Evidence map›Paper›PMID 30864076›Full record

ArticleJournal of clinical monitoring and computing2019

Optimizing intraoperative administration of propofol, remifentanil, and fentanyl through pharmacokinetic and pharmacodynamic simulations to increase the postoperative duration of analgesia.

Carl Tams, Noah Syroid, Terrie Vasilopoulos, Ken Johnson

Abstract read
PubMed Publisher
In one paragraph

Article in Journal of clinical monitoring and computing, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Trial
  2. End of year summary 2019: anaesthesia and airway management.Journal of clinical monitoring and computing · 2020
    Review
  3. Article
  4. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Carl TamsDepartment of Anesthesiology, University of Utah, 30 N 1900 E, Salt Lake City, UT, 84132, USA. carl.tams@utah.edu.ORCID http://orcid.org/0000-0002-2384-5220
Noah SyroidDepartment of Anesthesiology, University of Utah, 30 N 1900 E, Salt Lake City, UT, 84132, USA.
Terrie VasilopoulosDepartment of Anesthesiology, University of Florida, P.O. Box 100524, Gainesville, FL, 32610-0254, USA.
Ken JohnsonDepartment of Anesthesiology, University of Utah, 30 N 1900 E, Salt Lake City, UT, 84132, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Titrating an intraoperative anesthetic to achieve the postoperative goals of rapid emergence and prolonged analgesia can be difficult because of inter-patient variability and the need to provide intraoperative sedation and analgesia. Modeling pharmacokinetics and pharmacodynamics of anesthetic administrations estimates drug concentrations and predicted responses to stimuli during anesthesia. With utility of these PK/PD models we created an algorithm to optimize the intraoperative dosing regimen. We hypothesized the optimization algorithm would find a dosing regimen that would increase the postoperative duration of analgesia, not increase the time to emergence, and meet the intraoperative requirements of sedation and analgesia. To evaluate these hypotheses we performed a simulation study on previously collected anesthesia data. We developed an algorithm to recommend different intraoperative dosing regimens for improved post-operative results. To test the post-operative results of the algorithm we tested it on previously collected anesthesia data. An anesthetic dataset of 21 patients was obtained from a previous study from an anesthetic database at the University of Utah. Using the anesthetic records from these surgeries we modeled 21 patients using the same patient demographics and anesthetic requirements as the dataset. The anesthetic was simulated for each of the 21 patients with three different dosing regimens. The three dosing regimens are: from the anesthesiologist as recorded in the dataset (control group), from the algorithm in the clinical scenario one (test group), and from the algorithm in the clinical scenario two (test group). We created two clinical scenarios for the optimization algorithm to perform; one with normal general anesthesia constraints and goals, and a second condition where a delayed time to emergence is allowed to further maximize the duration of analgesia. The algorithm was evaluated by comparing the post-operative results of the control group to each of the test groups. Comparing results between the clinical scenario 1 dosing to the actual dosing showed a median increase in the duration of analgesia by 6 min and the time to emergence by 0.3 min. This was achieved by decreasing the intraoperative remifentanil infusion rate, increased the fentanyl dosing regimen, and not changing the propofol infusion rate. Comparing results between the clinical scenario 2 dosing to the actual dosing showed a median increase in the duration of analgesia by 26 min and emergence by 1.5 min. To dosing regimen from clinical scenario 2 greatly increased the fentanyl dosing regimen and greatly decreased the remifentanil infusion rate with no change to the propofol infusion rate. The results from this preliminary analysis of the optimization algorithm appear to imply that it can operate as intended. However a clinical study is warranted to determine to what extent the optimization algorithm determined optimal dosing regimens can maximize the postoperative duration of analgesia without delaying the time to emergence in a clinical setting.

Indexed as

AlgorithmsAnalgesiaAnesthesia, GeneralAnesthesiologyAnesthetics, IntravenousComputer SimulationDatabases, FactualFentanylHumansPain ManagementPostoperative PainPostoperative PeriodPropofolRemifentanilReproducibility of ResultsAnesthetics, IntravenousFentanylPropofolRemifentanilAnesthesiaOptimizationPharmacodynamicPharmacokineticTotal intravenous anesthesia

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.