A Study Using Artificial Intelligence to Identify Adults With Complex Perianal Fistulas Associated With Crohn's Disease

Completed

• Conditions: Crohn Disease; Rectal Fistula

• Registration Number: NCT04844593
• Lead Sponsor: Takeda

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.

Detailed 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.

Study Timeline

• Study First Submitted: 2021-04-13
• Study First Submitted QC: 2021-04-13
• Study First Posted: 2021-04-14
• Study Start: 2022-03-08
• Primary Completion: 2023-02-27
• Study Completion: 2024-04-29
• Status Verified: 2024-05
• Last Update Submitted: 2024-05-09
• Last Update Posted: 2024-05-10

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 32

Inclusion Criteria

1. CD participant diagnosed or not with CPF between January 1st 2015 and December 31st 2021.

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Exclusion Criteria

Not applicable.

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Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Percentage of Participants With CD and CPF Accurately Identified With the use of NLP and Medical Language (MEL)	Up to Month 36	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.

Secondary Outcome Measures

Name	Time	Method
Number of Participants With CD and CPF Characterized Using NLP and Machine Learning Techniques	Up to Month 36	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.

Trial Locations

Locations (3)

Hospital Universitario Fundacion Alcorcon; 🇪🇸 Madrid, Comunidad De Madrid, Spain

Hospital del Mar; 🇪🇸 Barcelona, Cataluna, Spain

Hospital Universitario Son Espases; 🇪🇸 Palma, Islas Baleares, Spain