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rsi family for resistance analysis

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dr. M.S. (Matthijs) Berends
2018-08-03 14:49:29 +02:00
parent 227121af3d
commit ae2433a020
15 changed files with 421 additions and 196 deletions
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6
man/as.bactid.Rd
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@ -84,3 +84,9 @@ df <- df \%>\%
\seealso{
\code{\link{microorganisms}} for the dataframe that is being used to determine ID's.
}
\keyword{Becker}
\keyword{Lancefield}
\keyword{bactid}
\keyword{becker}
\keyword{guess}
\keyword{lancefield}

4
man/microorganisms.Rd
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@ -4,7 +4,7 @@
\name{microorganisms}
\alias{microorganisms}
\title{Dataset with ~2500 microorganisms}
\format{A data.frame with 2456 observations and 12 variables:
\format{A data.frame with 2464 observations and 12 variables:
\describe{
\item{\code{bactid}}{ID of microorganism}
\item{\code{bactsys}}{Bactsyscode of microorganism}
@ -23,7 +23,7 @@
microorganisms
}
\description{
A dataset containing 2456 microorganisms. MO codes of the UMCG can be looked up using \code{\link{microorganisms.umcg}}.
A dataset containing 2464 microorganisms. MO codes of the UMCG can be looked up using \code{\link{microorganisms.umcg}}.
}
\seealso{
\code{\link{guess_bactid}} \code{\link{antibiotics}} \code{\link{microorganisms.umcg}}

2
man/resistance_predict.Rd
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@ -1,5 +1,5 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/resistance.R
% Please edit documentation in R/rsi_IR.R
\name{resistance_predict}
\alias{resistance_predict}
\alias{rsi_predict}

106
man/rsi.Rd Normal file
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@ -0,0 +1,106 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/rsi_IR.R
\name{rsi}
\alias{rsi}
\title{Calculate resistance of isolates}
\usage{
rsi(ab1, ab2 = NA, interpretation = "IR", minimum = 30,
as_percent = FALSE, info = FALSE, warning = TRUE)
}
\arguments{
\item{ab1}{vector of antibiotic interpretations, they will be transformed internally with \code{\link{as.rsi}}}
\item{ab2}{like \code{ab}, a vector of antibiotic interpretations. Use this to calculate (the lack of) co-resistance: the probability where one of two drugs have a susceptible result. See Examples.}
\item{interpretation}{antimicrobial interpretation}
\item{minimum}{minimal amount of available isolates. Any number lower than \code{minimum} will return \code{NA}. The default number of \code{30} isolates is advised by the CLSI as best practice, see Source.}
\item{as_percent}{logical to indicate whether the output must be returned as percent (text), will else be a double}
\item{info}{calculate the amount of available isolates and print it, like \code{n = 423}}
\item{warning}{show a warning when the available amount of isolates is below \code{minimum}}
}
\value{
Double or, when \code{as_percent = TRUE}, a character.
}
\description{
This function is deprecated. Use \code{\link{rsi_IR}} instead.
}
\details{
\strong{Remember that you should filter your table to let it contain only first isolates!} Use \code{\link{first_isolate}} to determine them in your data set.
The functions \code{resistance} and \code{susceptibility} are wrappers around \code{rsi_IR} and \code{rsi_S}, respectively. All functions use hybrid evaluation (i.e. using C++), which makes these functions 20-30 times faster than the old \code{\link{rsi}} function. This latter function is still available for backwards compatibility but is deprecated.
\if{html}{
\cr\cr
To calculate the probability (\emph{p}) of susceptibility of one antibiotic, we use this formula:
\out{<div style="text-align: center">}\figure{mono_therapy.png}\out{</div>}
To calculate the probability (\emph{p}) of susceptibility of more antibiotics (i.e. combination therapy), we need to check whether one of them has a susceptible result (as numerator) and count all cases where all antibiotics were tested (as denominator). \cr
\cr
For two antibiotics:
\out{<div style="text-align: center">}\figure{combi_therapy_2.png}\out{</div>}
\cr
Theoretically for three antibiotics:
\out{<div style="text-align: center">}\figure{combi_therapy_3.png}\out{</div>}
}
}
\examples{
# Calculate resistance
rsi_R(septic_patients$amox)
rsi_IR(septic_patients$amox)
# Or susceptibility
rsi_S(septic_patients$amox)
rsi_SI(septic_patients$amox)
# Since n_rsi counts available isolates (and is used as denominator),
# you can calculate back to e.g. count resistant isolates:
rsi_IR(septic_patients$amox) * n_rsi(septic_patients$amox)
library(dplyr)
septic_patients \%>\%
group_by(hospital_id) \%>\%
summarise(p = rsi_S(cipr),
n = rsi_n(cipr)) # n_rsi works like n_distinct in dplyr
septic_patients \%>\%
group_by(hospital_id) \%>\%
summarise(R = rsi_R(cipr, as_percent = TRUE),
I = rsi_I(cipr, as_percent = TRUE),
S = rsi_S(cipr, as_percent = TRUE),
n = rsi_n(cipr), # also: n_rsi, works like n_distinct in dplyr
total = n()) # this is the length, NOT the amount of tested isolates
# Calculate co-resistance between amoxicillin/clav acid and gentamicin,
# so we can see that combination therapy does a lot more than mono therapy:
rsi_S(septic_patients$amcl) # S = 67.3\%
rsi_n(septic_patients$amcl) # n = 1570
rsi_S(septic_patients$gent) # S = 74.0\%
rsi_n(septic_patients$gent) # n = 1842
with(septic_patients,
rsi_S(amcl, gent)) # S = 92.1\%
with(septic_patients, # n = 1504
rsi_n(amcl, gent))
septic_patients \%>\%
group_by(hospital_id) \%>\%
summarise(cipro_p = rsi_S(cipr, as_percent = TRUE),
cipro_n = rsi_n(cipr),
genta_p = rsi_S(gent, as_percent = TRUE),
genta_n = rsi_n(gent),
combination_p = rsi_S(cipr, gent, as_percent = TRUE),
combination_n = rsi_n(cipr, gent))
\dontrun{
# calculate current empiric combination therapy of Helicobacter gastritis:
my_table \%>\%
filter(first_isolate == TRUE,
genus == "Helicobacter") \%>\%
summarise(p = rsi_S(amox, metr), # amoxicillin with metronidazole
n = rsi_n(amox, metr))
}
}

