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Note: values on this page will change with every website update since they are based on randomly created values and the page was written in R Markdown. However, the methodology remains unchanged. This page was generated on 26 February 2023.

Introduction

Conducting AMR data analysis unfortunately requires in-depth knowledge from different scientific fields, which makes it hard to do right. At least, it requires:

  • Good questions (always start with those!) and reliable data
  • A thorough understanding of (clinical) epidemiology, to understand the clinical and epidemiological relevance and possible bias of results
  • A thorough understanding of (clinical) microbiology/infectious diseases, to understand which microorganisms are causal to which infections and the implications of pharmaceutical treatment, as well as understanding intrinsic and acquired microbial resistance
  • Experience with data analysis with microbiological tests and their results, to understand the determination and limitations of MIC values and their interpretations to SIR values
  • Availability of the biological taxonomy of microorganisms and probably normalisation factors for pharmaceuticals, such as defined daily doses (DDD)
  • Available (inter-)national guidelines, and profound methods to apply them

Of course, we cannot instantly provide you with knowledge and experience. But with this AMR package, we aimed at providing (1) tools to simplify antimicrobial resistance data cleaning, transformation and analysis, (2) methods to easily incorporate international guidelines and (3) scientifically reliable reference data, including the requirements mentioned above.

The AMR package enables standardised and reproducible AMR data analysis, with the application of evidence-based rules, determination of first isolates, translation of various codes for microorganisms and antimicrobial agents, determination of (multi-drug) resistant microorganisms, and calculation of antimicrobial resistance, prevalence and future trends.

Preparation

For this tutorial, we will create fake demonstration data to work with.

You can skip to Cleaning the data if you already have your own data ready. If you start your analysis, try to make the structure of your data generally look like this:

date patient_id mo AMX CIP
2023-02-26 abcd Escherichia coli S S
2023-02-26 abcd Escherichia coli S R
2023-02-26 efgh Escherichia coli R S

Needed R packages

As with many uses in R, we need some additional packages for AMR data analysis. Our package works closely together with the tidyverse packages dplyr and ggplot2 by RStudio. The tidyverse tremendously improves the way we conduct data science - it allows for a very natural way of writing syntaxes and creating beautiful plots in R.

We will also use the cleaner package, that can be used for cleaning data and creating frequency tables.

library(dplyr)
library(ggplot2)
library(AMR)

# (if not yet installed, install with:)
# install.packages(c("dplyr", "ggplot2", "AMR"))

The AMR package contains a data set example_isolates_unclean, which might look data that users have extracted from their laboratory systems:

example_isolates_unclean
#> # A tibble: 3,000 × 8
#>    patient_id hospital date       bacteria      AMX   AMC   CIP   GEN  
#>    <chr>      <chr>    <date>     <chr>         <chr> <chr> <chr> <chr>
#>  1 J3         A        2012-11-21 E. coli       R     I     S     S    
#>  2 R7         A        2018-04-03 K. pneumoniae R     I     S     S    
#>  3 P3         A        2014-09-19 E. coli       R     S     S     S    
#>  4 P10        A        2015-12-10 E. coli       S     I     S     S    
#>  5 B7         A        2015-03-02 E. coli       S     S     S     S    
#>  6 W3         A        2018-03-31 S. aureus     R     S     R     S    
#>  7 J8         A        2016-06-14 E. coli       R     S     S     S    
#>  8 M3         A        2015-10-25 E. coli       R     S     S     S    
#>  9 J3         A        2019-06-19 E. coli       S     S     S     S    
#> 10 G6         A        2015-04-27 S. aureus     S     S     S     S    
#> # … with 2,990 more rows

# we will use 'our_data' as the data set name for this tutorial
our_data <- example_isolates_unclean

For AMR data analysis, we would like the microorganism column to contain valid, up-to-date taxonomy, and the antibiotic columns to be cleaned as SIR values as well.

