-| 2025-10-13 |
+2025-11-24 |
efgh |
Escherichia coli |
R |
@@ -1254,7 +1254,7 @@ function on a grouped tibble, i.e., using
provides an extension to that function:
-
+
To calculate antimicrobial resistance in a more sensible way, also by
correcting for too few results, we use the resistance() and
susceptibility() functions.
@@ -1348,7 +1348,7 @@ categories.
labs(title = "MIC Distribution and SIR Interpretation",
x = "Sample Groups",
y = "MIC (mg/L)")
-
+
This plot provides an intuitive way to assess susceptibility patterns
across different groups while incorporating clinical breakpoints.
For a more straightforward and less manual approach,
@@ -1357,12 +1357,12 @@ extended by this package to directly plot MIC and disk diffusion
values:
-
+
# by providing `mo` and `ab`, colours will indicate the SIR interpretation:
autoplot(mic_values, mo = "K. pneumoniae", ab = "cipro", guideline = "EUCAST 2024")
+
Author: Dr. Matthijs Berends, 23rd Feb 2025
diff --git a/articles/AMR.md b/articles/AMR.md
new file mode 100644
index 000000000..516ecd23f
--- /dev/null
+++ b/articles/AMR.md
@@ -0,0 +1,951 @@
+# Conduct AMR data analysis
+
+**Note:** values on this page will change with every website update
+since they are based on randomly created values and the page was written
+in [R Markdown](https://rmarkdown.rstudio.com/). However, the
+methodology remains unchanged. This page was generated on 24 November
+2025.
+
+## Introduction
+
+Conducting AMR data analysis unfortunately requires in-depth knowledge
+from different scientific fields, which makes it hard to do right. At
+least, it requires:
+
+- Good questions (always start with those!) and reliable data
+- A thorough understanding of (clinical) epidemiology, to understand the
+ clinical and epidemiological relevance and possible bias of results
+- A thorough understanding of (clinical) microbiology/infectious
+ diseases, to understand which microorganisms are causal to which
+ infections and the implications of pharmaceutical treatment, as well
+ as understanding intrinsic and acquired microbial resistance
+- Experience with data analysis with microbiological tests and their
+ results, to understand the determination and limitations of MIC values
+ and their interpretations to SIR values
+- Availability of the biological taxonomy of microorganisms and probably
+ normalisation factors for pharmaceuticals, such as defined daily doses
+ (DDD)
+- Available (inter-)national guidelines, and profound methods to apply
+ them
+
+Of course, we cannot instantly provide you with knowledge and
+experience. But with this `AMR` package, we aimed at providing (1) tools
+to simplify antimicrobial resistance data cleaning, transformation and
+analysis, (2) methods to easily incorporate international guidelines and
+(3) scientifically reliable reference data, including the requirements
+mentioned above.
+
+The `AMR` package enables standardised and reproducible AMR data
+analysis, with the application of evidence-based rules, determination of
+first isolates, translation of various codes for microorganisms and
+antimicrobial agents, determination of (multi-drug) resistant
+microorganisms, and calculation of antimicrobial resistance, prevalence
+and future trends.
+
+## Preparation
+
+For this tutorial, we will create fake demonstration data to work with.
+
+You can skip to [Cleaning the data](#cleaning-the-data) if you already
+have your own data ready. If you start your analysis, try to make the
+structure of your data generally look like this:
+
+| date | patient_id | mo | AMX | CIP |
+|:----------:|:----------:|:----------------:|:---:|:---:|
+| 2025-11-24 | abcd | Escherichia coli | S | S |
+| 2025-11-24 | abcd | Escherichia coli | S | R |
+| 2025-11-24 | efgh | Escherichia coli | R | S |
+
+### Needed R packages
+
+As with many uses in R, we need some additional packages for AMR data
+analysis. Our package works closely together with the [tidyverse
+packages](https://www.tidyverse.org)
+[`dplyr`](https://dplyr.tidyverse.org/) and
+[`ggplot2`](https://ggplot2.tidyverse.org) by RStudio. The tidyverse
+tremendously improves the way we conduct data science - it allows for a
+very natural way of writing syntaxes and creating beautiful plots in R.
+
+We will also use the `cleaner` package, that can be used for cleaning
+data and creating frequency tables.
+
+``` r
+library(dplyr)
+library(ggplot2)
+library(AMR)
+
+# (if not yet installed, install with:)
+# install.packages(c("dplyr", "ggplot2", "AMR"))
+```
+
+The `AMR` package contains a data set `example_isolates_unclean`, which
+might look data that users have extracted from their laboratory systems:
+
+``` r
+example_isolates_unclean
+#> # A tibble: 3,000 × 8
+#> patient_id hospital date bacteria AMX AMC CIP GEN
+#>
+#> 1 J3 A 2012-11-21 E. coli R I S S
+#> 2 R7 A 2018-04-03 K. pneumoniae R I S S
+#> 3 P3 A 2014-09-19 E. coli R S S S
+#> 4 P10 A 2015-12-10 E. coli S I S S
+#> 5 B7 A 2015-03-02 E. coli S S S S
+#> 6 W3 A 2018-03-31 S. aureus R S R S
+#> 7 J8 A 2016-06-14 E. coli R S S S
+#> 8 M3 A 2015-10-25 E. coli R S S S
+#> 9 J3 A 2019-06-19 E. coli S S S S
+#> 10 G6 A 2015-04-27 S. aureus S S S S
+#> # ℹ 2,990 more rows
+
+# we will use 'our_data' as the data set name for this tutorial
+our_data <- example_isolates_unclean
+```
+
+For AMR data analysis, we would like the microorganism column to contain
+valid, up-to-date taxonomy, and the antibiotic columns to be cleaned as
+SIR values as well.
+
+### Taxonomy of microorganisms
+
+With [`as.mo()`](https://amr-for-r.org/reference/as.mo.md), users can
+transform arbitrary microorganism names or codes to current taxonomy.
+The `AMR` package contains up-to-date taxonomic data. To be specific,
+currently included data were retrieved on 24 Jun 2024.
