Prediction of IBD Disease Activity in Individual Patients Based on PROMs and Clinical Data

Study Overview
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Brief Summary
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The proposed study will use a PROM (Patient report Outcome Measurement)-tool in combination with clinical and biochemical data to train and validate a Relapse Prediction Model for individual patients.

Detailed Description
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The primary objective is to train and validate a relapse prediction model for individual patients available for daily (remote) care management. Besides that, risk-based care pathways for different prediction outcomes will be evaluated, prediction scores will be correlated to medication type, CRP/Calprotectin and/or endoscopy, and with known IBD clinical risk profiles. Moreover dietary intake will be correlated with the IBD risk profiles. Study design: Multicentre, retrospective analysis of two prospective cohorts. Study population: Adult IBD patients. Main study parameters/endpoints: The endpoint will be a prediction regarding step-up or step-down in the care pathways. In other words, the percentage of patients in each individual care pathway with agreement between risk score of the individual patient and actual flares during a follow-up time of 24 months. Furthermore insight will be gained in dietary patterns amongst patients with different IBD risk profiles. No benefits or risks are associated with participating in this study, because only standard of care is given.

Official Title
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Prediction of IBD Disease Activity in Individual Patients Based on PROMs and Clinical Data

Conditions
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IBD, Inflammatory Bone Disease

Intervention / Treatment
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* Other: No intervention


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Adult IBD patients Subjects willing and able to sign informed consent Own and are able to use a smart phone (Android or iOS) Exclusion Criteria: Unwilling or unable to adhere to the protocol Unwilling or unable to adhere to the informed consent Age <18y

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| training phase<br>The study procedures for both cohorts are the same. | Other: No intervention<br>* Patients will receive standard of care.<br>|
| validation phase<br>The study procedures for both cohorts are the same. | Other: No intervention<br>* Patients will receive standard of care.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Develop a relapse prediction model for individual patients (agreement between risk score of the individual patient and actual flares) based on both clinical parameters and biochemical parameters in the individual care pathways. | This model will be based on both clinical parameters and biochemical parameters in the individual care pathways. | After 2 years | 
| Validate the above mentioned prediction model and make it available for daily (remote) care management. | Based on the information form the validation cohort. The model will be validated retrospectively. | After 2 years | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| evaluate risk-based care pathways for different prediction outcomes in clinical practice e.g. high intensity monitoring care pathway for patients with a high prediction score. | Evaluate whether predefined risk-based care pathways are in line with prediction outcomes of the relapse prediction model. | After 2 years | 
| Correlate the prediction scores of the different care pathways to medication type. | See if there is a statistical correlation between medication type and prediction score | After 2 years | 
| Correlate prediction scores of the different pathways with biomarkers CRP/Calprotectin and/or endoscopy | See if there is a statistical correlation between prediction score and biomarkers CRP/Calprotectin and/or endoscopy | After 2 years | 
| Correlate prediction scores from the algorithm with known IBD clinical risk factors | See if there is a statistical correlation between prediction scores from the model to known clinical risk factors like e.g. operation history, presence of EIM. | After 2 years | 
| Correlate dietary intake with the assigned IBD clinical risk profiles | See if there is a statistical correlation between dietary intake and assigned IBD clinical risk profile. | After 2 years |