Interpret minimum inhibitory concentration (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 = deparse(substitute(x)), guideline = "EUCAST", uti = FALSE, conserve_capped_values = FALSE, ... ) # S3 method for disk as.rsi( x, mo, ab = deparse(substitute(x)), guideline = "EUCAST", uti = FALSE, ... ) # S3 method for data.frame as.rsi( x, col_mo = NULL, guideline = "EUCAST", uti = NULL, conserve_capped_values = FALSE, ... ) is.rsi(x) is.rsi.eligible(x, threshold = 0.05)
Arguments
| x | vector of values (for class |
|---|---|
| ... | parameters passed on to methods |
| mo | any (vector of) text that can be coerced to a valid microorganism code with |
| ab | any (vector of) text that can be coerced to a valid antimicrobial code with |
| guideline | defaults to the latest included EUCAST guideline, see Details for all options |
| uti | (Urinary Tract Infection) A vector with logicals ( |
| conserve_capped_values | a logical to indicate that MIC values starting with |
| col_mo | column name of the IDs of the microorganisms (see |
| threshold | maximum fraction of invalid antimicrobial interpretations of |
Value
Ordered factor with new class rsi
Details
When using 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 mic (using as.mic()) or class disk (using as.disk()), the data will be interpreted based on the guideline set with the guideline parameter.
Supported guidelines to be used as input for the 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 "CLSI" or "EUCAST" for input will automatically select the latest version of that guideline.
When using conserve_capped_values = TRUE, an MIC value of e.g. ">2" will always return "R", even if the breakpoint according to the chosen guideline is ">=4". This is to prevent that capped values from raw laboratory data would not be treated conservatively. The default behaviour (conserve_capped_values = FALSE) considers ">2" to be lower than ">=4" and will in this case return "S" or "I".
The repository of this package contains a machine readable version of all guidelines. This is a CSV file consisting of 18,650 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 allows for easy implementation of these rules in laboratory information systems (LIS). Note that it only contains interpretation guidelines for humans - interpretation guidelines from CLSI for animals were removed.
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, major changes are unlikely. This means that the unlying code will generally evolve by adding new arguments; removing arguments or changing the meaning of existing arguments will be avoided.
If the unlying code needs breaking changes, they will occur gradually. For example, a parameter will be deprecated and first 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.
Read more on our website!
On our website https://msberends.github.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. As we would like to better understand the backgrounds and needs of our users, please participate in our survey!
See also
Examples
summary(example_isolates) # see all R/SI results at a glance # 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) if (FALSE) { # 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 = .$microorganism) # to include information about urinary tract infections (UTI) data.frame(mo = "E. coli", NIT = c("<= 2", 32), from_the_bladder = c(TRUE, FALSE)) %>% as.rsi(uti = "from_the_bladder") data.frame(mo = "E. coli", NIT = c("<= 2", 32), specimen = c("urine", "blood")) %>% as.rsi() # automatically determines urine isolates df %>% mutate_at(vars(AMP:NIT), as.rsi, mo = "E. coli", uti = TRUE) } # for single values as.rsi(x = as.mic(2), mo = as.mo("S. pneumoniae"), ab = "AMP", guideline = "EUCAST") as.rsi(x = as.disk(18), mo = "Strep pneu", # `mo` will be coerced with as.mo() ab = "ampicillin", # and `ab` with as.ab() guideline = "EUCAST") # For CLEANING existing R/SI values ------------------------------------ as.rsi(c("S", "I", "R", "A", "B", "C")) as.rsi("<= 0.002; S") # will return "S" rsi_data <- as.rsi(c(rep("S", 474), rep("I", 36), rep("R", 370))) is.rsi(rsi_data) plot(rsi_data) # for percentages barplot(rsi_data) # for frequencies if (FALSE) { library(dplyr) example_isolates %>% mutate_at(vars(PEN:RIF), as.rsi) # fastest way to transform all columns with already valid AMR results to class `rsi`: example_isolates %>% mutate_if(is.rsi.eligible, as.rsi) # note: from dplyr 1.0.0 on, this will be: # example_isolates %>% # mutate(across(is.rsi.eligible, as.rsi)) # default threshold of `is.rsi.eligible` is 5%. is.rsi.eligible(WHONET$`First name`) # fails, >80% is invalid is.rsi.eligible(WHONET$`First name`, threshold = 0.99) # succeeds }