as.sir: Translate MIC and Disk Diffusion to SIR, or Clean Existing...

In AMR: Antimicrobial Resistance Data Analysis

Translate MIC and Disk Diffusion to SIR, or Clean Existing SIR Data

Description

Interpret minimum inhibitory concentration (MIC) values and disk diffusion diameters according to EUCAST or CLSI, or clean up existing SIR values. This transforms the input to a new class sir, which is an ordered factor with levels ⁠S < I < R⁠.

Currently available breakpoint guidelines are EUCAST 2011-2023 and CLSI 2011-2023, and available breakpoint types are "ECOFF", "animal", and "human".

All breakpoints used for interpretation are publicly available in the clinical_breakpoints data set.

Usage

as.sir(x, ...)

NA_sir_

is.sir(x)

is_sir_eligible(x, threshold = 0.05)

## S3 method for class 'mic'
as.sir(
  x,
  mo = NULL,
  ab = deparse(substitute(x)),
  guideline = getOption("AMR_guideline", "EUCAST"),
  uti = NULL,
  conserve_capped_values = FALSE,
  add_intrinsic_resistance = FALSE,
  reference_data = AMR::clinical_breakpoints,
  include_screening = getOption("AMR_include_screening", FALSE),
  include_PKPD = getOption("AMR_include_PKPD", TRUE),
  breakpoint_type = getOption("AMR_breakpoint_type", "human"),
  ...
)

## S3 method for class 'disk'
as.sir(
  x,
  mo = NULL,
  ab = deparse(substitute(x)),
  guideline = getOption("AMR_guideline", "EUCAST"),
  uti = NULL,
  add_intrinsic_resistance = FALSE,
  reference_data = AMR::clinical_breakpoints,
  include_screening = getOption("AMR_include_screening", FALSE),
  include_PKPD = getOption("AMR_include_PKPD", TRUE),
  breakpoint_type = getOption("AMR_breakpoint_type", "human"),
  ...
)

## S3 method for class 'data.frame'
as.sir(
  x,
  ...,
  col_mo = NULL,
  guideline = getOption("AMR_guideline", "EUCAST"),
  uti = NULL,
  conserve_capped_values = FALSE,
  add_intrinsic_resistance = FALSE,
  reference_data = AMR::clinical_breakpoints,
  include_screening = getOption("AMR_include_screening", FALSE),
  include_PKPD = getOption("AMR_include_PKPD", TRUE),
  breakpoint_type = getOption("AMR_breakpoint_type", "human")
)

sir_interpretation_history(clean = FALSE)

Arguments

x	vector of values (for class mic: MIC values in mg/L, for class disk: a disk diffusion radius in millimetres)
...	for using on a data.frame: names of columns to apply as.sir() on (supports tidy selection such as column1:column4). Otherwise: arguments passed on to methods.
threshold	maximum fraction of invalid antimicrobial interpretations of x, see Examples
mo	any (vector of) text that can be coerced to valid microorganism codes with as.mo(), can be left empty to determine it automatically
ab	any (vector of) text that can be coerced to a valid antimicrobial drug code with as.ab()
guideline	defaults to EUCAST 2023 (the latest implemented EUCAST guideline in the clinical_breakpoints data set), but can be set with the package option AMR_guideline. Currently supports EUCAST (2011-2023) and CLSI (2011-2023), see Details.
uti	(Urinary Tract Infection) A vector with logicals (TRUE or FALSE) to specify whether a UTI specific interpretation from the guideline should be chosen. For using as.sir() on a data.frame, this can also be a column containing logicals or when left blank, the data set will be searched for a column 'specimen', and rows within this column containing 'urin' (such as 'urine', 'urina') will be regarded isolates from a UTI. See Examples.
conserve_capped_values	a logical to indicate that MIC values starting with ">" (but not ">=") must always return "R" , and that MIC values starting with "<" (but not "<=") must always return "S"
add_intrinsic_resistance	(only useful when using a EUCAST guideline) a logical to indicate whether intrinsic antibiotic resistance must also be considered for applicable bug-drug combinations, meaning that e.g. ampicillin will always return "R" in Klebsiella species. Determination is based on the intrinsic_resistant data set, that itself is based on 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3 (2021).
reference_data	a data.frame to be used for interpretation, which defaults to the clinical_breakpoints data set. Changing this argument allows for using own interpretation guidelines. This argument must contain a data set that is equal in structure to the clinical_breakpoints data set (same column names and column types). Please note that the guideline argument will be ignored when reference_data is manually set.
include_screening	a logical to indicate that clinical breakpoints for screening are allowed - the default is FALSE. Can also be set with the package option AMR_include_screening.
include_PKPD	a logical to indicate that PK/PD clinical breakpoints must be applied as a last resort - the default is TRUE. Can also be set with the package option AMR_include_PKPD.
breakpoint_type	the type of breakpoints to use, either "ECOFF", "animal", or "human". ECOFF stands for Epidemiological Cut-Off values. The default is "human", which can also be set with the package option AMR_breakpoint_type.
col_mo	column name of the names or codes of the microorganisms (see as.mo()) - the default is the first column of class mo. Values will be coerced using as.mo().
clean	a logical to indicate whether previously stored results should be forgotten after returning the 'logbook' with results

