Background

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.

Materials and methods

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.

Results

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.

Conclusion

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.

中文翻译：

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使用短时框架方法预测谁对脊椎手法治疗有反应：一项来自238名参与者的研究结果

背景

脊椎手法治疗（SMT）是临床指南中针对腰痛患者推荐的非药物干预措施之一，但是，有些患者似乎比其他患者受益更多。多项研究已经检查了可能的因素，以在应用SMT之前改变患者的反应。这项研究的目的是确定是否可以通过使用限制性的，非实际的方法，确定的响应者状态变量以及新开发的物理方法随SMT改变而改善SMT响应者的基线预测。

材料和方法

我们对先前的研究进行了二级分析，该研究在1周的时间内提供了两次标准SMT应用。经过初步的探索性分析之后，使用主成分分析和最佳尺度分析来减少预测变量之间的多重共线性。使用前向Wald程序建立了多元逻辑回归模型，以探索那些可以预测1周重新评估反应状态的基线变量。

结果

238名参与者完成了为期1周的重新评估（年龄40.0±11.8岁；女性59.7％）。通过包含以下8个变量的模型预测对治疗的反应：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，患者对药物治疗和强化锻炼的期望以及伸展状态。我们的模型的灵敏度为72.2％（95％CI，58.1-83.1），特异性为84.2％（95％CI，78.0-89.0），正似然比为4.6（CI，3.2-6.7），负似然比0.3（CI，0.2-0.5），ROC曲线下面积0.79。

结论

在腰痛患者中应用SMT之前可以预测对治疗的反应。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来使患者和临床医生受益。