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)

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

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
)

## Source

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/.

## 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 antibiotics, see section Combination Therapy below

ab_result

antibiotic results to test against, must be one or more values of "S", "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 antibiotics 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 and I must be merged into one, so the output only consists of S+I vs. R (susceptible vs. resistant) - the default is TRUE

## 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().

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.

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.

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 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 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:

--------------------------------------------------------------------
only_all_tested = FALSE  only_all_tested = TRUE
-----------------------  -----------------------
Drug A    Drug B   include as  include as   include as  include as
numerator   denominator  numerator   denominator
--------  --------  ----------  -----------  ----------  -----------
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 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.

count() to count resistant and susceptible isolates.

## Examples

# example_isolates is a data set available in the AMR package.
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 antibiotic 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" (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
# }