+
+The codes of the AMR packages that come from
+[`as.mo()`](https://amr-for-r.org/reference/as.mo.md) are short, but
+still human readable. More importantly,
+[`as.mo()`](https://amr-for-r.org/reference/as.mo.md) supports all kinds
+of input:
+
+``` r
+as.mo("Klebsiella pneumoniae")
+#> Class 'mo'
+#> [1] B_KLBSL_PNMN
+as.mo("K. pneumoniae")
+#> Class 'mo'
+#> [1] B_KLBSL_PNMN
+as.mo("KLEPNE")
+#> Class 'mo'
+#> [1] B_KLBSL_PNMN
+as.mo("KLPN")
+#> Class 'mo'
+#> [1] B_KLBSL_PNMN
+```
+
+The first character in above codes denote their taxonomic kingdom, such
+as Bacteria (B), Fungi (F), and Protozoa (P).
+
+The `AMR` package also contain functions to directly retrieve taxonomic
+properties, such as the name, genus, species, family, order, and even
+Gram-stain. They all start with `mo_` and they use
+[`as.mo()`](https://amr-for-r.org/reference/as.mo.md) internally, so
+that still any arbitrary user input can be used:
+
+``` r
+mo_family("K. pneumoniae")
+#> [1] "Enterobacteriaceae"
+mo_genus("K. pneumoniae")
+#> [1] "Klebsiella"
+mo_species("K. pneumoniae")
+#> [1] "pneumoniae"
+
+mo_gramstain("Klebsiella pneumoniae")
+#> [1] "Gram-negative"
+
+mo_ref("K. pneumoniae")
+#> [1] "Trevisan, 1887"
+
+mo_snomed("K. pneumoniae")
+#> [[1]]
+#> [1] "1098101000112102" "446870005" "1098201000112108" "409801009"
+#> [5] "56415008" "714315002" "713926009"
+```
+
+Now we can thus clean our data:
+
+``` r
+our_data$bacteria <- as.mo(our_data$bacteria, info = TRUE)
+#> ℹ Retrieved values from the `microorganisms.codes` data set for "ESCCOL",
+#> "KLEPNE", "STAAUR", and "STRPNE".
+#> ℹ Microorganism translation was uncertain for four microorganisms. Run
+#> `mo_uncertainties()` to review these uncertainties, or use
+#> `add_custom_microorganisms()` to add custom entries.
+```
+
+Apparently, there was some uncertainty about the translation to
+taxonomic codes. Let’s check this:
+
+``` r
+mo_uncertainties()
+#> Matching scores are based on the resemblance between the input and the full
+#> taxonomic name, and the pathogenicity in humans. See `?mo_matching_score`.
+#> Colour keys: 0.000-0.549 0.550-0.649 0.650-0.749 0.750-1.000
+#>
+#> --------------------------------------------------------------------------------
+#> "E. coli" -> Escherichia coli (B_ESCHR_COLI, 0.688)
+#> Also matched: Enterococcus crotali (0.650), Escherichia coli coli
+#> (0.643), Escherichia coli expressing (0.611), Enterobacter cowanii
+#> (0.600), Enterococcus columbae (0.595), Enterococcus camelliae (0.591),
+#> Enterococcus casseliflavus (0.577), Enterobacter cloacae cloacae
+#> (0.571), Enterobacter cloacae complex (0.571), and Enterobacter cloacae
+#> dissolvens (0.565)
+#> --------------------------------------------------------------------------------
+#> "K. pneumoniae" -> Klebsiella pneumoniae (B_KLBSL_PNMN, 0.786)
+#> Also matched: Klebsiella pneumoniae complex (0.707), Klebsiella
+#> pneumoniae ozaenae (0.707), Klebsiella pneumoniae pneumoniae (0.688),
+#> Klebsiella pneumoniae rhinoscleromatis (0.658), Klebsiella pasteurii
+#> (0.500), Klebsiella planticola (0.500), Kingella potus (0.400),
+#> Kluyveromyces pseudotropicale (0.386), Kluyveromyces pseudotropicalis
+#> (0.363), and Kosakonia pseudosacchari (0.361)
+#> --------------------------------------------------------------------------------
+#> "S. aureus" -> Staphylococcus aureus (B_STPHY_AURS, 0.690)
+#> Also matched: Staphylococcus aureus aureus (0.643), Staphylococcus
+#> argenteus (0.625), Staphylococcus aureus anaerobius (0.625),
+#> Staphylococcus auricularis (0.615), Salmonella Aurelianis (0.595),
+#> Salmonella Aarhus (0.588), Salmonella Amounderness (0.587),
+#> Staphylococcus argensis (0.587), Streptococcus australis (0.587), and
+#> Salmonella choleraesuis arizonae (0.562)
+#> --------------------------------------------------------------------------------
+#> "S. pneumoniae" -> Streptococcus pneumoniae (B_STRPT_PNMN, 0.750)
+#> Also matched: Streptococcus pseudopneumoniae (0.700), Streptococcus
+#> phocae salmonis (0.552), Serratia proteamaculans quinovora (0.545),
+#> Streptococcus pseudoporcinus (0.536), Staphylococcus piscifermentans
+#> (0.533), Staphylococcus pseudintermedius (0.532), Serratia
+#> proteamaculans proteamaculans (0.526), Streptococcus gallolyticus
+#> pasteurianus (0.526), Salmonella Portanigra (0.524), and Streptococcus
+#> periodonticum (0.519)
+#>
+#> Only the first 10 other matches of each record are shown. Run
+#> `print(mo_uncertainties(), n = ...)` to view more entries, or save
+#> `mo_uncertainties()` to an object.
+```
+
+That’s all good.
+
+### Antibiotic results
+
+The column with antibiotic test results must also be cleaned. The `AMR`
+package comes with three new data types to work with such test results:
+`mic` for minimal inhibitory concentrations (MIC), `disk` for disk
+diffusion diameters, and `sir` for SIR data that have been interpreted
+already. This package can also determine SIR values based on MIC or disk
+diffusion values, read more about that on the
+[`as.sir()`](https://amr-for-r.org/reference/as.sir.md) page.
