J Orthop Sports Phys Ther. 2018 Jun;48(6):460-475. doi: 10.2519/jospt.2018.7811. Epub 2018 Apr 7.
Study Design Observational, prospective cohort. Background Musculoskeletal pain is a common reason to seek health care, and earlier nonpharmacological treatment and enhancement of personalized care options are 2 high-priority areas. Validating concise assessment tools is an important step toward establishing better care pathways. Objectives To determine the predictive validity of Optimal Screening for Prediction of Referral and Outcome (OSPRO) tools for individuals with neck, low back, shoulder, or knee pain. Methods A convenience sample (n = 440) was gathered by Orthopaedic Physical Therapy-Investigator Network clinics (n = 9). Participants completed demographic, clinical, and comorbidity questionnaires and the OSPRO tools, and were followed for 12-month outcomes in pain intensity, region-specific disability, quality of life, and comorbidity change. Analyses predicted these 12-month outcomes with models that included the OSPRO review-of-systems (OSPRO-ROS) and yellow flag (OSPRO-YF) tools and planned covariates (accounting for comorbidities and established demographic and clinical factors). Results The 10-item OSPRO-YF tool (baseline and 4-week change score) consistently added to predictive models for 12-month pain intensity, region-specific disability, and quality of life. The 10-item OSPRO-ROS tool added to a predictive model for quality of life (mental summary score), and 13 additional items of the OSPRO-ROS+ tool added to prediction of 12-month comorbidity change. Other consistent predictors included age, race, income, previous episode of pain in same region, comorbidity number, and baseline measure for the outcome of interest. Conclusion The OSPRO-ROS and OSPRO-YF tools statistically improved prediction of multiple 12-month outcomes. The additional variance explained was small, and future research is necessary to determine whether these tools can be used as measurement adjuncts to improve management of musculoskeletal pain. J Orthop Sports Phys Ther 2018;48(6):460-475. Epub 7 Apr 2018. doi:10.2519/jospt.2018.7811.
观察性、前瞻性队列研究。背景：肌肉骨骼疼痛是寻求医疗保健的常见原因，早期的非药物治疗和增强个性化护理选择是两个优先领域。验证简洁的评估工具是建立更好的护理途径的重要步骤。目的：确定用于预测颈部、下背部、肩部或膝关节疼痛患者的 Optimal Screening for Prediction of Referral and Outcome（OSPRO）工具的预测效度。方法：通过骨科物理治疗-调查员网络诊所（n = 9）收集了一个方便样本（n = 440）。参与者完成了人口统计学、临床和合并症问卷以及 OSPRO 工具，随访了 12 个月的疼痛强度、特定区域的残疾、生活质量和合并症变化。分析使用包括 OSPRO 系统回顾（OSPRO-ROS）和黄色标志（OSPRO-YF）工具以及计划协变量（考虑合并症和既定人口统计学和临床因素）的模型来预测这些 12 个月的结果。结果：10 项 OSPRO-YF 工具（基线和 4 周变化得分）始终增加了 12 个月疼痛强度、特定区域残疾和生活质量的预测模型。10 项 OSPRO-ROS 工具增加了生活质量（心理综合评分）的预测模型，而 OSPRO-ROS+工具的另外 13 项增加了 12 个月合并症变化的预测。其他一致的预测因素包括年龄、种族、收入、同一区域先前的疼痛发作、合并症数量和感兴趣的结局的基线测量。结论：OSPRO-ROS 和 OSPRO-YF 工具在统计学上提高了对多个 12 个月结局的预测。解释的方差增加很小，需要进一步的研究来确定这些工具是否可以作为测量辅助手段来改善肌肉骨骼疼痛的管理。J Orthop Sports Phys Ther 2018;48(6):460-475。2018 年 4 月 7 日在线发表。doi:10.2519/jospt.2018.7811.
