Abstract
OBJECTIVES: To develop predictive models for short-term and long-term clinically important improvement in women with non-specific chronic disabling neck pain during the clinical course of physiotherapy. DESIGN: Longitudinal cohort study based on data from a randomised controlled trial evaluating short-term and long-term effects on sensorimotor function over 11 weeks of physiotherapy. PARTICIPANTS AND SETTINGS: Eighty-nine women aged 31-65 years with non-specific chronic disabling neck pain from Gävle, Sweden. MEASURES: The outcome, clinically important improvement, was measured with the Patient Global Impression of Change Scale (PGICS) and the Neck Disability Index (NDI), assessed by self-administered questionnaires at 3, 9 and 15 months from the start of the interventions (baseline). Twelve baseline prognostic factors were considered in the analyses. The predictive models were built using random-effects logistic regression. The predictive ability of the models was measured by the area under the receiver operating characteristic curve (AUC). Internal validity was assessed with cross-validation using the bootstrap resampling technique. RESULTS: Factors included in the final PGICS model were neck disability and age, and in the NDI model, neck disability, depression and catastrophising. In both models, the odds for short-term and long-term improvement increased with higher baseline neck disability, while the odds decreased with increasing age (PGICS model), and with increasing level of depression (NDI model). In the NDI model, higher baseline levels of catastrophising indicated increased odds for short-term improvement and decreased odds for long-term improvement. Both models showed acceptable predictive validity with an AUC of 0.64 (95% CI 0.55 to 0.73) and 0.67 (95% CI 0.59 to 0.75), respectively. CONCLUSION: Age, neck disability and psychological factors seem to be important predictors of improvement, and may inform clinical decisions about physiotherapy in women with chronic neck pain. Before using the developed predictive models in clinical practice, however, they should be validated in other populations and tested in clinical settings.