Abstract
Opioids are increasingly used for treatment of chronic pain. However, they are only effective in a subset of patients and have multiple side effects. Thus, studies using biomarkers for response are highly warranted. The current study prospectively examined 63 opioid-naïve patients initiating opioid use for diverse types of chronic pain at five European centers. Quantitative sensory testing, electroencephalography (EEG) recordings, and assessment of pain catastrophizing were performed prior to treatment. The co-primary outcomes were change from baseline in ratings of chronic pain and quality of life after 14 days of opioid treatment. Secondary outcomes included patient’s global impression of clinical change and side effects. Logistic regression models adjusted for age and sex were used to identify biomarkers predictive for successful treatment, defined as at least a 30% reduction in average pain intensity or an improvement in quality of life of at least 10 scale points. Fifty-nine patients (94%) completed the study. The mean age was 55 ± 16 years and 69% were females. Pain reduction was predicted by cold pain intensity (OR: 0.69; P = 0.01), pain catastrophizing (OR: 0.82; P = 0.03), relative delta (OR: 0.76; P = 0.03) and beta EEG activity (OR: 1.18; P = 0.04) induced by experimental cold pain. None of the study variables were related to improvement in quality of life. For the first time, individual pain processing characteristics have been linked to opioid response in a mixed chronic pain population. This has the potential to personalize treatment of chronic pain and restrict opioid use to patients with high likelihood for response.
Highlights
Chronic pain is a highly prevalent condition that may impact negatively on the individual’s quality of life; it is an expensive condition for society
None of the study variables were predictive of improvement in quality of life (Table 3). In this cohort study on a heterogeneous study population of patients with various chronic pain conditions and different underlying pain mechanisms, initiating use of both weak and strong opioids, we found evidence to support the hypothesis that opioid efficacy can be predicted before treatment based on patients’ responses to experimental pain
Of note, concerning continuous predictors, the coefficient is the odds ratio for successful treatment per unit of measurement of difference in the predictor variable; odds ratios (OR): odds ratio; SE: standard error; 95% confidence intervals (95% CI): 95% Confidence Intervals; Significant P values are marked in bold; BPI: Brief Pain Inventory; EPT: Electrical pain threshold; CPM%: conditioned pain modulation (i.e. relative (%) difference between two test stimuli before and after a conditioning stimuli (120 s cold pressor test—CPT); S-PCS: the Situational Pain Catastrophizing Scale administered in connection with CPT; relative delta (1–4 Hz), theta (4–8 Hz), alpha (8–12 Hz) and beta (12–32 Hz) bandwidth activities; QLQ-C30: European Organization for Research and Treatment of Cancer
Summary
Chronic pain is a highly prevalent condition that may impact negatively on the individual’s quality of life; it is an expensive condition for society. Usually involving more than one drug, is seldom adjusted according to individual treatment response, but rather escalated or several therapies are tried in turn This strategy often leads to insufficient pain control, intolerable side effects, and psychosocial distress [3]. Quantitative sensory testing, electroencephalography (EEG) recordings, and coping strategies have previously been used to identify patient subgroups in experimental studies and in highly selected chronic pain populations [5,6,7,8]. This includes morphine response in healthy volunteers, duloxetine efficacy in painful diabetic neuropathy, and pregabalin efficacy in painful chronic pancreatitis [9,10,11]. No studies have yet examined whether individual pain processing can predict efficacy of unselected opioids in a mixed patient population with chronic pain
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