Skip to contents

# Calculate Antimicrobial Resistance

Source: `R/proportion.R`, `R/sir_df.R`
`proportion.Rd`

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.

## Usage

    resistance(
      ...,
      minimum = 30,
      as_percent = FALSE,
      only_all_tested = FALSE,
      guideline = getOption("AMR_guideline", "EUCAST")
    )

    susceptibility(
      ...,
      minimum = 30,
      as_percent = FALSE,
      only_all_tested = FALSE,
      guideline = getOption("AMR_guideline", "EUCAST")
    )

    sir_confidence_interval(
      ...,
      ab_result = "R",
      minimum = 30,
      as_percent = FALSE,
      only_all_tested = FALSE,
      confidence_level = 0.95,
      side = "both",
      collapse = 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,
      confidence_level = 0.95
    )

    sir_df(
      data,
      translate_ab = "name",
      language = get_AMR_locale(),
      minimum = 30,
      as_percent = FALSE,
      combine_SI = TRUE,
      confidence_level = 0.95
    )

## Arguments

...

  One or more vectors (or columns) with antibiotic interpretations. They will be transformed internally with `as.sir()` 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 antimicrobials, see section Combination Therapy below.

guideline

  Either `"EUCAST"` (default) or `"CLSI"`. With EUCAST, the 'I' category will be considered as susceptible (see EUCAST website), but with with CLSI, it will be considered resistant. Therefore:

  - EUCAST: `susceptibility()` \(= \%S + \%I\), `resistance()` \(= \%R\)

  - CLSI: `susceptibility()` \(= \%S + \%SDD\), `resistance()` \(= \%I + \%R\)

  You can also use e.g. `proportion_R()` or `proportion_S()` instead, to be explicit.

ab_result

  Antibiotic results to test against, must be one or more values of "S", "SDD", "I", or "R".

confidence_level

  The confidence level for the returned confidence interval. For the calculation, the number of S or SI isolates, and R isolates are compared with the total number of available isolates with R, S, or I by using `binom.test()`, i.e., the Clopper-Pearson method.

side

  The side of the confidence interval to return. The default is `"both"` for a length 2 vector, but can also be (abbreviated as) `"min"`/`"left"`/`"lower"`/`"less"` or `"max"`/`"right"`/`"higher"`/`"greater"`.

collapse

  A logical to indicate whether the output values should be 'collapsed', i.e. be merged together into one value, or a character value to use for collapsing.

data

  A data.frame containing columns with class `sir` (see `as.sir()`).

translate_ab

  A column name of the antimicrobials data set to translate the antibiotic abbreviations to, using `ab_property()`.

language

  Language of the returned text - the default is the current system language (see `get_AMR_locale()`) and can also be set with the package option `AMR_locale`. Use `language = NULL` or `language = ""` to prevent translation.

combine_SI

  A logical to indicate whether all values of S, SDD, and I must be merged into one, so the output only consists of S+SDD+I vs. R (susceptible vs. resistant) - the default is `TRUE`.

## Value

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

## Details

For a more automated and comprehensive analysis, consider using `antibiogram()` or `wisca()`, which streamline many aspects of susceptibility reporting and, importantly, also support WISCA. The functions described here offer a more hands-on, manual approach for greater customisation.

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 with one of the four available algorithms.

The function `resistance()` is equal to the function `proportion_R()`. The function `susceptibility()` is equal to the function `proportion_SI()`. Since AMR v3.0, `proportion_SI()` and `proportion_I()` include dose-dependent susceptibility ('SDD').

Use `sir_confidence_interval()` to calculate the confidence interval, which relies on `binom.test()`, i.e., the Clopper-Pearson method. This function returns a vector of length 2 at default for antimicrobial resistance. Change the `side` argument to "left"/"min" or "right"/"max" to return a single value, and change the `ab_result` argument to e.g. `c("S", "I")` to test for antimicrobial susceptibility, see Examples.

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 dependent on the `minimum` argument).

