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Clinical Trial Details — Status: Completed

Administrative data

NCT number NCT04844593
Other study ID # Darvadstrocel-5001
Secondary ID
Status Completed
Phase
First received
Last updated
Start date March 8, 2022
Est. completion date April 29, 2024

Study information

Verified date May 2024
Source Takeda
Contact n/a
Is FDA regulated No
Health authority
Study type Observational

Clinical Trial 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.


Description:

This is a non-interventional, retrospective study of participants with CD and CPF in a clinical practice setting. The study will enroll approximately 100 participants. The 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. All participants will be enrolled in one observational group. • Participants with CD This multi-center trial will be conducted in Spain. The overall duration of the study is approximately 36 months.


Recruitment information / eligibility

Status Completed
Enrollment 32
Est. completion date April 29, 2024
Est. primary completion date February 27, 2023
Accepts healthy volunteers No
Gender All
Age group 18 Years and older
Eligibility Inclusion Criteria: 1. CD participant diagnosed or not with CPF between January 1st 2015 and December 31st 2021. Exclusion Criteria: Not applicable.

Study Design


Related Conditions & MeSH terms


Locations

Country Name City State
Spain Hospital del Mar Barcelona Cataluna
Spain Hospital Universitario Fundacion Alcorcon Madrid Comunidad De Madrid
Spain Hospital Universitario Son Espases Palma Islas Baleares

Sponsors (1)

Lead Sponsor Collaborator
Takeda

Country where clinical trial is conducted

Spain, 

Outcome

Type Measure Description Time frame Safety issue
Primary 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 Number of Participants With CD and CPF Characterized Using NLP and Machine Learning Techniques 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. Up to Month 36
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