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312 lines
22 KiB
Markdown
# The `AMR` Package for R <a href="https://msberends.github.io/AMR/"><img src="./logo.svg" align="right" height="139" /></a>
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* Provides an **all-in-one solution** for antimicrobial resistance (AMR) data analysis in a One Health approach
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* Used in over 175 countries, available in 20 languages
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* Generates **antibiograms** - traditional, combined, syndromic, and even WISCA
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* Provides the **full microbiological taxonomy** and extensive info on **all antimicrobial drugs**
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* Applies all recent **CLSI** and **EUCAST** clinical and veterinary breakpoints for MICs, disk zones and ECOFFs
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* Corrects for duplicate isolates, **calculates** and **predicts** AMR per antibiotic class
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* Integrates with **WHONET**, ATC, **EARS-Net**, PubChem, **LOINC**, **SNOMED CT**, and **NCBI**
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* 100% free of costs and dependencies, highly suitable for places with **limited resources**
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<a href="https://chatgpt.com/g/g-M4UNLwFi5-amr-for-r-assistant"><img src="AMRforRGPT.svg" style="min-width: 300px; width: 10%;" /></a>
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<div style="display: flex; font-size: 0.8em;">
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<p style="text-align:left; width: 50%;"><small><a href="https://msberends.github.io/AMR/">https://msberends.github.io/AMR</a></small></p>
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<p style="text-align:right; width: 50%;"><small><a href="https://doi.org/10.18637/jss.v104.i03" target="_blank">https://doi.org/10.18637/jss.v104.i03</a></small></p>
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</div>
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<a href="./reference/clinical_breakpoints.html#response-from-clsi-and-eucast"><img src="./endorsement_clsi_eucast.jpg" class="endorse_img" align="right" height="120" /></a>
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----
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### Introduction
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The `AMR` package is a [free and open-source](#copyright) R package with [zero dependencies](https://en.wikipedia.org/wiki/Dependency_hell) to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial data and properties, by using evidence-based methods. **Our aim is to provide a standard** for clean and reproducible AMR data analysis, that can therefore empower epidemiological analyses to continuously enable surveillance and treatment evaluation in any setting. [Many different researchers](./authors.html) from around the globe are continually helping us to make this a successful and durable project!
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This work was published in the Journal of Statistical Software (Volume 104(3); [DOI 10.18637/jss.v104.i03](https://doi.org/10.18637/jss.v104.i03)) and formed the basis of two PhD theses ([DOI 10.33612/diss.177417131](https://doi.org/10.33612/diss.177417131) and [DOI 10.33612/diss.192486375](https://doi.org/10.33612/diss.192486375)).
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After installing this package, R knows [**~52,000 distinct microbial species**](./reference/microorganisms.html) (updated December 2022) and all [**~600 antibiotic, antimycotic and antiviral drugs**](./reference/antibiotics.html) by name and code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED CT), and knows all about valid SIR and MIC values. The integral clinical breakpoint guidelines from CLSI and EUCAST are included, even with epidemiological cut-off (ECOFF) values. It supports and can read any data format, including WHONET data. This package works on Windows, macOS and Linux with all versions of R since R-3.0 (April 2013). **It was designed to work in any setting, including those with very limited resources**. It was created for both routine data analysis and academic research at the Faculty of Medical Sciences of the [University of Groningen](https://www.rug.nl), in collaboration with non-profit organisations [Certe Medical Diagnostics and Advice Foundation](https://www.certe.nl) and [University Medical Center Groningen](https://www.umcg.nl).
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##### Used in over 175 countries, available in 20 languages
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<a href="./countries_large.png" target="_blank"><img src="./countries.png" align="right" style="max-width: 300px;" /></a>
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Since its first public release in early 2018, this R package has been used in almost all countries in the world. Click the map to enlarge and to see the country names.
