AMR/man/as.rsi.Rd

% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/rsi.R
\name{as.rsi}
\alias{as.rsi}
\alias{rsi}
\alias{as.rsi.mic}
\alias{as.rsi.disk}
\alias{as.rsi.data.frame}
\alias{is.rsi}
\alias{is.rsi.eligible}
\title{Class 'rsi'}
\usage{
as.rsi(x, ...)

\method{as.rsi}{mic}(
  x,
  mo,
  ab = deparse(substitute(x)),
  guideline = "EUCAST",
  uti = FALSE,
  ...
)

\method{as.rsi}{disk}(
  x,
  mo,
  ab = deparse(substitute(x)),
  guideline = "EUCAST",
  uti = FALSE,
  ...
)

\method{as.rsi}{data.frame}(x, col_mo = NULL, guideline = "EUCAST", uti = NULL, ...)

is.rsi(x)

is.rsi.eligible(x, threshold = 0.05)
}
\arguments{
\item{x}{vector of values (for class \code{\link{mic}}: an MIC value in mg/L, for class \code{\link{disk}}: a disk diffusion radius in millimetres)}

\item{...}{parameters passed on to methods}

\item{mo}{any (vector of) text that can be coerced to a valid microorganism code with \code{\link[=as.mo]{as.mo()}}}

\item{ab}{any (vector of) text that can be coerced to a valid antimicrobial code with \code{\link[=as.ab]{as.ab()}}}

\item{guideline}{defaults to the latest included EUCAST guideline, see Details for all options}

\item{uti}{(Urinary Tract Infection) A vector with \link{logical}s (\code{TRUE} or \code{FALSE}) to specify whether a UTI specific interpretation from the guideline should be chosen. For using \code{\link[=as.rsi]{as.rsi()}} on a \link{data.frame}, this can also be a column containing \link{logical}s or when left blank, the data set will be search for a 'specimen' and rows containing 'urin' in that column will be regarded isolates from a UTI. See \emph{Examples}.}

\item{col_mo}{column name of the IDs of the microorganisms (see \code{\link[=as.mo]{as.mo()}}), defaults to the first column of class \code{\link{mo}}. Values will be coerced using \code{\link[=as.mo]{as.mo()}}.}

\item{threshold}{maximum fraction of invalid antimicrobial interpretations of \code{x}, please see \emph{Examples}}
}
\value{
Ordered factor with new class \code{\link{rsi}}
}
\description{
Interpret MIC values and disk diffusion diameters according to EUCAST or CLSI, or clean up existing R/SI values. This transforms the input to a new class \code{\link{rsi}}, which is an ordered factor with levels \verb{S < I < R}. Invalid antimicrobial interpretations will be translated as \code{NA} with a warning.
}
\details{
When using \code{\link[=as.rsi]{as.rsi()}} on untransformed data, the data will be cleaned to only contain values S, I and R. When using the function on data with class \code{\link{mic}} (using \code{\link[=as.mic]{as.mic()}}) or class \code{\link{disk}} (using \code{\link[=as.disk]{as.disk()}}), the data will be interpreted based on the guideline set with the \code{guideline} parameter.

Supported guidelines to be used as input for the \code{guideline} parameter are: "CLSI 2010", "CLSI 2011", "CLSI 2012", "CLSI 2013", "CLSI 2014", "CLSI 2015", "CLSI 2016", "CLSI 2017", "CLSI 2018", "CLSI 2019", "EUCAST 2011", "EUCAST 2012", "EUCAST 2013", "EUCAST 2014", "EUCAST 2015", "EUCAST 2016", "EUCAST 2017", "EUCAST 2018", "EUCAST 2019", "EUCAST 2020". Simply using \code{"CLSI"} or \code{"EUCAST"} for input will automatically select the latest version of that guideline.

The repository of this package \href{https://gitlab.com/msberends/AMR/blob/master/data-raw/rsi_translation.txt}{contains a machine readable version} of all guidelines. This is a CSV file consisting of 18,964 rows and 10 columns. This file is machine readable, since it contains one row for every unique combination of the test method (MIC or disk diffusion), the antimicrobial agent and the microorganism. This \strong{allows for easy implementation of these rules in laboratory information systems (LIS)}.

After using \code{\link[=as.rsi]{as.rsi()}}, you can use \code{\link[=eucast_rules]{eucast_rules()}} to (1) apply inferred susceptibility and resistance based on results of other antimicrobials and (2) apply intrinsic resistance based on taxonomic properties of a microorganism.

