Prediction of response to tapentadol in chronic low back pain

Abstract

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.

Figure 1

Example of a nomogram. A nomogram can directly be used to calculate the predicted response. The nomogram visualizes the influence of the different predictive variables on different horizontal lines. Depending on the influence of each predictor, the different lines have different lengths. The longer a horizontal line the stronger is the influence. The influence of each predictor is visualized by a number of points on the respective horizontal line. By adding the points associated with each predictor, the anticipated magnitude of response can be read on the response horizontal line on the bottom of the nomogram. For the calculation of a response, three variables are required in the example: Response = x * VAR1 + x * VAR2 + x * VAR3. Calculation example of this nomogram: VAR1 = 58 (30 Points), VAR2 = 18 (10 Points), VAR3 = 2.5 (7.5 Points), Total Points = 47.5 (30 + 10 + 7.5), Response = −5.

Figure 2

Flowchart of the study.

Figure 3

Nomogram of the Function‐PCS‐response. PainDETECT_modified: PainDETECT score using six items excluding the item “painful attacks” (painDETECT‐6). m, male; f, female.

Figure 4

Nomogram of the QoL‐EQ‐5D‐response. Details see Fig. 3.

Figure 5

Nomogram of the QoL‐MCS‐response. Details see Fig. 3.

Figure 6

Receiver operating characteristic (ROC) curve for the model predicting discontinuation during titration period. A valid prediction model for discontinuation of the trial was identified during the drug titration period. Female patients and patients with high depression rates have a higher chance to discontinue. Patients with severe burning at the beginning have a higher chance to stay in the trial than other patients. The sensitivity and specificity values can be seen in the ROC curve.