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With the help of contributors from all corners of the world, the `AMR` package is available in <img src="lang_en.svg" style="height: 13px !important; border: 1px solid #cccccc; vertical-align: initial !important;"> English, <img src="lang_cs.svg" style="height: 13px !important; border: 1px solid #cccccc; vertical-align: initial !important;"> Czech, <img src="lang_zh.svg" style="height: 13px !important; border: 1px solid #cccccc; vertical-align: initial !important;"> Chinese, <img src="lang_da.svg" style="height: 13px !important; border: 1px solid #cccccc; vertical-align: initial !important;"> Danish, <img src="lang_nl.svg" style="height: 13px !important; border: 1px solid #cccccc; vertical-align: initial !important;"> Dutch, <img src="lang_fi.svg" style="height: 13px !important; border: 1px solid #cccccc; vertical-align: initial !important;"> Finnish, <img src="lang_fr.svg" style="height: 13px !important; border: 1px solid #cccccc; vertical-align: initial !important;"> French, <img src="lang_de.svg" style="height: 13px !important; border: 1px solid #cccccc; vertical-align: initial !important;"> German, <img src="lang_el.svg" style="height: 13px !important; border: 1px solid #cccccc; vertical-align: initial !important;"> Greek, <img src="lang_it.svg" style="height: 13px !important; border: 1px solid #cccccc; vertical-align: initial !important;"> Italian, <img src="lang_ja.svg" style="height: 13px !important; border: 1px solid #cccccc; vertical-align: initial !important;"> Japanese, <img src="lang_no.svg" style="height: 13px !important; border: 1px solid #cccccc; vertical-align: initial !important;"> Norwegian, <img src="lang_pl.svg" style="height: 13px !important; border: 1px solid #cccccc; vertical-align: initial !important;"> Polish, <img src="lang_pt.svg" style="height: 13px !important; border: 1px solid #cccccc; vertical-align: initial !important;"> Portuguese, <img src="lang_ro.svg" style="height: 13px !important; border: 1px solid #cccccc; vertical-align: initial !important;"> Romanian, <img src="lang_ru.svg" style="height: 13px !important; border: 1px solid #cccccc; vertical-align: initial !important;"> Russian, <img src="lang_es.svg" style="height: 13px !important; border: 1px solid #cccccc; vertical-align: initial !important;"> Spanish, <img src="lang_sv.svg" style="height: 13px !important; border: 1px solid #cccccc; vertical-align: initial !important;"> Swedish, <img src="lang_tr.svg" style="height: 13px !important; border: 1px solid #cccccc; vertical-align: initial !important;"> Turkish, and <img src="lang_uk.svg" style="height: 13px !important; border: 1px solid #cccccc; vertical-align: initial !important;"> Ukrainian. Antimicrobial drug (group) names and colloquial microorganism names are provided in these languages.
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### Practical examples
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#### Filtering and selecting data
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One of the most powerful functions of this package, aside from calculating and plotting AMR, is selecting and filtering based on antibiotic columns. This can be done using the so-called [antibiotic class selectors](https://msberends.github.io/AMR/reference/antibiotic_class_selectors.html) that work in base R, `dplyr` and `data.table`:
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```r
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# AMR works great with dplyr, but it's not required or neccesary
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library(AMR)
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library(dplyr)
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example_isolates %>%
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mutate(bacteria = mo_fullname()) %>%
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# filtering functions for microorganisms:
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filter(mo_is_gram_negative(),
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mo_is_intrinsic_resistant(ab = "cefotax")) %>%
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# antibiotic selectors:
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select(bacteria,
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aminoglycosides(),
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carbapenems())
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```
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With only having defined a row filter on Gram-negative bacteria with intrinsic resistance to cefotaxime (`mo_is_gram_negative()` and `mo_is_intrinsic_resistant()`) and a column selection on two antibiotic groups (`aminoglycosides()` and `carbapenems()`), the reference data about [all microorganisms](./reference/microorganisms.html) and [all antibiotics](./reference/antibiotics.html) in the `AMR` package make sure you get what you meant:
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|bacteria | GEN | TOB | AMK | KAN | IPM | MEM |
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|:------------------------------|:---:|:---:|:---:|:---:|:---:|:---:|
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|*Pseudomonas aeruginosa* | I | S | | R | S | |
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|*Pseudomonas aeruginosa* | I | S | | R | S | |
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|*Pseudomonas aeruginosa* | I | S | | R | S | |
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|*Pseudomonas aeruginosa* | S | S | S | R | | S |
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|*Pseudomonas aeruginosa* | S | S | S | R | S | S |
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|*Pseudomonas aeruginosa* | S | S | S | R | S | S |
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|*Stenotrophomonas maltophilia* | R | R | R | R | R | R |
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|*Pseudomonas aeruginosa* | S | S | S | R | | S |
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|*Pseudomonas aeruginosa* | S | S | S | R | | S |
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|*Pseudomonas aeruginosa* | S | S | S | R | S | S |
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A base R equivalent would be:
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```r
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library(AMR)
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example_isolates$bacteria <- mo_fullname(example_isolates$mo)
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example_isolates[which(mo_is_gram_negative() &
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mo_is_intrinsic_resistant(ab = "cefotax")),
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c("bacteria", aminoglycosides(), carbapenems())]
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```
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This base R code will work in any version of R since April 2013 (R-3.0). Moreover, this code works identically with the `data.table` package, only by starting with:
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```r
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example_isolates <- data.table::as.data.table(example_isolates)
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```
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#### Generating antibiograms
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The `AMR` package supports generating traditional, combined, syndromic, and even weighted-incidence syndromic combination antibiograms (WISCA).
