Predicting Successful Outcome of Interdisciplinary Biopsychosocial Rehabilitation in Osteoarthritis

Chronic Pain Clinical Trial

— CIR-predict  

Official title:

Development of a Clinical Prediction Model to Facilitate Decision Making in Interdisciplinary Biopsychosocial Rehabilitation in Patients With Osteoarthritis (OA)

Clinical Trial Details — Status: Active, not recruiting

Administrative data

• NCT number: NCT05661760
• Other study ID #: CIR-001
• Secondary ID: 41150323011N
• Status: Active, not recruiting
• Start date: January 1, 2019
• Est. completion date: December 31, 2023

Study information

• Verified date: November 2022
• Source: Maastricht University
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

The goal of this prospective study is to identify variables that can predict whether an interdisciplinary biopsychosocial intervention for patients with osteoarthritis will be successful. Using an observational design, patients admitted to this program during the 3-year period (2019-2021) will be included and data gathered during routine clinical practice at baseline and end of treatment of patients who gave informed consent, will be used. With these data a prediction model will be build and internal validation with bootstrapping will be done.

Description:

Rationale: To predict the probability of a positive outcome of interdisciplinary biopsychosocial intervention in individual patients with osteoarthritis (OA), a prediction model is needed. Research Question/Objective: The overall goal is to develop a clinical prediction model to facilitate decision making in interdisciplinary biopsychosocial rehabilitation in patients with OA. The model will predict the individual probability (in percentage) of a positive response to the treatment (treatment success). Design: A retrospective cohort design. Setting: A Dutch rehabilitation facility. Participants: Patients with OA diagnosed by a medical specialist and consisting at least three months. Intervention and procedures: The intervention of interest is interdisciplinary biopsychosocial rehabilitation. The procedures include the development of a clinical prediction model and assessment of its performance and internal validity. Measurements: Candidate predictors will be carefully selected. The outcome of the model will be treatment success, defined by the change on the Pain Disability Index (PDI). The model will be built with data gathered as part of routine practice. Expected outcome of the research: The deliverable will be a clinical predication model, which can be used in follow-up research.

Recruitment information / eligibility

• Status: Active, not recruiting
• Enrollment: 2309
• Est. completion date: December 31, 2023
• Est. primary completion date: January 17, 2022
• Accepts healthy volunteers: No
• Gender: All
• Age group: 16 Years and older

Eligibility

Inclusion Criteria:

• Chronic musculoskeletal pain > 3 months and osteoarthritis
• Large personal and social dysfunction
• Interplay of biological, social and psychological factors maintaining pain and/or disability

Exclusion criteria:

• Inability to actively participate in treatment (insufficient motivation, limited Dutch language skills, environmental factors, other pending treatments)
• Disagreement between patient and care providers on content of treatment
• Pending legal procedures that hinder full cooperation

Related Conditions & MeSH terms

• Chronic Pain
• Disability Physical
• Osteoarthritis

Intervention

Behavioral:
• interdisciplinary multimodal pain treatment
A combination of physical and psychosocial treatment methods is employed, including Emotional Awareness and Expression Therapy (EAET), Pain Neuroscience Education (PNE), Acceptance and Commitment Therapy (ACT), graded activity, exposure in vivo, and experiential learning through physical training.

Locations

Country	Name	City	State
Netherlands	Centrum voor Integrale Revalidatie	Zwolle	Overijssel

Sponsors (3)

Lead Sponsor	Collaborator
Maastricht University	Centrum voor Integrale Revalidatie (CIR), Pfizer

Country where clinical trial is conducted

Netherlands, 

References & Publications (1)

Breugelmans L, Scheffer E, Beckers LWME, Oosterwijk RFA, Nijland G, Smeets RJEM. Systematic description of an interdisciplinary multimodal pain treatment programme for patients with chronic musculoskeletal pain, using the TIDieR checklist. BMC Res Notes. 2022 Oct 11;15(1):320. doi: 10.1186/s13104-022-06211-z. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Other	Brief Illness Perception Questionnaire	Measures the cognitive and emotional representation of illness in a patient	Baseline
Other	Pain Self Efficacy Questionnaire	Measures how confident the patient is that he/she will be able to perform daily tasks despite being in pain	baseline
Other	Psychological Inflexibility in Pain Scale	Measures psychological inflexibility (avoidance of pain and cognitive fusion with pain)	Baseline
Other	Symptom Checklist 90	Measures to which degree the patient suffers from 90 different physical and psychological symptoms.	Baseline
Other	Hospital Anxiety and Depression Scale	Measures feelings of fear and depression, without looking at physical complaints	Baseline
Other	General CIR questionnaire	Records general socio-demographic characteristics as well as symptom-related information, co-morbidity and medication levels.	baseline
Other	Pain Catastrophising Scale	Measures degree of catastrophizing	Baseline
Other	Checklist Individual Strength	Measures subjective tiredness	Baseline
Other	Twelve-Item Short Form Health Survey	Measures physical and mental functioning	Baseline
Other	Patient Specific Complaint	Measures a patient's functional status by inquiring for three self-selected daily activities to which degree the patient is limited in this activity by his/her pain complaints	Baseline
Primary	Pain Disability Index	Measures to which degree pain prevents the patient from participating in daily activities. Improvement equal or higher than Minimal Clinically Important Change (MCID; which is 9 points) is defined as successful treatment, and all others as not successful treatment Population will be dichotomized into successful or non-successful treatment and this will be the dependent variable for the logistic regression analysis used to build the prediction model	baseline (start of treatment) and 10 weeks