stankovic A, Venermo M, Tulamo R, Behrendt CA, Smit N, Schijven M, van den Born BJ, Delewi R, Jongkind V, Ayyalasomayajula V, Yeung KK. Developing Trustworthy Artificial Intelligence Models to Predict Vascular Disease Progression: the VASCUL-AID-RETRO Study Protocol. J Endovasc Ther. 2025 Feb 7:15266028251313963. doi: 10.1177/15266028251313963. Online ahead of print.'}]}, 'descriptionModule': {'briefSummary': 'The VASCULAID-RETRO study, within the broader VASCULAID project, aims to create artificial intelligence (AI) algorithms that can predict cardiovascular events and the progression of abdominal aortic aneurysm (AAA) and peripheral arterial disease (PAD). The study plans to gather and analyze data from at least 5000 AAA and 6000 PAD patients, combining existing cohorts and retrospectively collected data. During this project, AI tools will be developed to perform automatic anatomical segmentation and analyses on multimodal imaging. AI prediction algorithms will be developed based on multisource data (imaging, medical history, -omics).', 'detailedDescription': 'To date, it is unknown which abdominal aortic aneurysm (AAA) and peripheral arterial disease (PAD) patients will suffer cardiovascular events or in which patients the AAA or PAD will progress. In the VASCULAID project, the VASCULAID-RETRO study aims to leverage data from existing cohorts and retrospectively collected data to develop artificial intelligence (AI) algorithms able to evaluate the risk of cardiovascular events and extent of disease progression.\n\nIn order to build and train the algorithms for the predictions, we plan to retrospectively enroll at least 5000 AAA and 6000 PAD patients AI-tools will be applied to the patient data. Automatic anatomical segmentation on images and image analysis on US, CTA and MRI will be performed. Also, algorithms to predict cardiovascular events and AAA or PAD progression based on multi-source data analysis will be developed.\n\nPatient data from European clinical consortium partners is available. This consortium has access to big cohorts with relevant data for the envisioned study that will be used to enrich the existing registries. These data will be used to refine the algorithms developed for the prediction of cardiovascular events and AAA/PAD progression.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '90 Years', 'minimumAge': '40 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'Males and females between 40 and 90 years of age with an abdominal aortic aneurysm and/or peripheral arterial disease.', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Males and females, 40-90 years old, with an AAA \\>3cm. This includes patients with infrarenal, juxtarenal, suprarenal, iliac (defined as 1.5x its normal diameter) aneurysms, as well as mycotic aneurysms. Patients that have had interventions or ruptures will also be included\n* Males and females, 40-90 years old, all PAD patients (Fontaine stages 1,2,3, and 4).\n\nExclusion Criteria:\n\n* Patients with an ascending, thoracic, thoracoabdominal (type 1-3) aneurysm.'}, 'identificationModule': {'nctId': 'NCT06206369', 'acronym': 'VASCULAIDRETRO', 'briefTitle': 'Developing Trustworthy Artificial Intelligence (AI)-Driven Tools to Predict Vascular Disease Risk and Progression', 'organization': {'class': 'OTHER', 'fullName': 'Amsterdam UMC, location VUmc'}, 'officialTitle': 'Developing Trustworthy Artificial Intelligence (AI)-Driven Tools to Predict Vascular Disease Risk and Progression', 'orgStudyIdInfo': {'id': '2011279'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Abdominal Aortic Aneurysm patients', 'interventionNames': ['Other: No intervention, retrospective study']}, {'label': 'Peripheral Arterial Disease patients', 'interventionNames': ['Other: No intervention, retrospective study']}], 'interventions': [{'name': 'No intervention, retrospective study', 'type': 'OTHER', 'description': 'No intervention, retrospective study', 'armGroupLabels': ['Abdominal Aortic Aneurysm patients', 'Peripheral Arterial Disease patients']}]}, 'contactsLocationsModule': {'locations': [{'city': 'Helsinki', 'status': 'RECRUITING', 'country': 'Finland', 'contacts': [{'name': 'Riikka Tulamo', 'role': 'CONTACT'}], 'facility': 'Hospital District of Helsinki and Uusimaa (HUS)', 'geoPoint': {'lat': 60.16952, 'lon': 24.93545}}, {'city': 'Hamburg', 'status': 'RECRUITING', 'country': 'Germany', 'contacts': [{'name': 'Christian Behrendt', 'role': 'CONTACT'}], 'facility': 'Asklepios kliniken hamburg', 'geoPoint': {'lat': 53.55073, 'lon': 9.99302}}, {'city': 'Amsterdam', 'status': 'RECRUITING', 'country': 'Netherlands', 'contacts': [{'name': 'Kak Khee Yeung', 'role': 'CONTACT'}], 'facility': 'Amsterdam UMC', 'geoPoint': {'lat': 52.37403, 'lon': 4.88969}}, {'city': 'Porto', 'status': 'RECRUITING', 'country': 'Portugal', 'contacts': [{'name': 'Marina Dias-Neto', 'role': 'CONTACT'}], 'facility': 'University Hospital Center of São João', 'geoPoint': {'lat': 41.1485, 'lon': -8.61097}}, {'city': 'Belgrade', 'status': 'RECRUITING', 'country': 'Serbia', 'contacts': [{'name': 'Igor Koncar', 'role': 'CONTACT'}], 'facility': 'University Clinical Centre of Serbia', 'geoPoint': {'lat': 44.80401, 'lon': 20.46513}}, {'city': 'Oxford', 'status': 'RECRUITING', 'country': 'United Kingdom', 'contacts': [{'name': 'Regent Lee', 'role': 'CONTACT'}], 'facility': 'Oxford University Hospitals', 'geoPoint': {'lat': 51.75222, 'lon': -1.25596}}], 'centralContacts': [{'name': 'Kak Khee Yeung, MD, PhD', 'role': 'CONTACT', 'email': 'k.yeung@amsterdamumc.nl', 'phone': '+31 6 14278725'}, {'name': 'Lotte Rijken, Msc.', 'role': 'CONTACT', 'email': 'l.rijken@amsterdamumc.nl'}]}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Amsterdam UMC, location VUmc', 'class': 'OTHER'}, 'collaborators': [{'name': 'Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)', 'class': 'OTHER'}, {'name': 'Technical University of Twente', 'class': 'OTHER'}, {'name': 'Universidade do Porto', 'class': 'OTHER'}, {'name': 'Centre Hospitalier Universitaire de Nice', 'class': 'OTHER'}, {'name': 'Stichting Allai', 'class': 'UNKNOWN'}, {'name': 'Faculty of Medicine, University of Belgrade', 'class': 'UNKNOWN'}, {'name': 'Brightfish Be', 'class': 'UNKNOWN'}, {'name': 'Hospital District of Helsinki and Uusimaa', 'class': 'OTHER'}, {'name': 'University of Bergen', 'class': 'OTHER'}, {'name': 'Asklepios Kliniken Hamburg GmbH', 'class': 'OTHER'}, {'name': 'University of Oxford', 'class': 'OTHER'}, {'name': 'VINČA INSTITUTE OF NUCLEAR SCIENCES Belgrado', 'class': 'UNKNOWN'}], 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'M.D., Ph.D., FEBVS', 'investigatorFullName': 'Kak Khee Yeung', 'investigatorAffiliation': 'Amsterdam UMC, location VUmc'}}}}