The function \code{\link[=is.rsi.eligible]{is.rsi.eligible()}} returns \code{TRUE} when a columns contains at most 5\% invalid antimicrobial interpretations (not S and/or I and/or R), and \code{FALSE} otherwise. The threshold of 5\% can be set with the \code{threshold} parameter.
}
\section{Interpretation of R and S/I}{

In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories R and S/I as shown below (\url{http://www.eucast.org/newsiandr/}).
\itemize{
\item \strong{R = Resistant}\cr
A microorganism is categorised as \emph{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.
\item \strong{S = Susceptible}\cr
A microorganism is categorised as \emph{Susceptible, standard dosing regimen}, when there is a high likelihood of therapeutic success using a standard dosing regimen of the agent.
\item \strong{I = Increased exposure, but still susceptible}\cr
A microorganism is categorised as \emph{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.
}

This AMR package honours this new insight. Use \code{\link[=susceptibility]{susceptibility()}} (equal to \code{\link[=proportion_SI]{proportion_SI()}}) to determine antimicrobial susceptibility and \code{\link[=count_susceptible]{count_susceptible()}} (equal to \code{\link[=count_SI]{count_SI()}}) to count susceptible isolates.
}

\section{Stable lifecycle}{

\if{html}{\figure{lifecycle_stable.svg}{options: style=margin-bottom:5px} \cr}
The \link[AMR:lifecycle]{lifecycle} of this function is \strong{stable}. In a stable function, we are largely happy with the unlying code, and major changes are unlikely. This means that the unlying code will generally evolve by adding new arguments; we will avoid removing arguments or changing the meaning of existing arguments.

If the unlying code needs breaking changes, they will occur gradually. To begin with, the function or argument will be deprecated; it will continue to work but will emit an message informing you of the change. Next, typically after at least one newly released version on CRAN, the message will be transformed to an error.
}

\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}.
}

\examples{
# For INTERPRETING disk diffusion and MIC values -----------------------
       
# a whole data set, even with combined MIC values and disk zones
df <- data.frame(microorganism = "E. coli",
                 AMP = as.mic(8),
                 CIP = as.mic(0.256),
                 GEN = as.disk(18),
                 TOB = as.disk(16),
                 NIT = as.mic(32))
as.rsi(df)

\dontrun{

# the dplyr way
library(dplyr)
df \%>\%
  mutate_at(vars(AMP:TOB), as.rsi, mo = "E. coli")
  
df \%>\%
  mutate_at(vars(AMP:TOB), as.rsi, mo = .$microorganism)
  
# to include information about urinary tract infections (UTI)
data.frame(mo = "E. coli",
           NIT = c("<= 2", 32),
           from_the_bladder = c(TRUE, FALSE)) \%>\%
  as.rsi(uti = "from_the_bladder")
  
data.frame(mo = "E. coli",
           NIT = c("<= 2", 32),
           specimen = c("urine", "blood")) \%>\%
  as.rsi() # automatically determines urine isolates

df \%>\%
  mutate_at(vars(AMP:NIT), as.rsi, mo = "E. coli", uti = TRUE)  
}

# for single values
as.rsi(x = as.mic(2),
       mo = as.mo("S. pneumoniae"),
       ab = "AMP",
       guideline = "EUCAST")

as.rsi(x = as.disk(18),
       mo = "Strep pneu",  # `mo` will be coerced with as.mo()
       ab = "ampicillin",  # and `ab` with as.ab()
       guideline = "EUCAST")


# For CLEANING existing R/SI values ------------------------------------

as.rsi(c("S", "I", "R", "A", "B", "C"))
as.rsi("<= 0.002; S") # will return "S"

rsi_data <- as.rsi(c(rep("S", 474), rep("I", 36), rep("R", 370)))
is.rsi(rsi_data)
plot(rsi_data)    # for percentages
barplot(rsi_data) # for frequencies

\dontrun{
library(dplyr)
example_isolates \%>\%
  mutate_at(vars(PEN:RIF), as.rsi)

# fastest way to transform all columns with already valid AMR results to class `rsi`:
example_isolates \%>\%
  mutate_if(is.rsi.eligible, as.rsi)
  
# note: from dplyr 1.0.0 on, this will be: 
# example_isolates \%>\%
#   mutate(across(is.rsi.eligible, as.rsi))

# default threshold of `is.rsi.eligible` is 5\%.
is.rsi.eligible(WHONET$`First name`) # fails, >80\% is invalid
is.rsi.eligible(WHONET$`First name`, threshold = 0.99) # succeeds
}
}
\seealso{
\code{\link[=as.mic]{as.mic()}}
}