Department of Physical Therapy, Faculty of Rehabilitation Medicine, University of Alberta, Edmonton, Alberta, Canada.
Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, Canada.
PLoS One. 2020 Nov 24;15(11):e0242831. doi: 10.1371/journal.pone.0242831. eCollection 2020.
Spinal manipulative therapy (SMT) is among the nonpharmacologic interventions that has been recommended in clinical guidelines for patients with low back pain, however, some patients appear to benefit substantially more from SMT than others. Several investigations have examined potential factors to modify patients' responses prior to SMT application. The objective of this study was to determine if the baseline prediction of SMT responders can be improved through the use of a restricted, non-pragmatic methodology, established variables of responder status, and newly developed physical measures observed to change with SMT.
We conducted a secondary analysis of a prior study that provided two applications of standardized SMT over a period of 1 week. After initial exploratory analysis, principal component analysis and optimal scaling analysis were used to reduce multicollinearity among predictors. A multiple logistic regression model was built using a forward Wald procedure to explore those baseline variables that could predict response status at 1-week reassessment.
Two hundred and thirty-eight participants completed the 1-week reassessment (age 40.0± 11.8 years; 59.7% female). Response to treatment was predicted by a model containing the following 8 variables: height, gender, neck or upper back pain, pain frequency in the past 6 months, the STarT Back Tool, patients' expectations about medication and strengthening exercises, and extension status. Our model had a sensitivity of 72.2% (95% CI, 58.1-83.1), specificity of 84.2% (95% CI, 78.0-89.0), a positive likelihood ratio of 4.6 (CI, 3.2-6.7), a negative likelihood ratio of 0.3 (CI, 0.2-0.5), and area under ROC curve, 0.79.
It is possible to predict response to treatment before application of SMT in low back pain patients. Our model may benefit both patients and clinicians by reducing the time needed to re-evaluate an initial trial of care.
脊柱手法治疗(SMT)是临床指南中推荐的非药物干预措施之一,用于治疗腰痛患者,但有些患者似乎比其他患者从 SMT 中获益更多。一些研究已经研究了在应用 SMT 之前改变患者反应的潜在因素。本研究的目的是确定是否可以通过使用受限的、非实用的方法、已建立的反应者状态变量和新开发的与 SMT 一起观察到的物理测量来提高 SMT 反应者的基线预测。
我们对一项先前的研究进行了二次分析,该研究在一周内提供了两次标准化 SMT 应用。在初始探索性分析后,使用主成分分析和最佳标度分析来减少预测因子之间的共线性。使用向前 Wald 程序构建了一个多逻辑回归模型,以探索那些可以预测 1 周重新评估时反应状态的基线变量。
238 名参与者完成了 1 周的重新评估(年龄 40.0±11.8 岁;59.7%为女性)。治疗反应由包含以下 8 个变量的模型预测:身高、性别、颈部或上背部疼痛、过去 6 个月的疼痛频率、STarT 背部工具、患者对药物和强化锻炼的期望以及伸展状态。我们的模型具有 72.2%(95%置信区间,58.1-83.1)的敏感性、84.2%(95%置信区间,78.0-89.0)的特异性、4.6(CI,3.2-6.7)的阳性似然比、0.3(CI,0.2-0.5)的阴性似然比和 ROC 曲线下面积 0.79。
在对腰痛患者进行 SMT 治疗之前,有可能预测治疗反应。我们的模型可以通过减少重新评估初始护理试验所需的时间,使患者和临床医生受益。