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993 lines
47 KiB
Markdown
# Conduct AMR data analysis
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**Note:** values on this page will change with every website update
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since they are based on randomly created values and the page was written
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in [R Markdown](https://rmarkdown.rstudio.com/). However, the
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methodology remains unchanged. This page was generated on 02 May 2026.
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## Introduction
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Conducting AMR data analysis unfortunately requires in-depth knowledge
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from different scientific fields, which makes it hard to do right. At
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least, it requires:
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- Good questions (always start with those!) and reliable data
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- A thorough understanding of (clinical) epidemiology, to understand the
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clinical and epidemiological relevance and possible bias of results
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- A thorough understanding of (clinical) microbiology/infectious
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diseases, to understand which microorganisms are causal to which
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infections and the implications of pharmaceutical treatment, as well
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as understanding intrinsic and acquired microbial resistance
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- Experience with data analysis with microbiological tests and their
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results, to understand the determination and limitations of MIC values
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and their interpretations to SIR values
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- Availability of the biological taxonomy of microorganisms and probably
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normalisation factors for pharmaceuticals, such as defined daily doses
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(DDD)
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- Available (inter-)national guidelines, and profound methods to apply
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them
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Of course, we cannot instantly provide you with knowledge and
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experience. But with this `AMR` package, we aimed at providing (1) tools
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to simplify antimicrobial resistance data cleaning, transformation and
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analysis, (2) methods to easily incorporate international guidelines and
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(3) scientifically reliable reference data, including the requirements
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mentioned above.
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The `AMR` package enables standardised and reproducible AMR data
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analysis, with the application of evidence-based rules, determination of
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first isolates, translation of various codes for microorganisms and
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antimicrobial drugs, determination of (multi-drug) resistant
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microorganisms, and calculation of antimicrobial resistance, prevalence
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and future trends.
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## Preparation
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For this tutorial, we will create fake demonstration data to work with.
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You can skip to [Cleaning the data](#cleaning-the-data) if you already
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have your own data ready. If you start your analysis, try to make the
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structure of your data generally look like this:
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| date | patient_id | mo | AMX | CIP |
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|:----------:|:----------:|:----------------:|:---:|:---:|
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| 2026-05-02 | abcd | Escherichia coli | S | S |
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| 2026-05-02 | abcd | Escherichia coli | S | R |
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| 2026-05-02 | efgh | Escherichia coli | R | S |
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### Needed R packages
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As with many uses in R, we need some additional packages for AMR data
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analysis. Our package works closely together with the [tidyverse
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packages](https://www.tidyverse.org)
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[`dplyr`](https://dplyr.tidyverse.org/) and
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[`ggplot2`](https://ggplot2.tidyverse.org) by RStudio. The tidyverse
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tremendously improves the way we conduct data science - it allows for a
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very natural way of writing syntaxes and creating beautiful plots in R.
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We will also use the `cleaner` package, that can be used for cleaning
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data and creating frequency tables.
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``` r
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library(dplyr)
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library(ggplot2)
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library(AMR)
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# (if not yet installed, install with:)
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# install.packages(c("dplyr", "ggplot2", "AMR"))
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```
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The `AMR` package contains a data set `example_isolates_unclean`, which
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might look data that users have extracted from their laboratory systems:
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``` r
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example_isolates_unclean
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#> # A tibble: 3,000 × 8
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#> patient_id hospital date bacteria AMX AMC CIP GEN
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#> <chr> <chr> <date> <chr> <chr> <chr> <chr> <chr>
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#> 1 J3 A 2012-11-21 E. coli R I S S
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#> 2 R7 A 2018-04-03 K. pneumoniae R I S S
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#> 3 P3 A 2014-09-19 E. coli R S S S
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#> 4 P10 A 2015-12-10 E. coli S I S S
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#> 5 B7 A 2015-03-02 E. coli S S S S
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#> 6 W3 A 2018-03-31 S. aureus R S R S
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#> 7 J8 A 2016-06-14 E. coli R S S S
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#> 8 M3 A 2015-10-25 E. coli R S S S
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#> 9 J3 A 2019-06-19 E. coli S S S S
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#> 10 G6 A 2015-04-27 S. aureus S S S S
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#> # ℹ 2,990 more rows
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# we will use 'our_data' as the data set name for this tutorial
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our_data <- example_isolates_unclean
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```
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For AMR data analysis, we would like the microorganism column to contain
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valid, up-to-date taxonomy, and the antibiotic columns to be cleaned as
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SIR values as well.