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If used inside R Markdown or Quarto, the table will be printed in the right output format automatically (such as markdown, LaTeX, HTML, etc.).
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```r
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antibiogram(example_isolates,
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antibiotics = c(aminoglycosides(), carbapenems()))
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```
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| Pathogen | Amikacin | Gentamicin | Imipenem | Kanamycin | Meropenem | Tobramycin |
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|:------------------------|:----------------:|:---------------:|:----------------:|:----------------:|:----------------:|:----------------:|
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| CoNS | 0% (0/43) | 86% (267/309) | 52% (25/48) | 0% (0/43) | 52% (25/48) | 22% (12/55) |
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| *E. coli* | 100% (171/171) | 98% (451/460) | 100% (422/422) | | 100% (418/418) | 97% (450/462) |
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| *E. faecalis* | 0% (0/39) | 0% (0/39) | 100% (38/38) | 0% (0/39) | | 0% (0/39) |
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| *K. pneumoniae* | | 90% (52/58) | 100% (51/51) | | 100% (53/53) | 90% (52/58) |
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| *P. aeruginosa* | | 100% (30/30) | | 0% (0/30) | | 100% (30/30) |
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| *P. mirabilis* | | 94% (32/34) | 94% (30/32) | | | 94% (32/34) |
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| *S. aureus* | | 99% (231/233) | | | | 98% (84/86) |
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| *S. epidermidis* | 0% (0/44) | 79% (128/163) | | 0% (0/44) | | 51% (45/89) |
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| *S. hominis* | | 92% (74/80) | | | | 85% (53/62) |
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| *S. pneumoniae* | 0% (0/117) | 0% (0/117) | | 0% (0/117) | | 0% (0/117) |
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In combination antibiograms, it is clear that combined antibiotics yield higher empiric coverage:
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```r
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antibiogram(example_isolates,
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antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
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mo_transform = "gramstain")
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```
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| Pathogen | Piperacillin/tazobactam | Piperacillin/tazobactam + Gentamicin | Piperacillin/tazobactam + Tobramycin |
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|:--------------|:-----------------------:|:-------------------------------------:|:------------------------------------:|
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| Gram-negative | 88% (565/641) | 99% (681/691) | 98% (679/693) |
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| Gram-positive | 86% (296/345) | 98% (1018/1044) | 95% (524/550) |
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Like many other functions in this package, `antibiogram()` comes with support for 20 languages that are often detected automatically based on system language:
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```r
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antibiogram(example_isolates,
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antibiotics = c("cipro", "tobra", "genta"), # any arbitrary name or code will work
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mo_transform = "gramstain",
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ab_transform = "name",
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language = "uk") # Ukrainian
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```
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| Збудник | Гентаміцин | Тобраміцин | Ципрофлоксацин |
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|:---------------|:--------------------:|:-------------------:|:------------------:|
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| Грамнегативні | 96% (659/684) | 96% (658/686) | 91% (621/684) |
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| Грампозитивні | 63% (740/1170) | 34% (228/665) | 77% (560/724) |
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#### Calculating resistance per group
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For a manual approach, you can use the `resistance` or `susceptibility()` function:
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```r
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example_isolates %>%
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# group by ward:
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group_by(ward) %>%
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# calculate AMR using resistance() for gentamicin and tobramycin
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# and get their 95% confidence intervals using sir_confidence_interval():
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summarise(across(c(GEN, TOB),
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list(total_R = resistance,
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conf_int = function(x) sir_confidence_interval(x, collapse = "-"))))
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```
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|ward | GEN_total_R|GEN_conf_int | TOB_total_R|TOB_conf_int |
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|:---------:|:----------:|:-----------:|:----------:|:-----------:|
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|Clinical | 0.229 |0.205-0.254 | 0.315 |0.284-0.347 |
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|ICU | 0.290 |0.253-0.330 | 0.400 |0.353-0.449 |
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|Outpatient | 0.200 |0.131-0.285 | 0.368 |0.254-0.493 |
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Or use [antibiotic class selectors](https://msberends.github.io/AMR/reference/antibiotic_class_selectors.html) to select a series of antibiotic columns:
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```r
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library(AMR)
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library(dplyr)
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out <- example_isolates %>%
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# group by ward:
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group_by(ward) %>%
