Skip to main content
Erschienen in: Clinical Pharmacokinetics 8/2016

25.02.2016 | Current Opinion

Challenges in Individualizing Drug Dosage for Intensive Care Unit Patients: Is Augmented Renal Clearance What We Really Want to Know? Some Suggested Management Approaches and Clinical Software Tools

verfasst von: Roger Jelliffe

Erschienen in: Clinical Pharmacokinetics | Ausgabe 8/2016

Einloggen, um Zugang zu erhalten

Abstract

Acutely ill intensive care unit (ICU) patients often have large apparent volumes of distribution of drugs and, because of this, their drug clearance (CL) is usually also increased. ‘Augmented renal Cl’ is a current issue in the management of drug therapy for acutely ill and unstable ICU patients; however, Cl, the product of volume and the rate constant for excretion, describes only a theoretical volume of drug cleared per unit of time. Information of the actual rate of movement of the drug itself is obscured. It is suggested that the most useful clinical information is given by describing drug volume and elimination rate constant separately. This also permits better understanding of the patient’s separate issues of fluid balance and drug elimination, especially when dialysis, renal replacement therapy, or extracorporeal membrane oxygenation (ECMO) may be used, and facilitates management of these two important separate clinical issues. Optimal management of drug therapy also requires optimal methods embodied in clinical software to describe drug behavior in these highly unstable patients, and considerably more data than for ordinary patients. The interacting multiple model (IMM) clinical software facilitates management of both fluid balance and drug therapy in these unstable patients. Illustrative cases are discussed, and new monitoring and management strategies are suggested. Like other ICU skills, physicians need to learn optimal tools for managing drug therapy in the ICU. Further work should help evaluate these new approaches.
Literatur
1.
Zurück zum Zitat D’Argenio D. Optimal sampling times for pharmacokinetic experiments. J Pharmacokinet Biopharm. 1981;9:739–56.CrossRefPubMed D’Argenio D. Optimal sampling times for pharmacokinetic experiments. J Pharmacokinet Biopharm. 1981;9:739–56.CrossRefPubMed
2.
Zurück zum Zitat Jelliffe R, Bayard D, Neely M. Optimal sampling times weighted for the task to be optimized—control of AUC, peak, trough, etc. Presented at the PODE Conference; 11 Sep 2014, Basel. Abstract and slides available on Google by searching for “pode 2014”. Jelliffe R, Bayard D, Neely M. Optimal sampling times weighted for the task to be optimized—control of AUC, peak, trough, etc. Presented at the PODE Conference; 11 Sep 2014, Basel. Abstract and slides available on Google by searching for “pode 2014”.
3.
Zurück zum Zitat Bustad A, Terziivanov D, Leary R, Port R, Schumitzky A, Jelliffe R. Parametric and nonparametric population methods: their comparative performance in analysing a clinical data set and two Monte Carlo simulation studies. Clin Pharmacokinet. 2006;45:365–83.CrossRefPubMed Bustad A, Terziivanov D, Leary R, Port R, Schumitzky A, Jelliffe R. Parametric and nonparametric population methods: their comparative performance in analysing a clinical data set and two Monte Carlo simulation studies. Clin Pharmacokinet. 2006;45:365–83.CrossRefPubMed
4.
Zurück zum Zitat Jelliffe R, Bayard D, Milman M, Van Guilder M, Schumitzky A. Achieving target goals most precisely using nonparametric compartmental models and “multiple model” design of dosage regimens. Ther Drug Monit. 2000;22:346–53.CrossRefPubMed Jelliffe R, Bayard D, Milman M, Van Guilder M, Schumitzky A. Achieving target goals most precisely using nonparametric compartmental models and “multiple model” design of dosage regimens. Ther Drug Monit. 2000;22:346–53.CrossRefPubMed
5.
Zurück zum Zitat Bayard D, Jelliffe R. A Bayesian approach to tracking patients having changing pharmacokinetic parameters. J Pharmacokinet Pharmacodyn. 2004;31:75–107.CrossRefPubMed Bayard D, Jelliffe R. A Bayesian approach to tracking patients having changing pharmacokinetic parameters. J Pharmacokinet Pharmacodyn. 2004;31:75–107.CrossRefPubMed
6.
Zurück zum Zitat Macdonald I, Staatz C, Jelliffe R, Thomson A. Evaluation and comparison of simple multiple model, richer data multiple model, and sequential interacting multiple model (IMM) Bayesian analyses of gentamicin and vancomycin data collected from patients undergoing cardiothoracic surgery. Ther Drug Monit. 2008;30:67–74.CrossRefPubMedPubMedCentral Macdonald I, Staatz C, Jelliffe R, Thomson A. Evaluation and comparison of simple multiple model, richer data multiple model, and sequential interacting multiple model (IMM) Bayesian analyses of gentamicin and vancomycin data collected from patients undergoing cardiothoracic surgery. Ther Drug Monit. 2008;30:67–74.CrossRefPubMedPubMedCentral
7.