Format

An object of class sir (inherits from ordered, factor) of length 1.

Details

Note: The clinical breakpoints in this package were validated through and imported from WHONET and the public use of this AMR package has been endorsed by CLSI and EUCAST, please see clinical_breakpoints for more information.

How it Works

The as.sir() function works in four ways:

1. For cleaning raw / untransformed data. The data will be cleaned to only contain values S, I and R and will try its best to determine this with some intelligence. For example, mixed values with SIR interpretations and MIC values such as "<0.25; S" will be coerced to "S". Combined interpretations for multiple test methods (as seen in laboratory records) such as "S; S" will be coerced to "S", but a value like "S; I" will return NA with a warning that the input is unclear.

2. For interpreting minimum inhibitory concentration (MIC) values according to EUCAST or CLSI. You must clean your MIC values first using as.mic(), that also gives your columns the new data class mic. Also, be sure to have a column with microorganism names or codes. It will be found automatically, but can be set manually using the mo argument.

   • Using dplyr, SIR interpretation can be done very easily with either:

     your_data %>% mutate_if(is.mic, as.sir)
     your_data %>% mutate(across(where(is.mic), as.sir))
     

   • Operators like "<=" will be stripped before interpretation. 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 might in this case return "S" or "I".

3. For interpreting disk diffusion diameters according to EUCAST or CLSI. You must clean your disk zones first using as.disk(), that also gives your columns the new data class disk. Also, be sure to have a column with microorganism names or codes. It will be found automatically, but can be set manually using the mo argument.

   • Using dplyr, SIR interpretation can be done very easily with either:

     your_data %>% mutate_if(is.disk, as.sir)
     your_data %>% mutate(across(where(is.disk), as.sir))
     

4. For interpreting a complete data set, with automatic determination of MIC values, disk diffusion diameters, microorganism names or codes, and antimicrobial test results. This is done very simply by running as.sir(your_data).

For points 2, 3 and 4: Use sir_interpretation_history() to retrieve a data.frame (or tibble if the tibble package is installed) with all results of the last as.sir() call.

Supported Guidelines

For interpreting MIC values as well as disk diffusion diameters, currently implemented guidelines are EUCAST (2011-2023) and CLSI (2011-2023).

Thus, the guideline argument must be set to e.g., "EUCAST 2023" or "CLSI 2023". By simply using "EUCAST" (the default) or "CLSI" as input, the latest included version of that guideline will automatically be selected. You can set your own data set using the reference_data argument. The guideline argument will then be ignored.

You can set the default guideline with the package option AMR_guideline (e.g. in your .Rprofile file), such as:

  options(AMR_guideline = "CLSI")
  options(AMR_guideline = "CLSI 2018")
  options(AMR_guideline = "EUCAST 2020")
  # or to reset:
  options(AMR_guideline = NULL)

After Interpretation

After using as.sir(), you can use the eucast_rules() defined by EUCAST 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.

