Online Program

284296
Determinants of responders in a dose-response trial of spinal manipulation for the care of chronic low back pain


Monday, November 4, 2013 : 2:50 p.m. - 3:00 p.m.

Darcy Vavrek, ND, MS, Center for Outcomes Studies, University of Western States, Portland, OR
Mitchell Haas, DC, MA, Center for Outcomes Studies, University of Western States, Portland, OR
David Peterson, DC, Center for Outcomes Studies, University of Western States, Portland, OR
Moni Blazej Neradilek, MS, Mountain-Whisper-Light Statistics, Seattle, WA
Nayak Polissar, PhD, Mountain-Whisper-Light Statistics, Seattle, WA
Background: The aim of this secondary analysis is to identify determinants of success of spinal manipulation (SMT) for the treatment of chronic low back pain (cLBP). Methods: We randomized 400 patients with cLBP to receive 18 sessions of lumbar SMT or a light massage control scheduled over 6-weeks; with SMT at 0, 6, 12, or 18 of the visits. Patients were followed for 52-weeks. Successful response to treatment is defined as a 50%-improvement in pain-score measured with a modified Von-Korff (MVK) 100-point pain-scale. Determinants of successful response are baseline measures of pain, disability, outside care, health status, age, gender, relative confidence in SMT/massage, any previous SMT/massage care, and time-point. Three-quarters of the data are randomly allocated into a data-set used to develop the predictive models. Models built by stepwise logistic regression are validated on the remaining data. Sensitivity and specificity for the predictive model will be reported. Results: Preliminary results of univariate models of the entire data-set show that 50%-improvement in the MVK pain-scale was predicted best by lower number of comorbidities followed by more baseline pain, younger age, more baseline disability, and better health status; with an increased count of the number of comorbidities preventing recovery at an OR per 1 comorbidity of 0.84 95% CI[0.72,0.97; p=0.02]. Prediction models developed using 75% of the data and their predictive performance on the remaining data will be presented. Conclusions: Findings from this analysis will assist with developing models for predicting which patients would especially benefit from SMT for their cLBP.

Learning Areas:

Biostatistics, economics
Chronic disease management and prevention
Clinical medicine applied in public health
Other professions or practice related to public health
Public health or related research

Learning Objectives:
Discuss determinants of response to treatment in a dose-response trial of spinal manipulation for the care of chronic low back pain. Explain the process of building and testing a prediction model. Explain sensitivity, specificity, and AUC.

Keyword(s): Chiropractic, Chronic Illness

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I am qualified to be an abstract author on the content because I am a co-investigator on the grant and I am primary author on the paper that will be written based on this presentation. I am actively performing research in this area.
Any relevant financial relationships? No

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.