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# calculate AMR using resistance(), over all aminoglycosides and polymyxins:
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summarise(across(c(aminoglycosides(), polymyxins()),
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resistance))
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out
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```
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| ward | GEN | TOB | AMK | KAN | COL |
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|:-----------|------:|------:|------:|------:|------:|
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| Clinical | 0.229 | 0.315 | 0.626 | 1 | 0.780 |
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| ICU | 0.290 | 0.400 | 0.662 | 1 | 0.857 |
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| Outpatient | 0.200 | 0.368 | 0.605 | | 0.889 |
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```r
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# transform the antibiotic columns to names:
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out %>% set_ab_names()
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```
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| ward | gentamicin | tobramycin | amikacin | kanamycin | colistin |
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|:-----------|-----------:|-----------:|----------|----------:|----------:|
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| Clinical | 0.229 | 0.315 | 0.626 | 1 | 0.780 |
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| ICU | 0.290 | 0.400 | 0.662 | 1 | 0.857 |
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| Outpatient | 0.200 | 0.368 | 0.605 | | 0.889 |
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```r
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# transform the antibiotic column to ATC codes:
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out %>% set_ab_names(property = "atc")
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```
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| ward | J01GB03 | J01GB01 | J01GB06 | J01GB04 | J01XB01 |
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|:-----------|-----------:|-----------:|----------|----------:|----------:|
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| Clinical | 0.229 | 0.315 | 0.626 | 1 | 0.780 |
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| ICU | 0.290 | 0.400 | 0.662 | 1 | 0.857 |
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| Outpatient | 0.200 | 0.368 | 0.605 | | 0.889 |
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### What else can you do with this package?
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This package was intended as a comprehensive toolbox for integrated AMR data analysis. This package can be used for:
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* Reference for the taxonomy of microorganisms, since the package contains all microbial (sub)species from the List of Prokaryotic names with Standing in Nomenclature ([LPSN]((https://lpsn.dsmz.de))) and the Global Biodiversity Information Facility ([GBIF](https://www.gbif.org)) ([manual](./reference/mo_property.html))
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* Interpreting raw MIC and disk diffusion values, based on any CLSI or EUCAST guideline ([manual](./reference/as.sir.html))
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* Retrieving antimicrobial drug names, doses and forms of administration from clinical health care records ([manual](./reference/ab_from_text.html))
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* Determining first isolates to be used for AMR data analysis ([manual](./reference/first_isolate.html))
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* Calculating antimicrobial resistance ([tutorial](./articles/AMR.html))
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* Determining multi-drug resistance (MDR) / multi-drug resistant organisms (MDRO) ([tutorial](./articles/MDR.html))
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* Calculating (empirical) susceptibility of both mono therapy and combination therapies ([tutorial](./articles/AMR.html))
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* Predicting future antimicrobial resistance using regression models ([tutorial](./articles/resistance_predict.html))
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* Getting properties for any microorganism (like Gram stain, species, genus or family) ([manual](./reference/mo_property.html))
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* Getting properties for any antibiotic (like name, code of EARS-Net/ATC/LOINC/PubChem, defined daily dose or trade name) ([manual](./reference/ab_property.html))
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* Plotting antimicrobial resistance ([tutorial](./articles/AMR.html))
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* Applying EUCAST expert rules ([manual](./reference/eucast_rules.html))
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* Getting SNOMED codes of a microorganism, or getting properties of a microorganism based on a SNOMED code ([manual](./reference/mo_property.html))
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* Getting LOINC codes of an antibiotic, or getting properties of an antibiotic based on a LOINC code ([manual](./reference/ab_property.html))
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* Machine reading the EUCAST and CLSI guidelines from 2011-2021 to translate MIC values and disk diffusion diameters to SIR ([link](./articles/datasets.html))
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* Principal component analysis for AMR ([tutorial](./articles/PCA.html))
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### Get this package
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#### Latest official version
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[](https://cran.r-project.org/package=AMR)
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[](https://cran.r-project.org/package=AMR)
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This package is available [here on the official R network (CRAN)](https://cran.r-project.org/package=AMR). Install this package in R from CRAN by using the command:
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```r
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install.packages("AMR")
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```
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It will be downloaded and installed automatically. For RStudio, click on the menu *Tools* > *Install Packages...* and then type in "AMR" and press <kbd>Install</kbd>.
