Murphy Myles C, Mosler Andrea B, Rio Ebonie K, Coventry Molly, Raj Isaac Selva, Chivers Paola T, Arendt-Nielsen Lars, Alfieri Fabio Marcon, Bjurström Martin F, Larsen Dennis Boye, Chang Wei-Ju, Olesen Anne Estrup, Hertel Emma, Holm Paetur Mikal, Graven-Nielsen Thomas, de Paula Gomes Cid André Fidelis, Henriksen Marius, Klinedinst N Jennifer, Mathew Jerin, Drewes Asbjørn Mohr, Nunes Guilherme S, O'Leary Helen, Østerås Håvard, Ozturk Ozgul, Pozsgai Miklos, Rampazo Érika Patrícia, Rasmussen Sten, Rice David, Sánchez-Romero Eleuterio A, Irani Anushka, Stausholm Martin Bjørn, Hince Dana, Petersen Kristian Kjær-Staal
Nutrition and Health Innovation Research Institute, School of Medical and Health Sciences, Edith Cowan University, Joondalup, WA, Australia.
Institute for Health Research, The University of Notre Dame Australia, Fremantle, WA, Australia.
Pain. 2025 Apr 29. doi: 10.1097/j.pain.0000000000003627.
An individual participant data (IPD) meta-analysis can assess the predictive value of data on outcomes at the individual level, offering a potential tool for developing personalized pain management. Pretreatment quantitative sensory testing (QST) may stratify patient groups, which are then linked to treatment outcomes. Our objective was to determine if measures of QST at baseline are related to treatment outcomes (at any time point) for pain and disability in lower-limb osteoarthritis. We performed a systematic review with an IPD meta-analysis. Searches were conducted in 9 databases until May 5, 2023 for intervention studies that measured baseline QST and longitudinal measures of participant-reported pain and disability. We performed a 2-stage approach to analyse longitudinal data. Individual models were fitted to each study and combined using random effects multivariate meta-analytic models. Study quality was assessed using the Joanna Briggs Institute checklist, and certainty of the evidence was assessed using GRADE. We identified 3082 records and included 1 hip and 28 knee datasets consisting of 2522 participants from 40 studies. Local warm detection thresholds (P = 0.024) predicted knee osteoarthritis pain outcomes (very-low certainty). Local warm detection thresholds (P = 0.030), remote cold detection thresholds (P = 0.05), and remote pressure tolerance thresholds (P = 0.007) predicted knee osteoarthritis disability outcomes (very-low certainty). Other QST variables were associated with hip and knee osteoarthritis pain and disability levels (eg, pressure pain thresholds), but this relationship did not change over time. This review finds that mechanism-based, QST methodologies do not consistently predict pain or disability on an individual level in hip or knee osteoarthritis.
个体参与者数据（IPD）荟萃分析可以在个体层面评估数据对结局的预测价值，为制定个性化疼痛管理提供了一种潜在工具。治疗前定量感觉测试（QST）可能会对患者群体进行分层，然后将其与治疗结局相关联。我们的目的是确定基线时的QST测量值是否与下肢骨关节炎疼痛和残疾的治疗结局（在任何时间点）相关。我们进行了一项包含IPD荟萃分析的系统评价。截至2023年5月5日，在9个数据库中进行检索，以查找测量基线QST以及参与者报告的疼痛和残疾纵向测量值的干预研究。我们采用两阶段方法分析纵向数据。为每项研究拟合个体模型，并使用随机效应多变量荟萃分析模型进行合并。使用乔安娜·布里格斯研究所清单评估研究质量，并使用GRADE评估证据的确定性。我们识别出3082条记录，纳入了1个髋部和28个膝部数据集，这些数据集由来自40项研究的2522名参与者组成。局部热觉检测阈值（P = 0.024）可预测膝骨关节炎疼痛结局（极低确定性）。局部热觉检测阈值（P = 0.030）、远端冷觉检测阈值（P = 0.05）和远端压力耐受阈值（P = 0.007）可预测膝骨关节炎残疾结局（极低确定性）。其他QST变量与髋部和膝部骨关节炎疼痛及残疾水平相关（如压痛阈值），但这种关系不会随时间变化。本综述发现，基于机制的QST方法并不能始终如一地在个体层面预测髋部或膝部骨关节炎的疼痛或残疾情况。