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### Taxonomy of microorganisms
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With [`as.mo()`](https://amr-for-r.org/reference/as.mo.md), users can
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transform arbitrary microorganism names or codes to current taxonomy.
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The `AMR` package contains up-to-date taxonomic data. To be specific,
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currently included data were retrieved on 24 Jun 2024.
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The codes of the AMR packages that come from
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[`as.mo()`](https://amr-for-r.org/reference/as.mo.md) are short, but
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still human readable. More importantly,
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[`as.mo()`](https://amr-for-r.org/reference/as.mo.md) supports all kinds
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of input:
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``` r
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as.mo("Klebsiella pneumoniae")
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#> Class <mo>
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#> [1] B_KLBSL_PNMN
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as.mo("K. pneumoniae")
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#> Class <mo>
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#> [1] B_KLBSL_PNMN
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as.mo("KLEPNE")
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#> Class <mo>
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#> [1] B_KLBSL_PNMN
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as.mo("KLPN")
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#> Class <mo>
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#> [1] B_KLBSL_PNMN
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```
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The first character in above codes denote their taxonomic kingdom, such
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as Bacteria (B), Fungi (F), and Protozoa (P).
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The `AMR` package also contain functions to directly retrieve taxonomic
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properties, such as the name, genus, species, family, order, and even
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Gram-stain. They all start with `mo_` and they use
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[`as.mo()`](https://amr-for-r.org/reference/as.mo.md) internally, so
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that still any arbitrary user input can be used:
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``` r
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mo_family("K. pneumoniae")
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#> [1] "Enterobacteriaceae"
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mo_genus("K. pneumoniae")
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#> [1] "Klebsiella"
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mo_species("K. pneumoniae")
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#> [1] "pneumoniae"
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mo_gramstain("Klebsiella pneumoniae")
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#> [1] "Gram-negative"
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mo_ref("K. pneumoniae")
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#> [1] "Trevisan, 1887"
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mo_snomed("K. pneumoniae")
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#> [[1]]
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#> [1] "1098101000112102" "446870005" "1098201000112108" "409801009"
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#> [5] "56415008" "714315002" "713926009"
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```
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Now we can thus clean our data:
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``` r
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our_data$bacteria <- as.mo(our_data$bacteria, info = TRUE)
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#> ℹ Retrieved values from the `microorganisms.codes` data set for "ESCCOL",
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#> "KLEPNE", "STAAUR", and "STRPNE".
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#> ℹ Microorganism translation was uncertain for four microorganisms. Run
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#> `mo_uncertainties()` to review these uncertainties, or use
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#> `add_custom_microorganisms()` to add custom entries.
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```
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Apparently, there was some uncertainty about the translation to
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taxonomic codes. Let’s check this:
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``` r
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mo_uncertainties()
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#> Matching scores are based on the resemblance between the input and the full
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#> taxonomic name, and the pathogenicity in humans. See `mo_matching_score()`.
