key_antimicrobials {AMR} | R Documentation |
(Key) Antimicrobials for First Weighted Isolates
Description
These functions can be used to determine first weighted isolates by considering the phenotype for isolate selection (see first_isolate()
). Using a phenotype-based method to determine first isolates is more reliable than methods that disregard phenotypes.
Usage
key_antimicrobials(
x = NULL,
col_mo = NULL,
universal = c("ampicillin", "amoxicillin/clavulanic acid", "cefuroxime",
"piperacillin/tazobactam", "ciprofloxacin", "trimethoprim/sulfamethoxazole"),
gram_negative = c("gentamicin", "tobramycin", "colistin", "cefotaxime", "ceftazidime",
"meropenem"),
gram_positive = c("vancomycin", "teicoplanin", "tetracycline", "erythromycin",
"oxacillin", "rifampin"),
antifungal = c("anidulafungin", "caspofungin", "fluconazole", "miconazole", "nystatin",
"voriconazole"),
only_sir_columns = FALSE,
...
)
all_antimicrobials(x = NULL, only_sir_columns = FALSE, ...)
antimicrobials_equal(
y,
z,
type = c("points", "keyantimicrobials"),
ignore_I = TRUE,
points_threshold = 2,
...
)
Arguments
x |
a data.frame with antibiotics columns, like |
col_mo |
column name of the names or codes of the microorganisms (see |
universal |
names of broad-spectrum antimicrobial drugs, case-insensitive. Set to |
gram_negative |
names of antibiotic drugs for Gram-positives, case-insensitive. Set to |
gram_positive |
names of antibiotic drugs for Gram-negatives, case-insensitive. Set to |
antifungal |
names of antifungal drugs for fungi, case-insensitive. Set to |
only_sir_columns |
a logical to indicate whether only columns must be included that were transformed to class |
... |
ignored, only in place to allow future extensions |
y , z |
character vectors to compare |
type |
type to determine weighed isolates; can be |
ignore_I |
logical to indicate whether antibiotic interpretations with |
points_threshold |
minimum number of points to require before differences in the antibiogram will lead to inclusion of an isolate when |
Details
The key_antimicrobials()
and all_antimicrobials()
functions are context-aware. This means that the x
argument can be left blank if used inside a data.frame call, see Examples.
The function key_antimicrobials()
returns a character vector with 12 antimicrobial results for every isolate. The function all_antimicrobials()
returns a character vector with all antimicrobial drug results for every isolate. These vectors can then be compared using antimicrobials_equal()
, to check if two isolates have generally the same antibiogram. Missing and invalid values are replaced with a dot ("."
) by key_antimicrobials()
and ignored by antimicrobials_equal()
.
Please see the first_isolate()
function how these important functions enable the 'phenotype-based' method for determination of first isolates.
The default antimicrobial drugs used for all rows (set in universal
) are:
Ampicillin
Amoxicillin/clavulanic acid
Cefuroxime
Ciprofloxacin
Piperacillin/tazobactam
Trimethoprim/sulfamethoxazole
The default antimicrobial drugs used for Gram-negative bacteria (set in gram_negative
) are:
Cefotaxime
Ceftazidime
Colistin
Gentamicin
Meropenem
Tobramycin
The default antimicrobial drugs used for Gram-positive bacteria (set in gram_positive
) are:
Erythromycin
Oxacillin
Rifampin
Teicoplanin
Tetracycline
Vancomycin
The default antimicrobial drugs used for fungi (set in antifungal
) are:
Anidulafungin
Caspofungin
Fluconazole
Miconazole
Nystatin
Voriconazole
See Also
Examples
# `example_isolates` is a data set available in the AMR package.
# See ?example_isolates.
# output of the `key_antimicrobials()` function could be like this:
strainA <- "SSSRR.S.R..S"
strainB <- "SSSIRSSSRSSS"
# those strings can be compared with:
antimicrobials_equal(strainA, strainB, type = "keyantimicrobials")
# TRUE, because I is ignored (as well as missing values)
antimicrobials_equal(strainA, strainB, type = "keyantimicrobials", ignore_I = FALSE)
# FALSE, because I is not ignored and so the 4th [character] differs
if (require("dplyr")) {
# set key antibiotics to a new variable
my_patients <- example_isolates %>%
mutate(keyab = key_antimicrobials(antifungal = NULL)) %>% # no need to define `x`
mutate(
# now calculate first isolates
first_regular = first_isolate(col_keyantimicrobials = FALSE),
# and first WEIGHTED isolates
first_weighted = first_isolate(col_keyantimicrobials = "keyab")
)
# Check the difference in this data set, 'weighted' results in more isolates:
sum(my_patients$first_regular, na.rm = TRUE)
sum(my_patients$first_weighted, na.rm = TRUE)
}