Katrine R Knøsgaard, M.Sc. (1), Rasmus V Juul, M.Sc. (1), Anne E Olesen, Ph.D. (1,2), Katja V Pedersen, Ph.D. (3,4), Mads Kreilgaard, Ph.D. (1), Lona L Christrup, Ph.D. (1), Palle J Osther M.D.; Ph.D. (3), Asbjørn M Drewes, M.D., Ph.D. (2), Trine M Lund, Ph.D. (1)
(1) Department of Drug Design and Pharmacology, University of Copenhagen, Denmark 2) Mech-Sense, Department of Gastroenterology & Hepatology, Aalborg University Hospital, Denmark 3) Urological Research Center, Lillebaelt Hospital, University of Southern Denmark, Fredericia, Denmark 4) Department of Clinical Genetics, Odense University Hospital, Denmark
Objectives: Joint analysis of pain intensity and opioid consumption is encouraged in trials of postoperative pain [1]. However, previous approaches have not appropriately addressed the complexity of their interrelation in time [2,3]. We hypothesized that adequate joint data analysis of pain intensity and opioid consumption required description of four key phases: A) pain intensity in time, B) probability of threshold pain, C) probability of opioid request and D) opioid effects on pain intensity. It was the aim of this study to demonstrate the application of a joint analysis approach spanning the four key phases using non-linear mixed effects modeling.
Methods: In this study we applied a non-linear mixed effects model to simultaneously study pain intensity and opioid consumption in a 4-hour postoperative period for 44 patients undergoing percutaneous kidney stone surgery. Analysis was based on 748 numerical rating scores (NRS) of pain intensity and 51 observed morphine and oxycodone dosing events.
Results: A joint model was developed to describe the recurrent pattern of four key phases determining the development of pain intensity and opioid consumption in time; A) Distribution of pain intensity scores which followed a truncated Poisson distribution with time-dependent mean score ranging from 0.93 to 2.45; B) Probability of transition to threshold pain levels (NRS≥3) which was strongly dependent on previous pain levels ranging from 2.8-15.2% after NRS of 0-2; C) Probability of requesting opioid when allowed (NRS≥3) which was strongly correlated with the number of previous doses, ranging from 89.8% for requesting the first dose to 26.1% after three previous doses; D) Reduction in pain scores after opioid dosing which was significantly related to the pain intensity at time of opioid request (p<0.001). This study highlights the importance of analyzing pain intensity and opioid consumption in an integrated manner.
Conclusions: Non-linear mixed effects modelling proved a valuable tool for analysis of interventions that affect pain intensity, probability of rescue dosing or the effect of opioids in the postoperative pain period.
References:
[1] US Food Drug Administration. Guidance for industry analgesic indications: developing drug and biological products., 2014
[2] McQuay HJ, Derry S, Eccleston C, Wiffen PJ, Andrew Moore R. Evidence for analgesic effect in acute pain – 50 years on. Pain 2012;153(7):1364-1367.
[2] Woolf CJ. Overcoming obstacles to developing new analgesics. Nature medicine 2010;16(11):1241-1247.
Reference: PAGE 25 (2016) Abstr 5720 [www.page-meeting.org/?abstract=5720]
Poster: Methodology - New Modelling Approaches