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#> Colour keys: 0.000-0.549 0.550-0.649 0.650-0.749 0.750-1.000
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#> -------------------------------------------------------------------------------
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#> "E. coli" -> Escherichia coli (B_ESCHR_COLI, 0.688)
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#> Also matched: Enterococcus crotali (0.650), Escherichia coli coli (0.643),
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#> Escherichia coli expressing (0.611), Enterobacter cowanii (0.600), Enterococcus
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#> columbae (0.595), Enterococcus camelliae (0.591), Enterococcus casseliflavus
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#> (0.577), Enterobacter cloacae cloacae (0.571), Enterobacter cloacae complex
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#> (0.571), and Enterobacter cloacae dissolvens (0.565)
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#> -------------------------------------------------------------------------------
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#> "K. pneumoniae" -> Klebsiella pneumoniae (B_KLBSL_PNMN, 0.786)
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#> Also matched: Klebsiella pneumoniae complex (0.707), Klebsiella pneumoniae
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#> ozaenae (0.707), Klebsiella pneumoniae pneumoniae (0.688), Klebsiella
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#> pneumoniae rhinoscleromatis (0.658), Klebsiella pasteurii (0.500), Klebsiella
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#> planticola (0.500), Kingella potus (0.400), Kluyveromyces pseudotropicale
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#> (0.386), Kluyveromyces pseudotropicalis (0.363), and Kosakonia pseudosacchari
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#> (0.361)
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#> -------------------------------------------------------------------------------
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#> "S. aureus" -> Staphylococcus aureus (B_STPHY_AURS, 0.690)
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#> Also matched: Staphylococcus aureus aureus (0.643), Staphylococcus argenteus
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#> (0.625), Staphylococcus aureus anaerobius (0.625), Staphylococcus auricularis
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#> (0.615), Salmonella Aurelianis (0.595), Salmonella Aarhus (0.588), Salmonella
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#> Amounderness (0.587), Staphylococcus argensis (0.587), Streptococcus australis
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#> (0.587), and Salmonella choleraesuis arizonae (0.562)
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#> -------------------------------------------------------------------------------
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#> "S. pneumoniae" -> Streptococcus pneumoniae (B_STRPT_PNMN, 0.750)
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#> Also matched: Streptococcus pseudopneumoniae (0.700), Streptococcus phocae
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#> salmonis (0.552), Serratia proteamaculans quinovora (0.545), Streptococcus
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#> pseudoporcinus (0.536), Staphylococcus piscifermentans (0.533), Staphylococcus
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#> pseudintermedius (0.532), Serratia proteamaculans proteamaculans (0.526),
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#> Streptococcus gallolyticus pasteurianus (0.526), Salmonella Portanigra (0.524),
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#> and Streptococcus periodonticum (0.519)
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#> ℹ Only the first 10 other matches of each record are shown. Run ``
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#> `print(mo_uncertainties(), n = ...)` `` to view more entries, or save
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#> `mo_uncertainties()` to an object.
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```
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That’s all good.
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### Antibiotic results
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The column with antibiotic test results must also be cleaned. The `AMR`
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package comes with three new data types to work with such test results:
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`mic` for minimal inhibitory concentrations (MIC), `disk` for disk
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diffusion diameters, and `sir` for SIR data that have been interpreted
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already. This package can also determine SIR values based on MIC or disk
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diffusion values, read more about that on the
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[`as.sir()`](https://amr-for-r.org/reference/as.sir.md) page.
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For now, we will just clean the SIR columns in our data using dplyr:
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``` r
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# method 1, be explicit about the columns:
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our_data <- our_data %>%
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mutate_at(vars(AMX:GEN), as.sir)
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# method 2, let the AMR package determine the eligible columns
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our_data <- our_data %>%
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mutate_if(is_sir_eligible, as.sir)
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# result:
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our_data
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#> # A tibble: 3,000 × 8
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#> patient_id hospital date bacteria AMX AMC CIP GEN
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#> <chr> <chr> <date> <mo> <sir> <sir> <sir> <sir>
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#> 1 J3 A 2012-11-21 B_ESCHR_COLI R I S S
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#> 2 R7 A 2018-04-03 B_KLBSL_PNMN R I S S
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#> 3 P3 A 2014-09-19 B_ESCHR_COLI R S S S
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#> 4 P10 A 2015-12-10 B_ESCHR_COLI S I S S
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#> 5 B7 A 2015-03-02 B_ESCHR_COLI S S S S
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#> 6 W3 A 2018-03-31 B_STPHY_AURS R S R S
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#> 7 J8 A 2016-06-14 B_ESCHR_COLI R S S S
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#> 8 M3 A 2015-10-25 B_ESCHR_COLI R S S S
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#> 9 J3 A 2019-06-19 B_ESCHR_COLI S S S S
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#> 10 G6 A 2015-04-27 B_STPHY_AURS S S S S
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#> # ℹ 2,990 more rows
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```
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This is basically it for the cleaning, time to start the data inclusion.
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### First isolates
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We need to know which isolates we can *actually* use for analysis
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without repetition bias.
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To conduct an analysis of antimicrobial resistance, you must [only
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include the first isolate of every patient per
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episode](https:/pubmed.ncbi.nlm.nih.gov/17304462/) (Hindler *et al.*,
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Clin Infect Dis. 2007). If you would not do this, you could easily get
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an overestimate or underestimate of the resistance of an antibiotic.
