Clinical prediction model for interdisciplinary biopsychosocial rehabilitation in osteoarthritis patients.
Sophie Vervullens, Lissa Breugelmans, Laura Beckers and 4 others
PMID 38059576WHAT IT FOUND
A prediction model identified factors like fewer pain locations, higher disability, and higher self-efficacy as predicting success in interdisciplinary rehabilitation.
With an AUC of 0.71, the model discriminates well but requires external validation before clinical use.
Key findings
01The internally validated model showed acceptable discriminative power with an optimism-corrected AUC of 0.71.
02Predictors of treatment success included lower age, female sex, fewer pain locations, higher baseline disability, lower worst pain severity, no pain medication use, higher work capacity, alcohol use, and specific illness perceptions.
03The authors recommend external validation before using the model as a clinical decision tool.
STILL TO COME
How it was doneWhat they foundWhat it means for PTsWhat it means for OTs
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What it does not show
The diagnosis of osteoarthritis was retrospectively identified from electronic patient files, which may introduce misclassification bias. Treatment success was defined using the minimal clinically important change for chronic low back pain, as a specific value for osteoarthritis patients is not yet available. The model has only been internally validated. The authors explicitly recommend external validation before using it in clinical practice. The study was conducted in a specific secondary care center in the Netherlands, which may limit generalizability to other healthcare systems.
Declared interests
The authors declared no conflicts of interest. The research was funded by a grant from the Global Awards for Advancing Chronic Pain Research (ADVANCE), which had no influence on the study design, data collection, analysis, or interpretation.
The easy way to misread this
Do not use this model to exclude patients from interdisciplinary rehabilitation. The authors state that cut-off scores should not be used as a gold standard for inclusion or exclusion, and the model requires external validation before it can be used as a clinical decision tool.