AMR

An R package to simplify the analysis and prediction of Antimicrobial Resistance (AMR) and work with antibiotic properties by using evidence-based methods.

This R package was created for academic research by PhD students of 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).

▶️ Download it with install.packages("AMR") or see below for other possibilities.

Authors

• Berends MS^1,2, PhD Student
• Luz CF¹, PhD Student
• Hassing EEA², Data Analyst (contributor)

¹ Department of Medical Microbiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands

² Certe Medical Diagnostics & Advice, Groningen, the Netherlands

Why this package?

This R package contains functions to make microbiological, epidemiological data analysis easier. It allows the use of some new classes to work with MIC values and antimicrobial interpretations (i.e. values S, I and R).

With AMR you can also:

• Conduct AMR analysis with the rsi function, that can also be used with the dplyr package (e.g. in conjunction with summarise) to calculate the resistance percentages (and even co-resistance) of different antibiotic columns of a table
• Predict antimicrobial resistance for the nextcoming years with the rsi_predict function
• Apply EUCAST rules to isolates with the EUCAST_rules function
• Identify first isolates of every patient using guidelines from the CLSI (Clinical and Laboratory Standards Institute) with the first_isolate function
• Get antimicrobial ATC properties from the WHO Collaborating Centre for Drug Statistics Methodology (WHOCC), to be able to:
  • Translate antibiotic codes (like AMOX), official names (like amoxicillin) and even trade names (like Amoxil or Trimox) to an ATC code (like J01CA04) and vice versa with the abname function
  • Get the latest antibiotic properties like hierarchic groups and defined daily dose (DDD) with units and administration form from the WHOCC website with the atc_property function
• Conduct descriptive statistics: calculate kurtosis, skewness and create frequency tables

And it contains:

• A recent data set with ~2500 human pathogenic microorganisms, including family, genus, species, gram stain and aerobic/anaerobic
• A recent data set with all antibiotics as defined by the WHOCC, including ATC code, official name and DDD's
• An example data set septic_patients, consisting of 2000 blood culture isolates from anonymised septic patients between 2001 and 2017.

With the MDRO function (abbreviation of Multi Drug Resistant Organisms), you can check your isolates for exceptional resistance with country-specific guidelines or EUCAST rules. Currently guidelines for Germany and the Netherlands are supported. Please suggest addition of your own country here: https://github.com/msberends/AMR/issues/new.

Read all changes and new functions in NEWS.md.

How to get it?

This package is available on CRAN and also here on GitHub.

From CRAN (recommended)

Latest released version on CRAN:

Downloads via RStudio CRAN server (downloads by all other CRAN mirrors not measured, including the official https://cran.r-project.org):

• In RStudio (recommended):

  • Click on Tools and then Install Packages...
  • Type in AMR and press Install

• In R directly:

  • install.packages("AMR")

• In Exploratory.io:

  • (Exploratory.io costs $40/month but the somewhat limited Community Plan is free for students and teachers, click here to enroll)
  • Start the software and log in
  • Click on your username at the right hand side top
  • Click on R Packages
  • Click on the Install tab
  • Type in AMR and press Install
  • Once it’s installed it will show up in the User Packages section under the Packages tab.

From GitHub (latest development version)

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

How to use it?

# Call it with:
library(AMR)

# For a list of functions:
help(package = "AMR")

Overwrite/force resistance based on EUCAST rules

This is also called interpretive reading.

before <- data.frame(bactid = c("STAAUR",  # Staphylococcus aureus
                                "ENCFAE",  # Enterococcus faecalis
                                "ESCCOL",  # Escherichia coli
                                "KLEPNE",  # Klebsiella pneumoniae
                                "PSEAER"), # Pseudomonas aeruginosa
                     vanc = "-",           # Vancomycin
                     amox = "-",           # Amoxicillin
                     coli = "-",           # Colistin
                     cfta = "-",           # Ceftazidime
                     cfur = "-",           # Cefuroxime
                     stringsAsFactors = FALSE)
before
#   bactid vanc amox coli cfta cfur
# 1 STAAUR    -    -    -    -    -
# 2 ENCFAE    -    -    -    -    -
# 3 ESCCOL    -    -    -    -    -
# 4 KLEPNE    -    -    -    -    -
# 5 PSEAER    -    -    -    -    -