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Imagine that a patient was admitted with an MRSA and that it was found
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in 5 different blood cultures the following weeks (yes, some countries
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like the Netherlands have these blood drawing policies). The resistance
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percentage of oxacillin of all isolates would be overestimated, because
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you included this MRSA more than once. It would clearly be [selection
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bias](https://en.wikipedia.org/wiki/Selection_bias).
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The Clinical and Laboratory Standards Institute (CLSI) appoints this as
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follows:
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> *(…) When preparing a cumulative antibiogram to guide clinical
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> decisions about empirical antimicrobial therapy of initial infections,
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> **only the first isolate of a given species per patient, per analysis
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> period (eg, one year) should be included, irrespective of body site,
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> antimicrobial susceptibility profile, or other phenotypical
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> characteristics (eg, biotype)**. The first isolate is easily
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> identified, and cumulative antimicrobial susceptibility test data
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> prepared using the first isolate are generally comparable to
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> cumulative antimicrobial susceptibility test data calculated by other
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> methods, providing duplicate isolates are excluded.*
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> [M39-A4 Analysis and Presentation of Cumulative Antimicrobial
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> Susceptibility Test Data, 4th Edition. CLSI, 2014. Chapter
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> 6.4](https://clsi.org/standards/products/microbiology/documents/m39/)
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This `AMR` package includes this methodology with the
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[`first_isolate()`](https://amr-for-r.org/reference/first_isolate.md)
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function and is able to apply the four different methods as defined by
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[Hindler *et al.* in
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2007](https://academic.oup.com/cid/article/44/6/867/364325):
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phenotype-based, episode-based, patient-based, isolate-based. The right
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method depends on your goals and analysis, but the default
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phenotype-based method is in any case the method to properly correct for
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most duplicate isolates. Read more about the methods on the
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[`first_isolate()`](https://amr-for-r.org/reference/first_isolate.md)
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page.
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The outcome of the function can easily be added to our data:
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``` r
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our_data <- our_data %>%
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mutate(first = first_isolate(info = TRUE))
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#> ℹ Determining first isolates using an episode length of 365 days
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#> ℹ Using column bacteria as input for `col_mo`.
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#> ℹ Column first is SIR eligible (despite only having empty values), since it
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#> seems to be cefozopran (ZOP)
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#> ℹ Using column date as input for `col_date`.
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#> ℹ Using column patient_id as input for `col_patient_id`.
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#> ℹ Basing inclusion on all antimicrobial results, using a points threshold of 2
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#> => Found 2,724 'phenotype-based' first isolates (90.8% of total where a
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#> microbial ID was available)
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```
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So only 91% is suitable for resistance analysis! We can now filter on it
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with the [`filter()`](https://dplyr.tidyverse.org/reference/filter.html)
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function, also from the `dplyr` package:
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``` r
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our_data_1st <- our_data %>%
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filter(first == TRUE)
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```
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For future use, the above two syntaxes can be shortened:
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``` r
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our_data_1st <- our_data %>%
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filter_first_isolate()
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```
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So we end up with 2 724 isolates for analysis. Now our data looks like:
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``` r
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our_data_1st
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#> # A tibble: 2,724 × 9
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#> patient_id hospital date bacteria AMX AMC CIP GEN first
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#> <chr> <chr> <date> <mo> <sir> <sir> <sir> <sir> <lgl>
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#> 1 J3 A 2012-11-21 B_ESCHR_COLI R I S S TRUE
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#> 2 R7 A 2018-04-03 B_KLBSL_PNMN R I S S TRUE
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#> 3 P3 A 2014-09-19 B_ESCHR_COLI R S S S TRUE
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#> 4 P10 A 2015-12-10 B_ESCHR_COLI S I S S TRUE
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#> 5 B7 A 2015-03-02 B_ESCHR_COLI S S S S TRUE
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#> 6 W3 A 2018-03-31 B_STPHY_AURS R S R S TRUE
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#> 7 M3 A 2015-10-25 B_ESCHR_COLI R S S S TRUE
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#> 8 J3 A 2019-06-19 B_ESCHR_COLI S S S S TRUE
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#> 9 G6 A 2015-04-27 B_STPHY_AURS S S S S TRUE
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#> 10 P4 A 2011-06-21 B_ESCHR_COLI S S S S TRUE
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#> # ℹ 2,714 more rows
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```
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Time for the analysis.