117
man/resistance.Rd → man/rsi_IR.Rd
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@ -1,66 +1,65 @@
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/resistance.R
\name{resistance}
% Please edit documentation in R/rsi_IR.R
\name{rsi_IR}
\alias{rsi_IR}
\alias{rsi_R}
\alias{rsi_I}
\alias{rsi_SI}
\alias{rsi_S}
\alias{resistance}
\alias{S}
\alias{SI}
\alias{IR}
\alias{R}
\alias{n_rsi}
\alias{intermediate}
\alias{susceptibility}
\alias{rsi}
\alias{rsi_n}
\alias{n_rsi}
\title{Calculate resistance of isolates}
\source{
\strong{M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition}, 2014, \emph{Clinical and Laboratory Standards Institute (CLSI)}. \url{https://clsi.org/standards/products/microbiology/documents/m39/}.
}
\usage{
S(ab1, ab2 = NULL, minimum = 30, as_percent = FALSE)
rsi_R(ab1, minimum = 30, as_percent = FALSE)
SI(ab1, ab2 = NULL, minimum = 30, as_percent = FALSE)
rsi_IR(ab1, minimum = 30, as_percent = FALSE)
IR(ab1, minimum = 30, as_percent = FALSE)
rsi_I(ab1, minimum = 30, as_percent = FALSE)
R(ab1, minimum = 30, as_percent = FALSE)
rsi_SI(ab1, ab2 = NULL, minimum = 30, as_percent = FALSE)
n_rsi(ab1, ab2 = NULL)
rsi_S(ab1, ab2 = NULL, minimum = 30, as_percent = FALSE)
resistance(ab1, include_I = TRUE, minimum = 30, as_percent = FALSE)
intermediate(ab1, minimum = 30, as_percent = FALSE)
susceptibility(ab1, ab2 = NULL, include_I = FALSE, minimum = 30,
as_percent = FALSE)
rsi(ab1, ab2 = NA, interpretation = "IR", minimum = 30,
as_percent = FALSE, info = FALSE, warning = TRUE)
rsi_n(ab1, ab2 = NULL)
n_rsi(ab1, ab2 = NULL)
}
\arguments{
\item{ab1}{vector of antibiotic interpretations, they will be transformed internally with \code{\link{as.rsi}}}
\item{ab2}{like \code{ab}, a vector of antibiotic interpretations. Use this to calculate (the lack of) co-resistance: the probability where one of two drugs have a susceptible result. See Examples.}
\item{minimum}{minimal amount of available isolates. Any number lower than \code{minimum} will return \code{NA}. The default number of \code{30} isolates is advised by the CLSI as best practice, see Source.}
\item{as_percent}{logical to indicate whether the output must be returned as percent (text), will else be a double}
\item{ab2}{like \code{ab}, a vector of antibiotic interpretations. Use this to calculate (the lack of) co-resistance: the probability where one of two drugs have a susceptible result. See Examples.}
\item{include_I}{logical to indicate whether antimicrobial interpretations of "I" should be included}
\item{interpretation}{antimicrobial interpretation}
\item{info}{\emph{DEPRECATED} calculate the amount of available isolates and print it, like \code{n = 423}}
\item{warning}{\emph{DEPRECATED} show a warning when the available amount of isolates is below \code{minimum}}
}
\value{
Double or, when \code{as_percent = TRUE}, a character.
}
\description{
These functions can be used to calculate the (co-)resistance of microbial isolates (i.e. percentage S, SI, IR or R). All functions can be used in \code{dplyr}s \code{\link[dplyr]{summarise}} and support grouped variables, see \emph{Examples}. \cr\cr
\code{R} and \code{IR} can be used to calculate resistance, \code{S} and \code{SI} can be used to calculate susceptibility.\cr
\code{n_rsi} counts all cases where antimicrobial interpretations are available.
These functions can be used to calculate the (co-)resistance of microbial isolates (i.e. percentage S, SI, I, IR or R). All functions can be used in \code{dplyr}s \code{\link[dplyr]{summarise}} and support grouped variables, see \emph{Examples}. \cr\cr
\code{rsi_R} and \code{rsi_IR} can be used to calculate resistance, \code{rsi_S} and \code{rsi_SI} can be used to calculate susceptibility.\cr
\code{rsi_n} counts all cases where antimicrobial interpretations are available.
