Description Usage Arguments Details References Examples
View source: R/simulate_amikacin_bayesian_auc_mic_ratio.R
Amikacin » Bayesian adaptive dosing » AUC to MIC ratio
1 2 3 4 5 6 7 8 9 10 11 12 | simulate_amikacin_bayesian_auc_mic_ratio(
PATID,
AGE,
HEIGHT,
WEIGHT,
GENDER,
MODEL,
MIC,
AUCPERMIC,
HISTORY,
REGIMENS
)
|
PATID |
Patient Identifier. User-provided free text (such as patient id, name or alias) to identify related simulations. Must be provided as string. |
AGE |
Age. Age of the patient in years. Must be provided as numeric (min. 18, max. 120 year). |
HEIGHT |
Height. Height of the patient. Must be provided as numeric (min. 100, max. 250 cm). |
WEIGHT |
Weight. Actual body weight of the patient. Must be provided as numeric (min. 20, max. 500 kg). |
GENDER |
Sex. Patient's sex for clinical decision-making. Must be provided as string ('Male' or 'Female'). |
MODEL |
Model for population of interest. Pharmacokinetic model to be used for specific patient type during simulations. Must be provided as string ('Saez Fernandez et al. (2019) - General ward'). |
MIC |
MIC. Minimum Inhibitory Concentration (MIC). Must be provided as numeric (min. 0.01, max. 1024 mg/L). |
AUCPERMIC |
AUC to MIC ratio target. The PK/PD target can be provided as 24 hour area under the concentration-time curve to minimum inhibitory concentration ratio (AUC/MIC). Must be provided as numeric (min. 10, max. 2000 ). |
HISTORY |
Historical Records. Must be provided as list of 3-48 'HISTCREATININE', 'HISTDOSE' or 'HISTCONCENTRATION' values. |
REGIMENS |
Dosing Regimens. List of dosing regimens to be used in simulating target attainment, from which the dosing regimen with the smallest absolute difference from the desired target will be automatically selected. Must be provided as list of 1-20 'REGIMEN' values. Use the |
Drug: Amikacin
Method: Estimate the pharmacokinetic parameters of the patient from past concentrations with Bayesian inverse modeling, then use that information to predict the steady state concentrations for multiple dosing regimens and select the optimal one, with regard to the target pharmacodynamic index.
PK/PD target: 24 hour area under the concentration-time curve to minimum inhibitory concentration ratio.
Saez Fernandez et al. (2019): Evaluation of renal function equations to predict amikacin clearance. In. Expert Review of Clinical Pharmacology. https://www.tandfonline.com/doi/full/10.1080/17512433.2019.1637253
K. Soetaert, T. Petzoldt (2010): Inverse Modelling, Sensitivity and Monte Carlo Analysis in R Using Package FME. In. Journal of Statistical Software. https://www.jstatsoft.org/article/view/v033i03
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simulate_amikacin_bayesian_auc_mic_ratio(PATID = "Anonymous",
AGE = 65, HEIGHT = 175,
WEIGHT = 75, GENDER = "Male",
MODEL = "Saez Fernandez et al. (2019) - General ward",
MIC = 1, AUCPERMIC = 50,
HISTORY = list(list(
DATETIME = structure(1601870400, class = c("POSIXct",
"POSIXt"), tzone = ""),
DOSE = 200, TINF = 0.5,
set = "HISTDOSE"),
list(DATETIME = structure(1601881200, class = c("POSIXct",
"POSIXt"), tzone = ""),
DOSE = 300,
TINF = 0.5,
set = "HISTDOSE"),
list(DATETIME = structure(1601899200, class = c("POSIXct",
"POSIXt"), tzone = ""),
DOSE = 400,
TINF = 0.5,
set = "HISTDOSE"),
list(DATETIME = structure(1601942400, class = c("POSIXct",
"POSIXt"), tzone = ""),
DOSE = 500,
TINF = 0.5,
set = "HISTDOSE"),
list(DATETIME = structure(1601866800, class = c("POSIXct",
"POSIXt"), tzone = ""),
CREATININE = 0.9,
set = "HISTCREATININE"),
list(DATETIME = structure(1601906400, class = c("POSIXct",
"POSIXt"), tzone = ""),
CREATININE = 0.7,
set = "HISTCREATININE"),
list(DATETIME = structure(1601888400, class = c("POSIXct",
"POSIXt"), tzone = ""),
CONCENTRATION = 8,
set = "HISTCONCENTRATION"),
list(DATETIME = structure(1601890200, class = c("POSIXct",
"POSIXt"), tzone = ""),
CONCENTRATION = 7,
set = "HISTCONCENTRATION"),
list(DATETIME = structure(1601906400, class = c("POSIXct",
"POSIXt"), tzone = ""),
CONCENTRATION = 10,
set = "HISTCONCENTRATION")),
REGIMENS = list(list(
set = "REGIMEN",
DOSE = 100, INTERVAL = 8,
TINF = 0.5),
list(set = "REGIMEN",
DOSE = 100,
INTERVAL = 12,
TINF = 0.5),
list(set = "REGIMEN",
DOSE = 125,
INTERVAL = 8,
TINF = 0.5),
list(set = "REGIMEN",
DOSE = 125,
INTERVAL = 12,
TINF = 0.5),
list(set = "REGIMEN",
DOSE = 150,
INTERVAL = 8,
TINF = 0.5),
list(set = "REGIMEN",
DOSE = 150,
INTERVAL = 12,
TINF = 0.5),
list(set = "REGIMEN",
DOSE = 175,
INTERVAL = 8,
TINF = 0.5),
list(set = "REGIMEN",
DOSE = 175,
INTERVAL = 12,
TINF = 0.5),
list(set = "REGIMEN",
DOSE = 200,
INTERVAL = 8,
TINF = 0.5),
list(set = "REGIMEN",
DOSE = 200,
INTERVAL = 12,
TINF = 0.5),
list(set = "REGIMEN",
DOSE = 400,
INTERVAL = 24,
TINF = 0.5),
list(set = "REGIMEN",
DOSE = 400,
INTERVAL = 36,
TINF = 0.5),
list(set = "REGIMEN",
DOSE = 500,
INTERVAL = 24,
TINF = 0.5),
list(set = "REGIMEN",
DOSE = 500,
INTERVAL = 36,
TINF = 0.5),
list(set = "REGIMEN",
DOSE = 600,
INTERVAL = 24,
TINF = 0.5),
list(set = "REGIMEN",
DOSE = 600,
INTERVAL = 36,
TINF = 0.5),
list(set = "REGIMEN",
DOSE = 700,
INTERVAL = 24,
TINF = 0.5),
list(set = "REGIMEN",
DOSE = 700,
INTERVAL = 36,
TINF = 0.5),
list(set = "REGIMEN",
DOSE = 800,
INTERVAL = 24,
TINF = 0.5),
list(set = "REGIMEN",
DOSE = 800,
INTERVAL = 36,
TINF = 0.5)))
## End(Not run)
|
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