+
+For now, we will just clean the SIR columns in our data using dplyr:
+
+``` r
+# method 1, be explicit about the columns:
+our_data <- our_data %>%
+ mutate_at(vars(AMX:GEN), as.sir)
+
+# method 2, let the AMR package determine the eligible columns
+our_data <- our_data %>%
+ mutate_if(is_sir_eligible, as.sir)
+
+# result:
+our_data
+#> # A tibble: 3,000 × 8
+#> patient_id hospital date bacteria AMX AMC CIP GEN
+#>
+#> 1 J3 A 2012-11-21 B_ESCHR_COLI R I S S
+#> 2 R7 A 2018-04-03 B_KLBSL_PNMN R I S S
+#> 3 P3 A 2014-09-19 B_ESCHR_COLI R S S S
+#> 4 P10 A 2015-12-10 B_ESCHR_COLI S I S S
+#> 5 B7 A 2015-03-02 B_ESCHR_COLI S S S S
+#> 6 W3 A 2018-03-31 B_STPHY_AURS R S R S
+#> 7 J8 A 2016-06-14 B_ESCHR_COLI R S S S
+#> 8 M3 A 2015-10-25 B_ESCHR_COLI R S S S
+#> 9 J3 A 2019-06-19 B_ESCHR_COLI S S S S
+#> 10 G6 A 2015-04-27 B_STPHY_AURS S S S S
+#> # ℹ 2,990 more rows
+```
+
+This is basically it for the cleaning, time to start the data inclusion.
+
+### First isolates
+
+We need to know which isolates we can *actually* use for analysis
+without repetition bias.
+
+To conduct an analysis of antimicrobial resistance, you must [only
+include the first isolate of every patient per
+episode](https:/pubmed.ncbi.nlm.nih.gov/17304462/) (Hindler *et al.*,
+Clin Infect Dis. 2007). If you would not do this, you could easily get
+an overestimate or underestimate of the resistance of an antibiotic.
+Imagine that a patient was admitted with an MRSA and that it was found
+in 5 different blood cultures the following weeks (yes, some countries
+like the Netherlands have these blood drawing policies). The resistance
+percentage of oxacillin of all isolates would be overestimated, because
+you included this MRSA more than once. It would clearly be [selection
+bias](https://en.wikipedia.org/wiki/Selection_bias).
+
+The Clinical and Laboratory Standards Institute (CLSI) appoints this as
+follows:
+
+> *(…) When preparing a cumulative antibiogram to guide clinical
+> decisions about empirical antimicrobial therapy of initial infections,
+> **only the first isolate of a given species per patient, per analysis
+> period (eg, one year) should be included, irrespective of body site,
+> antimicrobial susceptibility profile, or other phenotypical
+> characteristics (eg, biotype)**. The first isolate is easily
+> identified, and cumulative antimicrobial susceptibility test data
+> prepared using the first isolate are generally comparable to
+> cumulative antimicrobial susceptibility test data calculated by other
+> methods, providing duplicate isolates are excluded.*
+> [M39-A4 Analysis and Presentation of Cumulative Antimicrobial
+> Susceptibility Test Data, 4th Edition. CLSI, 2014. Chapter
+> 6.4](https://clsi.org/standards/products/microbiology/documents/m39/)
+
+This `AMR` package includes this methodology with the
+[`first_isolate()`](https://amr-for-r.org/reference/first_isolate.md)
+function and is able to apply the four different methods as defined by
+[Hindler *et al.* in
+2007](https://academic.oup.com/cid/article/44/6/867/364325):
+phenotype-based, episode-based, patient-based, isolate-based. The right
+method depends on your goals and analysis, but the default
+phenotype-based method is in any case the method to properly correct for
+most duplicate isolates. Read more about the methods on the
+[`first_isolate()`](https://amr-for-r.org/reference/first_isolate.md)
+page.
+
+The outcome of the function can easily be added to our data:
+
+``` r
+our_data <- our_data %>%
+ mutate(first = first_isolate(info = TRUE))
+#> ℹ Determining first isolates using an episode length of 365 days
+#> ℹ Using column 'bacteria' as input for `col_mo`.
+#> ℹ Using column 'date' as input for `col_date`.
+#> ℹ Using column 'patient_id' as input for `col_patient_id`.
+#> ℹ Basing inclusion on all antimicrobial results, using a points threshold
+#> of 2
+#> => Found 2,724 'phenotype-based' first isolates (90.8% of total where a
+#> microbial ID was available)
+```
+
+So only 91% is suitable for resistance analysis! We can now filter on it
+with the [`filter()`](https://dplyr.tidyverse.org/reference/filter.html)
+function, also from the `dplyr` package:
+
+``` r
+our_data_1st <- our_data %>%
+ filter(first == TRUE)
+```
+
+For future use, the above two syntaxes can be shortened:
+
+``` r
+our_data_1st <- our_data %>%
+ filter_first_isolate()
+```
+
+So we end up with 2 724 isolates for analysis. Now our data looks like:
+
+``` r
+our_data_1st
+#> # A tibble: 2,724 × 9
+#> patient_id hospital date bacteria AMX AMC CIP GEN first
+#>
+#> 1 J3 A 2012-11-21 B_ESCHR_COLI R I S S TRUE
+#> 2 R7 A 2018-04-03 B_KLBSL_PNMN R I S S TRUE
+#> 3 P3 A 2014-09-19 B_ESCHR_COLI R S S S TRUE
+#> 4 P10 A 2015-12-10 B_ESCHR_COLI S I S S TRUE
+#> 5 B7 A 2015-03-02 B_ESCHR_COLI S S S S TRUE
+#> 6 W3 A 2018-03-31 B_STPHY_AURS R S R S TRUE
+#> 7 M3 A 2015-10-25 B_ESCHR_COLI R S S S TRUE
+#> 8 J3 A 2019-06-19 B_ESCHR_COLI S S S S TRUE
+#> 9 G6 A 2015-04-27 B_STPHY_AURS S S S S TRUE
+#> 10 P4 A 2011-06-21 B_ESCHR_COLI S S S S TRUE
+#> # ℹ 2,714 more rows
+```
+
+Time for the analysis.
