Prediction of response to tapentadol in chronic low back pain.

BACKGROUND

Many chronic low back pain (cLBP) patients do not satisfactorily respond to treatment. The knowledge of responders and non-responders before initiating treatment would improve decision making and reduce health care costs. The aims of this exploratory prediction study in cLBP patients treated with tapentadol were to identify predictors of treatment outcome based on baseline characteristics, to evaluate quality-of-life and functionality as alternative outcome parameters and to develop nomograms to calculate the individual probability of response.

METHODS

In a retrospective analysis of an open-label phase 3b trial, 46 baseline characteristics were included into statistical prediction modelling. One hundred and twenty-one patients were followed up during the titration and treatment period and 67 patients were analysed who discontinued the trial.

RESULTS

Demographic data were not relevant for response prediction. Nine baseline co-variables were robust: painDETECT score, intensity of burning and painful attacks, SF36 Health Survey score (MCS, PCS), EuroQol-5, Hospital Anxiety/Depression Scale. Gender had a minor influence. Alternative outcomes (quality-of-life, functionality) were more important for response prediction than conventional pain intensity measures. Neuropathic symptoms (high painDETECT score) had a positive predictive validity. Painful attacks and classical yellow flags (depression, anxiety) negatively influenced the treatment response. High depression scores, female gender and low burning predicted discontinuation during titration.

CONCLUSION

In this exploratory study, predictive baseline characteristics have been identified that can be used to calculate the individual probability of tapentadol response in cLBP. The small sample size in relation to the number of initial variables is a limitation of this approach.

SIGNIFICANCE

Predictors for treatment response of tapentadol were identified in patients with chronic low back pain based on clinical pre-treatment characteristics that can guide personalized treatment. Quality-of-life and functionality were the most relevant outcomes for response prediction.