Ignite Creation Date:
2025-12-25 @ 1:24 AM
Ignite Modification Date:
2025-12-31 @ 11:01 AM
Study NCT ID:
NCT04844593
Status:
COMPLETED
Last Update Posted:
2024-05-10
First Post:
2021-04-13
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
A Study Using Artificial Intelligence to Identify Adults With Complex Perianal Fistulas Associated With Crohn's Disease
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D003424', 'term': 'Crohn Disease'}, {'id': 'D012003', 'term': 'Rectal Fistula'}], 'ancestors': [{'id': 'D015212', 'term': 'Inflammatory Bowel Diseases'}, {'id': 'D005759', 'term': 'Gastroenteritis'}, {'id': 'D005767', 'term': 'Gastrointestinal Diseases'}, {'id': 'D004066', 'term': 'Digestive System Diseases'}, {'id': 'D007410', 'term': 'Intestinal Diseases'}, {'id': 'D007412', 'term': 'Intestinal Fistula'}, {'id': 'D016154', 'term': 'Digestive System Fistula'}, {'id': 'D012002', 'term': 'Rectal Diseases'}, {'id': 'D005402', 'term': 'Fistula'}, {'id': 'D020763', 'term': 'Pathological Conditions, Anatomical'}, {'id': 'D013568', 'term': 'Pathological Conditions, Signs and Symptoms'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'RETROSPECTIVE', 'observationalModel': 'COHORT'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 32}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2022-03-08', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2024-05', 'completionDateStruct': {'date': '2024-04-29', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2024-05-09', 'studyFirstSubmitDate': '2021-04-13', 'studyFirstSubmitQcDate': '2021-04-13', 'lastUpdatePostDateStruct': {'date': '2024-05-10', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2021-04-14', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2023-02-27', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Percentage of Participants With CD and CPF Accurately Identified With the use of NLP and Medical Language (MEL)', 'timeFrame': 'Up to Month 36', 'description': 'Percentage of participants will be measured in terms of accuracy and precision (sensitivity and specificity) of the "algorithm" used to identify participants with CPF associated with CD. Data obtained through the artificial intelligence (AI) technology will be compared with data obtained through traditional electronic data capture (EDC) and source data verification methods.'}], 'secondaryOutcomes': [{'measure': 'Number of Participants With CD and CPF Characterized Using NLP and Machine Learning Techniques', 'timeFrame': 'Up to Month 36', 'description': 'The following information at the moment of CPF diagnosis will be extracted from the electronical medical records (EMRs): age, gender, date of diagnosis of CPF, smoking status, date of diagnosis of CD, luminal disease characteristics (localization, behaviour and activity) at diagnosis, treatments (medical and surgical) established for luminal disease in the study period, treatments (medical and surgical) established for CPF since first occurrence, fistula characteristics at diagnosis: type of fistula (following American Gastroenterological Association \\[AGA\\] classification) number of fistula internal and external openings, fistula activity.'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Drug Therapy'], 'conditions': ['Crohn Disease', 'Rectal Fistula']}, 'referencesModule': {'seeAlsoLinks': [{'url': 'https://clinicaltrials.takeda.com/study-detail/60774675688ad8001f42fba3', 'label': 'To obtain more information on the study, click here/on this link'}]}, 'descriptionModule': {'briefSummary': "Natural Language Processing and machine learning are examples of artificial intelligence tools. This study will check if these tools correctly identify people with Crohn's disease with complex perianal fistulas from their medical records.", 'detailedDescription': 'This is a non-interventional, retrospective study of participants with CD and CPF in a clinical practice setting.\n\nThe study will enroll approximately 100 participants.\n\nThe study will have a retrospective data collection to select and analyze information from EMRs processed by an AI based analytics framework that uses machine learning and NLP methodologies.\n\nAll participants will be enrolled in one observational group.\n\n• Participants with CD\n\nThis multi-center trial will be conducted in Spain. The overall duration of the study is approximately 36 months.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'Participants with CD diagnosed with or without CPF during the study period.', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n1\\. CD participant diagnosed or not with CPF between January 1st 2015 and December 31st 2021.\n\nExclusion Criteria:\n\nNot applicable.'}, 'identificationModule': {'nctId': 'NCT04844593', 'acronym': 'INTUITION-CPF', 'briefTitle': "A Study Using Artificial Intelligence to Identify Adults With Complex Perianal Fistulas Associated With Crohn's Disease", 'organization': {'class': 'INDUSTRY', 'fullName': 'Takeda'}, 'officialTitle': "Use of Natural Language Processing (NLP) and Machine Learning (ML) for the Identification of Patients With Crohn's Disease (CD) and Complex Perianal Fistulas (CPF) and Their Characterization in Terms of Clinical and Demographic Characteristics. A Multicentre, Retrospective, NLP Based Study", 'orgStudyIdInfo': {'id': 'Darvadstrocel-5001'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Participants With CD', 'description': 'Participants with CD diagnosed with or without CPF will be identified from EMRs through medical language application program interface (API) software. The AI will apply NLP and machine learning to identify and analyse text information in EMRs and thereby, extract medical information. The data will be collected retrospectively from January 1st 2015 and December 31st 2021.'}]}, 'contactsLocationsModule': {'locations': [{'zip': '07010', 'city': 'Palma', 'state': 'Balearic Islands', 'country': 'Spain', 'facility': 'Hospital Universitario Son Espases', 'geoPoint': {'lat': 39.56939, 'lon': 2.65024}}, {'zip': '08003', 'city': 'Barcelona', 'state': 'Catalonia', 'country': 'Spain', 'facility': 'Hospital del Mar', 'geoPoint': {'lat': 41.38879, 'lon': 2.15899}}, {'zip': '28922', 'city': 'Madrid', 'state': 'Madrid', 'country': 'Spain', 'facility': 'Hospital Universitario Fundacion Alcorcon', 'geoPoint': {'lat': 40.4165, 'lon': -3.70256}}], 'overallOfficials': [{'name': 'Study Director', 'role': 'STUDY_DIRECTOR', 'affiliation': 'Takeda'}]}, 'ipdSharingStatementModule': {'url': 'https://vivli.org/ourmember/takeda/', 'infoTypes': ['STUDY_PROTOCOL', 'SAP', 'CSR'], 'ipdSharing': 'YES', 'description': "Takeda provides access to the de-identified individual participant data (IPD) for eligible studies to aid qualified researchers in addressing legitimate scientific objectives (Takeda's data sharing commitment is available on https://clinicaltrials.takeda.com/takedas-commitment?commitment=5). These IPDs will be provided in a secure research environment following approval of a data sharing request, and under the terms of a data sharing agreement.", 'accessCriteria': 'IPD from eligible studies will be shared with qualified researchers according to the criteria and process described on https://vivli.org/ourmember/takeda/. For approved requests, the researchers will be provided access to anonymized data (to respect patient privacy in line with applicable laws and regulations) and with information necessary to address the research objectives under the terms of a data sharing agreement.'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Takeda', 'class': 'INDUSTRY'}, 'responsibleParty': {'type': 'SPONSOR'}}}}