Calculate Microbial Resistance

These functions can be used to calculate the (co-)resistance or susceptibility of microbial isolates (i.e. percentage of S, SI, I, IR or R). All functions support quasiquotation with pipes, can be used in summarise() from the dplyr package and also support grouped variables, see Examples.

resistance() should be used to calculate resistance, susceptibility() should be used to calculate susceptibility.

resistance(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)

susceptibility(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)

proportion_R(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)

proportion_IR(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)

proportion_I(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)

proportion_SI(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)

proportion_S(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE)

proportion_df(
  data,
  translate_ab = "name",
  language = get_AMR_locale(),
  minimum = 30,
  as_percent = FALSE,
  combine_SI = TRUE,
  combine_IR = FALSE
)

rsi_df(
  data,
  translate_ab = "name",
  language = get_AMR_locale(),
  minimum = 30,
  as_percent = FALSE,
  combine_SI = TRUE,
  combine_IR = FALSE
)

Source

M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition, 2014, Clinical and Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m39/.

Arguments

...: one or more vectors (or columns) with antibiotic interpretations. They will be transformed internally with as.rsi() if needed. Use multiple columns to calculate (the lack of) co-resistance: the probability where one of two drugs have a resistant or susceptible result. See Examples.
minimum: the minimum allowed number of available (tested) isolates. Any isolate count lower than minimum will return NA with a warning. The default number of 30 isolates is advised by the Clinical and Laboratory Standards Institute (CLSI) as best practice, see Source.
as_percent: a logical to indicate whether the output must be returned as a hundred fold with % sign (a character). A value of 0.123456 will then be returned as "12.3%".
only_all_tested: (for combination therapies, i.e. using more than one variable for ...): a logical to indicate that isolates must be tested for all antibiotics, see section Combination Therapy below
data: a data.frame containing columns with class rsi (see as.rsi())
translate_ab: a column name of the antibiotics data set to translate the antibiotic abbreviations to, using ab_property()
language: language of the returned text, defaults to system language (see get_AMR_locale()) and can also be set with getOption("AMR_locale"). Use language = NULL or language = "" to prevent translation.
combine_SI: a logical to indicate whether all values of S and I must be merged into one, so the output only consists of S+I vs. R (susceptible vs. resistant). This used to be the argument combine_IR, but this now follows the redefinition by EUCAST about the interpretation of I (increased exposure) in 2019, see section 'Interpretation of S, I and R' below. Default is TRUE.
combine_IR: a logical to indicate whether all values of I and R must be merged into one, so the output only consists of S vs. I+R (susceptible vs. non-susceptible). This is outdated, see argument combine_SI.

Value

A double or, when as_percent = TRUE, a character.

Details

The function resistance() is equal to the function proportion_R(). The function susceptibility() is equal to the function proportion_SI().

Remember that you should filter your data to let it contain only first isolates! This is needed to exclude duplicates and to reduce selection bias. Use first_isolate() to determine them in your data set.

These functions are not meant to count isolates, but to calculate the proportion of resistance/susceptibility. Use the count() functions to count isolates. The function susceptibility() is essentially equal to count_susceptible() / count_all(). Low counts can influence the outcome - the proportion functions may camouflage this, since they only return the proportion (albeit being dependent on the minimum argument).

The function proportion_df() takes any variable from data that has an rsi class (created with as.rsi()) and calculates the proportions R, I and S. It also supports grouped variables. The function rsi_df() works exactly like proportion_df(), but adds the number of isolates.

Combination Therapy

When using more than one variable for ... (= combination therapy), use only_all_tested to only count isolates that are tested for all antibiotics/variables that you test them for. See this example for two antibiotics, Drug A and Drug B, about how susceptibility() works to calculate the %SI:

Please note that, in combination therapies, for only_all_tested = TRUE applies that:

and that, in combination therapies, for only_all_tested = FALSE applies that:

Using only_all_tested has no impact when only using one antibiotic as input.

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, an argument will be deprecated and first continue to work, but will emit a 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.

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 (https://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 = 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.

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.

Examples

# example_isolates is a data set available in the AMR package.
?example_isolates

resistance(example_isolates$AMX)     # determines %R
susceptibility(example_isolates$AMX) # determines %S+I

# be more specific
proportion_S(example_isolates$AMX)
proportion_SI(example_isolates$AMX)
proportion_I(example_isolates$AMX)
proportion_IR(example_isolates$AMX)
proportion_R(example_isolates$AMX)

# \donttest{
if (require("dplyr")) {
  example_isolates %>%
    group_by(hospital_id) %>%
    summarise(r = resistance(CIP),
              n = n_rsi(CIP)) # n_rsi works like n_distinct in dplyr, see ?n_rsi
 
  example_isolates %>%
    group_by(hospital_id) %>%
    summarise(R  = resistance(CIP, as_percent = TRUE),
              SI = susceptibility(CIP, as_percent = TRUE),
              n1 = count_all(CIP),  # the actual total; sum of all three
              n2 = n_rsi(CIP),      # same - analogous to n_distinct
              total = n())          # NOT the number 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:
  example_isolates %>% susceptibility(AMC)  # %SI = 76.3%
  example_isolates %>% count_all(AMC)       #   n = 1879
 
  example_isolates %>% susceptibility(GEN)  # %SI = 75.4%
  example_isolates %>% count_all(GEN)       #   n = 1855
 
  example_isolates %>% susceptibility(AMC, GEN) # %SI = 94.1%
  example_isolates %>% count_all(AMC, GEN)      #   n = 1939
 
 
  # See Details on how `only_all_tested` works. Example:
  example_isolates %>%
    summarise(numerator = count_susceptible(AMC, GEN),
              denominator = count_all(AMC, GEN),
              proportion = susceptibility(AMC, GEN))
 
  example_isolates %>%
    summarise(numerator = count_susceptible(AMC, GEN, only_all_tested = TRUE),
              denominator = count_all(AMC, GEN, only_all_tested = TRUE),
              proportion = susceptibility(AMC, GEN, only_all_tested = TRUE))
 
 
  example_isolates %>%
    group_by(hospital_id) %>%
    summarise(cipro_p = susceptibility(CIP, as_percent = TRUE),
              cipro_n = count_all(CIP),
              genta_p = susceptibility(GEN, as_percent = TRUE),
              genta_n = count_all(GEN),
              combination_p = susceptibility(CIP, GEN, as_percent = TRUE),
              combination_n = count_all(CIP, GEN))
 
  # Get proportions S/I/R immediately of all rsi columns
  example_isolates %>%
    select(AMX, CIP) %>%
    proportion_df(translate = FALSE)
 
  # It also supports grouping variables
  example_isolates %>%
    select(hospital_id, AMX, CIP) %>%
    group_by(hospital_id) %>%
    proportion_df(translate = FALSE)
}
# }