Johan Haumann1,2, Sander M J van Kuijk1,3, Elbert A Joosten1,4, Marieke H J van den Beuken-van Everdingen1,5. 1. 1 Department of Anaesthesiology and Pain Management, Maastricht University Medical Centre, University Pain Centre Maastricht (UPCM), Maastricht, The Netherlands. 2. 2 Department of Anaesthesiology and Pain Management, OLVG, Amsterdam, The Netherlands. 3. 3 Department of Clinical Epidemiology and Medical Technology, Maastricht University Medical Centre, Maastricht, The Netherlands. 4. 4 Department of Translational Neuroscience, School for Health and Neuroscience, Maastricht University, Maastricht, The Netherlands. 5. 5 Centre of Expertise for Palliative Care, Maastricht University Medical Centre (MUMC+), Maastricht, The Netherlands.
Abstract
BACKGROUND: Cancer pain remains a difficult problem, for which opioids are often necessary. At present it is difficult to predict the effectiveness of opioid therapy. OBJECTIVES: We aim to assess the association between patient characteristics and opioid treatment response in cancer patients, and develop a model to predict probability of response. SUBJECTS: We used data from two previously published randomized clinical trials, in which patients with head and neck cancer were treated with fentanyl or methadone (total N = 134). MEASUREMENTS: Treatment success was defined as ≥50% pain reduction at one and five weeks. We analyzed patient characteristics (age, sex, depression, and anxiety), treatment characteristics (having had chemotherapy, radiotherapy, surgery, methadone, or fentanyl) and pain characteristics (neuropathic and nociceptive). DESIGN: Based on univariable and multivariable regression analyses determinants of therapy success were assessed. Based on these analyses a prediction model was developed. RESULTS: Our analyses show that one-week therapy success was associated with methadone (odds ratio [OR] = 5.21), duration of pain in months (OR = 1.12), neuropathic pain (OR = 3.36), and age of the patient in years (OR = 0.95). Inclusion of these four characteristics into our prediction model resulted in an area under the curve of 81.6%. CONCLUSIONS: Careful analyses of patient attributes, treatment, and pain type of patients with head and neck cancer resulted in a prediction model that allowed to predict short-term pain relief and the opioid treatment response in neuropathic and nociceptive pain owing to cancer.
BACKGROUND:Cancer pain remains a difficult problem, for which opioids are often necessary. At present it is difficult to predict the effectiveness of opioid therapy. OBJECTIVES: We aim to assess the association between patient characteristics and opioid treatment response in cancerpatients, and develop a model to predict probability of response. SUBJECTS: We used data from two previously published randomized clinical trials, in which patients with head and neck cancer were treated with fentanyl or methadone (total N = 134). MEASUREMENTS: Treatment success was defined as ≥50% pain reduction at one and five weeks. We analyzed patient characteristics (age, sex, depression, and anxiety), treatment characteristics (having had chemotherapy, radiotherapy, surgery, methadone, or fentanyl) and pain characteristics (neuropathic and nociceptive). DESIGN: Based on univariable and multivariable regression analyses determinants of therapy success were assessed. Based on these analyses a prediction model was developed. RESULTS: Our analyses show that one-week therapy success was associated with methadone (odds ratio [OR] = 5.21), duration of pain in months (OR = 1.12), neuropathic pain (OR = 3.36), and age of the patient in years (OR = 0.95). Inclusion of these four characteristics into our prediction model resulted in an area under the curve of 81.6%. CONCLUSIONS: Careful analyses of patient attributes, treatment, and pain type of patients with head and neck cancer resulted in a prediction model that allowed to predict short-term pain relief and the opioid treatment response in neuropathic and nociceptive pain owing to cancer.
Entities:
Keywords:
association; characteristics; fentanyl; methadone; pain control; prediction model
Authors: Megan L Uhelski; Aysegul Gorur; Ted Shi; German Corrales; Kim N Du; Yan Li; Moran Amit; Claudio E Tatsui; Laurence D Rhines; Patrick M Dougherty; Juan P Cata Journal: Pain Rep Date: 2022-05-16