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

Iona Macdonald, C.E. Staatz, Roger W. Jellifife, A.H. Thomson

Research output: Contribution to journalArticle

31 Citations (Scopus)

Abstract

This study compared the abilities of three Bayesian algorithms-simple multiple model (SMM) using a single creatinine measurement; richer data multiple model (RMM) using all creatinine measurements; and the sequential interacting multiple model (IMM)to describe gentamicin and vancomycin concentration-time data from patients within a cardiothoracic surgery unit who had variable renal function. All algorithms start with multiple sets of discrete parameter support points obtained from nonparametric population modeling. The SMM and RMM Bayesian algorithms then estimate their Bayesian posterior probabilitiis by conventionally assuming that the estimated parameter distributions are fixed and unchanging throughout the period of data analysis. In contrast, the IMM sequential Bayesian algorithm permits parameter estimates to jump from one population model support point to another, as new data are analyzed, if the probability of a different support point fitting the more recent data is more likely. Several initial IMM jump probability settings were examined-0.0001%, 0.1%, 3 %, and 10%-and a probability range of 0.0001% to 50%. The data sets comprised 550 gentamicin concentration measurements from 135 patients and 555 vancomycin concentration measurements from 139 patients. The SMM algorithm performed poorly with both antibiotics. Improved precision was obtained with the RMM algorithm. However, the IMM algorithm fitted the data with the highest precision. A 3% jump probability gave the best estimates. In contrast, the IMM 0.0001% to 50% range setting performed poorly, especially for vancomycin. In summary, the IMM algorithm described and tracked drug concentration data well in these clinically unstable patients. Further investigation of this new approach in routine clinical care and optimal dosage design is warranted.
LanguageEnglish
Pages67-74
Number of pages7
JournalTherapeutic Drug Monitoring
Volume30
Issue number1
DOIs
Publication statusPublished - Feb 2008

Fingerprint

Bayes Theorem
Vancomycin
Gentamicins
Creatinine
Population
Demography
Anti-Bacterial Agents
Kidney
Pharmaceutical Preparations

Keywords

  • gentamicin
  • vancomycin
  • MAP Bayesian algorithms
  • interacting multiple model
  • unstable renal function

Cite this

@article{3d007484e61c408f947db901a4f6389e,
title = "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",
abstract = "This study compared the abilities of three Bayesian algorithms-simple multiple model (SMM) using a single creatinine measurement; richer data multiple model (RMM) using all creatinine measurements; and the sequential interacting multiple model (IMM)to describe gentamicin and vancomycin concentration-time data from patients within a cardiothoracic surgery unit who had variable renal function. All algorithms start with multiple sets of discrete parameter support points obtained from nonparametric population modeling. The SMM and RMM Bayesian algorithms then estimate their Bayesian posterior probabilitiis by conventionally assuming that the estimated parameter distributions are fixed and unchanging throughout the period of data analysis. In contrast, the IMM sequential Bayesian algorithm permits parameter estimates to jump from one population model support point to another, as new data are analyzed, if the probability of a different support point fitting the more recent data is more likely. Several initial IMM jump probability settings were examined-0.0001{\%}, 0.1{\%}, 3 {\%}, and 10{\%}-and a probability range of 0.0001{\%} to 50{\%}. The data sets comprised 550 gentamicin concentration measurements from 135 patients and 555 vancomycin concentration measurements from 139 patients. The SMM algorithm performed poorly with both antibiotics. Improved precision was obtained with the RMM algorithm. However, the IMM algorithm fitted the data with the highest precision. A 3{\%} jump probability gave the best estimates. In contrast, the IMM 0.0001{\%} to 50{\%} range setting performed poorly, especially for vancomycin. In summary, the IMM algorithm described and tracked drug concentration data well in these clinically unstable patients. Further investigation of this new approach in routine clinical care and optimal dosage design is warranted.",
keywords = "gentamicin, vancomycin, MAP Bayesian algorithms, interacting multiple model, unstable renal function",
author = "Iona Macdonald and C.E. Staatz and Jellifife, {Roger W.} and A.H. Thomson",
year = "2008",
month = "2",
doi = "10.1097/FTD.0b013e318161a38c",
language = "English",
volume = "30",
pages = "67--74",
journal = "Therapeutic Drug Monitoring",
issn = "0163-4356",
number = "1",

}

TY - JOUR

T1 - 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

AU - Macdonald, Iona

AU - Staatz, C.E.

