Piperacillin & Tazobactam » Bayesian » fCmin/MIC

Piperacillin & Tazobactam » Bayesian adaptive dosing » Free minimum concentration to MIC ratio

simulate_piperacillin_tazobactam_bayesian_fcmin_mic_ratio(
  PATID,
  AGE,
  HEIGHT,
  WEIGHT,
  GENDER,
  MODEL,
  MIC,
  CMINPERMIC,
  CRCLCAP,
  HISTORY,
  REGIMENS
)

Arguments

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 ('Felton et al. (2014) - ICU' or 'Patel et. al. (2010) - General ward').
MIC	MIC. Minimum Inhibitory Concentration (MIC). Must be provided as numeric (min. 0.01, max. 1024 mg/L).
CMINPERMIC	Minimum concentration to MIC ratio target. The PK/PD target can be provided as minimum concentration to minimum inhibitory concentration ratio (Cmin/MIC). Must be provided as numeric (min. 0.1, max. 50 ).
CRCLCAP	Capping Creatinine Clearance. Whether to use capping for creatinine clearance. Must be provided as string ('No cap', '120 ml/min', '130 ml/min', '140 ml/min' or '150 ml/min').
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 regimen helper function to define the REGIMEN values.

Details

Drug: Piperacillin & Tazobactam

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: Free minimum concentration (mg/L) to minimum inhibitory concentration ratio (mg/L).

References

• Felton, T. W. et al. (2014): Individualization of Piperacillin Dosing for Critically Ill Patients: Dosing Software To Optimize Antimicrobial Therapy. In. Antimicrobial Agents and Chemotherapy. https://aac.asm.org/content/58/7/4094

• Patel, N. et al. (2010): Identification of Optimal Renal Dosage Adjustments for Traditional and Extended-Infusion Piperacillin-Tazobactam Dosing Regimens in Hospitalized Patients. In. Antimicrobial Agents and Chemotherapy. https://aac.asm.org/content/54/1/460

• 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

Examples

if (FALSE) {
simulate_piperacillin_tazobactam_bayesian_fcmin_mic_ratio(PATID = "Anonymous", 
    AGE = 65, HEIGHT = 175, 
    WEIGHT = 75, GENDER = "Male", 
    MODEL = "Patel et. al. (2010) - General ward", 
    MIC = 4, CMINPERMIC = 2, 
    CRCLCAP = "No cap", 
    HISTORY = list(list(
        DATETIME = structure(1601870400, class = c("POSIXct", 
        "POSIXt"), tzone = ""), 
        DOSE = 2250, 
        TINF = 0.5, set = "HISTDOSE"), 
        list(DATETIME = structure(1601881200, class = c("POSIXct", 
        "POSIXt"), tzone = ""), 
            DOSE = 3375, 
            TINF = 0.5, 
            set = "HISTDOSE"), 
        list(DATETIME = structure(1601899200, class = c("POSIXct", 
        "POSIXt"), tzone = ""), 
            DOSE = 4500, 
            TINF = 3, 
            set = "HISTDOSE"), 
        list(DATETIME = structure(1601942400, class = c("POSIXct", 
        "POSIXt"), tzone = ""), 
            DOSE = 2250, 
            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(1601886600, class = c("POSIXct", 
        "POSIXt"), tzone = ""), 
            CONCENTRATION = 40, 
            set = "HISTCONCENTRATION"), 
        list(DATETIME = structure(1601890200, class = c("POSIXct", 
        "POSIXt"), tzone = ""), 
            CONCENTRATION = 25, 
            set = "HISTCONCENTRATION"), 
        list(DATETIME = structure(1601917200, class = c("POSIXct", 
        "POSIXt"), tzone = ""), 
            CONCENTRATION = 20, 
            set = "HISTCONCENTRATION")), 
    REGIMENS = list(list(
        DOSE = 2250, 
        INTERVAL = 4, 
        TINF = 0.5, set = "REGIMEN"), 
        list(DOSE = 2250, 
            INTERVAL = 6, 
            TINF = 0.5, 
            set = "REGIMEN"), 
        list(DOSE = 2250, 
            INTERVAL = 8, 
            TINF = 0.5, 
            set = "REGIMEN"), 
        list(DOSE = 3375, 
            INTERVAL = 4, 
            TINF = 0.5, 
            set = "REGIMEN"), 
        list(DOSE = 3375, 
            INTERVAL = 6, 
            TINF = 0.5, 
            set = "REGIMEN"), 
        list(DOSE = 3375, 
            INTERVAL = 8, 
            TINF = 0.5, 
            set = "REGIMEN"), 
        list(DOSE = 4500, 
            INTERVAL = 4, 
            TINF = 0.5, 
            set = "REGIMEN"), 
        list(DOSE = 4500, 
            INTERVAL = 6, 
            TINF = 0.5, 
            set = "REGIMEN")))
}