AMR (for R)

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

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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.

We created this package for both academic research and routine analysis at the Faculty of Medical Sciences of the University of Groningen and the Medical Microbiology & Infection Prevention (MMBI) department of the University Medical Center Groningen (UMCG). This R package is actively maintained and free software; you can freely use and distribute it for both personal and commercial (but not patent) purposes under the terms of the GNU General Public Licence version 2.0 (GPL-2), as published by the Free Software Foundation. Read the full licence here.

This package can be used for:

• Calculating antimicrobial resistance
• Predicting antimicrobial resistance using regression models
• Getting properties for any microorganism (like Gram stain, species, genus or family)
• Getting properties for any antibiotic (like name, ATC code, defined daily dose or trade name)
• Plotting antimicrobial resistance
• Determining first isolates to be used for AMR analysis
• Applying EUCAST rules
• Determining multi-drug resistance organisms (MDRO)
• Descriptive statistics: frequency tables, kurtosis and skewness

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

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

install.packages("AMR")

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

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 microbial taxonomic data (with all nine taxonomic ranks - from kingdom to subspecies) from the publicly available Integrated Taxonomic Information System (ITIS, https://www.itis.gov).

All ~20,000 (sub)species from the taxonomic kingdoms Bacteria, Fungi and Protozoa are included in this package, as well as all their ~2,500 previously accepted names known to ITIS. Furthermore, the responsible authors and year of publication are available. This allows users to use authoritative taxonomic information for their data analysis on any microorganism, not only human pathogens. It also helps to quickly determine the Gram stain of bacteria, since all bacteria are classified into subkingdom Negibacteria or Posibacteria.

Read more about the data from ITIS in our manual.

Antimicrobial reference data

WHO Collaborating Centre for Drug Statistics Methodology

This package contains all ~500 antimicrobial drugs and their Anatomical Therapeutic Chemical (ATC) codes, ATC groups and Defined Daily Dose (DDD) 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.

Read more about the data from WHOCC in our manual.

Overview of functions

The AMR package basically does four important things:

1. It cleanses existing data, by transforming it to reproducible and profound classes, making the most efficient use of R. These functions all use artificial intelligence to guess results that you would expect:

   • Use as.mo() to get an ID of a microorganism. The IDs are human readable for the trained eye - the ID of Klebsiella pneumoniae is “B_KLBSL_PNE” (B stands for Bacteria) and the ID of S. aureus is “B_STPHY_AUR”. 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 artificial intelligence (AI) on the included ITIS data set, consisting of almost 20,000 microorganisms. It is very fast, 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.rsi() to transform 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”.
   • Use as.atc() to get the ATC code of an antibiotic as defined by the WHO. This package contains a database with most LIS codes, official names, DDDs and even trade names of antibiotics. For example, the values “Furabid”, “Furadantin”, “nitro” all return the ATC code of Nitrofurantoine.

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.
   • 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 more than 18,000 microorganisms (bacteria, fungi/yeasts and protozoa). Furthermore, the colloquial name and Gram stain 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 artificial intelligence. 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 the ATC code, LIS codes, official name, trivial name and DDD of both oral and parenteral administration. It also contains a total of 298 trade names. Use functions like ab_name() and ab_tradenames() to look up values. The ab_* functions use as.atc() internally so they support AI to guess your expected 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.

   • Calculate the resistance (and even co-resistance) of microbial isolates with the portion_R(), portion_IR(), portion_I(), portion_SI() and portion_S() functions. Similarly, the number of isolates can be determined with the count_R(), count_IR(), count_I(), count_SI() and count_S() functions. All these functions can be used with the dplyr package (e.g. in conjunction with summarise())
   • Plot AMR results with geom_rsi(), a function made for the ggplot2 package
   • Predict antimicrobial resistance for the nextcoming years using logistic regression models with the resistance_predict() function
   • Conduct descriptive statistics to enhance base R: calculate kurtosis(), skewness() and create frequency tables with freq()

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

   • The package contains extensive help pages with many examples.
   • It also contains an example data set called septic_patients. 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 of 38,414 antimicrobial results
     • Real and genuine data

Partners

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