Repeated time to event modelling of opioid consumption in postoperative pain
Rasmus Vestergaard Juul (1), Joakim Nyberg (2), Mads Kreilgaard (1), Lona Louring Christrup (1), Ulrika SH Simonsson (2), Trine Meldgaard Lund (1)
1: Department of Drug Design and Pharmacology, University of Copenhagen, Denmark, 2: Department of Pharmaceutical Biosciences, Uppsala, Sweden
Objectives: Postoperative pain trials often rely on patient controlled analgesic consumption as an endpoint of analgesic effect despite great concerns on traditional approaches to data analysis [1,2]. Pain intensity and consumption of rescue medication follows recurrent patterns of pain progression, pain events, patient controlled rescue medication, exposure to rescue analgesics and pain relief in time . How to meaningfully analyse patient controlled analgesic consumption remain an obstacle to advances in postoperative pain management . This work aimed to explore Repeated Time-to-Event (RTTE) modelling for analysis of consecutive analgesic consumption.
Methods: Data of patient requested morphine with three formulations and a range of doses (2.5-30 mg) in the postoperative pain period until 96 h or loss to follow-up after hip fracture surgery was obtained [5,6]. RTTE modelling was used to describe the timing of morphine consumption events . A PK-PD model for the effects of morphine on the probability of subsequent morphine consumption was developed . A RTTE simulation approach was developed of adaptive dosing in the presence of time-varying covariates . A simulation study was performed to compare RTTE modelling to traditional analysis approaches; t-test, Mann-Whitney test and time-to-event modelling .
Results: A Gompertz distribution RTTE model described the data well. The probability of analgesic events decreased in time, was reduced to 50% after 3.3 days after surgery and was significantly lower (32%) during night compared to day. For the first time, the PK-PD relationship between morphine and the probability of subsequent morphine consumption could be determined (EC50 = 1.9 ng/mL, Emax = 78% reduction). A hypothetical adjuvant drug X was simulated to have 37% morphine sparing effect. In a 24 h trial allowing morphine as rescue medication, a sample size of 50 patients was required to detect a significant reduction in morphine consumption with at least 80% power with RTTE modeling. In comparison the required sample size was 200 for Mann-Whitney, 180 for t-test and 76 for time-to-event modelling.
Conclusion: This study demonstrates the value of RTTE modelling for the study of analgesic consumption in postoperative pain. RTTE modelling allows future studies to use opioid consumption data to study analgesic interventions accounting for time-varying factors such as pain intensity, analgesic exposure and pain relief .
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