Class 'rsi'

Interpret MIC values according to EUCAST or CLSI, or clean up existing RSI 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(AMR::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, see Examples

Value

Ordered factor with new class rsi

Details

Run unique(AMR::rsi_translation$guideline) for a list of all supported guidelines.

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 S, I and R

In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories S, I and R as shown below (http://www.eucast.org/newsiandr/). Results of several consultations on the new definitions are available on the EUCAST website under "Consultations".

  • 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.

  • 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.

  • 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.

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.

Read more on our website!

On our website https://msberends.gitlab.io/AMR you can find a tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data.

See also

as.mic

Examples

rsi_data <- as.rsi(c(rep("S", 474), rep("I", 36), rep("R", 370)))
rsi_data <- as.rsi(c(rep("S", 474), rep("I", 36), rep("R", 370), "A", "B", "C"))
is.rsi(rsi_data)

# this can also coerce combined MIC/RSI values:
as.rsi("<= 0.002; S") # will return S

# interpret MIC values
as.rsi(x = as.mic(2),
       mo = as.mo("S. pneumoniae"),
       ab = "AMX",
       guideline = "EUCAST")
as.rsi(x = as.mic(4),
       mo = as.mo("S. pneumoniae"),
       ab = "AMX",
       guideline = "EUCAST")

plot(rsi_data)    # for percentages
barplot(rsi_data) # for frequencies
freq(rsi_data)    # frequency table with informative header

# using dplyr's mutate
library(dplyr)
example_isolates %>%
  mutate_at(vars(PEN:RIF), as.rsi)


# fastest way to transform all columns with already valid AB results to class `rsi`:
example_isolates %>%
  mutate_if(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