Taxonomy of microorganisms

With as.mo(), users can transform arbitrary microorganism names or codes to current taxonomy. The AMR package contains up-to-date taxonomic data. To be specific, currently included data were retrieved on 11 Dec 2022.

The codes of the AMR packages that come from as.mo() are short, but still human readable. More importantly, as.mo() supports all kinds of input:

as.mo("Klebsiella pneumoniae")
#> Class 'mo'
#> [1] B_KLBSL_PNMN
as.mo("K. pneumoniae")
#> Class 'mo'
#> [1] B_KLBSL_PNMN
as.mo("KLEPNE")
#> Class 'mo'
#> [1] B_KLBSL_PNMN
as.mo("KLPN")
#> Class 'mo'
#> [1] B_KLBSL_PNMN

The first character in above codes denote their taxonomic kingdom, such as Bacteria (B), Fungi (F), and Protozoa (P).

The AMR package also contain functions to directly retrieve taxonomic properties, such as the name, genus, species, family, order, and even Gram-stain. They all start with mo_ and they use as.mo() internally, so that still any arbitrary user input can be used:

mo_family("K. pneumoniae")
#> [1] "Enterobacteriaceae"
mo_genus("K. pneumoniae")
#> [1] "Klebsiella"
mo_species("K. pneumoniae")
#> [1] "pneumoniae"

mo_gramstain("Klebsiella pneumoniae")
#> [1] "Gram-negative"

mo_ref("K. pneumoniae")
#> [1] "Trevisan, 1887"

mo_snomed("K. pneumoniae")
#> [[1]]
#> [1] "1098101000112102" "446870005"        "1098201000112108" "409801009"       
#> [5] "56415008"         "714315002"        "713926009"

Now we can thus clean our data:

our_data$bacteria <- as.mo(our_data$bacteria, info = TRUE)
#> ℹ Microorganism translation was uncertain for four microorganisms. Run
#>   mo_uncertainties() to review these uncertainties, or use
#>   add_custom_microorganisms() to add custom entries.

Apparently, there was some uncertainty about the translation to taxonomic codes. Let’s check this:

mo_uncertainties()
#> Matching scores are based on the resemblance between the input and the full
#> taxonomic name, and the pathogenicity in humans. See ?mo_matching_score.
#> 
#> --------------------------------------------------------------------------------
#> "E. coli" -> Escherichia coli (B_ESCHR_COLI, 0.688)
#>              Based on input "E coli"
#> Also matched: Enterobacter cowanii (0.600), Eubacterium combesii
#>               (0.600), Eggerthia catenaformis (0.591), Eubacterium callanderi
#>               (0.591), Enterocloster citroniae (0.587), Eubacterium cylindroides
#>               (0.583), Enterococcus casseliflavus (0.577), Enterobacter cloacae
#>               cloacae (0.571), Ehrlichia canis (0.567), and Enterobacter cloacae
#>               dissolvens (0.565)
#> --------------------------------------------------------------------------------
#> "K. pneumoniae" -> Klebsiella pneumoniae (B_KLBSL_PNMN, 0.786)
#>                    Based on input "K pneumoniae"
#> Also matched: Klebsiella pneumoniae ozaenae (0.707), Klebsiella
#>               pneumoniae pneumoniae (0.688), Klebsiella pneumoniae rhinoscleromatis
#>               (0.658), Klebsiella pasteurii (0.500), Klebsiella planticola (0.500),
#>               Kingella potus (0.400), Kosakonia pseudosacchari (0.361), Kaistella
#>               palustris (0.333), Kocuria palustris (0.333), and Kocuria pelophila
#>               (0.333)
#> --------------------------------------------------------------------------------
#> "S. aureus" -> Staphylococcus aureus (B_STPHY_AURS, 0.690)
#>                Based on input "S aureus"
#> Also matched: Staphylococcus aureus aureus (0.643), Staphylococcus
#>               argenteus (0.625), Staphylococcus aureus anaerobius (0.625),
#>               Streptomyces argenteolus (0.483), Streptomyces aureus (0.474),
#>               Streptomyces azureus (0.467), Streptomyces aureorectus (0.444),
#>               Streptomyces auratus (0.433), Streptomyces aurantiogriseus (0.429), and
#>               Streptomyces aureocirculatus (0.429)
#> --------------------------------------------------------------------------------
#> "S. pneumoniae" -> Streptococcus pneumoniae (B_STRPT_PNMN, 0.750)
#>                    Based on input "S pneumoniae"
#> Also matched: Streptococcus pseudopneumoniae (0.700), Serratia
#>               proteamaculans quinovora (0.545), Streptococcus pseudoporcinus (0.536),
#>               Staphylococcus pseudintermedius (0.532), Serratia proteamaculans
#>               proteamaculans (0.526), Salmonella Portanigra (0.524), Sphingomonas
#>               paucimobilis (0.520), Streptococcus pluranimalium (0.519),
#>               Streptococcus constellatus pharyngis (0.514), and Salmonella Pakistan
#>               (0.500)
#> 
#> Only the first 10 other matches of each record are shown. Run
#> print(mo_uncertainties(), n = ...) to view more entries, or save
#> mo_uncertainties() to an object.