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## Analysing the data
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The base R [`summary()`](https://rdrr.io/r/base/summary.html) function
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gives a good first impression, as it comes with support for the new `mo`
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and `sir` classes that we now have in our data set:
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``` r
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summary(our_data_1st)
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#> patient_id hospital date bacteria
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#> Length :2724 Length :2724 Min. :2011-01-01 Class :mo
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#> N.unique : 260 N.unique : 3 1st Qu.:2013-04-07 <NA> :0
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#> N.blank : 0 N.blank : 0 Median :2015-06-03 Unique:4
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#> Min.nchar: 2 Min.nchar: 1 Mean :2015-06-09 #1 :B_ESCHR_COLI
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#> Max.nchar: 3 Max.nchar: 1 3rd Qu.:2017-08-11 #2 :B_STPHY_AURS
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#> Max. :2019-12-27 #3 :B_STRPT_PNMN
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#> AMX AMC CIP
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#> Class:sir Class:sir Class:sir
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#> %S :41.6% (n=1133) %S :52.6% (n=1432) %S :52.5% (n=1431)
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#> %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) %SDD : 0.0% (n=0)
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#> %I :16.4% (n=446) %I :12.2% (n=333) %I : 6.5% (n=176)
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#> %R :42.0% (n=1145) %R :35.2% (n=959) %R :41.0% (n=1117)
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#> %NI : 0.0% (n=0) %NI : 0.0% (n=0) %NI : 0.0% (n=0)
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#> GEN first
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#> Class:sir Mode:logical
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#> %S :61.0% (n=1661) TRUE:2724
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#> %SDD : 0.0% (n=0)
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#> %I : 3.0% (n=82)
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#> %R :36.0% (n=981)
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#> %NI : 0.0% (n=0)
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glimpse(our_data_1st)
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#> Rows: 2,724
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#> Columns: 9
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#> $ patient_id <chr> "J3", "R7", "P3", "P10", "B7", "W3", "M3", "J3", "G6", "P4"…
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#> $ hospital <chr> "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A",…
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#> $ date <date> 2012-11-21, 2018-04-03, 2014-09-19, 2015-12-10, 2015-03-02…
