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dr. M.S. (Matthijs) Berends
2019-05-13 10:10:16 +02:00
parent 0444c4ed9d
commit 38a4421450
36 changed files with 475 additions and 213 deletions
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man/count.Rd
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@@ -30,7 +30,7 @@ count_all(...)
n_rsi(...)
count_df(data, translate_ab = "name", language = get_locale(),
combine_IR = FALSE)
combine_SI = TRUE, combine_IR = FALSE)
}
\arguments{
\item{...}{one or more vectors (or columns) with antibiotic interpretations. They will be transformed internally with \code{\link{as.rsi}} if needed.}
@@ -43,7 +43,7 @@ count_df(data, translate_ab = "name", language = get_locale(),
\item{language}{language of the returned text, defaults to system language (see \code{\link{get_locale}}) and can also be set with \code{\link{getOption}("AMR_locale")}. Use \code{language = NULL} or \code{language = ""} to prevent translation.}
\item{combine_IR}{a logical to indicate whether all values of I and R must be merged into one, so the output only consists of S vs. IR (susceptible vs. non-susceptible)}
\item{combine_SI}{a logical to indicate whether all values of I and S must be merged into one, so the output only consists of S+I vs. R (susceptible vs. resistant). This used to be the parameter \code{combine_IR}, but this now follows the redefinition by EUCAST about the interpretion of I (increased exposure) in 2019, see below. Default is now \code{TRUE}.}
}
\value{
Integer
@@ -60,6 +60,23 @@ These functions are meant to count isolates. Use the \code{\link{portion}_*} fun
\code{count_df} takes any variable from \code{data} that has an \code{"rsi"} class (created with \code{\link{as.rsi}}) and counts the amounts of R, I and S. The resulting \emph{tidy data} (see Source) \code{data.frame} will have three rows (S/I/R) and a column for each variable with class \code{"rsi"}.
}
\section{Interpretation of S, I and R}{
In 2019, EUCAST has decided to change the definitions of susceptibility testing categories S, I and R as shown below. Results of several consultations on the new definitions are available on the EUCAST website under "Consultations".
\itemize{
\item{\strong{S}}{Susceptible, standard dosing regimen: A microorganism is categorised as "Susceptible, standard dosing regimen", when there is a high likelihood of therapeutic success using a standard dosing regimen of the agent.}
\item{\strong{I}}{Susceptible, increased exposure: A microorganism is categorised as "Susceptible, Increased exposure" when there is a high likelihood of therapeutic success because exposure to the agent is increased by adjusting the dosing regimen or by its concentration at the site of infection.}
\item{\strong{R}}{Resistant: A microorganism is categorised as "Resistant" when there is a high likelihood of therapeutic failure even when there is increased exposure.}
}
Exposure is a function of how the mode of administration, dose, dosing interval, infusion time, as well as distribution and excretion of the antimicrobial agent will influence the infecting organism at the site of infection.
Source: \url{http://www.eucast.org/newsiandr/}.
\strong{This AMR package honours this new insight.}
}
\section{Read more on our website!}{
On our website \url{https://msberends.gitlab.io/AMR} you can find \href{https://msberends.gitlab.io/AMR/articles/AMR.html}{a comprehensive tutorial} about how to conduct AMR analysis, the \href{https://msberends.gitlab.io/AMR/reference}{complete documentation of all functions} (which reads a lot easier than here in R) and \href{https://msberends.gitlab.io/AMR/articles/WHONET.html}{an example analysis using WHONET data}.