A Study Using Artificial Intelligence to Identify Adults With Complex Perianal Fistulas Associated With Crohn's Disease
INTUITION-CPF
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
1 other identifier
observational
32
1 country
3
Brief Summary
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.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P25-P50 for all trials
Started Mar 2022
Typical duration for all trials
3 active sites
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
First Submitted
Initial submission to the registry
April 13, 2021
CompletedFirst Posted
Study publicly available on registry
April 14, 2021
CompletedStudy Start
First participant enrolled
March 8, 2022
CompletedPrimary Completion
Last participant's last visit for primary outcome
February 27, 2023
CompletedStudy Completion
Last participant's last visit for all outcomes
April 29, 2024
CompletedMay 10, 2024
May 1, 2024
12 months
April 13, 2021
May 9, 2024
Conditions
Keywords
Outcome Measures
Primary Outcomes (1)
Percentage of Participants With CD and CPF Accurately Identified With the use of NLP and Medical Language (MEL)
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.
Up to Month 36
Secondary Outcomes (1)
Number of Participants With CD and CPF Characterized Using NLP and Machine Learning Techniques
Up to Month 36
Study Arms (1)
Participants With CD
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.
Eligibility Criteria
Participants with CD diagnosed with or without CPF during the study period.
You may qualify if:
- \. CD participant diagnosed or not with CPF between January 1st 2015 and December 31st 2021.
You may not qualify if:
- Not applicable.
Contact the study team to confirm eligibility.
Sponsors & Collaborators
- Takedalead
Study Sites (3)
Hospital Universitario Son Espases
Palma, Balearic Islands, 07010, Spain
Hospital del Mar
Barcelona, Catalonia, 08003, Spain
Hospital Universitario Fundacion Alcorcon
Madrid, Madrid, 28922, Spain
Related Links
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Study Officials
- STUDY DIRECTOR
Study Director
Takeda
Study Design
- Study Type
- observational
- Observational Model
- COHORT
- Time Perspective
- RETROSPECTIVE
- Sponsor Type
- INDUSTRY
- Responsible Party
- SPONSOR
Study Record Dates
First Submitted
April 13, 2021
First Posted
April 14, 2021
Study Start
March 8, 2022
Primary Completion
February 27, 2023
Study Completion
April 29, 2024
Last Updated
May 10, 2024
Record last verified: 2024-05
Data Sharing
- IPD Sharing
- Will share
- Shared Documents
- STUDY PROTOCOL, SAP, CSR
- Access Criteria
- 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.
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.