+
+## Analysing the data
+
+The base R [`summary()`](https://rdrr.io/r/base/summary.html) function
+gives a good first impression, as it comes with support for the new `mo`
+and `sir` classes that we now have in our data set:
+
+``` r
+summary(our_data_1st)
+#> patient_id hospital date
+#> Length:2724 Length:2724 Min. :2011-01-01
+#> Class :character Class :character 1st Qu.:2013-04-07
+#> Mode :character Mode :character Median :2015-06-03
+#> Mean :2015-06-09
+#> 3rd Qu.:2017-08-11
+#> Max. :2019-12-27
+#> bacteria AMX AMC
+#> Class :mo Class:sir Class:sir
+#> :0 %S :41.6% (n=1133) %S :52.6% (n=1432)
+#> Unique:4 %SDD : 0.0% (n=0) %SDD : 0.0% (n=0)
+#> #1 :B_ESCHR_COLI %I :16.4% (n=446) %I :12.2% (n=333)
+#> #2 :B_STPHY_AURS %R :42.0% (n=1145) %R :35.2% (n=959)
+#> #3 :B_STRPT_PNMN %NI : 0.0% (n=0) %NI : 0.0% (n=0)
+#> CIP GEN first
+#> Class:sir Class:sir Mode:logical
+#> %S :52.5% (n=1431) %S :61.0% (n=1661) TRUE:2724
+#> %SDD : 0.0% (n=0) %SDD : 0.0% (n=0)
+#> %I : 6.5% (n=176) %I : 3.0% (n=82)
+#> %R :41.0% (n=1117) %R :36.0% (n=981)
+#> %NI : 0.0% (n=0) %NI : 0.0% (n=0)
+
+glimpse(our_data_1st)
+#> Rows: 2,724
+#> Columns: 9
+#> $ patient_id "J3", "R7", "P3", "P10", "B7", "W3", "M3", "J3", "G6", "P4"…
+#> $ hospital "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A",…
+#> $ date 2012-11-21, 2018-04-03, 2014-09-19, 2015-12-10, 2015-03-02…
+#> $ bacteria "B_ESCHR_COLI", "B_KLBSL_PNMN", "B_ESCHR_COLI", "B_ESCHR_COL…
+#> $ AMX R, R, R, S, S, R, R, S, S, S, S, R, S, S, R, R, R, R, S, R,…
+#> $ AMC I, I, S, I, S, S, S, S, S, S, S, S, S, S, S, S, S, R, S, S,…
+#> $ CIP S, S, S, S, S, R, S, S, S, S, S, S, S, S, S, S, S, S, S, S,…
+#> $ GEN S, S, S, S, S, S, S, S, S, S, S, R, S, S, S, S, S, S, S, S,…
+#> $ first TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,…
+
+# number of unique values per column:
+sapply(our_data_1st, n_distinct)
+#> patient_id hospital date bacteria AMX AMC CIP
+#> 260 3 1854 4 3 3 3
+#> GEN first
+#> 3 1
+```
+
+### Availability of species
+
+To just get an idea how the species are distributed, create a frequency
+table with [`count()`](https://amr-for-r.org/reference/count.md) based
+on the name of the microorganisms:
+
+``` r
+our_data %>%
+ count(mo_name(bacteria), sort = TRUE)
+#> # A tibble: 4 × 2
+#> `mo_name(bacteria)` n
+#>
+#> 1 Escherichia coli 1518
+#> 2 Staphylococcus aureus 730
+#> 3 Streptococcus pneumoniae 426
+#> 4 Klebsiella pneumoniae 326
+
+our_data_1st %>%
+ count(mo_name(bacteria), sort = TRUE)
+#> # A tibble: 4 × 2
+#> `mo_name(bacteria)` n
+#>
+#> 1 Escherichia coli 1321
+#> 2 Staphylococcus aureus 682
+#> 3 Streptococcus pneumoniae 402
+#> 4 Klebsiella pneumoniae 319
+```
+
+### Select and filter with antibiotic selectors
+
+Using so-called antibiotic class selectors, you can select or filter
+columns based on the antibiotic class that your antibiotic results are
+in:
+
+``` r
+our_data_1st %>%
+ select(date, aminoglycosides())
+#> ℹ For `aminoglycosides()` using column 'GEN' (gentamicin)
+#> # A tibble: 2,724 × 2
+#> date GEN
+#>
+#> 1 2012-11-21 S
+#> 2 2018-04-03 S
+#> 3 2014-09-19 S
+#> 4 2015-12-10 S
+#> 5 2015-03-02 S
+#> 6 2018-03-31 S
+#> 7 2015-10-25 S
+#> 8 2019-06-19 S
+#> 9 2015-04-27 S
+#> 10 2011-06-21 S
+#> # ℹ 2,714 more rows
+
+our_data_1st %>%
+ select(bacteria, betalactams())
