Recurrence of low back pain: a difficult outcome to predict. Development and validation of a multivariable prediction model for recurrence in patients recently recovered from an episode of non-specific low back pain

Background: Recurrence of low back pain (LBP) is common. If clinicians could identify an individual's risk of recurrence, this would enhance clinical decision-making and tailored patient care. Objective/design: To develop and validate a simple tool to predict the probability of a recurrence of LBP by 3- or 12-months following recovery. Methods: Data utilised for the prediction model development came from a prospective inception cohort study of participants (n = 250) recently recovered from LBP, who had sought care from chiropractic or physiotherapy services. The outcome measure was a recurrence of activity-limiting LBP. Candidate predictor variables (e.g., basic demographics, LBP history, levels of physical activity, etc) collected at baseline were considered for inclusion in a multivariable Cox model. The model's performance was tested in a separate validation dataset of participants (n = 261) involved in a randomised controlled trial investigating exercise for the prevention of LBP recurrences. Results: The final model included the number of previous episodes, total sitting time, and level of education. In the development sample, discrimination was acceptable (Harrell's C-statistic = 0.61, 95% CI, 0.59–0.62), but in the validation sample, discrimination was poor (0.56, 95% CI, 0.54–0.58). Calibration of the model in the validation dataset was acceptable at 3 months but was less precise at 12 months. Conclusion: The developed prediction model, which included number of previous episodes, total sitting time, and level of education, did not perform adequately in the validation sample to recommend its use in clinical practice. Predicting recurrence of LBP in clinical practice remains challenging.