Predicting who responds to spinal manipulative therapy using a short-time frame methodology: Results from a 238-participant study

Maliheh Hadizadeh, Gregory Neil Kawchuk , Narasimha Prasad, Julie M. Fritz
Published: November 24, 2020 https://doi.org/10.1371/journal.pone.0242831

Abstract
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.

Full Text Article

Table 7. Logistic regression analysis of 238 participants with low back pain for relative changes in Oswestry disability index following spinal manipulative therapy resulting in an 8-variable model.

Height:  Shorter, more improvement
Gender:  male, more improvement
Current pain duration: No changes
Depression:  Not significant
Neck or upper back pain: No neck or upper back pain, more improvement
Pain frequency in past 6 months: More pain frequency, more improvement
Patient’s expectation on medication: Lower expectation, more improvement
Patient’s expectation on strengthening exercises:  Higher expectation, more improvement
The STarT Back Tool: Lower score, more improvement
Extension status: Peripheralized pain with extension, more improvement

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