+#> ℹ For `betalactams()` using columns 'AMX' (amoxicillin) and 'AMC'
+#> (amoxicillin/clavulanic acid)
+#> # A tibble: 2,724 × 3
+#> bacteria AMX AMC
+#>
+#> 1 B_ESCHR_COLI R I
+#> 2 B_KLBSL_PNMN R I
+#> 3 B_ESCHR_COLI R S
+#> 4 B_ESCHR_COLI S I
+#> 5 B_ESCHR_COLI S S
+#> 6 B_STPHY_AURS R S
+#> 7 B_ESCHR_COLI R S
+#> 8 B_ESCHR_COLI S S
+#> 9 B_STPHY_AURS S S
+#> 10 B_ESCHR_COLI S S
+#> # ℹ 2,714 more rows
+
+our_data_1st %>%
+ select(bacteria, where(is.sir))
+#> # A tibble: 2,724 × 5
+#> bacteria AMX AMC CIP GEN
+#>
+#> 1 B_ESCHR_COLI R I S S
+#> 2 B_KLBSL_PNMN R I S S
+#> 3 B_ESCHR_COLI R S S S
+#> 4 B_ESCHR_COLI S I S S
+#> 5 B_ESCHR_COLI S S S S
+#> 6 B_STPHY_AURS R S R S
+#> 7 B_ESCHR_COLI R S S S
+#> 8 B_ESCHR_COLI S S S S
+#> 9 B_STPHY_AURS S S S S
+#> 10 B_ESCHR_COLI S S S S
+#> # ℹ 2,714 more rows
+
+# filtering using AB selectors is also possible:
+our_data_1st %>%
+ filter(any(aminoglycosides() == "R"))
+#> ℹ For `aminoglycosides()` using column 'GEN' (gentamicin)
+#> # A tibble: 981 × 9
+#> patient_id hospital date bacteria AMX AMC CIP GEN first
+#>
+#> 1 J5 A 2017-12-25 B_STRPT_PNMN R S S R TRUE
+#> 2 X1 A 2017-07-04 B_STPHY_AURS R S S R TRUE
+#> 3 B3 A 2016-07-24 B_ESCHR_COLI S S S R TRUE
+#> 4 V7 A 2012-04-03 B_ESCHR_COLI S S S R TRUE
+#> 5 C9 A 2017-03-23 B_ESCHR_COLI S S S R TRUE
+#> 6 R1 A 2018-06-10 B_STPHY_AURS S S S R TRUE
+#> 7 S2 A 2013-07-19 B_STRPT_PNMN S S S R TRUE
+#> 8 P5 A 2019-03-09 B_STPHY_AURS S S S R TRUE
+#> 9 Q8 A 2019-08-10 B_STPHY_AURS S S S R TRUE
+#> 10 K5 A 2013-03-15 B_STRPT_PNMN S S S R TRUE
+#> # ℹ 971 more rows
+
+our_data_1st %>%
+ filter(all(betalactams() == "R"))
+#> ℹ For `betalactams()` using columns 'AMX' (amoxicillin) and 'AMC'
+#> (amoxicillin/clavulanic acid)
+#> # A tibble: 462 × 9
+#> patient_id hospital date bacteria AMX AMC CIP GEN first
+#>
+#> 1 M7 A 2013-07-22 B_STRPT_PNMN R R S S TRUE
+#> 2 R10 A 2013-12-20 B_STPHY_AURS R R S S TRUE
+#> 3 R7 A 2015-10-25 B_STPHY_AURS R R S S TRUE
+#> 4 R8 A 2019-10-25 B_STPHY_AURS R R S S TRUE
+#> 5 B6 A 2016-11-20 B_ESCHR_COLI R R R R TRUE
+#> 6 I7 A 2015-08-19 B_ESCHR_COLI R R S S TRUE
+#> 7 N3 A 2014-12-29 B_STRPT_PNMN R R R S TRUE
+#> 8 Q2 A 2019-09-22 B_ESCHR_COLI R R S S TRUE
+#> 9 X7 A 2011-03-20 B_ESCHR_COLI R R S R TRUE
+#> 10 V1 A 2018-08-07 B_STPHY_AURS R R S S TRUE
+#> # ℹ 452 more rows
+
+# even works in base R (since R 3.0):
+our_data_1st[all(betalactams() == "R"), ]
+#> ℹ For `betalactams()` using columns 'AMX' (amoxicillin) and 'AMC'
+#> (amoxicillin/clavulanic acid)
+#> # A tibble: 462 × 9
+#> patient_id hospital date bacteria AMX AMC CIP GEN first
+#>
+#> 1 M7 A 2013-07-22 B_STRPT_PNMN R R S S TRUE
+#> 2 R10 A 2013-12-20 B_STPHY_AURS R R S S TRUE
+#> 3 R7 A 2015-10-25 B_STPHY_AURS R R S S TRUE
+#> 4 R8 A 2019-10-25 B_STPHY_AURS R R S S TRUE
+#> 5 B6 A 2016-11-20 B_ESCHR_COLI R R R R TRUE
+#> 6 I7 A 2015-08-19 B_ESCHR_COLI R R S S TRUE
+#> 7 N3 A 2014-12-29 B_STRPT_PNMN R R R S TRUE
+#> 8 Q2 A 2019-09-22 B_ESCHR_COLI R R S S TRUE
+#> 9 X7 A 2011-03-20 B_ESCHR_COLI R R S R TRUE
+#> 10 V1 A 2018-08-07 B_STPHY_AURS R R S S TRUE
+#> # ℹ 452 more rows
+```
+
+### Generate antibiograms
+
+Since AMR v2.0 (March 2023), it is very easy to create different types
+of antibiograms, with support for 20 different languages.
