Covariate analysis yielded a set of important predictors that in part may explain the efficacy of capsaicin 8% treatment. Increasing age and a longer duration of disease reduced the probability of falling into subgroup 5 (Figure 4A and B). These factors are possibly interdependent (i.e., an older patient may have a more prolonged disease process than a younger patient) but confirm the notion of the complexity of treating chronic pain in the elderly. Opioid use and high baseline pain scores reduce the probability of a full analgesic response (Figure 4D and E). It may possibly be that the lack of opioid efficacy is a symptom of (general) therapy resistance, although further study is required to understand this important issue. The finding that run-in pain score variability influences the efficacy of treatment (for test drug and active control) is an interesting and novel observation. Patients that show high variability in their run-in pain scores have an almost 80% probability of a full analgesic response and almost 0% probability of falling into the non-responder group (Figure 4F). In contrast, low variability (a constant NPRS value for 14 days) results in a probability for subgroups 2 and 5 of about 30%. Possibly the low variability NRPSs are an indication of a rigid and fully manifested chronic pain process with severe central plastic changes unresponsive to therapy. The reverse may be the case in patients that experience variable pain scores over time. We believe that this is an important first observation that needs further confirmation. Finally, pain scores following lidocaine pretreatment over the skin where the capsaicin 8% patch will be applied predicted treatment efficacy. The probability of being a non-responder increases from 14% at a pain score of zero to 51% at a pain score of 10. The reverse was observed for subgroups 4 and 5 (Figure 4C). While lidocaine and capsaicin have distinct mechanisms of action, the current finding indicates a common target, i.e., the peripheral nerves, and suggests that synergistic treatment effects are possible when combining the two treatments.
We restricted our covariate analysis to a set of 14 variables (Table 1). It may well be that other factors alone or in combination with the current set may enhance our ability to predict therapy efficacy; e.g., by adding functional or neurosensory testing [such as quantitative sensory testing (QST), responses to electrical or laser stimulation of the skin, conditioned pain modulation (CPM)]. Specific patterns in the QST profile may represent specific disease modalities with a greater or lesser probability of therapy efficacy. There are various examples in the literature that concur with our suggestion. Eisenberg et al. showed that the magnitude of heat pain thresholds predicts the magnitude of reduction of pain intensity in response to oxycodone treatment (the greater the heat pain threshold the greater the opioid effect; R2 = 0.17) in healthy volunteers.18 Yarnitsky et al. showed
that patients with less efficient CPM have greater analgesic responses to duloxetine (R2 = 0.39) in patients with painful diabetic neuropathy.19 We recently showed a significant
Chapter 4 | A novel approach to identify responder subgroups and predictors of response to low- and high-dose capsaicin patches in postherpetic neuralgia
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correlation between the magnitude of CMP and pain relief in patients with chronic neuropathic pain and treated with placebo, morphine and ketamine.20 Finally, also
genetic factors, such as variations in the TRPV1 gene, may be used in predictive analyses of treatment effect.
Conclusions and final remarks
Pain data from 1248 patients with PHN and treated with a capsaicin 8% patch or an active control were successfully divided into five response groups using time-course and mixture analyses. Covariate analysis identified factors that enhanced prediction of treatment responses. The most important variables identified include age, disease duration, pain score following local anaesthetic pretreatment of the affected skin area, baseline pain and pretreatment pain score variability. Further studies should address whether incorporation of additional variables (TRPV1 gene-related, sensory testing- related, symptom-related) may enhance prediction of treatment effect. Our current and previous analytical approach provides strong indications of the existence of specific subgroups and predicting variables related to treatment of chronic pain patients. This indicates not only that our approach may be applied across different chronic pain syndromes, but suggests also that our approach is a step towards individually tailored treatment. An example could be to treat patients with Qutenza who display a large variability in their pain scores at baseline or those patients that respond favourably to a baseline trial with topical lidocaine. This latter approach is similar to the approach of Campbell et al. who determined the effect of capsaicin as a predictor of topical clonidine treatment in painful diabetic neuropathy.21 As discussed by Sindrup and Finnerup and
shown here, drugs have different efficacies in subgroups of patients, possibly related to different pain mechanisms despite a presumed common underlying disease.22
APPENDIX 1: THE LONGITUDINAL MODEL AND MIXTURE ANALYSIS
The longitudinal model was developed within the non-linear mixed effect modelling software (NONMEM version VII level 1, ICON Development Solutions, Ellicott City, MD) to describe the time course of NPRS following application of the low- (0.04%) and high- concentration capsaicin path.23 The first-order conditional estimation (FOCE) was used formodel development. The performance of the analysis was evaluated by various selection criteria, including visual inspection of the goodness of fit plot, changes in the objective function value and parameter estimates and their respective standard errors. Using the likelihood ratio test, the significance level was set at a = 0.01, which corresponds
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