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,
confidence_level = 0.95,
guideline = getOption("AMR_guideline", "EUCAST")
)
sir_df(
data,
translate_ab = "name",
language = get_AMR_locale(),
minimum = 30,
as_percent = FALSE,
combine_SI,
confidence_level = 0.95,
guideline = getOption("AMR_guideline", "EUCAST")
)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
minimumwill returnNAwith a warning. The default number of30isolates 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
0.123456will 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. Usesir_combine()to create a new variable for a combination therapy.- guideline
A guideline name,
"EUCAST"or"CLSI"(or any version thereof, e.g."CLSI 2025"), to determine whether the 'I' category is considered susceptible (EUCAST) or resistant (CLSI). The default is the package optionAMR_guideline, or"EUCAST"if it is not set. For functions that return results per category, i.e.,count_df(),proportion_df(),sir_df(),ggplot_sir(),geom_sir(), andlabels_sir_count(), this can also beNULLorFALSEto keep S, I, and R separate. See section Interpretation of SIR below.- 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(seeas.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 optionAMR_locale. Uselanguage = NULLorlanguage = ""to prevent translation.- combine_SI
(deprecated in favour of
guideline) A logical. If set, it takes precedence overguideline:TRUEmerges S, SDD, and I into"SI"vs."R", andFALSEkeeps S, I, and R separate.
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.
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/).
In aggregated analyses, such as proportions, counts, antibiograms, and plots, the 'I' category must be considered either susceptible or resistant. In epidemiological AMR data analysis, this follows the guideline: EUCAST considers 'I' susceptible, while CLSI considers 'I' resistant. The dose-dependent susceptibility of CLSI ('SDD') is always considered susceptible, as it equals what EUCAST defines as 'I'. All functions with a guideline argument for this purpose use these categories:
guideline | Susceptible | Resistant |
"EUCAST" | S, SDD, I, WT | R, NWT, NS |
"CLSI" | S, SDD, WT | I, R, NWT, NS |
The values 'WT' (wild type) and 'NWT' (non-wild type) result from epidemiological cut-off values (ECOFFs) instead of clinical breakpoints. Since they are no clinical categories, 'WT' is always considered susceptible and 'NWT' always resistant, regardless of the guideline. The value 'NS' (non-susceptible) is always considered resistant, and 'NI' (non-interpretable) is always considered a missing result.
The guideline argument can be:
Left unset, to use the package option
AMR_guideline, or"EUCAST"if this option is not set. In the latter case, a message is shown once per session (but not in R Markdown or Quarto documents); set the option to silence it."EUCAST", or any EUCAST version such as"EUCAST 2025", to consider 'I' susceptible, e.g., to return"SI"vs."R"inproportion_df()."CLSI", or any CLSI version such as"CLSI 2025", to consider 'I' resistant, e.g., to return"S"vs."IR"inproportion_df().NULLorFALSE, to keep S, I, and R separate. This is only possible in functions that return results per category:count_df(),proportion_df(),sir_df(),ggplot_sir(),geom_sir(),labels_sir_count(), andbug_drug_combinations(). Functions that return a single proportion or count, such assusceptibility(),count_resistant(),antibiogram(),wisca(), andformat()on the result ofbug_drug_combinations(), require 'I' to be considered either susceptible or resistant, and return an error.
Use susceptibility() and resistance() (or count_susceptible() and count_resistant()) to follow the guideline, or e.g. proportion_SI() and proportion_R() to be explicit regardless of the guideline.
The combine_SI argument of the functions that return results per category, antibiogram(), and wisca() is deprecated in favour of guideline. If set, it is followed literally and takes precedence over guideline.
Combination Therapy
Results are ordered from susceptible to resistant: S (and WT), SDD, I, R (and NWT, NS). With method = "most_susceptible" (in sir_combine()), the most susceptible result of the antimicrobials is returned, and with method = "most_resistant", the most resistant result. The value "NI" (non-interpretable) is considered a missing result.
A missing result can only be ignored if it cannot change the conclusion: a susceptible result for "most_susceptible", and a non-susceptible result for "most_resistant". Under EUCAST, the 'I' category is considered susceptible (susceptible, increased exposure), and under CLSI, it is not. With only_all_tested = TRUE, any missing result leads to NA.
This is an example for two antimicrobials, Drug A and Drug B, under EUCAST (the order of the antimicrobials does not matter):
-------------------------------------------------------------------------
Result of method = Result of method =
"most_susceptible" "most_resistant"
----------------------- -----------------------------
only_all_tested = only_all_tested =
Drug A Drug B FALSE TRUE FALSE TRUE
-------- -------- --------- --------- ----------- -------------
S S S S S S
S I S S I I
S R S S R R
I R I I R R
R R R R R R
S <NA> S <NA> <NA> <NA>
I <NA> I <NA> <NA> <NA>
R <NA> <NA> <NA> R <NA>
<NA> <NA> <NA> <NA> <NA> <NA>
-------------------------------------------------------------------------Under CLSI, the combination of I with a missing result is <NA> for "most_susceptible" and I for "most_resistant".
The count_*() and proportion_*() functions apply these rules when more than one variable is used. With only_all_tested = FALSE (default), the functions for susceptibility, such as susceptibility() and count_susceptible(), count an isolate as susceptible if any of the antimicrobials is susceptible ("most_susceptible"), and the functions for resistance, such as resistance() and count_resistant(), count an isolate as resistant if any of the antimicrobials is resistant ("most_resistant"). An isolate is considered tested if it has such a result, or if it was tested for all antimicrobials. Therefore, for combinations with only_all_tested = FALSE:
count_S() + count_I() + count_R() >= count_all()
proportion_S() + proportion_I() + proportion_R() >= 1With only_all_tested = TRUE, all these functions count the result of the combination therapy ("most_susceptible") of isolates tested for all antimicrobials, so that:
count_S() + count_I() + count_R() = count_all()
proportion_S() + proportion_I() + proportion_R() = 1In antibiogram() and wisca(), the result of the combination therapy ("most_susceptible") is used, the 'I' category is considered susceptible or not following the guideline argument ('SDD' is always considered susceptible), and the result is then reduced to S or R.
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)
}
#> ℹ `proportion_df()` assumes the EUCAST guideline and thus considers the 'I'
#> category susceptible (S+SDD+I vs. R). Use `guideline = "CLSI"` for S+SDD vs.
#> I+R, or `guideline = NULL` to keep S, I, and R separate. Set the `guideline`
#> argument, or set the `AMR_guideline` option to change the default (see
#> `?AMR-options`). This message is shown once per session.
#> ℹ `sir_df()` assumes the EUCAST guideline and thus considers the 'I' category
#> susceptible (S+SDD+I vs. R). Use `guideline = "CLSI"` for S+SDD vs. I+R, or
#> `guideline = NULL` to keep S, I, and R separate. Set the `guideline`
#> argument, or set the `AMR_guideline` option to change the default (see
#> `?AMR-options`). This message is shown once per session.
#> # 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
# }