+
+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()`](https://amr-for-r.org/reference/antibiogram.md)
+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()`](https://amr-for-r.org/reference/antibiogram.md)
+function to create any of the above antibiogram types. For starters,
+this is what the included `example_isolates` data set looks like:
+
+``` r
+example_isolates
+#> # A tibble: 2,000 × 46
+#> date patient age gender ward mo PEN OXA FLC AMX
+#>
+#> 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 , AMP , TZP , CZO , FEP ,
+#> # CXM , FOX , CTX , CAZ , CRO , GEN ,
+#> # TOB , AMK , KAN , TMP , SXT , NIT ,
+#> # FOS , LNZ , CIP , MFX , VAN , TEC ,
+#> # TCY , TGC , DOX , ERY , CLI , AZM ,
+#> # IPM , MEM , MTR , CHL , COL , MUP , …
+```
+
+#### Traditional Antibiogram
+
+To create a traditional antibiogram, simply state which antibiotics
+should be used. The `antibiotics` argument in the
+[`antibiogram()`](https://amr-for-r.org/reference/antibiogram.md)
+function supports any (combination) of the previously mentioned
+antibiotic class selectors:
+
+``` r
+antibiogram(example_isolates,
+ antibiotics = c(aminoglycosides(), carbapenems()))
+#> ℹ For `aminoglycosides()` using columns 'GEN' (gentamicin), 'TOB'
+#> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin)
+#> ℹ For `carbapenems()` using columns 'IPM' (imipenem) and 'MEM' (meropenem)
+```
+
+| Pathogen | Amikacin | Gentamicin | Imipenem | Kanamycin | Meropenem | Tobramycin |
+|:-----------------|:---------------------|:--------------------|:---------------------|:----------------|:---------------------|:--------------------|
+| CoNS | 0% (0-8%,N=43) | 86% (82-90%,N=309) | 52% (37-67%,N=48) | 0% (0-8%,N=43) | 52% (37-67%,N=48) | 22% (12-35%,N=55) |
+| *E. coli* | 100% (98-100%,N=171) | 98% (96-99%,N=460) | 100% (99-100%,N=422) | NA | 100% (99-100%,N=418) | 97% (96-99%,N=462) |
+| *E. faecalis* | 0% (0-9%,N=39) | 0% (0-9%,N=39) | 100% (91-100%,N=38) | 0% (0-9%,N=39) | NA | 0% (0-9%,N=39) |
+| *K. pneumoniae* | NA | 90% (79-96%,N=58) | 100% (93-100%,N=51) | NA | 100% (93-100%,N=53) | 90% (79-96%,N=58) |
+| *P. aeruginosa* | NA | 100% (88-100%,N=30) | NA | 0% (0-12%,N=30) | NA | 100% (88-100%,N=30) |
+| *P. mirabilis* | NA | 94% (80-99%,N=34) | 94% (79-99%,N=32) | NA | NA | 94% (80-99%,N=34) |
+| *S. aureus* | NA | 99% (97-100%,N=233) | NA | NA | NA | 98% (92-100%,N=86) |
+| *S. epidermidis* | 0% (0-8%,N=44) | 79% (71-85%,N=163) | NA | 0% (0-8%,N=44) | NA | 51% (40-61%,N=89) |
+| *S. hominis* | NA | 92% (84-97%,N=80) | NA | NA | NA | 85% (74-93%,N=62) |
+| *S. pneumoniae* | 0% (0-3%,N=117) | 0% (0-3%,N=117) | NA | 0% (0-3%,N=117) | NA | 0% (0-3%,N=117) |
+
+Notice that the
+[`antibiogram()`](https://amr-for-r.org/reference/antibiogram.md)
+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()`](https://amr-for-r.org/reference/italicise_taxonomy.md)
+internally).
+
+It also uses the language of your OS if this is either English, Arabic,
+Bengali, Chinese, Czech, Danish, Dutch, Finnish, French, German, Greek,
+Hindi, Indonesian, Italian, Japanese, Korean, Norwegian, Polish,
+Portuguese, Romanian, Russian, Spanish, Swahili, Swedish, Turkish,
+Ukrainian, Urdu, or Vietnamese. In this next example, we force the
+language to be Spanish using the `language` argument:
+
+``` r
+antibiogram(example_isolates,
+ mo_transform = "gramstain",
+ antibiotics = aminoglycosides(),
+ ab_transform = "name",
+ language = "es")
+#> ℹ For `aminoglycosides()` using columns 'GEN' (gentamicin), 'TOB'
+#> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin)
+```
+
+| Patógeno | Amikacina | Gentamicina | Kanamicina | Tobramicina |
+|:--------------|:-------------------|:--------------------|:----------------|:-------------------|
+| Gram negativo | 98% (96-99%,N=256) | 96% (95-98%,N=684) | 0% (0-10%,N=35) | 96% (94-97%,N=686) |
+| Gram positivo | 0% (0-1%,N=436) | 63% (60-66%,N=1170) | 0% (0-1%,N=436) | 34% (31-38%,N=665) |
+
+#### Combined Antibiogram
+
+To create a combined antibiogram, use antibiotic codes or names with a
+plus `+` character like this:
+
+``` r
+combined_ab <- antibiogram(example_isolates,
+ antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
+ ab_transform = NULL)
+combined_ab
+```
+
+| Pathogen | TZP | TZP + GEN | TZP + TOB |
+|:-----------------|:---------------------|:---------------------|:---------------------|
+| CoNS | 30% (16-49%,N=33) | 97% (95-99%,N=274) | NA |
+| *E. coli* | 94% (92-96%,N=416) | 100% (98-100%,N=459) | 99% (97-100%,N=461) |
+| *K. pneumoniae* | 89% (77-96%,N=53) | 93% (83-98%,N=58) | 93% (83-98%,N=58) |
+| *P. aeruginosa* | NA | 100% (88-100%,N=30) | 100% (88-100%,N=30) |
+| *P. mirabilis* | NA | 100% (90-100%,N=34) | 100% (90-100%,N=34) |
+| *S. aureus* | NA | 100% (98-100%,N=231) | 100% (96-100%,N=91) |
+| *S. epidermidis* | NA | 100% (97-100%,N=128) | 100% (92-100%,N=46) |
+| *S. hominis* | NA | 100% (95-100%,N=74) | 100% (93-100%,N=53) |
+| *S. pneumoniae* | 100% (97-100%,N=112) | 100% (97-100%,N=112) | 100% (97-100%,N=112) |
+
+#### 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()`](https://rdrr.io/r/base/ifelse.html) with calculations based
+on certain columns:
+
+``` r
+antibiogram(example_isolates,
+ antibiotics = c(aminoglycosides(), carbapenems()),
+ syndromic_group = "ward")
