AMR (for R)

(TLDR - to find out how to conduct AMR analysis, please continue reading here to get started.

18 October 2019
METHODS PAPER PREPRINTED
A methods paper about this package has been preprinted at bioRxiv. It was updated on 8 November 2019. Please click here for the publishers page.

What is AMR (for R)?

AMR is a free and open-source R package to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and to work with microbial and antimicrobial properties by using evidence-based methods. Since its first public release in early 2018, this package has been downloaded over 25,000 times from more than 60 countries (source: CRAN logs, 2019).

After installing this package, R knows ~70,000 microorganisms (distinct microbial species) and ~450 antibiotics by name and code, and knows all about valid RSI and MIC values. It supports any data format, including WHONET/EARS-Net data.

We created this package for both routine analysis and academic research (as part of our PhD theses) at the Faculty of Medical Sciences of the University of Groningen, the Netherlands, and the Medical Microbiology & Infection Prevention (MMBI) department of the University Medical Center Groningen (UMCG). This R package is actively maintained and is free software (see Copyright).

Used to SPSS? Read our tutorial on how to import data from SPSS, SAS or Stata.

Partners

The development of this package is part of, related to, or made possible by:

What can you do with this package?

This package can be used for:

  • Reference for the taxonomy of microorganisms, since the package contains all microbial (sub)species from the Catalogue of Life (manual)
  • Interpreting raw MIC and disk diffusion values, based on the latest CLSI or EUCAST guidelines (manual)
  • Determining first isolates to be used for AMR analysis (manual)
  • Calculating antimicrobial resistance (tutorial)
  • Determining multi-drug resistance (MDR) / multi-drug resistant organisms (MDRO) (tutorial)
  • Calculating (empirical) susceptibility of both mono therapy and combination therapies (tutorial)
  • Predicting future antimicrobial resistance using regression models (tutorial)
  • Getting properties for any microorganism (like Gram stain, species, genus or family) (manual)
  • Getting properties for any antibiotic (like name, EARS-Net code, ATC code, PubChem code, defined daily dose or trade name) (manual)
  • Plotting antimicrobial resistance (tutorial)
  • Applying EUCAST expert rules (manual)

This package is ready-to-use for a professional environment by specialists in the following fields:

Medical Microbiology

  • Epidemiologists (both clinical microbiological and research)
  • Research Microbiologists
  • Biomedical Researchers
  • Research Pharmacologists
  • Data Scientists / Data Analysts

Veterinary Microbiology

  • Research Veterinarians
  • Veterinary Epidemiologists

Microbial Ecology

  • Soil Microbiologists
  • Extremophile Researchers
  • Astrobiologists

Developers

  • Package developers for R
  • Software developers
  • Web application / Shiny developers

Get this package

Latest released version

This package is available on the official R network (CRAN), which has a peer-reviewed submission process. Install this package in R with:

It will be downloaded and installed automatically. For RStudio, click on the menu Tools > Install Packages… and then type in “AMR” and press Install.

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.

Latest development version

The latest and unpublished development version can be installed with (precaution: may be unstable):

install.packages("devtools")
devtools::install_gitlab("msberends/AMR")

Get started

To find out how to conduct AMR analysis, please continue reading here to get started or click the links in the ‘How to’ menu.

Short introduction

Microbial (taxonomic) reference data

This package contains the complete taxonomic tree of almost all 70,000 microorganisms from the authoritative and comprehensive Catalogue of Life (CoL, www.catalogueoflife.org). With catalogue_of_life_version() can be checked which version of the CoL is included in this package.

Read more about which data from the Catalogue of Life in our manual.

Antimicrobial reference data

This package contains all ~450 antimicrobial drugs and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD, oral and IV) from the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC, https://www.whocc.no) and the Pharmaceuticals Community Register of the European Commission.

NOTE: The WHOCC copyright does not allow use for commercial purposes, unlike any other info from this package. See https://www.whocc.no/copyright_disclaimer/.

Read more about the data from WHOCC in our manual.

WHONET / EARS-Net

We support WHONET and EARS-Net data. Exported files from WHONET can be imported into R and can be analysed easily using this package. For education purposes, we created an example data set WHONET with the exact same structure as a WHONET export file. Furthermore, this package also contains a data set antibiotics with all EARS-Net antibiotic abbreviations, and knows almost all WHONET abbreviations for microorganisms. When using WHONET data as input for analysis, all input parameters will be set automatically.

Read our tutorial about how to work with WHONET data here.

