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Package: AMR
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Version: 2.0.0.9012
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Date: 2023-04-20
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Version: 2.0.0.9013
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Date: 2023-04-21
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Title: Antimicrobial Resistance Data Analysis
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Description: Functions to simplify and standardise antimicrobial resistance (AMR)
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data analysis and to work with microbial and antimicrobial properties by
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2
NEWS.md
2
NEWS.md
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# AMR 2.0.0.9012
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# AMR 2.0.0.9013
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## Changed
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* formatting fix for `sir_interpretation_history()`
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# how to conduct AMR data analysis: https://msberends.github.io/AMR/ #
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# ==================================================================== #
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expect_identical(mo_genus("B_GRAMP", language = "pt"), "(Gram positivos desconhecidos)")
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expect_identical(mo_genus("B_GRAMP", language = "pt"), "(gênero desconhecido)")
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expect_identical(mo_fullname("CoNS", "cs"), "Koaguláza-negativní stafylokok (KNS)")
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expect_identical(mo_fullname("CoNS", "da"), "Koagulase-negative stafylokokker (KNS)")
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@ -247,60 +247,79 @@ our_data_1st[all(betalactams() == "R"), ]
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## Generate antibiograms
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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.
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Since AMR v2.0 (March 2023), it is very easy to create different types of antibiograms, with support for 20 different languages.
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Below are some suggestions for how to generate the different antibiograms:
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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:
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1. **Traditional Antibiogram (TA)** e.g, for the susceptibility of *Pseudomonas aeruginosa* to piperacillin/tazobactam (TZP)
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2. **Combination Antibiogram (CA)** e.g, for the sdditional susceptibility of *Pseudomonas aeruginosa* to TZP + tobramycin versus TZP alone
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3. **Syndromic Antibiogram (SA)** e.g, for the susceptibility of *Pseudomonas aeruginosa* to TZP among respiratory specimens (obtained among ICU patients only)
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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
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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:
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```{r}
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# traditional:
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antibiogram(our_data_1st)
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antibiogram(our_data_1st,
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ab_transform = "name"
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)
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antibiogram(our_data_1st,
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ab_transform = "name",
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language = "es"
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) # support for 20 languages
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example_isolates
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```
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```{r}
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# combined:
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antibiogram(our_data_1st,
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antibiotics = c("AMC", "AMC+CIP", "AMC+GEN")
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)
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### Traditional Antibiogram
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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:
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```{r trad}
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antibiogram(example_isolates,
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antibiotics = c(aminoglycosides(), carbapenems()))
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```
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```{r}
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# for a syndromic antibiogram, we must fake some clinical conditions:
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our_data_1st$condition <- sample(c("Cardial", "Respiratory", "Rheumatic"),
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size = nrow(our_data_1st),
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replace = TRUE
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)
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Notice that the `antibiogram()` function automatically prints in the right format when using Quarto or R Markdown (such as this page), and even applies italics for taxonomic names (by using `italicise_taxonomy()` internally).
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# syndromic:
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antibiogram(our_data_1st,
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syndromic_group = "condition"
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)
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antibiogram(our_data_1st,
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# you can use AB selectors here as well:
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antibiotics = c(penicillins(), aminoglycosides()),
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syndromic_group = "condition",
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mo_transform = "gramstain"
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)
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It also uses the language of your OS if this is either `r AMR:::vector_or(vapply(FUN.VALUE = character(1), AMR:::LANGUAGES_SUPPORTED_NAMES, function(x) x$exonym), quotes = FALSE, sort = FALSE)`. In this next example, we force the language to be Spanish using the `language` argument:
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```{r trad2}
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antibiogram(example_isolates,
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mo_transform = "gramstain",
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antibiotics = aminoglycosides(),
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ab_transform = "name",
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language = "es")
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```
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```{r}
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# WISCA:
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# (we lack some details, but it could contain a filter on e.g. >65 year-old males)
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wisca <- antibiogram(our_data_1st,
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antibiotics = c("AMC", "AMC+CIP", "AMC+GEN"),
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syndromic_group = "condition",
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mo_transform = "gramstain"
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)
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### Combined Antibiogram
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To create a combined antibiogram, use antibiotic codes or names with a plus `+` character like this:
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```{r comb}
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antibiogram(example_isolates,
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antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"))
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```
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### Syndromic Antibiogram
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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:
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```{r synd}
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antibiogram(example_isolates,
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antibiotics = c(aminoglycosides(), carbapenems()),
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syndromic_group = "ward")
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```
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### Weighted-Incidence Syndromic Combination Antibiogram (WISCA)
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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:
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```{r wisca}
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wisca <- antibiogram(example_isolates,
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antibiotics = c("AMC", "AMC+CIP", "TZP", "TZP+TOB"),
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mo_transform = "gramstain",
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minimum = 10, # this should be >= 30, but now just as example
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syndromic_group = ifelse(example_isolates$age >= 65 &
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example_isolates$gender == "M",
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"WISCA Group 1", "WISCA Group 2"))
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wisca
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```
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Antibiograms can be plotted using `autoplot()` from the `ggplot2` packages, since this package provides an extension to that function:
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### Plotting antibiograms
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Antibiograms can be plotted using `autoplot()` from the `ggplot2` packages, since this `AMR` package provides an extension to that function:
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```{r}
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autoplot(wisca)
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