DATA SYNTHESIS AND SIMULATION OF DOSING SCENARIOS
Data were collated using Microsoft Excel. Our primary analysis was a
mixed narrative and meta-analysis. Descriptive analytics and multiple
regression modelling were performed using IBM SPSS Statistics 28. In
cases where specific values were not reported (e.g. ultrafiltration
rate, filtration fraction, volume of distribution) these were calculated
using the available data and standard formulae where possible.
Statistical heterogeneity was not assessed as the Cochrane Q Test and
I2 statistic are not applicable to our dependent
variable which is continuous rather than dichotomous.
Simulations were undertaken in R using tidyverse and linpk packages[29-31] . Individual patient level data was used for
elimination rate constant and volume of distribution. Elimination rate
constant was taken directly from published manuscripts or derived from
other published pharmacokinetic parameters, where available. Volume of
distribution was not available for 8 patients (Chappell). Simulated
patients (n=10,000) were created from these values using the MASS
package and assuming a multivariate log-normal distribution of
parameters [32] . For pharmacokinetic profiles, a one
compartment model was assumed. Profiles were simulated to 72-hours, on
the assumption that steady state would be achieved by then and that this
early period of treatment was likely to be the most important in
achieving therapeutic concentrations. We utilised a target trough
concentration range of 12 – 46 mcg/mL as this is the most widely used
therapeutic index [6, 25, 34, 38] .