Repeated Time-to-event Analysis of Consecutive Analgesic Events in Postoperative Pain
Research output: Contribution to journal › Journal article › Research › peer-review
Standard
Repeated Time-to-event Analysis of Consecutive Analgesic Events in Postoperative Pain. / Juul, Rasmus Vestergaard; Rasmussen, Sten; Kreilgaard, Mads; Christrup, Lona Louring; Simonsson, Ulrika S H; Lund, Trine Meldgaard.
In: Anesthesiology, Vol. 123, No. 12, 2015, p. 1411-1419.Research output: Contribution to journal › Journal article › Research › peer-review
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
TY - JOUR
T1 - Repeated Time-to-event Analysis of Consecutive Analgesic Events in Postoperative Pain
AU - Juul, Rasmus Vestergaard
AU - Rasmussen, Sten
AU - Kreilgaard, Mads
AU - Christrup, Lona Louring
AU - Simonsson, Ulrika S H
AU - Lund, Trine Meldgaard
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
U2 - 10.1097/ALN.0000000000000917
DO - 10.1097/ALN.0000000000000917
M3 - Journal article
C2 - 26495978
VL - 123
SP - 1411
EP - 1419
JO - Anesthesiology
JF - Anesthesiology
SN - 0003-3022
IS - 12
ER -
ID: 146774245