That’s all good.

Antibiotic results

The column with antibiotic test results must also be cleaned. The AMR package comes with three new data types to work with such test results: mic for minimal inhibitory concentrations (MIC), disk for disk diffusion diameters, and sir for SIR data that have been interpreted already. This package can also determine SIR values based on MIC or disk diffusion values, read more about that on the as.sir() page.

For now, we will just clean the SIR columns in our data using dplyr:

# method 1, be explicit about the columns:
our_data <- our_data %>% 
  mutate_at(vars(AMX:GEN), as.sir)

# method 2, let the AMR package determine the eligible columns
our_data <- our_data %>% 
  mutate_if(is_sir_eligible, as.sir)

# result:
our_data
#> # A tibble: 3,000 × 8
#>    patient_id hospital date       bacteria     AMX   AMC   CIP   GEN  
#>    <chr>      <chr>    <date>     <mo>         <sir> <sir> <sir> <sir>
#>  1 J3         A        2012-11-21 B_ESCHR_COLI R     I     S     S    
#>  2 R7         A        2018-04-03 B_KLBSL_PNMN R     I     S     S    
#>  3 P3         A        2014-09-19 B_ESCHR_COLI R     S     S     S    
#>  4 P10        A        2015-12-10 B_ESCHR_COLI S     I     S     S    
#>  5 B7         A        2015-03-02 B_ESCHR_COLI S     S     S     S    
#>  6 W3         A        2018-03-31 B_STPHY_AURS R     S     R     S    
#>  7 J8         A        2016-06-14 B_ESCHR_COLI R     S     S     S    
#>  8 M3         A        2015-10-25 B_ESCHR_COLI R     S     S     S    
#>  9 J3         A        2019-06-19 B_ESCHR_COLI S     S     S     S    
#> 10 G6         A        2015-04-27 B_STPHY_AURS S     S     S     S    
#> # … with 2,990 more rows

This is basically it for the cleaning, time to start the data inclusion.

First isolates

We need to know which isolates we can actually use for analysis without repetition bias.

To conduct an analysis of antimicrobial resistance, you must only include the first isolate of every patient per episode (Hindler et al., Clin Infect Dis. 2007). If you would not do this, you could easily get an overestimate or underestimate of the resistance of an antibiotic. Imagine that a patient was admitted with an MRSA and that it was found in 5 different blood cultures the following weeks (yes, some countries like the Netherlands have these blood drawing policies). The resistance percentage of oxacillin of all isolates would be overestimated, because you included this MRSA more than once. It would clearly be selection bias.