The function `proportion_df()` takes any variable from `data` that has an `sir` class (created with `as.sir()`) and calculates the proportions S, I, and R. It also supports grouped variables. The function `sir_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 antimicrobials/variables that you test them for. See this example for two antimicrobials, Drug A and Drug B, about how `susceptibility()` works to calculate the %SI:

    --------------------------------------------------------------------
                        only_all_tested = FALSE  only_all_tested = TRUE
                        -----------------------  -----------------------
     Drug A    Drug B   considered   considered  considered   considered
                        susceptible    tested    susceptible    tested
    --------  --------  -----------  ----------  -----------  ----------
     S or I    S or I        X            X           X            X
       R       S or I        X            X           X            X
      <NA>     S or I        X            X           -            -
     S or I      R           X            X           X            X
       R         R           -            X           -            X
      <NA>       R           -            -           -            -
     S or I     <NA>         X            X           -            -
       R        <NA>         -            -           -            -
      <NA>      <NA>         -            -           -            -
    --------------------------------------------------------------------

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

        count_S()    +   count_I()    +   count_R()    = count_all()
      proportion_S() + proportion_I() + proportion_R() = 1

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

        count_S()    +   count_I()    +   count_R()    >= count_all()
      proportion_S() + proportion_I() + proportion_R() >= 1

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

## 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 (https://www.eucast.org/bacteria/clinical-breakpoints-and-interpretation/definition-of-s-i-and-r/).

This AMR package follows insight; use `susceptibility()` (equal to `proportion_SI()`) to determine antimicrobial susceptibility and `count_susceptible()` (equal to `count_SI()`) to count susceptible isolates.

## References

M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 5th Edition, 2022, Clinical and Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m39/.

## See also

`count()` to count resistant and susceptible isolates.

## Examples

    # example_isolates is a data set available in the AMR package.
    # run ?example_isolates for more info.
    example_isolates
    #> # A tibble: 2,000 × 46
    #>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX
    #>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir>
    #>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA
    #>  2 2002-01-03 A77334     65 F      Clinical B_ESCHR_COLI   R     NA    NA    NA
    #>  3 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA
    #>  4 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA
    #>  5 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA
    #>  6 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA
    #>  7 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R
    #>  8 2002-01-14 462729     78 M      Clinical B_STPHY_AURS   R     NA    S     R
    #>  9 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR   R     NA    R     NA
    #> 10 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR   R     NA    S     NA
    #> # ℹ 1,990 more rows
    #> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>,
    #> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>,
    #> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>,
    #> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>,
    #> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>,
    #> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …

    # base R ------------------------------------------------------------
    # determines %R
    resistance(example_isolates$AMX)
    #> [1] 0.5955556
    sir_confidence_interval(example_isolates$AMX)
    #> [1] 0.5688204 0.6218738
    sir_confidence_interval(example_isolates$AMX,
      confidence_level = 0.975
    )
    #> [1] 0.5650148 0.6255670
    sir_confidence_interval(example_isolates$AMX,
      confidence_level = 0.975,
      collapse = ", "
    )
    #> [1] "0.565, 0.626"

    # determines %S+I:
    susceptibility(example_isolates$AMX)
    #> [1] 0.4044444
    sir_confidence_interval(example_isolates$AMX,
      ab_result = c("S", "I")
    )
    #> [1] 0.3781262 0.4311796

    # be more specific
    proportion_S(example_isolates$AMX)
    #> [1] 0.4022222
    proportion_SI(example_isolates$AMX)
    #> [1] 0.4044444
    proportion_I(example_isolates$AMX)
    #> [1] 0.002222222
    proportion_IR(example_isolates$AMX)
    #> [1] 0.5977778
    proportion_R(example_isolates$AMX)
    #> [1] 0.5955556