Machine-Readable Clinical Breakpoints

The repository of this package contains a machine-readable version of all guidelines. This is a CSV file consisting of 29 747 rows and 12 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 drug 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.

Other

The function is.sir() detects if the input contains class sir. If the input is a data.frame, it iterates over all columns and returns a logical vector.

The function is_sir_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 argument. If the input is a data.frame, it iterates over all columns and returns a logical vector.

NA_sir_ is a missing value of the new sir class, analogous to e.g. base R's NA_character_.

Value

Ordered factor with new class sir

Interpretation of SIR

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

• 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 insight. Use susceptibility() (equal to proportion_SI()) to determine antimicrobial susceptibility and count_susceptible() (equal to count_SI()) to count susceptible isolates.

Reference Data Publicly Available

All data sets in this AMR package (about microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) are publicly and freely available for download in the following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, and Stata. We also provide tab-separated plain text files that are machine-readable and suitable for input in any software program, such as laboratory information systems. Please visit our website for the download links. The actual files are of course available on our GitHub repository.

Source

For interpretations of minimum inhibitory concentration (MIC) values and disk diffusion diameters:

• M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 2011-2023, Clinical and Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m39/.

• M100 Performance Standard for Antimicrobial Susceptibility Testing, 2011-2023, Clinical and Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m100/.

• Breakpoint tables for interpretation of MICs and zone diameters, 2011-2023, European Committee on Antimicrobial Susceptibility Testing (EUCAST). https://www.eucast.org/clinical_breakpoints.

See Also

as.mic(), as.disk(), as.mo()

Examples

example_isolates
summary(example_isolates) # see all SIR 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 = "Escherichia coli",
  AMP = as.mic(8),
  CIP = as.mic(0.256),
  GEN = as.disk(18),
  TOB = as.disk(16),
  ERY = "R"
)
as.sir(df)

# return a 'logbook' about the results:
sir_interpretation_history()

# for single values
as.sir(
  x = as.mic(2),
  mo = as.mo("S. pneumoniae"),
  ab = "AMP",
  guideline = "EUCAST"
)

as.sir(
  x = as.disk(18),
  mo = "Strep pneu", # `mo` will be coerced with as.mo()
  ab = "ampicillin", # and `ab` with as.ab()
  guideline = "EUCAST"
)


# the dplyr way
if (require("dplyr")) {
  df %>% mutate_if(is.mic, as.sir)
  df %>% mutate_if(function(x) is.mic(x) | is.disk(x), as.sir)
  df %>% mutate(across(where(is.mic), as.sir))
  df %>% mutate_at(vars(AMP:TOB), as.sir)
  df %>% mutate(across(AMP:TOB, as.sir))

  df %>%
    mutate_at(vars(AMP:TOB), as.sir, 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.sir(uti = "from_the_bladder")

  data.frame(
    mo = "E. coli",
    NIT = c("<= 2", 32),
    specimen = c("urine", "blood")
  ) %>%
    as.sir() # automatically determines urine isolates

  df %>%
    mutate_at(vars(AMP:TOB), as.sir, mo = "E. coli", uti = TRUE)
}

# For CLEANING existing SIR values ------------------------------------

as.sir(c("S", "I", "R", "A", "B", "C"))
as.sir("<= 0.002; S") # will return "S"
sir_data <- as.sir(c(rep("S", 474), rep("I", 36), rep("R", 370)))
is.sir(sir_data)
plot(sir_data) # for percentages
barplot(sir_data) # for frequencies

# the dplyr way
if (require("dplyr")) {
  example_isolates %>%
    mutate_at(vars(PEN:RIF), as.sir)
  # same:
  example_isolates %>%
    as.sir(PEN:RIF)

  # fastest way to transform all columns with already valid AMR results to class `sir`:
  example_isolates %>%
    mutate_if(is_sir_eligible, as.sir)

  # since dplyr 1.0.0, this can also be:
  # example_isolates %>%
  #   mutate(across(where(is_sir_eligible), as.sir))
}

AMR documentation built on Oct. 22, 2023, 1:08 a.m.