van der Windt Danielle A, Burke Danielle L, Babatunde Opeyemi, Hattle Miriam, McRobert Cliona, Littlewood Chris, Wynne-Jones Gwenllian, Chesterton Linda, van der Heijden Geert J M G, Winters Jan C, Rhon Daniel I, Bennell Kim, Roddy Edward, Heneghan Carl, Beard David, Rees Jonathan L, Riley Richard D
1Institute for Primary Care and Health Sciences, Arthritis Research UK Primary Care Centre, Centre for Prognosis Research, Keele University, Keele, UK.
2School of Health Sciences, University of Liverpool, Liverpool, UK.
Diagn Progn Res. 2019 Aug 8;3:15. doi: 10.1186/s41512-019-0061-x. eCollection 2019.
Shoulder pain is one of the most common presentations of musculoskeletal pain with a 1-month population prevalence of between 7 and 26%. The overall prognosis of shoulder pain is highly variable with 40% of patients reporting persistent pain 1 year after consulting their primary care clinician. Despite evidence for prognostic value of a range of patient and disease characteristics, it is not clear whether these factors also predict (moderate) the effect of specific treatments (such as corticosteroid injection, exercise, or surgery).
This study aims to identify predictors of treatment effect (i.e. treatment moderators or effect modifiers) by investigating the association between a number of pre-defined individual-level factors and the effects of commonly used treatments on shoulder pain and disability outcomes.
This will be a meta-analysis using individual participant data (IPD). Eligible trials investigating the effectiveness of advice and analgesics, corticosteroid injection, physiotherapy-led exercise, psychological interventions, and/or surgical treatment in patients with shoulder conditions will be identified from systematic reviews and an updated systematic search for trials, and risk of bias will be assessed. Authors of all eligible trials will be approached for data sharing. Outcomes measured will be shoulder pain and disability, and our previous work has identified candidate predictors. The main analysis will be conducted using hierarchical one-stage IPD meta-analysis models, examining the effect of treatment-predictor interaction on outcome for each of the candidate predictors and describing relevant subgroup effects where significant interaction effects are detected. Random effects will be used to account for clustering and heterogeneity. Sensitivity analyses will be based on (i) exclusion of trials at high risk of bias, (ii) use of restricted cubic splines to model potential non-linear associations for candidate predictors measured on a continuous scale, and (iii) the use of a two-stage IPD meta-analysis framework.
Our study will collate, appraise, and synthesise IPD from multiple studies to examine potential predictors of treatment effect in order to assess the potential for better and more efficient targeting of specific treatments for individuals with shoulder pain.
PROSPERO CRD42018088298.
肩痛是肌肉骨骼疼痛最常见的表现之一，1个月的人群患病率在7%至26%之间。肩痛的总体预后差异很大，40%的患者在咨询初级保健医生1年后仍报告有持续性疼痛。尽管有证据表明一系列患者和疾病特征具有预后价值，但尚不清楚这些因素是否也能预测（调节）特定治疗（如皮质类固醇注射、运动或手术）的效果。
本研究旨在通过调查一些预先定义的个体水平因素与常用治疗对肩痛和残疾结局的影响之间的关联，确定治疗效果的预测因素（即治疗调节因素或效应修饰因素）。
这将是一项使用个体参与者数据（IPD）的荟萃分析。将从系统评价和对试验的更新系统检索中识别出符合条件的试验，这些试验调查了建议和镇痛药、皮质类固醇注射、物理治疗主导的运动、心理干预和/或手术治疗对肩部疾病患者的有效性，并将评估偏倚风险。将与所有符合条件的试验的作者联系以共享数据。测量的结局将是肩痛和残疾，我们之前的工作已经确定了候选预测因素。主要分析将使用分层单阶段IPD荟萃分析模型进行，检查治疗预测因素相互作用对每个候选预测因素结局的影响，并在检测到显著相互作用效应时描述相关亚组效应。将使用随机效应来考虑聚类和异质性。敏感性分析将基于：（i）排除高偏倚风险的试验；（ii）使用受限立方样条对连续量表测量的候选预测因素的潜在非线性关联进行建模；（iii）使用两阶段IPD荟萃分析框架。
我们的研究将整理、评估和综合来自多项研究的IPD，以检查治疗效果的潜在预测因素，以便评估针对肩痛个体更好、更有效地靶向特定治疗的潜力。
PROSPERO CRD42018088298。