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**Note:** Not all functions on this website may be available in this latest release. To use all functions and data sets mentioned on this website, install the latest development version.
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#### Latest development version
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[](https://github.com/msberends/AMR/actions/workflows/check-old.yaml?query=branch%3Amain)
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[](https://github.com/msberends/AMR/actions/workflows/check-recent.yaml?query=branch%3Amain)
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[](https://www.codefactor.io/repository/github/msberends/amr)
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[](https://codecov.io/gh/msberends/AMR?branch=main)
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Please read our [Developer Guideline here](https://github.com/msberends/AMR/wiki/Developer-Guideline).
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The latest and unpublished development version can be installed from GitHub in two ways:
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1. Manually, using:
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```r
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install.packages("remotes") # if you haven't already
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remotes::install_github("msberends/AMR")
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```
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2. Automatically, using the [rOpenSci R-universe platform](https://ropensci.org/r-universe/), by adding [our R-universe address](https://msberends.r-universe.dev) to your list of repositories ('repos'):
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```r
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options(repos = c(getOption("repos"),
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msberends = "https://msberends.r-universe.dev"))
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```
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After this, you can install and update this `AMR` package like any official release (e.g., using `install.packages("AMR")` or in RStudio via *Tools* > *Check for Package Updates...*).
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### Get started
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To find out how to conduct AMR data analysis, please [continue reading here to get started](./articles/AMR.html) or click a link in the ['How to' menu](https://msberends.github.io/AMR/articles/).
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### Partners
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The development of this package is part of, related to, or made possible by the following non-profit organisations and initiatives:
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<div align="center">
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<a href="https://www.rug.nl" title="University of Groningen"><img src="./logo_rug.svg" style="max-width: 200px;"></a>
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<a href="https://www.umcg.nl" title="University Medical Center Groningen"><img src="./logo_umcg.svg" style="max-width: 200px;"></a>
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<a href="https://www.certe.nl" title="Certe Medical Diagnostics and Advice Foundation"><img src="./logo_certe.svg" style="max-width: 200px;"></a>
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<a href="https://www.deutschland-nederland.eu" title="EurHealth-1-Health"><img src="./logo_eh1h.png" style="max-width: 200px;"></a>
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<a href="https://www.deutschland-nederland.eu" title="INTERREG"><img src="./logo_interreg.png" style="max-width: 200px;"></a>
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</div>
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### Copyright
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This R package is free, open-source software and licensed under the [GNU General Public License v2.0 (GPL-2)](./LICENSE-text.html). In a nutshell, this means that this package:
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- May be used for commercial purposes
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- May be used for private purposes
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- May **not** be used for patent purposes
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- May be modified, although:
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- Modifications **must** be released under the same license when distributing the package
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- Changes made to the code **must** be documented
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- May be distributed, although:
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- Source code **must** be made available when the package is distributed
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- A copy of the license and copyright notice **must** be included with the package.
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- Comes with a LIMITATION of liability
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- Comes with NO warranty
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