Personalised Prediction of Disease Course in Ulcerative C... | NCT05479617

Studies
Personalised Prediction of Disease Course in Ulcerative Colitis Using Multimodal Machine Learning - Part of the Presager Project

NCT05479617

Sponsor

Copenhagen University Hospital, Hvidovre

Status

Recruiting

Last Update Posted

Feb 27, 2025Actual

Enrollment

400Estimated

Phase

Not provided

Conditions

Ulcerative Colitis

Interventions

Deep learning model

Countries

Denmark

Protocol Section

Identification Module Status Module Sponsor/Collaborators Module Oversight Module Description Module Conditions Module Design Module Arms/Interventions Module Outcomes Module Eligibility Module Contacts/Locations Module References Module IPD Sharing Statement Module

Results Section

Participant Flow Module Baseline Characteristics Module Outcome Measures Module Adverse Events Module More Info Module

Annotation Section

Annotation Module

Document Section

Large Document Module

Derived Section

Miscellaneous Info Module Condition Browse Module Intervention Browse Module

Protocol Section

Identification Module

NCT ID

Results Section

No data available

No data is available for this block.

Annotation Section

No data available

No data is available for this block.

Document Section

No data available

No data is available for this block.

Derived Section

Miscellaneous Info Module

Version Holder

Status Module

Record Verification Date

Feb 2025

Overall Recruitment Status or Expanded Access Status

Recruiting

Last Known Status

Not provided

Delayed Posting

Not provided

Why Stopped

Not provided

Expanded Access Info

Start Date

Jun 20, 2022Actual

Primary Completion Date

Dec 2025Estimated

Completion Date

Jul 2026Estimated

First Submitted Date

Jun 29, 2022

First Submission Date that Met QC Criteria

Jul 26, 2022

First Posted Date

Jul 29, 2022Actual

Results Waived

Not provided

Results First Submitted Date

Not provided

Results First Submitted that Met QC Criteria

Not provided

Results First Posted Date

Not provided

Certification/Extension (aka Delayed Results) First Submitted Date

Not provided

Certification/Extension First Submitted that Passed QC Review

Not provided

Certification/Extension First Posted Date

Not provided

Last Update Submitted Date

Feb 25, 2025

Last Update Posted Date

Feb 27, 2025Actual

Sponsor/Collaborators Module

Responsible Party, by Official Title

Bobby Lo, Principal Investigator, Copenhagen University Hospital, HvidovrePrincipal Investigator

Lead Sponsor

Copenhagen University Hospital, HvidovreOTHER

Collaborators

Not provided

Oversight Module

Has Data Monitoring Committee (DMC)

Is FDA Regulated Drug

Is FDA Regulated Device

Is Unapproved Device

Not provided

Pediatric Postmarket Surveillance of a Device Product

Not provided

Product Exported from US

FDAAA801 Violation

Not provided

Description Module

Brief Summary

In patients achieving clinical remission following a flare, artificial intelligence can reliably predict a new flare within the next 12 months utilizing clinical and objective information at day 0 and week 8.

Secondary endpoints:

An artificial intelligence model's precision in predicting a new flare within 2 and 3 years
An artificial intelligence model's precision to rule out patients who will not experience a new flare within 1, 2 and 3 year

Detailed Description

Not provided

Design Module

Study Type

Observational

Number of References to an Expanded Access Study

Not provided

Expanded Access Types

Not provided

Patient Registry

Target Follow-Up Duration

Not provided

Phases

Not provided

Interventional Study Design

Allocation

Not provided

Intervention Model

Biospecimen

No data available

No data is available for this block.

Enrollment

400Estimated

Arms/Interventions Module

Arm Groups

Not provided

Interventions

Name	Type	Description	Arm Group Labels	Other Names
Deep learning model	Other	Use of deep learning model to predict individual patients disease course

Outcomes Module

Primary Outcomes

Measure	Description	Time Frame
Flare I	Evaluate the accuracy in predicting a flare within 1 year after the patient's initial flare using machine learning methods.	within 1 year

Secondary Outcomes

Measure	Description	Time Frame
Flare II	Evaluate the accuracy in predicting a flare within 2 years, after the patient's initial flare, using machine learning methods.	within 2 years
Flare III	Evaluate the accuracy in predicting a flare within 3 years, after the patient's initial flare, using machine learning methods.

Other Outcomes

Not provided

Eligibility Module

Eligibility Criteria

Inclusion criteria:

Signed informed consent
Diagnosis of UC for at least 1 year
Relapse due to UC.
First endoscopic and histological evaluation of the flare Exclusion criteria
Well-founded doubt that the flare is due to other than the patients UC

Accepts Healthy Volunteers

Sex

All

Sex/Gender Based

Not provided

Sex/Gender Description

Not provided

Minimum Age

18 Years

Maximum Age

Not provided

Standard Ages

AdultOlder Adult

Study Population

This is a prospective cohort of a total of 400 UC patients with an active flare

Sampling Method

Probability Sample

Contacts/Locations Module

Central Contacts

Not provided

Overall Officials

Not provided

Locations

Facility	Status	City	State	ZIP	Country	Contacts
Gastrounit, medical section, Copenhagen University Hospital Hvidovre	Recruiting	Hvidovre	Denmark	2650	Denmark

Label	URL
The description is in Danish	View source

IPD Sharing Statement Module

Plan to Share IPD

Undecided

Description

Not provided

Types

Not provided

Time Frame

Not provided

Access Criteria

Not provided

URL

Not provided

Condition Browse Module

MeSH Terms

ID	Term
D003093	Colitis, Ulcerative

Ancestor Terms

ID	Term
D003092	Colitis
D005759	Gastroenteritis
D005767	Gastrointestinal Diseases
D004066	Digestive System Diseases

Browse Leaves

Not provided

Browse Branches

Not provided