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DESCRIPTION
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Package: AMR Package: AMR
Version: 2.0.0.9012 Version: 2.0.0.9013
Date: 2023-04-20 Date: 2023-04-21
Title: Antimicrobial Resistance Data Analysis Title: Antimicrobial Resistance Data Analysis
Description: Functions to simplify and standardise antimicrobial resistance (AMR) Description: Functions to simplify and standardise antimicrobial resistance (AMR)
data analysis and to work with microbial and antimicrobial properties by data analysis and to work with microbial and antimicrobial properties by

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NEWS.md
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# AMR 2.0.0.9012 # AMR 2.0.0.9013
## Changed ## Changed
* formatting fix for `sir_interpretation_history()` * formatting fix for `sir_interpretation_history()`

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inst/tinytest/test-translate.R
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# how to conduct AMR data analysis: https://msberends.github.io/AMR/ # # how to conduct AMR data analysis: https://msberends.github.io/AMR/ #
# ==================================================================== # # ==================================================================== #
expect_identical(mo_genus("B_GRAMP", language = "pt"), "(Gram positivos desconhecidos)") expect_identical(mo_genus("B_GRAMP", language = "pt"), "(gênero desconhecido)")
expect_identical(mo_fullname("CoNS", "cs"), "Koaguláza-negativní stafylokok (KNS)") expect_identical(mo_fullname("CoNS", "cs"), "Koaguláza-negativní stafylokok (KNS)")
expect_identical(mo_fullname("CoNS", "da"), "Koagulase-negative stafylokokker (KNS)") expect_identical(mo_fullname("CoNS", "da"), "Koagulase-negative stafylokokker (KNS)")

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vignettes/AMR.Rmd
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## Generate antibiograms ## Generate antibiograms
This package comes with `antibiogram()`, a function that automatically generates traditional, combined, syndromic, and even weighted-incidence syndromic combination antibiograms (WISCA). For R Markdown (such as this page) it automatically prints in the right table format. Since AMR v2.0 (March 2023), it is very easy to create different types of antibiograms, with support for 20 different languages.
Below are some suggestions for how to generate the different antibiograms: There are four antibiogram types, as proposed by Klinker *et al.* (2021, [DOI 10.1177/20499361211011373](https://doi.org/10.1177/20499361211011373)), and they are all supported by the new `antibiogram()` function:
1. **Traditional Antibiogram (TA)** e.g, for the susceptibility of *Pseudomonas aeruginosa* to piperacillin/tazobactam (TZP)
2. **Combination Antibiogram (CA)** e.g, for the sdditional susceptibility of *Pseudomonas aeruginosa* to TZP + tobramycin versus TZP alone
3. **Syndromic Antibiogram (SA)** e.g, for the susceptibility of *Pseudomonas aeruginosa* to TZP among respiratory specimens (obtained among ICU patients only)
4. **Weighted-Incidence Syndromic Combination Antibiogram (WISCA)** e.g, for the susceptibility of *Pseudomonas aeruginosa* to TZP among respiratory specimens (obtained among ICU patients only) for male patients age >=65 years with heart failure
In this section, we show how to use the `antibiogram()` function to create any of the above antibiogram types. For starters, this is what the included `example_isolates` data set looks like:
```{r} ```{r}
# traditional: example_isolates
antibiogram(our_data_1st)
antibiogram(our_data_1st,
ab_transform = "name"
)
antibiogram(our_data_1st,
ab_transform = "name",
language = "es"
) # support for 20 languages
``` ```
```{r} ### Traditional Antibiogram
# combined:
antibiogram(our_data_1st, To create a traditional antibiogram, simply state which antibiotics should be used. The `antibiotics` argument in the `antibiogram()` function supports any (combination) of the previously mentioned antibiotic class selectors:
antibiotics = c("AMC", "AMC+CIP", "AMC+GEN")
) ```{r trad}
antibiogram(example_isolates,
antibiotics = c(aminoglycosides(), carbapenems()))
``` ```
```{r} Notice that the `antibiogram()` function automatically prints in the right format when using Quarto or R Markdown (such as this page), and even applies italics for taxonomic names (by using `italicise_taxonomy()` internally).
# for a syndromic antibiogram, we must fake some clinical conditions:
our_data_1st$condition <- sample(c("Cardial", "Respiratory", "Rheumatic"),
size = nrow(our_data_1st),
replace = TRUE
)
# syndromic: It also uses the language of your OS if this is either `r AMR:::vector_or(vapply(FUN.VALUE = character(1), AMR:::LANGUAGES_SUPPORTED_NAMES, function(x) x$exonym), quotes = FALSE, sort = FALSE)`. In this next example, we force the language to be Spanish using the `language` argument:
antibiogram(our_data_1st,
syndromic_group = "condition" ```{r trad2}
) antibiogram(example_isolates,
antibiogram(our_data_1st, mo_transform = "gramstain",
# you can use AB selectors here as well: antibiotics = aminoglycosides(),
antibiotics = c(penicillins(), aminoglycosides()), ab_transform = "name",
syndromic_group = "condition", language = "es")
mo_transform = "gramstain"
)
``` ```
```{r} ### Combined Antibiogram
# WISCA:
# (we lack some details, but it could contain a filter on e.g. >65 year-old males) To create a combined antibiogram, use antibiotic codes or names with a plus `+` character like this:
wisca <- antibiogram(our_data_1st,
antibiotics = c("AMC", "AMC+CIP", "AMC+GEN"), ```{r comb}
syndromic_group = "condition", antibiogram(example_isolates,
mo_transform = "gramstain" antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"))
) ```
### Syndromic Antibiogram
To create a syndromic antibiogram, the `syndromic_group` argument must be used. This can be any column in the data, or e.g. an `ifelse()` with calculations based on certain columns:
```{r synd}
antibiogram(example_isolates,
antibiotics = c(aminoglycosides(), carbapenems()),
syndromic_group = "ward")
```
### Weighted-Incidence Syndromic Combination Antibiogram (WISCA)
To create a WISCA, you must state combination therapy in the `antibiotics` argument (similar to the Combination Antibiogram), define a syndromic group with the `syndromic_group` argument (similar to the Syndromic Antibiogram) in which cases are predefined based on clinical or demographic characteristics (e.g., endocarditis in 75+ females). This next example is a simplification without clinical characteristics, but just gives an idea of how a WISCA can be created:
```{r wisca}
wisca <- antibiogram(example_isolates,
antibiotics = c("AMC", "AMC+CIP", "TZP", "TZP+TOB"),
mo_transform = "gramstain",
minimum = 10, # this should be >= 30, but now just as example
syndromic_group = ifelse(example_isolates$age >= 65 &
example_isolates$gender == "M",
"WISCA Group 1", "WISCA Group 2"))
wisca wisca
``` ```
Antibiograms can be plotted using `autoplot()` from the `ggplot2` packages, since this package provides an extension to that function: ### Plotting antibiograms
Antibiograms can be plotted using `autoplot()` from the `ggplot2` packages, since this `AMR` package provides an extension to that function:
```{r} ```{r}
autoplot(wisca) autoplot(wisca)