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Title: Predicting pain and pain responses to opioids
Authors: Wilder-Smith, C.H. 
Keywords: Analgesia
Pain relief
Psychosocial factors
Quantitative sensory testing
Issue Date: Sep-2007
Citation: Wilder-Smith, C.H. (2007-09). Predicting pain and pain responses to opioids. European Journal of Pain Supplements 1 (1) : 31-37. ScholarBank@NUS Repository.
Abstract: The mechanisms behind the wide individual variability in pain experience and relief are an area of intense research activity. Predicting individual clinical pain and the responsiveness to analgesics should increase the efficacy and tolerability of analgesic treatments, and improve the overall treatment outcome. Several factors have shown validity in the prediction of pain, with most studies having been performed in postoperative pain. These factors include younger age, female gender, multiple psychosocial contributors (e.g. negative affect, somatisation, depressive mood, expectations, anxiety), pre-existing physical comorbidities and pain, information provided by carers, and the nature of the pain insult. Recent studies have shown quantitiative sensory testing at high stimulus intensity (e.g. pain thresholds), as well as specific genetic factors and the functional testing of the endogenous pain modulatory pathways as emerging useful tools in the study of individual pain variability. Many of the above factors are also relevant in the prediction of analgesic responsiveness. The predominant pain characteristics, pain chronicity and neuroplastic changes, opioid-related genetic factors and psychological factors (including placebo response components) are major determinants of analgesic efficacy. Prediction of analgesia using quantitative sensory tests has been studied with some success. Better prediction of individual pain and analgesic responsiveness promises to improve pain control and general treatment outcome, and reduce adverse events as well as costs. Further prospective studies with interdisciplinary input validating the usefulness of predictive variables within algorithms are encouraged in large patient cohorts. © 2007 European Federation of Chapters of the International Association for the Study of Pain.
Source Title: European Journal of Pain Supplements
ISSN: 17543207
DOI: 10.1016/S1754-3207(08)60009-7
Appears in Collections:Staff Publications

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