|
||
#> $ bacteria <mo> "B_ESCHR_COLI", "B_KLBSL_PNMN", "B_ESCHR_COLI", "B_ESCHR_COL…
|
||
#> $ AMX <sir> R, R, R, S, S, R, R, S, S, S, S, R, S, S, R, R, R, R, S, R,…
|
||
#> $ AMC <sir> I, I, S, I, S, S, S, S, S, S, S, S, S, S, S, S, S, R, S, S,…
|
||
#> $ CIP <sir> S, S, S, S, S, R, S, S, S, S, S, S, S, S, S, S, S, S, S, S,…
|
||
#> $ GEN <sir> S, S, S, S, S, S, S, S, S, S, S, R, S, S, S, S, S, S, S, S,…
|
||
#> $ first <lgl> 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
|
||
#> <chr> <int>
|
||
#> 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
|
||
#> <chr> <int>
|
||
#> 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
|
||
#> <date> <sir>
|
||
#> 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
|
||
#> <mo> <sir> <sir>
|
||
#> 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
|
||
#> <mo> <sir> <sir> <sir> <sir>
|
||
#> 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
|
||
#> <chr> <chr> <date> <mo> <sir> <sir> <sir> <sir> <lgl>
|
||
#> 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
|
||
#> <chr> <chr> <date> <mo> <sir> <sir> <sir> <sir> <lgl>
|
||
#> 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
|
||
#> <chr> <chr> <date> <mo> <sir> <sir> <sir> <sir> <lgl>
|
||
#> 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
|
||
#> <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
|
||
#> # ℹ 1,990 more rows
|
||
#> # ℹ 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>, …
|
||
```
|
||
|
||
#### 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 | Amikacin | Amoxicillin | Amoxicillin/clavulanic acid | Ampicillin | Azithromycin | Benzylpenicillin | Cefazolin | Cefepime | Cefotaxime | Cefoxitin | Ceftazidime | Ceftriaxone | Cefuroxime | Chloramphenicol | Ciprofloxacin | Clindamycin | Colistin | Doxycycline | Erythromycin | Flucloxacillin | Fosfomycin | Gentamicin | Imipenem | Kanamycin | Linezolid | Meropenem | Metronidazole | Moxifloxacin | Mupirocin | Nitrofurantoin | Oxacillin | Piperacillin/tazobactam | Rifampicin | Teicoplanin | Tetracycline | Tigecycline | Tobramycin | Trimethoprim | Trimethoprim/sulfamethoxazole | Vancomycin |
|
||
|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|:---|
|
||
| 0-24 | F | 45.4% (15.4-79%) | 50.1% (20.5-77.6%) | 69% (44.5-88.5%) | 50.4% (20.6-77.3%) | 41.9% (18.1-65.6%) | 36.1% (12.3-64.3%) | NA | NA | 63.9% (34.6-87.6%) | 56.7% (25.9-85.8%) | 51.5% (25.6-74.4%) | 63.4% (32-88.1%) | 70.4% (45.4-89.1%) | 54% (22.3-85.3%) | 69.8% (45.9-88.9%) | 39.3% (17.7-64.6%) | 45.3% (18.1-75.9%) | 50.1% (21.5-80.5%) | 41.7% (19.1-67.6%) | 55.8% (23.7-83.3%) | 63.5% (32.6-89.4%) | 69.3% (44.6-88.3%) | 63.6% (36.1-88.2%) | 45.5% (15.7-77.7%) | 43.3% (17.8-71.2%) | 55.9% (24.3-82.2%) | NA | NA | 56.5% (24.3-85%) | 56.8% (30.9-82.3%) | 50.5% (19.4-80.8%) | 56.9% (26.7-85%) | 42.3% (18.3-68.8%) | 40.2% (17.6-67.7%) | 49.8% (20-79.3%) | 56.1% (22-85.4%) | 64.5% (39.6-85.5%) | 69.7% (42.3-90.4%) | 75.4% (52.1-91.7%) | 48.5% (24.3-72.6%) |
|
||
