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

Study Overview
=================
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.

Official Title
-----------------
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

Conditions
-----------------
Crohn Disease, Rectal Fistula

Participation Criteria
=================
Eligibility Criteria
-----------------
Inclusion Criteria: 1. CD participant diagnosed or not with CPF between January 1st 2015 and December 31st 2021. Exclusion Criteria: Not applicable.

Ages Eligible for Study
-----------------
Minimum Age: 18 Years

Sexes Eligible for Study
-----------------
All

Accepts Healthy Volunteers
-----------------
No

Study Plan
=================
How is the study designed?
-----------------

Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Participants With CD<br>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. | |

What is the study measuring?
-----------------
Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 | 

 Terms related to the study
=================
Keywords Provided by Centre Hospitalier Valida
-----------------
Drug Therapy