mirror of https://github.com/msberends/AMR.git
303 lines
13 KiB
Markdown
303 lines
13 KiB
Markdown
# `AMR`
|
||
This is an [R package](https://www.r-project.org) to simplify the analysis and prediction of Antimicrobial Resistance (AMR).
|
||
|
||
|
||
|
||
This R package was created for academic research by PhD students of the Faculty of Medical Sciences of the [University of Groningen (rug.nl)](https://www.rug.nl/) and the Medical Microbiology & Infection Prevention department of the [University Medical Center Groningen (UMCG, umcg.nl)](https://www.umcg.nl). They also maintain this package, see [Authors](#authors).
|
||
|
||
## 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 apply EUCAST rules to isolates, identify first isolates of every patient, translate antibiotic codes from the lab (like `"AMOX"`) or the [WHO](https://www.whocc.no/atc_ddd_index/?code=J01CA04&showdescription=no) (like `"J01CA04"`) to trivial names (like `"amoxicillin"`), or predict antimicrobial resistance for the nextcoming years with the `rsi_predict` function.
|
||
|
||
With the `MDRO` function (abbreviation of mutli-drug resistant organisms), you can check your isolates for exceptional resistance with country-specific guidelines. Currently guidelines for Germany and the Netherlands are supported. Please suggest addition of your own country here: [https://github.com/msberends/AMR/issues](https://github.com/msberends/AMR/issues/new?title=New%20guideline%20for%20MDRO&body=%3C--%20Please%20add%20your%20country%20code,%20guideline%20name,%20version%20and%20source%20below%20and%20remove%20this%20line--%3E).
|
||
|
||
For regular AMR analysis, the `rsi` function can be used. This function als works with the `dplyr` package (e.g. in conjunction with `summarise`) to calculate the resistance percentages of different antibiotic columns of a table.
|
||
|
||
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)
|
||
[](http://cran.r-project.org/package=AMR)
|
||
[](http://cran.r-project.org/package=AMR)
|
||
[](http://cran.r-project.org/package=AMR)
|
||
|
||
- <img src="http://www.rstudio.com/favicon.ico" alt="RStudio favicon" height="20px"> In [RStudio](http://www.rstudio.com) (recommended):
|
||
- Click on `Tools` and then `Install Packages...`
|
||
- Type in `AMR` and press <kbd>Install</kbd>
|
||
|
||
- <img src="https://cran.r-project.org/favicon.ico" alt="R favicon" height="20px"> In R directly:
|
||
- `install.packages("AMR")`
|
||
|
||
- <img src="https://exploratory.io/favicon.ico" alt="Exploratory favicon" height="20px"> In [Exploratory.io](https://exploratory.io):
|
||
- (Exploratory.io costs $40/month, but is free for students and teachers; if you have an `@umcg.nl` or `@rug.nl` email address, [click here to enroll](https://exploratory.io/plan?plan=Community))
|
||
- 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 <kbd>Install</kbd>
|
||
- Once it’s installed it will show up in the `User Packages` section under the `Packages` tab.
|
||
|
||
### From GitHub (latest development version)
|
||
[](https://travis-ci.org/msberends/AMR)
|
||
[](https://github.com/msberends/AMR/commits/master)
|
||
[](https://github.com/msberends/AMR/commits/master)
|
||
[](https://codecov.io/gh/msberends/AMR)
|
||
|
||
```r
|
||
install.packages("devtools")
|
||
devtools::install_github("msberends/AMR")
|
||
```
|
||
|
||
## How to use it?
|
||
```r
|
||
# 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*.
|
||
```r
|
||
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()`).
|
||
```r
|
||
## 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:
|
||
```r
|
||
?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`.
|
||
|
||
```r
|
||
# 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:
|
||
```r
|
||
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`:
|
||
```r
|
||
plot(rsi_data)
|
||
```
|
||
|
||
|
||
|
||
A plot of `mic_data` (defaults to bar plot):
|
||
```r
|
||
plot(mic_data)
|
||
```
|
||
|
||
|
||
|
||
Other epidemiological functions:
|
||
|
||
```r
|
||
# 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.
|
||
```r
|
||
# Dataset with ATC antibiotics codes, official names and DDD's (oral and parenteral)
|
||
ablist # A tibble: 420 x 12
|
||
|
||
# Dataset with bacteria codes and properties like gram stain and aerobic/anaerobic
|
||
bactlist # A tibble: 2,507 x 10
|
||
```
|
||
|
||
|
||
## Authors
|
||
|
||
- [Berends MS](https://github.com/msberends)<sup>1,2</sup>, PhD Student
|
||
- [Luz CF](https://github.com/ceefluz)<sup>1</sup>, PhD Student
|
||
- [Hassing EEA](https://github.com/erwinhassing)<sup>2</sup>, Data Analyst (contributor)
|
||
|
||
<sup>1</sup> Department of Medical Microbiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
|
||
|
||
<sup>2</sup> Department of Medical, Market and Innovation (MMI), Certe Medische diagnostiek & advies, Groningen, the Netherlands
|
||
|
||
## Copyright
|
||
[](https://github.com/msberends/AMR/blob/master/LICENSE)
|
||
|
||
This R package is licensed under the [GNU General Public License (GPL) v2.0](https://github.com/msberends/AMR/blob/master/LICENSE). In a nutshell, this means that this package:
|
||
|
||
- May be used for commercial purposes
|
||
|
||
- May be used for private 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
|