| 0-24 | M | 41.9% (15.2-72.5%) | 49.4% (23.3-75.5%) | 73.8% (51.8-90.1%) | 49.3% (22.7-76%) | 63.4% (40.7-83.5%) | 41.8% (20.4-64.8%) | 56.8% (25.2-83.5%) | 58.2% (29.1-85.8%) | 59.7% (29.1-87.4%) | 59.3% (29.1-86.6%) | 24.9% (8.9-47.3%) | 58.5% (28-86.5%) | 72.1% (47.9-90.5%) | NA | 77.2% (53-93.2%) | 61.6% (36.2-83.6%) | 25.5% (8.7-46.1%) | 69.4% (44.6-89.4%) | 63.4% (41.8-82.7%) | 64% (37.6-85.6%) | NA | 63.5% (40.9-83.1%) | 58.7% (27.6-86.5%) | 41.8% (13.6-71.2%) | 48.3% (17.9-78%) | 59.2% (27.4-86.4%) | NA | NA | NA | 53% (21.3-83.7%) | 57.2% (24.6-84.6%) | 59.9% (29.7-85.6%) | 48.2% (16.1-80.4%) | 48.4% (17.4-79.8%) | 68% (43.5-87.3%) | 65.7% (36-89.2%) | 44.3% (17.2-73.4%) | 69.4% (46.9-87.8%) | 74% (50.8-90.9%) | 75.3% (52.4-92.2%) |
|
||
| 25-49 | F | 46.8% (26.7-65.6%) | 39% (26.3-52.9%) | 73.8% (63.5-82.6%) | 39.3% (27.2-54.4%) | 54.8% (44.9-64.8%) | 36.5% (26.3-47.1%) | 66.4% (46.1-85%) | 69.2% (49.1-86.2%) | 70.2% (50.5-86.2%) | 68.1% (48.6-85.4%) | 27.9% (19.2-37.9%) | 70.1% (50.5-87.1%) | 71.4% (61.7-80.4%) | 58.2% (35.3-79.9%) | 85.5% (74.1-94.2%) | 67.1% (55.8-77.4%) | 25.8% (17-36%) | 75.5% (61.2-88.2%) | 54.9% (44.8-65.6%) | 55.2% (37.8-72.5%) | 60.9% (38.3-81.8%) | 75.2% (65.7-83.5%) | 69.9% (50.1-86.6%) | 37.5% (17.7-57.9%) | 50.7% (30.8-68.7%) | 69.4% (48.6-86.7%) | NA | 56.9% (36.2-77.7%) | 53.3% (30.9-75.6%) | 60.1% (38.1-81.8%) | 64.3% (43.2-83.6%) | 66% (45.7-85.2%) | 50.1% (30-69.6%) | 38.8% (19.6-58.9%) | 75.8% (61.6-88.1%) | 73.3% (56.6-89.5%) | 62.7% (47.6-77.1%) | 70.4% (58.7-80.2%) | 90% (82.9-95.4%) | 71.6% (61.7-80.4%) |
|
||
| 25-49 | M | 49.8% (24.2-75.8%) | 16.5% (8.1-27.4%) | 72.4% (60.5-83.5%) | 16.6% (7.7-28.2%) | 55.9% (43.6-67.9%) | 24.9% (14.7-37.6%) | 60.3% (33.2-82.4%) | 55.3% (27.6-81.8%) | 55.9% (29.7-81.1%) | 56.2% (27.7-82.1%) | 22.2% (12.7-33.9%) | 55.6% (29.1-81.8%) | 73.7% (62.6-83.8%) | 52.9% (25.2-79.6%) | 67.1% (53-79.8%) | 57.8% (43.5-71.8%) | 22.3% (12.6-33.6%) | 73% (57.8-85.5%) | 55.8% (43-68.2%) | 66.5% (51.6-79.4%) | 63.1% (40.3-84.5%) | 83.9% (74.5-91.7%) | 56.4% (28.4-84%) | 45.4% (18.9-73.8%) | 59.4% (37.6-77.9%) | 56.3% (28.8-81.1%) | NA | 52.8% (24.7-78.7%) | 64.2% (40.2-84.5%) | 62.9% (37.7-85.1%) | 60.5% (37.1-80.7%) | 55.8% (29.4-82.9%) | 65.4% (48.7-80.8%) | 54.5% (31.7-73.7%) | 72.8% (58.7-84.8%) | 84.8% (72.4-93.6%) | 66.7% (44.5-84.1%) | 71.4% (58.9-82.6%) | 86.6% (77.9-93.7%) | 77.1% (65.5-87.1%) |
|
||
| 50-74 | F | 44.8% (35.8-54.1%) | 30.1% (24.9-35.3%) | 74.1% (69.2-78.7%) | 30% (24.6-35.4%) | 41.9% (36.5-47.3%) | 23.5% (18.6-29%) | 73.1% (62-82.9%) | 76.6% (66.1-86%) | 74.8% (64.9-84.5%) | 74.6% (64.2-83.3%) | 37.5% (32.3-43.4%) | 74.8% (64.4-83.8%) | 74.5% (69.7-78.9%) | 61.2% (40.3-82.4%) | 79.4% (73-85%) | 44.9% (38.7-51%) | 37.8% (32.7-43.3%) | 63.8% (47.6-80.1%) | 41.7% (36.6-46.9%) | 58.1% (40-75.1%) | 65.2% (53.5-76.6%) | 78.7% (73.8-83.2%) | 80.6% (70.3-90%) | 28.1% (10.1-46.6%) | 53.2% (42.9-62.4%) | 79.3% (68.7-88.6%) | NA | 49.5% (37.5-61.8%) | 67.8% (48.5-86%) | 75.1% (63.3-86.3%) | 56.6% (37.8-74.2%) | 67.7% (56.4-79.6%) | 50.6% (40.9-59.1%) | 41.3% (31.5-50.4%) | 59% (48.3-74.5%) | 87.7% (80.4-94.1%) | 62.2% (55.4-68.4%) | 55.5% (49.8-61.1%) | 68% (62.7-73.3%) | 60.9% (55.8-66.1%) |
