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

• Published in: PloS one 2020-11, Vol.15 (11), p.e0242831-e0242831
• Main Authors: Hadizadeh, Maliheh, Kawchuk, Gregory Neil, Prasad, Narasimha, Fritz, Julie M
• Format: Article
• Language: English
• Subjects: Adolescent; Adult; Back pain; Biology and Life Sciences; Care and treatment; Chiropractic - methods; Chiropractic medicine; Clinical medicine; Engineering and Technology; Female; Humans; Likelihood ratio; Low back pain; Low Back Pain - physiopathology; Low Back Pain - therapy; Male; Manipulation, Spinal - adverse effects; Manipulation, Spinal - methods; Manipulative therapy; Medicine and Health Sciences; Middle Aged; Pain; Patients; Physical Sciences; Physical therapy; Principal components analysis; Prognosis; Regression analysis; Regression models; Secondary analysis; Spinal adjustment; Spinal manipulation; Spine - physiopathology; Sports Medicine - trends; Treatment Outcome; Validation studies; Young Adult
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0242831

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.