The Clinical and Laboratory Standards Institute (CLSI) appoints this as follows:

(…) When preparing a cumulative antibiogram to guide clinical decisions about empirical antimicrobial therapy of initial infections, only the first isolate of a given species per patient, per analysis period (eg, one year) should be included, irrespective of body site, antimicrobial susceptibility profile, or other phenotypical characteristics (eg, biotype). The first isolate is easily identified, and cumulative antimicrobial susceptibility test data prepared using the first isolate are generally comparable to cumulative antimicrobial susceptibility test data calculated by other methods, providing duplicate isolates are excluded.
M39-A4 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition. CLSI, 2014. Chapter 6.4

This AMR package includes this methodology with the first_isolate() function and is able to apply the four different methods as defined by Hindler et al. in 2007: phenotype-based, episode-based, patient-based, isolate-based. The right method depends on your goals and analysis, but the default phenotype-based method is in any case the method to properly correct for most duplicate isolates. Read more about the methods on the first_isolate() page.

The outcome of the function can easily be added to our data:

our_data <- our_data %>%
  mutate(first = first_isolate(info = TRUE))
#> Determining first isolates using an episode length of 365 days
#> ℹ Using column 'bacteria' as input for col_mo.
#> ℹ Using column 'date' as input for col_date.
#> ℹ Using column 'patient_id' as input for col_patient_id.
#> Basing inclusion on all antimicrobial results, using a points threshold of
#> 2
#> Including isolates from ICU.
#> => Found 2,626 'phenotype-based' first isolates (87.6% within scope and
#>    87.5% of total where a microbial ID was available)

So only 88% is suitable for resistance analysis! We can now filter on it with the filter() function, also from the dplyr package:

our_data_1st <- our_data %>%
  filter(first == TRUE)

For future use, the above two syntaxes can be shortened:

our_data_1st <- our_data %>%
  filter_first_isolate()

So we end up with 2 626 isolates for analysis. Now our data looks like:

our_data_1st
#> # A tibble: 2,626 × 9
#>    patient_id hospital date       bacteria     AMX   AMC   CIP   GEN   first
#>    <chr>      <chr>    <date>     <mo>         <sir> <sir> <sir> <sir> <lgl>
#>  1 J3         A        2012-11-21 B_ESCHR_COLI R     I     S     S     TRUE 
#>  2 R7         A        2018-04-03 B_KLBSL_PNMN R     I     S     S     TRUE 
#>  3 P10        A        2015-12-10 B_ESCHR_COLI S     I     S     S     TRUE 
#>  4 B7         A        2015-03-02 B_ESCHR_COLI S     S     S     S     TRUE 
#>  5 W3         A        2018-03-31 B_STPHY_AURS R     S     R     S     TRUE 
#>  6 J8         A        2016-06-14 B_ESCHR_COLI R     S     S     S     TRUE 
#>  7 M3         A        2015-10-25 B_ESCHR_COLI R     S     S     S     TRUE 
#>  8 J3         A        2019-06-19 B_ESCHR_COLI S     S     S     S     TRUE 
#>  9 G6         A        2015-04-27 B_STPHY_AURS S     S     S     S     TRUE 
#> 10 P4         A        2011-06-21 B_ESCHR_COLI S     S     S     S     TRUE 
#> # … with 2,616 more rows

Time for the analysis.

Analysing the data

The base R summary() function gives a good first impression, as it comes with support for the new mo and sir classes that we now have in our data set:

summary(our_data_1st)
#>   patient_id          hospital              date           
#>  Length:2626        Length:2626        Min.   :2011-01-01  
#>  Class :character   Class :character   1st Qu.:2013-04-14  
#>  Mode  :character   Mode  :character   Median :2015-06-05  
#>                                        Mean   :2015-06-15  
#>                                        3rd Qu.:2017-08-23  
#>                                        Max.   :2020-01-01  
#>    bacteria               AMX                    AMC                
#>  Class :mo             Class:sir              Class:sir             
#>  <NA>  :0              %R   :43.2% (n=1134)   %R   :36.1% (n=947)   
#>  Unique:4              %SI  :56.8% (n=1492)   %SI  :63.9% (n=1679)  
#>  #1    :B_ESCHR_COLI   - %S :41.1% (n=1080)   - %S :52.7% (n=1383)  
#>  #2    :B_STPHY_AURS   - %I :15.7% (n=412)    - %I :11.3% (n=296)   
#>  #3    :B_STRPT_PNMN                                                
#>     CIP                    GEN                  first        
#>  Class:sir              Class:sir              Mode:logical  
#>  %R   :42.0% (n=1102)   %R   :37.0% (n=971)    TRUE:2626     
#>  %SI  :58.0% (n=1524)   %SI  :63.0% (n=1655)                 
#>  - %S :51.9% (n=1362)   - %S :59.9% (n=1574)                 
#>  - %I : 6.2% (n=162)    - %I : 3.1% (n=81)                   
#> 

