Predicting recovery in patients with acute low back pain: a clinical prediction model

T. da Silva*, P. Macaskill, K. Mills, C. Maher, C. Williams, C. Lin, M. J. Hancock

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)

Abstract

Background: There is substantial variability in the prognosis of acute low back pain (LBP). The ability to identify the probability of individual patients recovering by key time points would be valuable in making informed decisions about the amount and type of treatment to provide. Predicting recovery based on presentation 1-week after initially seeking care is clinically important and may be more accurate than predictions made at initial presentation. The aim of this study was to predict the probability of recovery at 1-week, 1-month and 3-months after 1-week review in patients who still have LBP 1-week after initially seeking care. Methods: The study sample comprised 1070 patients with acute LBP, with a pain score of ≥2 1-week after initially seeking care. The primary outcome measure was days to recovery from pain. Ten potential prognostic factors were considered for inclusion in a multivariable Cox regression model. Results: The final model included duration of current episode, number of previous episodes, depressive symptoms, intensity of pain at 1-week, and change in pain over the first week after seeking care. Depending on values of the predictor variables, the probability of recovery at 1-week, 1-month and 3-months after 1-week review ranged from 4% to 59%, 19% to 91% and 30% to 97%, respectively. The model had good discrimination (C = 0.758) and calibration. Conclusions: This study found that a model based on five easily collected variables could predict the probability of recovery at key time points in people who still have LBP 1-week after seeking care. Significance: A clinical prediction model based on five easily collected variables was able to predict the likelihood of recovery from an episode of acute LBP at three key time points. The model had good discrimination (C = 0.758) and calibration.

Original languageEnglish
Pages (from-to)716-726
Number of pages11
JournalEuropean Journal of Pain (United Kingdom)
Volume21
Issue number4
DOIs
Publication statusPublished - Jan 2017

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