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


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Structural and chiropractic

PLoS One.2020 Nov 24;15(11):e0242831.


M Hadizadeh, G N Kawchuk, N Prasad, J M Fritz


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.

Publication Date: 

2020 Nov



Hadizadeh, M., Kawchuk, NG., Prasad, N., Fritz, MJ. (2020) 'Predicting who responds to spinal manipulative therapy using a short-time frame methodology: Results from a 238-participant study', PLoS One.2020 Nov 24;15(11):e0242831.

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