+#> ℹ For `aminoglycosides()` using columns 'GEN' (gentamicin), 'TOB'
+#> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin)
+#> ℹ For `carbapenems()` using columns 'IPM' (imipenem) and 'MEM' (meropenem)
+```
+
+| Syndromic Group | Pathogen | Amikacin | Gentamicin | Imipenem | Kanamycin | Meropenem | Tobramycin |
+|:----------------|:-----------------|:---------------------|:--------------------|:---------------------|:----------------|:---------------------|:--------------------|
+| Clinical | CoNS | NA | 89% (84-93%,N=205) | 57% (39-74%,N=35) | NA | 57% (39-74%,N=35) | 26% (12-45%,N=31) |
+| ICU | CoNS | NA | 79% (68-88%,N=73) | NA | NA | NA | NA |
+| Outpatient | CoNS | NA | 84% (66-95%,N=31) | NA | NA | NA | NA |
+| Clinical | *E. coli* | 100% (97-100%,N=104) | 98% (96-99%,N=297) | 100% (99-100%,N=266) | NA | 100% (99-100%,N=276) | 98% (96-99%,N=299) |
+| ICU | *E. coli* | 100% (93-100%,N=52) | 99% (95-100%,N=137) | 100% (97-100%,N=133) | NA | 100% (97-100%,N=118) | 96% (92-99%,N=137) |
+| Clinical | *K. pneumoniae* | NA | 92% (81-98%,N=51) | 100% (92-100%,N=44) | NA | 100% (92-100%,N=46) | 92% (81-98%,N=51) |
+| Clinical | *P. mirabilis* | NA | 100% (88-100%,N=30) | NA | NA | NA | 100% (88-100%,N=30) |
+| Clinical | *S. aureus* | NA | 99% (95-100%,N=150) | NA | NA | NA | 97% (89-100%,N=63) |
+| ICU | *S. aureus* | NA | 100% (95-100%,N=66) | NA | NA | NA | NA |
+| Clinical | *S. epidermidis* | NA | 82% (72-90%,N=79) | NA | NA | NA | 55% (39-70%,N=44) |
+| ICU | *S. epidermidis* | NA | 72% (60-82%,N=75) | NA | NA | NA | 41% (26-58%,N=41) |
+| Clinical | *S. hominis* | NA | 96% (85-99%,N=45) | NA | NA | NA | 94% (79-99%,N=31) |
+| Clinical | *S. pneumoniae* | 0% (0-5%,N=78) | 0% (0-5%,N=78) | NA | 0% (0-5%,N=78) | NA | 0% (0-5%,N=78) |
+| ICU | *S. pneumoniae* | 0% (0-12%,N=30) | 0% (0-12%,N=30) | NA | 0% (0-12%,N=30) | NA | 0% (0-12%,N=30) |
+
+#### Weighted-Incidence Syndromic Combination Antibiogram (WISCA)
+
+To create a **Weighted-Incidence Syndromic Combination Antibiogram
+(WISCA)**, simply set `wisca = TRUE` in the
+[`antibiogram()`](https://amr-for-r.org/reference/antibiogram.md)
+function, or use the dedicated
+[`wisca()`](https://amr-for-r.org/reference/antibiogram.md) function.
+Unlike traditional antibiograms, WISCA provides syndrome-based
+susceptibility estimates, weighted by pathogen incidence and
+antimicrobial susceptibility patterns.
+
+``` r
+example_isolates %>%
+ wisca(antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
+ minimum = 10) # Recommended threshold: ≥30
+```
+
+| Piperacillin/tazobactam | Piperacillin/tazobactam + Gentamicin | Piperacillin/tazobactam + Tobramycin |
+|:------------------------|:-------------------------------------|:-------------------------------------|
+| 69.4% (64.3-74.3%) | 92.6% (91.1-93.9%) | 88.7% (85.8-91.2%) |
+
+WISCA uses a **Bayesian decision model** to integrate data from multiple
+pathogens, improving empirical therapy guidance, especially for
+low-incidence infections. It is **pathogen-agnostic**, meaning results
+are syndrome-based rather than stratified by microorganism.
+
+For reliable results, ensure your data includes **only first isolates**
+(use
+[`first_isolate()`](https://amr-for-r.org/reference/first_isolate.md))
+and consider filtering for **the top *n* species** (use
+[`top_n_microorganisms()`](https://amr-for-r.org/reference/top_n_microorganisms.md)),
+as WISCA outcomes are most meaningful when based on robust incidence
+estimates.
+
+For **patient- or syndrome-specific WISCA**, run the function on a
+grouped `tibble`, i.e., using
+[`group_by()`](https://dplyr.tidyverse.org/reference/group_by.html)
+first:
+
+``` r
+example_isolates %>%
+ top_n_microorganisms(n = 10) %>%
+ group_by(age_group = age_groups(age, c(25, 50, 75)),
+ gender) %>%
+ wisca(antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"))
+```
+
+| age_group | gender | Piperacillin/tazobactam | Piperacillin/tazobactam + Gentamicin | Piperacillin/tazobactam + Tobramycin |
+|:----------|:-------|:------------------------|:-------------------------------------|:-------------------------------------|
+| 0-24 | F | 56.6% (25.2-83.9%) | 73.6% (48-91.6%) | 68.6% (42.9-89.5%) |
+| 0-24 | M | 60.3% (28.4-87.1%) | 79.7% (57.6-94.2%) | 60.1% (29.5-87.7%) |
+| 25-49 | F | 66.6% (45.6-85.5%) | 91.7% (84.6-96.7%) | 83% (67.9-94%) |
+| 25-49 | M | 56.4% (29.1-81.7%) | 89.2% (80.3-95.7%) | 72.4% (49.7-90%) |
+| 50-74 | F | 67.8% (55.8-80.1%) | 95.6% (93.2-97.5%) | 88.1% (80.4-94.6%) |
+| 50-74 | M | 66.2% (54.8-75.8%) | 95.2% (92.4-97.4%) | 84.4% (74.4-92.5%) |
+| 75+ | F | 71.7% (61-81.7%) | 96.6% (94.4-98.2%) | 90.6% (84.6-95.3%) |
+| 75+ | M | 72.9% (63.8-82%) | 96.6% (94.6-98.1%) | 92.8% (87.8-96.5%) |
+
+#### Plotting antibiograms
+
+Antibiograms can be plotted using
+[`autoplot()`](https://ggplot2.tidyverse.org/reference/autoplot.html)
+from the `ggplot2` packages, since this `AMR` package provides an
+extension to that function:
+
+``` r
+autoplot(combined_ab)
+```
+
+
+
+To calculate antimicrobial resistance in a more sensible way, also by
+correcting for too few results, we use the
+[`resistance()`](https://amr-for-r.org/reference/proportion.md) and
+[`susceptibility()`](https://amr-for-r.org/reference/proportion.md)
+functions.
