Tapentadol for chronic low back pain
The patients suffering from chronic low back pain (cLBP) do not satisfactorily respond to treatment. Before starting treatment, the knowledge of responders and non-responders would improve decision making and decrease health care costs. The prime focus of this exploratory prediction study in cLBP patients treated with tapentadol were to put-forward the predictors of treatment outcome based on baseline characteristics, to evaluate quality-of-life and functionality as alternative outcome parameters. Also, to develop nomograms to calculate the individual probability of response.
Total 46 baseline characteristics were included into a statistical prediction modelling in an open-label phase 3b trial. A total of 121 patients were followed up during the titration and treatment period, of which 67 patients were analysed who discontinued the trial.
It was found that the demographic data were not relevant for response prediction. Nine baseline co-variables were durable: painDETECT score, intensity of burning and painful attacks, SF36 Health Survey score (MCS, PCS), EuroQol-5, Hospital Anxiety/Depression Scale. Alternative outcomes (quality-of-life, functionality) were more significant for response prediction than conventional pain intensity measures. A positive predictive validity was observed for the neuropathic symptoms (high painDETECT score). Painful attacks and classical yellow ﬂags (depression, anxiety) negatively affected the treatment response. During titration, high depression scores, female gender and low burning predicted discontinuation was observed.
The predictive baseline characteristics have been revealed in this exploratory study that can be used to calculate the individual probability of tapentadol response in cLBP. The limitation of this study was the small sample size in relation to the number of initial variables. The notable result was that the predictors for treatment response of tapentadol were recognized in patients with chronic low back pain based on clinical pretreatment characteristics that can guide personalized treatment. The most appropriate outcomes for response prediction were the quality-of-life and the functionality.