Mertens Michel Gcam, van Kuijk Sander Mj, Beckers Laura Wme, Zmudzki Fredrick, Winkens Bjorn, Smeets Rob Jem
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).
Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Centre, PO Box 5800, 6202 AZ Maastricht, the Netherlands.
Semin Arthritis Rheum. 2025 Feb;70:152592. doi: 10.1016/j.semarthrit.2024.152592. Epub 2024 Nov 16.
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.
慢性肌肉骨骼疼痛（CMP）是一个广泛存在的健康和社会经济问题，是最常见的慢性疼痛病症。跨学科多模式疼痛治疗（IMPT）被认为是金标准，可提供具有成本效益的长期护理。不幸的是，只有一部分患者在接受IMPT后在疼痛、疲劳和残疾方面有临床相关改善。建立一个包含各种结果指标的预测模型可以加强康复和个性化医疗。因此，目的是开发并验证一个预测CMP患者IMPT成功的模型。在常规护理中进行了一项前瞻性队列研究，纳入了接受为期10周IMPT的CMP患者。确定了四项结果指标的成功情况：患者的康复前景、生活质量（身体和心理）以及残疾情况。检查了65个人口统计学和候选预测因素（主要是患者报告的结果指标）。最后，2309名患者参与研究，IMPT成功率在30%至57%之间。开发了包含33个预测因素的四个模型，治疗控制是所有模型中唯一一致的预测因素。此外，预测因素在模型中的影响方向各不相同。所有模型均显示出良好的校准、中等至良好的区分度，并经过内部验证（乐观校正AUC范围为0.69 - 0.80）。我们的研究结果表明，使用标准化的患者报告指标可以预测治疗成功，具有很强的区分能力。然而，预测因素因结果而异，强调了预先选择合适指标的重要性。临床上，这些结果表明以患者为中心的护理具有潜力，并可能有助于开发一个科学合理的决策工具。
