Prediction models for treatment success after an interdisciplinary multimodal pain treatment program

Michel Gcam Mertens; Sander Mj van Kuijk; Laura Wme Beckers; Fredrick Zmudzki; Bjorn Winkens; Rob Jem Smeets

doi:10.1016/j.semarthrit.2024.152592

Prediction models for treatment success after an interdisciplinary multimodal pain treatment program

Semin Arthritis Rheum. 2024 Nov 16:70:152592. doi: 10.1016/j.semarthrit.2024.152592. Online ahead of print.

Authors

Michel Gcam Mertens¹, Sander Mj van Kuijk², Laura Wme Beckers³, Fredrick Zmudzki⁴, Bjorn Winkens⁵, Rob Jem Smeets⁶

Affiliations

¹ Research School CAPHRI, Department of Rehabilitation Medicine, Maastricht University, PO Box 616, 6200 MD Maastricht, the Netherlands; Research Group MOVANT, Department of Rehabilitation Sciences and Physiotherapy (REVAKI), University of Antwerp, Universiteitsplein 1, 2610 Wilrijk, Belgium; Pain in Motion International Research Group (PiM). Electronic address: Michel.mertens@uantwerpen.be.
² Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Centre, PO Box 5800, 6202 AZ Maastricht, the Netherlands. Electronic address: sander.van.kuijk@mumc.nl.
³ Research School CAPHRI, Department of Rehabilitation Medicine, Maastricht University, PO Box 616, 6200 MD Maastricht, the Netherlands. Electronic address: laura.beckers@maastrichtuniversity.nl.
⁴ Research School CAPHRI, Department of Rehabilitation Medicine, Maastricht University, PO Box 616, 6200 MD Maastricht, the Netherlands; Époque Consulting, Level 57 MLC Centre, 19-29 Martin Place Sydney, NSW, Australia; Social Policy Research Centre, University of New South Wales, John Goodsell Building (F20), Library Rd, UNSW Sydney, NSW, Australia. Electronic address: fzmudzki@epoqueconsulting.com.au.
⁵ Research School CAPHRI, Department of Methodology and Statistics, Maastricht University, PO Box 616, 6200 MD Maastricht, the Netherlands. Electronic address: bjorn.winkens@maastrichtuniversity.nl.
⁶ Research School CAPHRI, Department of Rehabilitation Medicine, Maastricht University, PO Box 616, 6200 MD Maastricht, the Netherlands; Pain in Motion International Research Group (PiM); CIR Clinics in Rehabilitation, location Eindhoven, Anderlechtstraat 15, 5628 WB Eindhoven, the Netherlands. Electronic address: r.smeets@maastrichtuniversity.nl.

PMID: 39577031
DOI: 10.1016/j.semarthrit.2024.152592

Abstract

Chronic musculoskeletal pain (CMP) poses a widespread health and socioeconomic problem, being the most prevalent chronic pain condition. Interdisciplinary multimodal pain treatment (IMPT) is considered the gold standard, offering cost-effective long-term care. Unfortunately, only a subset of patients experiences clinically relevant improvements in pain, fatigue, and disability post-IMPT. Establishing a prediction model encompassing various outcome measures could enhance rehabilitation and personalized healthcare. Thus, the aim was to develop and validate a prediction model for IMPT success in patients with CMP. A prospective cohort study within routine care was performed, including patients with CMP undergoing a 10-week IMPT. Success across four outcome measures was determined: patients' recovery perspective, quality of life (physical and mental), and disability. Sixty-five demographic and candidate predictors (mainly patient reported outcome measures) were examined. Finally, 2309 patients participated, with IMPT success rates ranging from 30% to 57%. Four models incorporating 33 predictors were developed, with treatment control being the sole consistent predictor across all models. Additionally, predictors effects varied in direction in the models. All models demonstrated strong calibration, fair to good discrimination, and were internally validated (optimism-corrected AUC range 0.69-0.80). Our findings show that treatment success can be predicted using standardized patient-reported measures, exhibiting strong discriminatory power. However, predictors vary depending on the outcome, underscoring the importance of selecting the appropriate measure upfront. Clinically, these results suggest potential for patient-centered care and may contribute to the development of a scientifically sound decision tool.

Keywords: Chronic musculoskeletal pain; Disability; Interdisciplinary multimodal pain treatment; Patients' recovery perspective; Prediction; Quality of life.