Literature DB >> 1588504

Measuring the predictive performance of computer-controlled infusion pumps.

J R Varvel1, D L Donoho, S L Shafer.   

Abstract

Current measures of the performance of computer-controlled infusion pumps (CCIPs) are poorly defined, of little use to the clinician using the CCIP, and pharmacostatistically incorrect. We propose four measures be used to quantitate the performance of CCIPs: median absolute performance error (MDAPE), median performance error (MDPE), divergence, and wobble. These measures offer several significant advantages over previous measures. First, their definitions are based on the performance error as a fraction of the predicted (rather than measured) drug concentration, making the measures much more useful to the clinician. Second, the measures are defined in a way that addresses the pharmacostatistical issue of appropriate estimation of population parameters. Finally, the measure of inaccuracy, MDAPE, is defined in a way that is consistent with iteratively reweighted least squares nonlinear regression, a commonly used method of estimating pharmacokinetic parameters. These measures make it possible to quantitate the overall performance of a CCIP or to compare the predictive performance of CCIPs which differ in either general approach (e.g., compartmental model driven vs. plasma efflux approach), pump mechanics, software algorithms, or pharmacokinetic parameter sets.

Mesh:

Substances:

Year:  1992        PMID: 1588504     DOI: 10.1007/bf01143186

Source DB:  PubMed          Journal:  J Pharmacokinet Biopharm        ISSN: 0090-466X


  15 in total

1.  Population pharmacokinetics of alfentanil: the average dose-plasma concentration relationship and interindividual variability in patients.

Authors:  P O Maitre; S Vozeh; J Heykants; D A Thomson; D R Stanski
Journal:  Anesthesiology       Date:  1987-01       Impact factor: 7.892

2.  Computer-assisted continuous infusions of fentanyl during cardiac anesthesia: comparison with a manual method.

Authors:  J M Alvis; J G Reves; A V Govier; P G Menkhaus; C E Henling; J A Spain; E Bradley
Journal:  Anesthesiology       Date:  1985-07       Impact factor: 7.892

3.  Evaluating the accuracy of using population pharmacokinetic data to predict plasma concentrations of alfentanil.

Authors:  P O Maitre; M E Ausems; S Vozeh; D R Stanski
Journal:  Anesthesiology       Date:  1988-01       Impact factor: 7.892

4.  Some suggestions for measuring predictive performance.

Authors:  L B Sheiner; S L Beal
Journal:  J Pharmacokinet Biopharm       Date:  1981-08

5.  Pharmacokinetics of fentanyl administered by computer-controlled infusion pump.

Authors:  S L Shafer; J R Varvel; N Aziz; J C Scott
Journal:  Anesthesiology       Date:  1990-12       Impact factor: 7.892

6.  Pharmacokinetic model-driven infusion of fentanyl: assessment of accuracy.

Authors:  P S Glass; J R Jacobs; L R Smith; B Ginsberg; T J Quill; S A Bai; J G Reves
Journal:  Anesthesiology       Date:  1990-12       Impact factor: 7.892

7.  Decreased fentanyl and alfentanil dose requirements with age. A simultaneous pharmacokinetic and pharmacodynamic evaluation.

Authors:  J C Scott; D R Stanski
Journal:  J Pharmacol Exp Ther       Date:  1987-01       Impact factor: 4.030

8.  The prospective use of population pharmacokinetics in a computer-driven infusion system for alfentanil.

Authors:  D B Raemer; A Buschman; J R Varvel; B K Philip; M D Johnson; D A Stein; S L Shafer
Journal:  Anesthesiology       Date:  1990-07       Impact factor: 7.892

9.  The population approach to pharmacokinetic data analysis: rationale and standard data analysis methods.

Authors:  L B Sheiner
Journal:  Drug Metab Rev       Date:  1984       Impact factor: 4.518

10.  An evaluation of the accuracy of pharmacokinetic data for the computer assisted infusion of alfentanil.

Authors:  M E Ausems; D R Stanski; C C Hug
Journal:  Br J Anaesth       Date:  1985-12       Impact factor: 9.166

View more
  75 in total

1.  Nonlinear model-based predictive control of non-depolarizing muscle relaxants using neural networks.

Authors:  M Lendl; U H Schwarz; H J Romeiser; R Unbehauen; M Georgieff; G F Geldner
Journal:  J Clin Monit Comput       Date:  1999-07       Impact factor: 2.502

Review 2.  [Modern concepts in pharmacokinetics of intravenous anesthetics].

Authors:  T Heidegger; C F Minto; T W Schnider
Journal:  Anaesthesist       Date:  2004-01       Impact factor: 1.041

3.  Performance evaluation of a whole blood propofol analyser.

Authors:  Bo Liu; David M Pettigrew; Stephen Bates; Peter G Laitenberger; Gavin Troughton
Journal:  J Clin Monit Comput       Date:  2012-01-01       Impact factor: 2.502

4.  Pharmacodynamic modeling of propofol-induced tidal volume depression in children.

Authors:  Jin-Oh Hahn; Sara Khosravi; Maryam Dosani; Guy A Dumont; J Mark Ansermino
Journal:  J Clin Monit Comput       Date:  2011-09-23       Impact factor: 2.502

5.  Vancomycin in Pediatric Patients with Solid or Hematological Malignant Disease: Predictive Performance of a Population Pharmacokinetic Model and New Optimized Dosing Regimens.

Authors:  Amélie Marsot; F Gallais; C Galambrun; C Coze; O Blin; N Andre; R Guilhaumou
Journal:  Paediatr Drugs       Date:  2018-08       Impact factor: 3.022

6.  Predictive performance of 'Diprifusor' TCI system in patients during upper abdominal surgery under propofol/fentanyl anesthesia.

Authors:  Yu-hong Li; Jian-hong Xu; Jian-jun Yang; Jie Tian; Jian-guo Xu
Journal:  J Zhejiang Univ Sci B       Date:  2005-01       Impact factor: 3.066

7.  Induction speed is not a determinant of propofol pharmacodynamics.

Authors:  Anthony G Doufas; Maryam Bakhshandeh; Andrew R Bjorksten; Steven L Shafer; Daniel I Sessler
Journal:  Anesthesiology       Date:  2004-11       Impact factor: 7.892

8.  AZD-3043: a novel, metabolically labile sedative-hypnotic agent with rapid and predictable emergence from hypnosis.

Authors:  Talmage D Egan; Shinju Obara; Thomas E Jenkins; Sarah S Jaw-Tsai; Shanti Amagasu; Daniel R Cook; Scott C Steffensen; David T Beattie
Journal:  Anesthesiology       Date:  2012-06       Impact factor: 7.892

9.  Performance of computer simulated inhalational anesthetic uptake model in comparison with real time isoflurane concentration.

Authors:  Umeshkumar Athiraman; M Ravishankar; Sameer Jahagirdhar
Journal:  J Clin Monit Comput       Date:  2015-09-19       Impact factor: 2.502

10.  Population pharmacokinetics of intravenous acetaminophen in Japanese patients undergoing elective surgery.

Authors:  Tsuyoshi Imaizumi; Shinju Obara; Midori Mogami; Yuzo Iseki; Makiko Hasegawa; Masahiro Murakawa
Journal:  J Anesth       Date:  2017-04-21       Impact factor: 2.078

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.