glimpse(our_data_1st)
#> Rows: 2,626
#> Columns: 9
#> $ patient_id <chr> "J3", "R7", "P10", "B7", "W3", "J8", "M3", "J3", "G6", "P4"…
#> $ hospital   <chr> "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A", "A",…
#> $ date       <date> 2012-11-21, 2018-04-03, 2015-12-10, 2015-03-02, 2018-03-31…
#> $ bacteria   <mo> "B_ESCHR_COLI", "B_KLBSL_PNMN", "B_ESCHR_COLI", "B_ESCHR_COL…
#> $ AMX        <sir> R, R, S, S, R, R, R, S, S, S, S, R, S, S, R, R, R, R, I, S,…
#> $ AMC        <sir> I, I, I, S, S, S, S, S, S, S, S, S, S, S, S, S, S, R, S, R,…
#> $ CIP        <sir> S, S, S, S, R, S, S, S, S, S, S, S, S, S, S, S, S, S, S, S,…
#> $ GEN        <sir> S, S, S, S, S, S, S, S, S, S, S, R, S, S, S, S, S, S, S, S,…
#> $ first      <lgl> TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,…

# number of unique values per column:
sapply(our_data_1st, n_distinct)
#> patient_id   hospital       date   bacteria        AMX        AMC        CIP 
#>        260          3       1808          4          3          3          3 
#>        GEN      first 
#>          3          1

Availability of species

To just get an idea how the species are distributed, create a frequency table with count() based on the name of the microorganisms:

our_data %>% 
  count(mo_name(bacteria), sort = TRUE)
#> # A tibble: 4 × 2
#>   `mo_name(bacteria)`          n
#>   <chr>                    <int>
#> 1 Escherichia coli          1518
#> 2 Staphylococcus aureus      730
#> 3 Streptococcus pneumoniae   426
#> 4 Klebsiella pneumoniae      326

our_data_1st %>% 
  count(mo_name(bacteria), sort = TRUE)
#> # A tibble: 4 × 2
#>   `mo_name(bacteria)`          n
#>   <chr>                    <int>
#> 1 Escherichia coli          1250
#> 2 Staphylococcus aureus      661
#> 3 Streptococcus pneumoniae   399
#> 4 Klebsiella pneumoniae      316

Select and filter with antibiotic selectors

Using so-called antibiotic class selectors, you can select or filter columns based on the antibiotic class that your antibiotic results are in:

our_data_1st %>%
  select(date, aminoglycosides())
#> ℹ For aminoglycosides() using column 'GEN' (gentamicin)
#> # A tibble: 2,626 × 2
#>    date       GEN  
#>    <date>     <sir>
#>  1 2012-11-21 S    
#>  2 2018-04-03 S    
#>  3 2015-12-10 S    
#>  4 2015-03-02 S    
#>  5 2018-03-31 S    
#>  6 2016-06-14 S    
#>  7 2015-10-25 S    
#>  8 2019-06-19 S    
#>  9 2015-04-27 S    
#> 10 2011-06-21 S    
#> # … with 2,616 more rows