}
\details{
\strong{Remember that you should filter your table to let it contain only first isolates!} Use \code{\link{first_isolate}} to determine them in your data set.
The functions \code{resistance} and \code{susceptibility} are wrappers around \code{IR} and \code{S}, respectively. All functions except \code{rsi} use hybrid evaluation (i.e. using C++), which makes these functions 20-30 times faster than the old \code{rsi} function. This latter function is still available for backwards compatibility but is deprecated.
The functions \code{resistance} and \code{susceptibility} are wrappers around \code{rsi_IR} and \code{rsi_S}, respectively. All functions use hybrid evaluation (i.e. using C++), which makes these functions 20-30 times faster than the old \code{\link{rsi}} function. This latter function is still available for backwards compatibility but is deprecated.
\if{html}{
\cr\cr
To calculate the probability (\emph{p}) of susceptibility of one antibiotic, we use this formula:
@ -76,44 +75,52 @@ The functions \code{resistance} and \code{susceptibility} are wrappers around \c
}
\examples{
# Calculate resistance
R(septic_patients$amox)
IR(septic_patients$amox)
rsi_R(septic_patients$amox)
rsi_IR(septic_patients$amox)
# Or susceptibility
S(septic_patients$amox)
SI(septic_patients$amox)
rsi_S(septic_patients$amox)
rsi_SI(septic_patients$amox)
# Since n_rsi counts available isolates (and is used as denominator),
# you can calculate back to e.g. count resistant isolates:
IR(septic_patients$amox) * n_rsi(septic_patients$amox)
rsi_IR(septic_patients$amox) * n_rsi(septic_patients$amox)
library(dplyr)
septic_patients \%>\%
group_by(hospital_id) \%>\%
summarise(p = S(cipr),
n = n_rsi(cipr)) # n_rsi works like n_distinct in dplyr
# Calculate co-resistance between amoxicillin/clav acid and gentamicin,
# so we can see that combination therapy does a lot more than mono therapy:
S(septic_patients$amcl) # p = 67.3\%
n_rsi(septic_patients$amcl) # n = 1570
S(septic_patients$gent) # p = 74.0\%
n_rsi(septic_patients$gent) # n = 1842
with(septic_patients,
S(amcl, gent)) # p = 92.1\%
with(septic_patients,
n_rsi(amcl, gent)) # n = 1504
summarise(p = rsi_S(cipr),
n = rsi_n(cipr)) # n_rsi works like n_distinct in dplyr
septic_patients \%>\%
group_by(hospital_id) \%>\%
summarise(cipro_p = S(cipr, as_percent = TRUE),
cipro_n = n_rsi(cipr),
genta_p = S(gent, as_percent = TRUE),
genta_n = n_rsi(gent),
combination_p = S(cipr, gent, as_percent = TRUE),
combination_n = n_rsi(cipr, gent))
summarise(R = rsi_R(cipr, as_percent = TRUE),
I = rsi_I(cipr, as_percent = TRUE),
S = rsi_S(cipr, as_percent = TRUE),
n = rsi_n(cipr), # also: n_rsi, works like n_distinct in dplyr
total = n()) # this is the length, NOT the amount of tested isolates
# Calculate co-resistance between amoxicillin/clav acid and gentamicin,
# so we can see that combination therapy does a lot more than mono therapy:
rsi_S(septic_patients$amcl) # S = 67.3\%
rsi_n(septic_patients$amcl) # n = 1570
rsi_S(septic_patients$gent) # S = 74.0\%
rsi_n(septic_patients$gent) # n = 1842
with(septic_patients,
rsi_S(amcl, gent)) # S = 92.1\%
with(septic_patients, # n = 1504
rsi_n(amcl, gent))
septic_patients \%>\%
group_by(hospital_id) \%>\%
summarise(cipro_p = rsi_S(cipr, as_percent = TRUE),
cipro_n = rsi_n(cipr),
genta_p = rsi_S(gent, as_percent = TRUE),
genta_n = rsi_n(gent),
combination_p = rsi_S(cipr, gent, as_percent = TRUE),
combination_n = rsi_n(cipr, gent))
\dontrun{
@ -121,8 +128,8 @@ septic_patients \%>\%
my_table \%>\%
filter(first_isolate == TRUE,
genus == "Helicobacter") \%>\%
summarise(p = S(amox, metr), # amoxicillin with metronidazole
n = n_rsi(amox, metr))
summarise(p = rsi_S(amox, metr), # amoxicillin with metronidazole
n = rsi_n(amox, metr))
}
}
\keyword{antibiotics}