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
• Create frequency tables with the freq function

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.

This package contains an example data set septic_patients, consisting of 2000 isolates from anonymised septic patients between 2001 and 2017.

How to get it?

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

From CRAN (recommended)

• 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).

## Factors sort on item by default:

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


## This can be changed with the `sort.count` parameter:

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


## Other types, like numbers or dates, sort on count by default:

> freq(septic_patients$date)
# Class:     Date
# Length:    2000 (of which NA: 0 = 0.0%)
# Unique:    1662
# 
# Oldest:    2 January 2001
# Newest:    18 October 2017 (+6133)
# 
# Item          Count   Percent   Cum. Count   Cum. Percent
# -----------  ------  --------  -----------  -------------
# 2008-12-24        5      0.2%            5           0.2%
# 2010-12-10        4      0.2%            9           0.4%
# 2011-03-03        4      0.2%           13           0.6%
# 2013-06-24        4      0.2%           17           0.8%
# 2017-09-01        4      0.2%           21           1.1%
# 2002-09-02        3      0.2%           24           1.2%
# 2003-10-14        3      0.2%           27           1.4%
# 2004-06-25        3      0.2%           30           1.5%
# 2004-06-27        3      0.2%           33           1.7%
# 2004-10-29        3      0.2%           36           1.8%
# 2005-09-27        3      0.2%           39           2.0%
# 2006-08-01        3      0.2%           42           2.1%
# 2006-10-10        3      0.2%           45           2.2%
# 2007-11-16        3      0.2%           48           2.4%
# 2008-03-09        3      0.2%           51           2.5%
# ... and 1647 more (n = 1949; 97.5%). Use `nmax` to show more rows.


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

> freq(runif(n = 10, min = 1, max = 5))
# Class:     numeric
# Length:    10 (of which NA: 0 = 0.0%)
# Unique:    10
#   
# Mean:      3
# Std. dev.: 0.93 (CV: 0.31)
# Five-Num:  1.1  |  2.3  |  3.1  |  3.8  |  4.0 (CQV: 0.25)
# 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