Predicting outcomes of neuroreflexotherapy in patients with subacute or chronic neck or low back pain

Background context: In the context of shared decision-making, a valid estimation of the probability that a given patient will improve after a specific treatment is valuable.

Purpose: To develop models that predict the improvement of spinal pain, referred pain, and disability in patients with subacute or chronic neck or low back pain undergoing a conservative treatment.

Study design and setting: Analysis of data from a prospective registry in routine practice.

Patient sample: All patients who had been discharged after receiving a conservative treatment within the Spanish National Health Service (SNHS) (n=8,778).

Outcome measures: Spinal pain, referred pain, and disability were assessed before the conservative treatment and at discharge by the use of previously validated methods.

Methods: Improvement in spinal pain, referred pain, and disability was defined as a reduction in score greater than the minimal clinically important change. A predictive model that included demographic, clinical, and work-related variables was developed for each outcome using multivariate logistic regression. Missing data were addressed using multiple imputation. Discrimination and calibration were assessed for each model. The models were validated by bootstrap, and nomograms were developed.

Results: The following variables showed a predictive value in the three models: baseline scores for pain and disability, pain duration, having undergone X-ray, having undergone spine surgery, and receiving financial assistance for neck or low back pain. Discrimination of the three models ranged from slight to moderate, and calibration was good.

Conclusions: A registry in routine practice can be used to develop models that estimate the probability of improvement for each individual patient undergoing a specific form of treatment. Generalizing this approach to other treatments can be valuable for shared decision making.