Rasmus Vestergaard Juul1, Sten Rasmussen, Mads Kreilgaard, Lona Louring Christrup, Ulrika S H Simonsson, Trine Meldgaard Lund. 1. From the Department of Drug Design and Pharmacology, University of Copenhagen, Copenhagen, Denmark (R.V.J., M.K., L.L.C., T.M.L.); Orthopaedic Surgery Research Unit and Department of Clinical Medicine, Aalborg University Hospital, Aalborg, Denmark (S.R.); and Department of Pharmaceutical Biosciences, Uppsala University, Uppsala, Sweden (U.S.H.S.).
Abstract
BACKGROUND: Reduction in consumption of opioid rescue medication is often used as an endpoint when investigating analgesic efficacy of drugs by adjunct treatment, but appropriate methods are needed to analyze analgesic consumption in time. Repeated time-to-event (RTTE) modeling is proposed as a way to describe analgesic consumption by analyzing the timing of consecutive analgesic events. METHODS: Retrospective data were obtained from 63 patients receiving standard analgesic treatment including morphine on request after surgery following hip fracture. Times of analgesic events up to 96 h after surgery were extracted from hospital medical records. Parametric RTTE analysis was performed with exponential, Weibull, or Gompertz distribution of analgesic events using NONMEM, version 7.2 (ICON Development Solutions, USA). The potential influences of night versus day, sex, and age were investigated on the probability. RESULTS: A Gompertz distribution RTTE model described the data well. The probability of having one or more analgesic events within 24 h was 80% for the first event, 55% for the second event, 31% for the third event, and 18% for fourth or more events for a typical woman of age 80 yr. 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 with day. CONCLUSIONS: RTTE modeling described analgesic consumption data well and could account for time-dependent changes in probability of analgesic events. Thus, RTTE modeling of analgesic events is proposed as a valuable tool when investigating new approaches to pain management such as opioid-sparing analgesia.
BACKGROUND: Reduction in consumption of opioid rescue medication is often used as an endpoint when investigating analgesic efficacy of drugs by adjunct treatment, but appropriate methods are needed to analyze analgesic consumption in time. Repeated time-to-event (RTTE) modeling is proposed as a way to describe analgesic consumption by analyzing the timing of consecutive analgesic events. METHODS: Retrospective data were obtained from 63 patients receiving standard analgesic treatment including morphine on request after surgery following hip fracture. Times of analgesic events up to 96 h after surgery were extracted from hospital medical records. Parametric RTTE analysis was performed with exponential, Weibull, or Gompertz distribution of analgesic events using NONMEM, version 7.2 (ICON Development Solutions, USA). The potential influences of night versus day, sex, and age were investigated on the probability. RESULTS: A Gompertz distribution RTTE model described the data well. The probability of having one or more analgesic events within 24 h was 80% for the first event, 55% for the second event, 31% for the third event, and 18% for fourth or more events for a typical woman of age 80 yr. 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 with day. CONCLUSIONS: RTTE modeling described analgesic consumption data well and could account for time-dependent changes in probability of analgesic events. Thus, RTTE modeling of analgesic events is proposed as a valuable tool when investigating new approaches to pain management such as opioid-sparing analgesia.
Authors: Mohammed H Elkomy; David R Drover; Jeffery L Galinkin; Gregory B Hammer; Kristi L Glotzbach Journal: Clin Pharmacokinet Date: 2016-10 Impact factor: 6.447
Authors: Rasmus V Juul; Katrine R Knøsgaard; Anne E Olesen; Katja V Pedersen; Mads Kreilgaard; Lona L Christrup; Palle J Osther; Asbjørn M Drewes; Trine M Lund Journal: AAPS J Date: 2016-04-26 Impact factor: 4.009
Authors: João A Abrantes; Alexander Solms; Dirk Garmann; Elisabet I Nielsen; Siv Jönsson; Mats O Karlsson Journal: Haematologica Date: 2019-08-01 Impact factor: 9.941