# Now apply those rules; just need a column with bacteria ID's and antibiotic results:
after <- EUCAST_rules(before)
after
#   bactid vanc amox coli cfta cfur
# 1 STAAUR    -    -    R    R    -
# 2 ENCFAE    -    -    R    R    R
# 3 ESCCOL    R    -    -    -    -
# 4 KLEPNE    R    R    -    -    -
# 5 PSEAER    R    R    -    -    R

Frequency tables

Base R lacks a simple function to create frequency tables. We created such a function that works with almost all data types: freq (or frequency_tbl). It can be used in two ways:

# Like base R:
freq(mydata$myvariable)

# And like tidyverse:
mydata %>% freq(myvariable)

Factors sort on item by default:

septic_patients %>% freq(hospital_id)
# Frequency table of `hospital_id`
# Class:     factor
# Length:    2000 (of which NA: 0 = 0.0%)
# Unique:    5
# 
#      Item    Count   Percent   Cum. Count   Cum. Percent   (Factor Level)
# ---  -----  ------  --------  -----------  -------------  ---------------
# 1    A         233     11.7%          233          11.7%                1
# 2    B         583     29.1%          816          40.8%                2
# 3    C         221     11.1%         1037          51.8%                3
# 4    D         650     32.5%         1687          84.4%                4
# 5    E         313     15.7%         2000         100.0%                5

This can be changed with the sort.count parameter:

septic_patients %>% freq(hospital_id, sort.count = TRUE)
# Frequency table of `hospital_id`
# Class:     factor
# Length:    2000 (of which NA: 0 = 0.0%)
# Unique:    5
# 
#      Item    Count   Percent   Cum. Count   Cum. Percent   (Factor Level)
# ---  -----  ------  --------  -----------  -------------  ---------------
# 1    D         650     32.5%          650          32.5%                4
# 2    B         583     29.1%         1233          61.7%                2
# 3    E         313     15.7%         1546          77.3%                5
# 4    A         233     11.7%         1779          88.9%                1
# 5    C         221     11.1%         2000         100.0%                3

All other types, like numbers, characters and dates, sort on count by default:

septic_patients %>% freq(date)
# Frequency table of `date` 
# Class:     Date
# Length:    2000 (of which NA: 0 = 0.0%)
# Unique:    1662
# 
# Oldest:    2 January 2001
# Newest:    18 October 2017 (+6133)
# Median:    6 December 2009 (~53%)
# 
#      Item          Count   Percent   Cum. Count   Cum. Percent
# ---  -----------  ------  --------  -----------  -------------
# 1    2008-12-24        5      0.2%            5           0.2%
# 2    2010-12-10        4      0.2%            9           0.4%
# 3    2011-03-03        4      0.2%           13           0.6%
# 4    2013-06-24        4      0.2%           17           0.8%
# 5    2017-09-01        4      0.2%           21           1.1%
# 6    2002-09-02        3      0.2%           24           1.2%
# 7    2003-10-14        3      0.2%           27           1.4%
# 8    2004-06-25        3      0.2%           30           1.5%
# 9    2004-06-27        3      0.2%           33           1.7%
# 10   2004-10-29        3      0.2%           36           1.8%
# 11   2005-09-27        3      0.2%           39           2.0%
# 12   2006-08-01        3      0.2%           42           2.1%
# 13   2006-10-10        3      0.2%           45           2.2%
# 14   2007-11-16        3      0.2%           48           2.4%
# 15   2008-03-09        3      0.2%           51           2.5%
# [ reached getOption("max.print.freq") -- omitted 1647 entries, n = 1949 (97.5%) ]