AU - Jellifife, Roger W.

AU - Thomson, A.H.

PY - 2008/2

Y1 - 2008/2

N2 - This study compared the abilities of three Bayesian algorithms-simple multiple model (SMM) using a single creatinine measurement; richer data multiple model (RMM) using all creatinine measurements; and the sequential interacting multiple model (IMM)to describe gentamicin and vancomycin concentration-time data from patients within a cardiothoracic surgery unit who had variable renal function. All algorithms start with multiple sets of discrete parameter support points obtained from nonparametric population modeling. The SMM and RMM Bayesian algorithms then estimate their Bayesian posterior probabilitiis by conventionally assuming that the estimated parameter distributions are fixed and unchanging throughout the period of data analysis. In contrast, the IMM sequential Bayesian algorithm permits parameter estimates to jump from one population model support point to another, as new data are analyzed, if the probability of a different support point fitting the more recent data is more likely. Several initial IMM jump probability settings were examined-0.0001%, 0.1%, 3 %, and 10%-and a probability range of 0.0001% to 50%. The data sets comprised 550 gentamicin concentration measurements from 135 patients and 555 vancomycin concentration measurements from 139 patients. The SMM algorithm performed poorly with both antibiotics. Improved precision was obtained with the RMM algorithm. However, the IMM algorithm fitted the data with the highest precision. A 3% jump probability gave the best estimates. In contrast, the IMM 0.0001% to 50% range setting performed poorly, especially for vancomycin. In summary, the IMM algorithm described and tracked drug concentration data well in these clinically unstable patients. Further investigation of this new approach in routine clinical care and optimal dosage design is warranted.

AB - This study compared the abilities of three Bayesian algorithms-simple multiple model (SMM) using a single creatinine measurement; richer data multiple model (RMM) using all creatinine measurements; and the sequential interacting multiple model (IMM)to describe gentamicin and vancomycin concentration-time data from patients within a cardiothoracic surgery unit who had variable renal function. All algorithms start with multiple sets of discrete parameter support points obtained from nonparametric population modeling. The SMM and RMM Bayesian algorithms then estimate their Bayesian posterior probabilitiis by conventionally assuming that the estimated parameter distributions are fixed and unchanging throughout the period of data analysis. In contrast, the IMM sequential Bayesian algorithm permits parameter estimates to jump from one population model support point to another, as new data are analyzed, if the probability of a different support point fitting the more recent data is more likely. Several initial IMM jump probability settings were examined-0.0001%, 0.1%, 3 %, and 10%-and a probability range of 0.0001% to 50%. The data sets comprised 550 gentamicin concentration measurements from 135 patients and 555 vancomycin concentration measurements from 139 patients. The SMM algorithm performed poorly with both antibiotics. Improved precision was obtained with the RMM algorithm. However, the IMM algorithm fitted the data with the highest precision. A 3% jump probability gave the best estimates. In contrast, the IMM 0.0001% to 50% range setting performed poorly, especially for vancomycin. In summary, the IMM algorithm described and tracked drug concentration data well in these clinically unstable patients. Further investigation of this new approach in routine clinical care and optimal dosage design is warranted.

KW - gentamicin

KW - vancomycin

KW - MAP Bayesian algorithms

KW - interacting multiple model

KW - unstable renal function

UR - http://dx.doi.org/10.1097/FTD.0b013e318161a38c

U2 - 10.1097/FTD.0b013e318161a38c

DO - 10.1097/FTD.0b013e318161a38c

M3 - Article

VL - 30

SP - 67

EP - 74

JO - Therapeutic Drug Monitoring

T2 - Therapeutic Drug Monitoring

JF - Therapeutic Drug Monitoring

SN - 0163-4356

IS - 1

ER -