Class 'rsi'

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 rsi, which is an ordered factor with levels S < I < R. Invalid antimicrobial interpretations will be translated as NA with a warning.

as.rsi(x, ...)

# S3 method for mic
as.rsi(x, mo, ab, guideline = "EUCAST", ...)

# S3 method for disk
as.rsi(x, mo, ab, guideline = "EUCAST", ...)

# S3 method for data.frame
as.rsi(x, col_mo = NULL, guideline = "EUCAST", ...)

is.rsi(x)

is.rsi.eligible(x, threshold = 0.05)

Arguments

x	vector of values (for class `mic`: an MIC value in mg/L, for class `disk`: a disk diffusion radius in millimeters)
...	parameters passed on to methods
mo	a microorganism code, generated with `as.mo()`
ab	an antimicrobial code, generated with `as.ab()`
guideline	defaults to the latest included EUCAST guideline, run `unique(rsi_translation$guideline)` for all options
col_mo	column name of the IDs of the microorganisms (see `as.mo()`), defaults to the first column of class `mo`. Values will be coerced using `as.mo()`.
threshold	maximum fraction of invalid antimicrobial interpretations of `x`, please see Examples

Value

Ordered factor with new class rsi

Details

Run unique(rsi_translation$guideline) for a list of all supported guidelines. The repository of this package contains this machine readable version of these guidelines.

These guidelines are machine readable, since .

After using as.rsi(), you can use 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 is.rsi.eligible() returns TRUE when a columns contains at most 5% invalid antimicrobial interpretations (not S and/or I and/or R), and FALSE otherwise. The threshold of 5% can be set with the threshold parameter.

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 (http://www.eucast.org/newsiandr/).

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.
S = Susceptible
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.
I = Increased exposure, but still susceptible
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.

This AMR package honours this new insight. Use susceptibility() (equal to proportion_SI()) to determine antimicrobial susceptibility and count_susceptible() (equal to count_SI()) to count susceptible isolates.

Stable lifecycle

The lifecycle of this function is 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.

Examples

# For INTERPRETING disk diffusion and MIC values -----------------------

# 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")

# a whole data set, even with combined MIC values and disk zones
df <- data.frame(microorganism = "E. coli",
                 AMP = as.mic(12),
                 GEN = as.disk(18))
as.rsi(df)


# 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
freq(rsi_data)    # frequency table with informative header

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

Arguments

Value

Details

Interpretation of R and S/I

Stable lifecycle

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See also

Examples

Contents