For numeric values, some extra descriptive statistics will be calculated:

freq(runif(n = 10, min = 1, max = 5))
# Frequency table 
# Class:     numeric
# Length:    10 (of which NA: 0 = 0.0%)
# Unique:    10
#   
# Mean:      2.9
# Std. dev.: 1.3 (CV: 0.43, MAD: 1.5)
# Five-Num:  1.5 | 1.7 | 2.6 | 4.0 | 4.7 (IQR: 2.3, CQV: 0.4)
# Outliers:  0
# 
#      Item   Count   Percent   Cum. Count   Cum. Percent
# ---------  ------  --------  -----------  -------------
#  1.132033       1     10.0%            1          10.0%
#  2.226903       1     10.0%            2          20.0%
#  2.280779       1     10.0%            3          30.0%
#  2.640898       1     10.0%            4          40.0%
#  2.913462       1     10.0%            5          50.0%
#  3.364201       1     10.0%            6          60.0%
#  3.771975       1     10.0%            7          70.0%
#  3.802861       1     10.0%            8          80.0%
#  3.803547       1     10.0%            9          90.0%
#  3.985691       1     10.0%           10         100.0%
# 
# Warning message:
# All observations are unique. 

Learn more about this function with:

?freq

New classes

This package contains two new S3 classes: mic for MIC values (e.g. from Vitek or Phoenix) and rsi for antimicrobial drug interpretations (i.e. S, I and R). Both are actually ordered factors under the hood (an MIC of 2 being higher than <=1 but lower than >=32, and for class rsi factors are ordered as S < I < R). Both classes have extensions for existing generic functions like print, summary and plot.

# Transform values to new classes
mic_data <- as.mic(c(">=32", "1.0", "8", "<=0.128", "8", "16", "16"))
rsi_data <- as.rsi(c(rep("S", 474), rep("I", 36), rep("R", 370)))

These functions also try to coerce valid values.

Quick overviews when just printing objects:

mic_data
# Class 'mic': 7 isolates
# 
# <NA>  0
# 
# <=0.128       1       8      16    >=32
#       1       1       2       2       1

rsi_data
# Class 'rsi': 880 isolates
# 
# <NA>:       0 
# Sum of S:   474 
# Sum of IR:  406 
# - Sum of R: 370 
# - Sum of I: 36 
# 
#   %S  %IR   %I   %R 
# 53.9 46.1  4.1 42.0 

A plot of rsi_data:

plot(rsi_data)

A plot of mic_data (defaults to bar plot):

plot(mic_data)

Other epidemiological functions:

# Determine key antibiotic based on bacteria ID
key_antibiotics(...)

# Selection of first isolates of any patient
first_isolate(...)

# Calculate resistance levels of antibiotics, can be used with `summarise` (dplyr)
rsi(...)
# Predict resistance levels of antibiotics
rsi_predict(...)

# Get name of antibiotic by ATC code
abname(...)
abname("J01CR02", from = "atc", to = "umcg") # "AMCL"

Databases included in package

Datasets to work with antibiotics and bacteria properties.

# Dataset with 2000 random blood culture isolates from anonymised
# septic patients between 2001 and 2017 in 5 Dutch hospitals
septic_patients   # A tibble: 4,000 x 47

# Dataset with ATC antibiotics codes, official names, trade names 
# and DDD's (oral and parenteral)
antibiotics       # A tibble: 420 x 18

# Dataset with bacteria codes and properties like gram stain and 
# aerobic/anaerobic
microorganisms    # A tibble: 2,453 x 12

Copyright

This R package is licensed under the GNU General Public License (GPL) v2.0. In a nutshell, this means that this package:

• May be used for commercial purposes

• May be used for private purposes

• May not be used for patent purposes

• May be modified, although:

  • Modifications must be released under the same license when distributing the package
  • Changes made to the code must be documented

• May be distributed, although:

  • Source code must be made available when the package is distributed
  • A copy of the license and copyright notice must be included with the package.

• Comes with a LIMITATION of liability

• Comes with NO warranty