Prediction of pain outcomes in a randomized controlled trial of dose-response of spinal manipulation for the care of chronic low back pain

Vavrek, Darcy; Haas, Mitchell; Blazej Neradilek, Moni; Polissar, Nayak
August 2015
BMC Musculoskeletal Disorders;Aug2015, Vol. 16 Issue 1, p1
Academic Journal
Background: No previous studies have created and validated prediction models for outcomes in patients receiving spinal manipulation for care of chronic low back pain (cLBP). We therefore conducted a secondary analysis alongside a dose-response, randomized controlled trial of spinal manipulation. Methods: We investigated dose, pain and disability, sociodemographics, general health, psychosocial measures, and objective exam findings as potential predictors of pain outcomes utilizing 400 participants from a randomized controlled trial. Participants received 18 sessions of treatment over 6-weeks and were followed for a year. Spinal manipulation was performed by a chiropractor at 0, 6, 12, or 18 visits (dose), with a light-massage control at all remaining visits. Pain intensity was evaluated with the modified von Korff pain scale (0-100). Predictor variables evaluated came from several domains: condition-specific pain and disability, sociodemographics, general health status, psychosocial, and objective physical measures. Three-quarters of cases (training-set) were used to develop 4 longitudinal models with forward selection to predict individual "responders" (≥50 % improvement from baseline) and future pain intensity using either pretreatment characteristics or post-treatment variables collected shortly after completion of care. The internal validity of the predictor models were then evaluated on the remaining 25 % of cases (test-set) using area under the receiver operating curve (AUC), R², and root mean squared error (RMSE). Results: The pretreatment responder model performed no better than chance in identifying participants who became responders (AUC = 0.479). Similarly, the pretreatment pain intensity model predicted future pain intensity poorly with low proportion of variance explained (R² = .065). The post-treatment predictor models performed better with AUC = 0.665 for the responder model and R² = 0.261 for the future pain model. Post-treatment pain alone actually predicted future pain better than the full post-treatment predictor model (R² = 0.350). The prediction errors (RMSE) were large (19.4 and 17.5 for the pre- and post-treatment predictor models, respectively). Conclusions: Internal validation of prediction models showed that participant characteristics preceding the start of care were poor predictors of at least 50 % improvement and the individual's future pain intensity. Pain collected shortly after completion of 6 weeks of study intervention predicted future pain the best.


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