+
+### Resistance percentages
+
+The functions
+[`resistance()`](https://amr-for-r.org/reference/proportion.md) and
+[`susceptibility()`](https://amr-for-r.org/reference/proportion.md) can
+be used to calculate antimicrobial resistance or susceptibility. For
+more specific analyses, the functions
+[`proportion_S()`](https://amr-for-r.org/reference/proportion.md),
+[`proportion_SI()`](https://amr-for-r.org/reference/proportion.md),
+[`proportion_I()`](https://amr-for-r.org/reference/proportion.md),
+[`proportion_IR()`](https://amr-for-r.org/reference/proportion.md) and
+[`proportion_R()`](https://amr-for-r.org/reference/proportion.md) can be
+used to determine the proportion of a specific antimicrobial outcome.
+
+All these functions contain a `minimum` argument, denoting the minimum
+required number of test results for returning a value. These functions
+will otherwise return `NA`. The default is `minimum = 30`, following the
+[CLSI M39-A4
+guideline](https://clsi.org/standards/products/microbiology/documents/m39/)
+for applying microbial epidemiology.
+
+As per the EUCAST guideline of 2019, we calculate resistance as the
+proportion of R
+([`proportion_R()`](https://amr-for-r.org/reference/proportion.md),
+equal to
+[`resistance()`](https://amr-for-r.org/reference/proportion.md)) and
+susceptibility as the proportion of S and I
+([`proportion_SI()`](https://amr-for-r.org/reference/proportion.md),
+equal to
+[`susceptibility()`](https://amr-for-r.org/reference/proportion.md)).
+These functions can be used on their own:
+
+``` r
+our_data_1st %>% resistance(AMX)
+#> [1] 0.4203377
+```
+
+Or can be used in conjunction with
+[`group_by()`](https://dplyr.tidyverse.org/reference/group_by.html) and
+[`summarise()`](https://dplyr.tidyverse.org/reference/summarise.html),
+both from the `dplyr` package:
+
+``` r
+our_data_1st %>%
+ group_by(hospital) %>%
+ summarise(amoxicillin = resistance(AMX))
+#> # A tibble: 3 × 2
+#> hospital amoxicillin
+#>
+#> 1 A 0.340
+#> 2 B 0.551
+#> 3 C 0.370
+```
+
+### Interpreting MIC and Disk Diffusion Values
+
+Minimal inhibitory concentration (MIC) values and disk diffusion
+diameters can be interpreted into clinical breakpoints (SIR) using
+[`as.sir()`](https://amr-for-r.org/reference/as.sir.md). Here’s an
+example with randomly generated MIC values for *Klebsiella pneumoniae*
+and ciprofloxacin:
+
+``` r
+set.seed(123)
+mic_values <- random_mic(100)
+sir_values <- as.sir(mic_values, mo = "K. pneumoniae", ab = "cipro", guideline = "EUCAST 2024")
+
+my_data <- tibble(MIC = mic_values, SIR = sir_values)
+my_data
+#> # A tibble: 100 × 2
+#> MIC SIR
+#>
+#> 1 <=0.0001 S
+#> 2 0.0160 S
+#> 3 >=8.0000 R
+#> 4 0.0320 S
+#> 5 0.0080 S
+#> 6 64.0000 R
+#> 7 0.0080 S
+#> 8 0.1250 S
+#> 9 0.0320 S
+#> 10 0.0002 S
+#> # ℹ 90 more rows
+```
+
+This allows direct interpretation according to EUCAST or CLSI
+breakpoints, facilitating automated AMR data processing.
+
+### Plotting MIC and SIR Interpretations
+
+We can visualise MIC distributions and their SIR interpretations using
+`ggplot2`, using the new
+[`scale_y_mic()`](https://amr-for-r.org/reference/plot.md) for the
+y-axis and
+[`scale_colour_sir()`](https://amr-for-r.org/reference/plot.md) to
+colour-code SIR categories.
+
+``` r
+# add a group
+my_data$group <- rep(c("A", "B", "C", "D"), each = 25)
+
+ggplot(my_data,
+ aes(x = group, y = MIC, colour = SIR)) +
+ geom_jitter(width = 0.2, size = 2) +
+ geom_boxplot(fill = NA, colour = "grey40") +
+ scale_y_mic() +
+ scale_colour_sir() +
+ labs(title = "MIC Distribution and SIR Interpretation",
+ x = "Sample Groups",
+ y = "MIC (mg/L)")
+```
+
+
+
+This plot provides an intuitive way to assess susceptibility patterns
+across different groups while incorporating clinical breakpoints.
+
+For a more straightforward and less manual approach, `ggplot2`’s
+function
+[`autoplot()`](https://ggplot2.tidyverse.org/reference/autoplot.html)
+has been extended by this package to directly plot MIC and disk
+diffusion values:
+
+``` r
+autoplot(mic_values)
+```
+
+
+
+``` r
+
+# by providing `mo` and `ab`, colours will indicate the SIR interpretation:
+autoplot(mic_values, mo = "K. pneumoniae", ab = "cipro", guideline = "EUCAST 2024")
+```
+
+
+
+------------------------------------------------------------------------
+
+*Author: Dr. Matthijs Berends, 23rd Feb 2025*
diff --git a/articles/AMR_for_Python.html b/articles/AMR_for_Python.html
index addb946e1..65d08935e 100644
--- a/articles/AMR_for_Python.html
+++ b/articles/AMR_for_Python.html
@@ -30,7 +30,7 @@
AMR (for R)
- 3.0.1.9002
+ 3.0.1.9003