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"
)
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
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 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. Defaults to
"both"for a length 2 vector, but can also be (abbreviated as)"min"/"left"/"lower"/"less"or"max"/"right"/"higher"/"greater".- data
a data.frame containing columns with class
sir(seeas.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, defaults to system language (see
get_AMR_locale()) and can also be set with the optionAMR_locale. Uselanguage = NULLorlanguage = ""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), defaults to
TRUE
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() = 1and 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() >= 1Using 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.
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
#> # … with 1,990 more rows, and 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>, RIF <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$AMX,
confidence_level = 0.975
)
#> [1] 0.5650148 0.6255670
# determines %S+I:
susceptibility(example_isolates$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
# }