Zurück zum Zitat Mehvar R. Teachers topics. The relationship among pharmacokinetic parameters: effects of altered kinetics on the drug plasma concentration–time profiles. Am J Pharmaceut Educ. 2004;68(2):article 36. Mehvar R. Teachers topics. The relationship among pharmacokinetic parameters: effects of altered kinetics on the drug plasma concentration–time profiles. Am J Pharmaceut Educ. 2004;68(2):article 36.
8.
Zurück zum Zitat Jelliffe RW, D’Argenio DZ, Schumitzky A, Hu L, Liu M. The USC PC-PACK programs for planning, monitoring, and adjusting drug dosage regimens. Presented at the American Association of Medical Instrumentation annual meeting; 14–18 May 1988, Washington, D.C. Jelliffe RW, D’Argenio DZ, Schumitzky A, Hu L, Liu M. The USC PC-PACK programs for planning, monitoring, and adjusting drug dosage regimens. Presented at the American Association of Medical Instrumentation annual meeting; 14–18 May 1988, Washington, D.C.
9.
Zurück zum Zitat Niemiec P, Allo M, Miller C. Effect of altered volume of distribution on aminoglycoside levels in patients in surgical intensive care. Arch Surg. 1987;122:207–12.CrossRefPubMed Niemiec P, Allo M, Miller C. Effect of altered volume of distribution on aminoglycoside levels in patients in surgical intensive care. Arch Surg. 1987;122:207–12.CrossRefPubMed
10.
Zurück zum Zitat Haug MT III, Slugg PH. Antibiotic pharmacokinetics. In: Sivak E, Higgins T, Seiver A, editors. The high risk patient: management of the critically ill. Media (PA): Williams & Wilkins; 1995. p. 1338–64. Haug MT III, Slugg PH. Antibiotic pharmacokinetics. In: Sivak E, Higgins T, Seiver A, editors. The high risk patient: management of the critically ill. Media (PA): Williams & Wilkins; 1995. p. 1338–64.
11.
Zurück zum Zitat Jelliffe R, Schumitzky A, Bayard D, Van Guilder M, Botnen A, Neely M, et al. The USC Pmetrics and Bestdose software—the only software with integrated population modeling, simulation, and maximally precise dosage. Laboratory of Applied Pharmacokinetics, USC Keck School of Medicine, Los Angeles, CA, USA. A software demonstration at the Population Approach Group Europe, Glasgow, Scotland; 11–14 June 2013. http://www.lapk.org. Jelliffe R, Schumitzky A, Bayard D, Van Guilder M, Botnen A, Neely M, et al. The USC Pmetrics and Bestdose software—the only software with integrated population modeling, simulation, and maximally precise dosage. Laboratory of Applied Pharmacokinetics, USC Keck School of Medicine, Los Angeles, CA, USA. A software demonstration at the Population Approach Group Europe, Glasgow, Scotland; 11–14 June 2013. http://​www.​lapk.​org.
12.
Zurück zum Zitat Blom H. An efficient filter for abruptly changing systems. Proceedings of the 23rd Conference on Decision and Control; December 1984, Las Vegas (NV). p. 656–658. Blom H. An efficient filter for abruptly changing systems. Proceedings of the 23rd Conference on Decision and Control; December 1984, Las Vegas (NV). p. 656–658.
13.
Zurück zum Zitat Blom H, Bar-Shalom Y. The interacting multiple model algorithm for systems with Markovian switching coefficients. IEEE Trans Autom Control. 1988;33:780–3.CrossRef Blom H, Bar-Shalom Y. The interacting multiple model algorithm for systems with Markovian switching coefficients. IEEE Trans Autom Control. 1988;33:780–3.CrossRef
14.
Zurück zum Zitat Mazor E, Averbuch A, Bar-Shalom Y, Dayan J. Interacting multiple model methods in target tracking: a survey. IEEE Trans Aerosp Electron Syst. 1998;34:103–23.CrossRef Mazor E, Averbuch A, Bar-Shalom Y, Dayan J. Interacting multiple model methods in target tracking: a survey. IEEE Trans Aerosp Electron Syst. 1998;34:103–23.CrossRef
15.
Zurück zum Zitat Jelliffe R. Optimal methodology is important for optimal pharmacokinetic studies, therapeutic drug monitoring, and patient care [commentary]. Clin Pharmacokinet. 2015;54(9):887–92.CrossRefPubMed Jelliffe R. Optimal methodology is important for optimal pharmacokinetic studies, therapeutic drug monitoring, and patient care [commentary]. Clin Pharmacokinet. 2015;54(9):887–92.CrossRefPubMed
16.
Zurück zum Zitat Jelliffe R. Estimation of creatinine clearance in patients with unstable renal function, without a urine specimen. Am J Nephrol. 2002;22:320–4.CrossRefPubMed Jelliffe R. Estimation of creatinine clearance in patients with unstable renal function, without a urine specimen. Am J Nephrol. 2002;22:320–4.CrossRefPubMed
17.
Zurück zum Zitat Cotta M, Roberts J, Lipman J. Antibiotic dose optimization in critically ill patients. Med Intensiva. 2015;39(9):563–72.CrossRefPubMed Cotta M, Roberts J, Lipman J. Antibiotic dose optimization in critically ill patients. Med Intensiva. 2015;39(9):563–72.CrossRefPubMed
Metadaten
Titel
Challenges in Individualizing Drug Dosage for Intensive Care Unit Patients: Is Augmented Renal Clearance What We Really Want to Know? Some Suggested Management Approaches and Clinical Software Tools
verfasst von
Roger Jelliffe
Publikationsdatum
25.02.2016
Verlag
Springer International Publishing
Erschienen in
Clinical Pharmacokinetics / Ausgabe 8/2016
Print ISSN: 0312-5963
Elektronische ISSN: 1179-1926
DOI
https://doi.org/10.1007/s40262-016-0369-4

Weitere Artikel der Ausgabe 8/2016

Clinical Pharmacokinetics 8/2016 Zur Ausgabe