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BMC Musculoskelet Disord. 2015 Aug 19;16:205. doi: 10.1186/s12891-015-0632-0.

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

Author information

1
University of Western States, 2900 NE 132nd Ave, Portland, OR, 97230, USA. dvavrek@uws.edu.
2
University of Western States, 2900 NE 132nd Ave, Portland, OR, 97230, USA. mhaas@uws.edu.
3
The Mountain-Whisper-Light Statistics, 1827 23rd Ave. East, Seattle, WA, 98112-2913, USA. moni@mwlight.com.
4
The Mountain-Whisper-Light Statistics, 1827 23rd Ave. East, Seattle, WA, 98112-2913, USA. nayak@mwlight.com.

Abstract

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), R(2), 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 (R(2) = .065). The post-treatment predictor models performed better with AUC = 0.665 for the responder model and R(2) = 0.261 for the future pain model. Post-treatment pain alone actually predicted future pain better than the full post-treatment predictor model (R(2) = 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.

PMID:
26286532
PMCID:
PMC4545558
DOI:
10.1186/s12891-015-0632-0
[Indexed for MEDLINE]
Free PMC Article

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