    # dplyr -------------------------------------------------------------
    # \donttest{
    if (require("dplyr")) {
      example_isolates %>%
        group_by(ward) %>%
        summarise(
          r = resistance(CIP),
          n = n_sir(CIP)
        ) # n_sir works like n_distinct in dplyr, see ?n_sir
    }
    #> # A tibble: 3 × 3
    #>   ward           r     n
    #>   <chr>      <dbl> <int>
    #> 1 Clinical   0.147   869
    #> 2 ICU        0.190   447
    #> 3 Outpatient 0.161    93
    if (require("dplyr")) {
      example_isolates %>%
        group_by(ward) %>%
        summarise(
          cipro_R = resistance(CIP),
          ci_min = sir_confidence_interval(CIP, side = "min"),
          ci_max = sir_confidence_interval(CIP, side = "max"),
        )
    }
    #> # A tibble: 3 × 4
    #>   ward       cipro_R ci_min ci_max
    #>   <chr>        <dbl>  <dbl>  <dbl>
    #> 1 Clinical     0.147 0.124   0.173
    #> 2 ICU          0.190 0.155   0.230
    #> 3 Outpatient   0.161 0.0932  0.252
    if (require("dplyr")) {
      # scoped dplyr verbs with antimicrobial selectors
      # (you could also use across() of course)
      example_isolates %>%
        group_by(ward) %>%
        summarise_at(
          c(aminoglycosides(), carbapenems()),
          resistance
        )
    }
    #> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK
    #>   (amikacin), and KAN (kanamycin)
    #> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM (meropenem)
    #> Warning: There was 1 warning in `summarise()`.
    #> ℹ In argument: `KAN = (function (..., minimum = 30, as_percent = FALSE,
    #>   only_all_tested = FALSE, ...`.
    #> ℹ In group 3: `ward = "Outpatient"`.
    #> Caused by warning:
    #> ! Introducing NA: only 23 results available for KAN in group: ward = "Outpatient"
    #> (whilst `minimum = 30`).
    #> # A tibble: 3 × 7
    #>   ward         GEN   TOB   AMK   KAN    IPM    MEM
    #>   <chr>      <dbl> <dbl> <dbl> <dbl>  <dbl>  <dbl>
    #> 1 Clinical   0.229 0.315 0.626     1 0.0498 0.0458
    #> 2 ICU        0.290 0.400 0.662     1 0.0862 0.0894
    #> 3 Outpatient 0.2   0.368 0.605    NA 0.0541 0.0541
    if (require("dplyr")) {
      example_isolates %>%
        group_by(ward) %>%
        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_sir(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(ward) %>%
        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 sir columns
      example_isolates %>%
        select(AMX, CIP) %>%
        proportion_df(translate = FALSE)

      # It also supports grouping variables
      # (use sir_df to also include the count)
      example_isolates %>%
        select(ward, AMX, CIP) %>%
        group_by(ward) %>%
        sir_df(translate = FALSE)
    }
    #> # A tibble: 12 × 7
    #>    ward       antibiotic interpretation value ci_min ci_max isolates
    #>    <chr>      <chr>      <ord>          <dbl>  <dbl>  <dbl>    <int>
    #>  1 Clinical   AMX        SI             0.423 0.389   0.457      357
    #>  2 Clinical   AMX        R              0.577 0.543   0.611      487
    #>  3 Clinical   CIP        SI             0.853 0.827   0.876      741
    #>  4 Clinical   CIP        R              0.147 0.124   0.173      128
    #>  5 ICU        AMX        SI             0.369 0.323   0.417      158
    #>  6 ICU        AMX        R              0.631 0.583   0.677      270
    #>  7 ICU        CIP        SI             0.810 0.770   0.845      362
    #>  8 ICU        CIP        R              0.190 0.155   0.230       85
    #>  9 Outpatient AMX        SI             0.397 0.288   0.515       31
    #> 10 Outpatient AMX        R              0.603 0.485   0.712       47
    #> 11 Outpatient CIP        SI             0.839 0.748   0.907       78
    #> 12 Outpatient CIP        R              0.161 0.0932  0.252       15
    # }