Overview of functions

The AMR package basically does four important things:

  1. It cleanses existing data by providing new classes for microoganisms, antibiotics and antimicrobial results (both S/I/R and MIC). By installing this package, you teach R everything about microbiology that is needed for analysis. These functions all use intelligent rules to guess results that you would expect:

    • Use as.mo() to get a microbial ID. The IDs are human readable for the trained eye - the ID of Klebsiella pneumoniae is “B_KLBSL_PNMN” (B stands for Bacteria) and the ID of S. aureus is “B_STPHY_AURS”. The function takes almost any text as input that looks like the name or code of a microorganism like “E. coli”, “esco” or “esccol” and tries to find expected results using intelligent rules combined with the included Catalogue of Life data set. It only takes milliseconds to find results, please see our benchmarks. Moreover, it can group Staphylococci into coagulase negative and positive (CoNS and CoPS, see source) and can categorise Streptococci into Lancefield groups (like beta-haemolytic Streptococcus Group B, source).
    • Use as.ab() to get an antibiotic ID. Like microbial IDs, these IDs are also human readable based on those used by EARS-Net. For example, the ID of amoxicillin is AMX and the ID of gentamicin is GEN. The as.ab() function also uses intelligent rules to find results like accepting misspelling, trade names and abbrevations used in many laboratory systems. For instance, the values “Furabid”, “Furadantin”, “nitro” all return the ID of Nitrofurantoine. To accomplish this, the package contains a database with most LIS codes, official names, trade names, ATC codes, defined daily doses (DDD) and drug categories of antibiotics.
    • Use as.rsi() to get antibiotic interpretations based on raw MIC values (in mg/L) or disk diffusion values (in mm), or transform existing values to valid antimicrobial results. It produces just S, I or R based on your input and warns about invalid values. Even values like “<=0.002; S” (combined MIC/RSI) will result in “S”.
    • Use as.mic() to cleanse your MIC values. It produces a so-called factor (called ordinal in SPSS) with valid MIC values as levels. A value like “<=0.002; S” (combined MIC/RSI) will result in “<=0.002”.

  2. It enhances existing data and adds new data from data sets included in this package.

    • Use eucast_rules() to apply EUCAST expert rules to isolates (not the translation from MIC to RSI values, use as.rsi() for that).
    • Use first_isolate() to identify the first isolates of every patient using guidelines from the CLSI (Clinical and Laboratory Standards Institute).
      • You can also identify first weighted isolates of every patient, an adjusted version of the CLSI guideline. This takes into account key antibiotics of every strain and compares them.
    • Use mdro() (abbreviation of Multi Drug Resistant Organisms) to check your isolates for exceptional resistance with country-specific guidelines or EUCAST rules. Currently, national guidelines for Germany and the Netherlands are supported.
    • The data set microorganisms contains the complete taxonomic tree of ~70,000 microorganisms. Furthermore, some colloquial names and all Gram stains are available, which enables resistance analysis of e.g. different antibiotics per Gram stain. The package also contains functions to look up values in this data set like mo_genus(), mo_family(), mo_gramstain() or even mo_phylum(). As they use as.mo() internally, they also use the same intelligent rules for determination. For example, mo_genus("MRSA") and mo_genus("S. aureus") will both return "Staphylococcus". They also come with support for German, Dutch, Spanish, Italian, French and Portuguese. These functions can be used to add new variables to your data.
    • The data set antibiotics contains ~450 antimicrobial drugs with their EARS-Net code, ATC code, PubChem compound ID, official name, common LIS codes and DDDs of both oral and parenteral administration. It also contains all (thousands of) trade names found in PubChem. The function ab_atc() will return the ATC code of an antibiotic as defined by the WHO. Use functions like ab_name(), ab_group() and ab_tradenames() to look up values. The ab_* functions use as.ab() internally so they support the same intelligent rules to guess the most probable result. For example, ab_name("Fluclox"), ab_name("Floxapen") and ab_name("J01CF05") will all return "Flucloxacillin". These functions can again be used to add new variables to your data.

  3. It analyses the data with convenient functions that use well-known methods.

  4. It teaches the user how to use all the above actions.

    • Aside from this website with many tutorials, the package itself contains extensive help pages with many examples for all functions.
    • The package also contains example data sets:
      • The example_isolates data set. This data set contains:
        • 2,000 blood culture isolates from anonymised septic patients between 2001 and 2017 in the Northern Netherlands
        • Results of 40 antibiotics (each antibiotic in its own column) with a total ~40,000 antimicrobial results
        • Real and genuine data
      • The WHONET data set. This data set only contains fake data, but with the exact same structure as files exported by WHONET. Read more about WHONET on its tutorial page.