Chronic Musculoskeletal Pain Treatment Success Predictors for 2,204 Patients

2204 individuals with chronic musculoskeletal pain underwent a 10-week interdisciplinary multimodal pain treatment, with success rates ranging from 28% to 52% across four different outcome measures. Michel GCAM Mertens externally validated and updated four prediction models using 63 demographic and patient-reported candidate predictors. The updated models, last shared in March 2026, demonstrated strong calibration and acceptable discrimination, with 'treatment control' emerging as the most consistent predictor across outcomes.

Use Cases

• Validate clinical prediction models for treatment success based on patient-reported outcome measures.
• Identify key predictors like 'treatment control' and 'pain self-efficacy' for tailoring patient interventions.
• Guide shared decision-making in consultations by applying models at clinically relevant benefit thresholds.
• Compare model performance across different success definitions like recovery perspective, quality of life, and disability.

Strengths

• Data from 2204 individuals provides a substantial sample for model validation.
• Models were evaluated on four distinct clinical outcomes with success rates quantified (38%, 28%, 29%, 52%).
• Analysis considered 63 candidate predictors, primarily patient-reported outcome measures.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown for the underlying dataset files, which may limit suitability assessment.
• The primary files are PDF and DOCX formats, which may require extraction to access structured data.

Provenance

Source

figshare

Collection Method

Routine care data from individuals undergoing a 10-week interdisciplinary multimodal pain treatment.

Time Range

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Freshness

Last updated 2026-03-22 12:16:07

Geography

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