|
||
| 50-74 | M | 38.8% (30.6-48.6%) | 34.6% (29.1-40.3%) | 75% (70-79.5%) | 34.7% (29.2-40.5%) | 43.4% (37.8-48.5%) | 21% (16.5-26.4%) | 64.3% (54.1-74.1%) | 65.9% (56.5-75.4%) | 67.3% (58.3-77%) | 65.9% (56.1-75.9%) | 32.9% (27.6-38%) | 67.3% (57.4-76.8%) | 74.1% (69.2-78.8%) | 63.5% (42.4-83%) | 76.9% (71.6-81.9%) | 47.3% (40.9-53.8%) | 30.8% (26.1-36.1%) | 68.5% (53.5-81.9%) | 43.4% (37.7-48.8%) | 58.1% (42.4-73.2%) | 68.1% (53.5-82.2%) | 79.1% (74.4-83.1%) | 69% (59.7-78.3%) | 24.8% (9.5-40.5%) | 49.7% (35-63.2%) | 68.1% (58.1-77.6%) | 53.8% (32-75%) | 51.7% (36.1-67.3%) | 68.8% (51.1-85.7%) | 70.2% (54.7-85.3%) | 53.2% (37.5-68.7%) | 66.5% (55-76.8%) | 56.2% (45.8-65.4%) | 44% (30.3-57.5%) | 71.9% (58.2-82.2%) | 86.8% (77.3-93.6%) | 54.1% (46.9-61.4%) | 67.1% (61.5-72.5%) | 81% (76.4-85.2%) | 66.3% (61-71.2%) |
|
||
| 75+ | F | 51.4% (41.7-62%) | 30.9% (26.2-36.5%) | 74.4% (70.3-78.6%) | 30.9% (25.7-36.1%) | 36.6% (32-41.6%) | 20.7% (16.2-25.4%) | 73.6% (63.6-82.5%) | 79.1% (70.6-86.8%) | 78.6% (69.9-86.3%) | 76% (67.5-83.7%) | 43.1% (38.6-48%) | 78.9% (70.5-86.4%) | 77% (72.6-81.3%) | 63.2% (43.2-84.1%) | 77.7% (72.1-83.2%) | 41.2% (36-46.4%) | 39.1% (34.2-44.4%) | 63.7% (46.3-80.6%) | 36.5% (31.9-41.2%) | 57.1% (39.8-76%) | 65.8% (57.2-73.5%) | 84.6% (80.6-88%) | 81.9% (73.7-89.5%) | 33.3% (13.7-53%) | 49.6% (42.3-56.1%) | 81.3% (73.2-88.9%) | 55.9% (33.5-76.5%) | 41% (31.4-51.8%) | 63.7% (43.8-82.3%) | 77.8% (66-87.4%) | 56.3% (37.3-75.1%) | 71.8% (62-82.2%) | 48.3% (41.5-54.9%) | 43.3% (36.2-50.7%) | 63% (45.3-80.3%) | 85.9% (79.9-90.9%) | 70.4% (64.1-76.8%) | 60.4% (55.1-65.8%) | 77.6% (73.4-82.1%) | 55.3% (50.4-60.1%) |
|
||
| 75+ | M | 52.6% (43.3-62.6%) | 33% (28.1-38%) | 77.4% (73.3-81.5%) | 33% (28.2-38.2%) | 36.8% (32.3-41.8%) | 17.9% (12.6-23.2%) | 64.4% (55.4-73.3%) | 71.2% (63.1-79.1%) | 67.9% (59.5-75.8%) | 65.3% (56.3-73.6%) | 42.6% (37.8-47.4%) | 68.2% (59.7-76.4%) | 75.1% (70.9-79.2%) | 64.1% (45.8-81.8%) | 77.6% (72-82.6%) | 41% (36-46.4%) | 39.9% (35.1-44.5%) | 62.1% (46-78.8%) | 36.9% (32.4-41.4%) | 59.7% (43.4-76.6%) | 64.7% (56.6-73.6%) | 83% (79.4-86.7%) | 75.7% (66.6-83%) | 31.6% (12.1-51.7%) | 51.8% (44.9-58%) | 74.2% (65.8-82.7%) | NA | 52.2% (41.5-60.8%) | 69.3% (50.5-86.4%) | 72.2% (58.7-83.4%) | 59.3% (41.6-76.7%) | 73.1% (64.3-81.4%) | 49.9% (42.4-56.9%) | 46.3% (38.2-53.1%) | 59.7% (44.2-75.7%) | 86.8% (81.4-91.3%) | 72% (66.3-77.6%) | 55.8% (50.3-61.1%) | 73.3% (68.9-77.6%) | 57% (52.2-61.6%) |
|
||
|
||
#### 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)
|
||
#> ℹ `resistance()` assumes the EUCAST guideline and thus considers the 'I'
|
||
#> category susceptible. Set the `guideline` argument or the `AMR_guideline`
|
||
#> option to either "CLSI" or "EUCAST", see `?AMR-options`.
|
||
#> ℹ This message will be shown once per session.
|
||
#> [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
|
||
#> <chr> <dbl>
|
||
#> 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
|
||
#> <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*
|