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 antibiotic 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 unique IDs of the microorganisms (see mo), defaults to the first column of class mo. Values will be coerced using as.mo.
threshold	maximum fraction of x that is allowed to fail transformation, 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 antibiotics 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.

Read more on our website!

On our website https://msberends.gitlab.io/AMR you can find a comprehensive 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

# NOT RUN {
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)
septic_patients %>%
  mutate_at(vars(PEN:RIF), as.rsi)


# fastest way to transform all columns with already valid AB results to class `rsi`:
septic_patients %>%
  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
# }