TY - CONF

T1 - How Repeated Time To Event (RTTE) modelling of opioid requests after surgery may improve future post-operative pain management

AU - Juul, Rasmus Vestergaard

AU - Rasmussen, Sten

AU - Kreilgaard, Mads

AU - simonsson, ulrika

AU - Christrup, Lona Louring

AU - Lund, Trine Meldgaard

PY - 2014/7/11

Y1 - 2014/7/11

N2 - Title: How Repeated Time To Event (RTTE) modelling of opioid requests after surgery may improve future post-operative pain management Author: Rasmus Vestergaard Juul (1) Sten Rasmussen (2) Mads Kreilgaard (1) Ulrika S. H. Simonsson (3) Lona Louring Christrup (1) Trine Meldgaard Lund (1) Institution: (1) Dept. of Drug Design and Pharmacology, University of Copenhagen, Denmark (2) Orthopaedic Surgery Research Unit, Aalborg University Hospital, Denmark (3) Dept. of Pharmaceutical Biosciences, Uppsala University, Sweden Type: Poster: Drug/Disease modelling – CNSObjectives: Amount of opioid (eg. morphine) required by patients after surgery is often used as a surrogate measure for pain intensity in post-operative pain. However, the dynamic development of pain intensity over time is often ignored when investigating new analgesic treatments for post-operative pain1. This work included a Repeated Time to Event (RTTE) modelling2 approach of repeated opioid request in order to increase the understanding of pain breakthrough patterns in severe surgeries and improve patients’ pain management.Methods: 68 patients (F:45,M:23, Age:76±15) were included from a population receiving surgery after hip fracture at Orthopaedic Department, Aalborg University Hospital, Denmark during the period May-Dec 2012. Morphine administration times (estimated precision: ±5mins), formulations and doses were extracted from medical journals in the hospitalization period or until 96 hours after surgery. RTTE modelling was performed in NONMEM 7.2 with Pirana, PsN and Xpose- and ggplot2 libraries for R3,4. Weibull and Gompertz distributions were investigated as hazard models. Visual Predictive Check (VPC) of Kaplan Meier survival curves as well objective function value was used to evaluate the model fit.Results: A base RTTE model based on a Weibull distribution fitted the pooled data. However, VPCs showed that morphine request was not adequately described by the base models on all surgery types. This suggests that pain events do not occur in similar patterns in different surgeries. The developed RTTE model provide a base for investigation of surgery specific, drug concentration related, population specific and/or time-varying covariates of opioid requests and pain events.Conclusions: A framework has been developed based on RTTE modelling that may help improve future pain management by 1) Identification of surgery specific patterns in pain events and 2) Evaluation of concentration related effects of opioids.References:[1] McQuay et al. 2008. Br J Anaesth. 101(1):69-76 [2] Plan et al. 2011. J Pharmacol Exp Ther. 339(3):878-85[3] Keizer et al 2013. CPT Pharmacometrics Syst Pharmacol. 26;2:e50[4] Wickham 2009. ggplot2: elegant graphics for data analysis. Springer.

AB - Title: How Repeated Time To Event (RTTE) modelling of opioid requests after surgery may improve future post-operative pain management Author: Rasmus Vestergaard Juul (1) Sten Rasmussen (2) Mads Kreilgaard (1) Ulrika S. H. Simonsson (3) Lona Louring Christrup (1) Trine Meldgaard Lund (1) Institution: (1) Dept. of Drug Design and Pharmacology, University of Copenhagen, Denmark (2) Orthopaedic Surgery Research Unit, Aalborg University Hospital, Denmark (3) Dept. of Pharmaceutical Biosciences, Uppsala University, Sweden Type: Poster: Drug/Disease modelling – CNSObjectives: Amount of opioid (eg. morphine) required by patients after surgery is often used as a surrogate measure for pain intensity in post-operative pain. However, the dynamic development of pain intensity over time is often ignored when investigating new analgesic treatments for post-operative pain1. This work included a Repeated Time to Event (RTTE) modelling2 approach of repeated opioid request in order to increase the understanding of pain breakthrough patterns in severe surgeries and improve patients’ pain management.Methods: 68 patients (F:45,M:23, Age:76±15) were included from a population receiving surgery after hip fracture at Orthopaedic Department, Aalborg University Hospital, Denmark during the period May-Dec 2012. Morphine administration times (estimated precision: ±5mins), formulations and doses were extracted from medical journals in the hospitalization period or until 96 hours after surgery. RTTE modelling was performed in NONMEM 7.2 with Pirana, PsN and Xpose- and ggplot2 libraries for R3,4. Weibull and Gompertz distributions were investigated as hazard models. Visual Predictive Check (VPC) of Kaplan Meier survival curves as well objective function value was used to evaluate the model fit.Results: A base RTTE model based on a Weibull distribution fitted the pooled data. However, VPCs showed that morphine request was not adequately described by the base models on all surgery types. This suggests that pain events do not occur in similar patterns in different surgeries. The developed RTTE model provide a base for investigation of surgery specific, drug concentration related, population specific and/or time-varying covariates of opioid requests and pain events.Conclusions: A framework has been developed based on RTTE modelling that may help improve future pain management by 1) Identification of surgery specific patterns in pain events and 2) Evaluation of concentration related effects of opioids.References:[1] McQuay et al. 2008. Br J Anaesth. 101(1):69-76 [2] Plan et al. 2011. J Pharmacol Exp Ther. 339(3):878-85[3] Keizer et al 2013. CPT Pharmacometrics Syst Pharmacol. 26;2:e50[4] Wickham 2009. ggplot2: elegant graphics for data analysis. Springer.

M3 - Poster

T2 - PAGE 2014: Population Approach Group Europe

Y2 - 10 June 2014 through 13 June 2014

ER -