our_data_1st %>%
  select(bacteria, betalactams())
#> ℹ For betalactams() using columns 'AMX' (amoxicillin) and 'AMC'
#>   (amoxicillin/clavulanic acid)
#> # A tibble: 2,626 × 3
#>    bacteria     AMX   AMC  
#>    <mo>         <sir> <sir>
#>  1 B_ESCHR_COLI R     I    
#>  2 B_KLBSL_PNMN R     I    
#>  3 B_ESCHR_COLI S     I    
#>  4 B_ESCHR_COLI S     S    
#>  5 B_STPHY_AURS R     S    
#>  6 B_ESCHR_COLI R     S    
#>  7 B_ESCHR_COLI R     S    
#>  8 B_ESCHR_COLI S     S    
#>  9 B_STPHY_AURS S     S    
#> 10 B_ESCHR_COLI S     S    
#> # … with 2,616 more rows

our_data_1st %>%
  select(bacteria, where(is.sir))
#> # A tibble: 2,626 × 5
#>    bacteria     AMX   AMC   CIP   GEN  
#>    <mo>         <sir> <sir> <sir> <sir>
#>  1 B_ESCHR_COLI R     I     S     S    
#>  2 B_KLBSL_PNMN R     I     S     S    
#>  3 B_ESCHR_COLI S     I     S     S    
#>  4 B_ESCHR_COLI S     S     S     S    
#>  5 B_STPHY_AURS R     S     R     S    
#>  6 B_ESCHR_COLI R     S     S     S    
#>  7 B_ESCHR_COLI R     S     S     S    
#>  8 B_ESCHR_COLI S     S     S     S    
#>  9 B_STPHY_AURS S     S     S     S    
#> 10 B_ESCHR_COLI S     S     S     S    
#> # … with 2,616 more rows

# filtering using AB selectors is also possible:
our_data_1st %>%
  filter(any(aminoglycosides() == "R"))
#> ℹ For aminoglycosides() using column 'GEN' (gentamicin)
#> # A tibble: 971 × 9
#>    patient_id hospital date       bacteria     AMX   AMC   CIP   GEN   first
#>    <chr>      <chr>    <date>     <mo>         <sir> <sir> <sir> <sir> <lgl>
#>  1 J5         A        2017-12-25 B_STRPT_PNMN R     S     S     R     TRUE 
#>  2 X1         A        2017-07-04 B_STPHY_AURS R     S     S     R     TRUE 
#>  3 B3         A        2016-07-24 B_ESCHR_COLI S     S     S     R     TRUE 
#>  4 V7         A        2012-04-03 B_ESCHR_COLI S     S     S     R     TRUE 
#>  5 C9         A        2017-03-23 B_ESCHR_COLI S     S     S     R     TRUE 
#>  6 R1         A        2018-06-10 B_STPHY_AURS S     S     S     R     TRUE 
#>  7 S2         A        2013-07-19 B_STRPT_PNMN S     S     S     R     TRUE 
#>  8 P5         A        2019-03-09 B_STPHY_AURS S     S     S     R     TRUE 
#>  9 Q8         A        2019-08-10 B_STPHY_AURS S     S     S     R     TRUE 
#> 10 K5         A        2013-03-15 B_STRPT_PNMN S     S     S     R     TRUE 
#> # … with 961 more rows

our_data_1st %>%
  filter(all(betalactams() == "R"))
#> ℹ For betalactams() using columns 'AMX' (amoxicillin) and 'AMC'
#>   (amoxicillin/clavulanic acid)
#> # A tibble: 471 × 9
#>    patient_id hospital date       bacteria     AMX   AMC   CIP   GEN   first
#>    <chr>      <chr>    <date>     <mo>         <sir> <sir> <sir> <sir> <lgl>
#>  1 M7         A        2013-07-22 B_STRPT_PNMN R     R     S     S     TRUE 
#>  2 R10        A        2013-12-20 B_STPHY_AURS R     R     S     S     TRUE 
#>  3 R7         A        2015-10-25 B_STPHY_AURS R     R     S     S     TRUE 
#>  4 R8         A        2019-10-25 B_STPHY_AURS R     R     S     S     TRUE 
#>  5 I7         A        2015-08-19 B_ESCHR_COLI R     R     S     S     TRUE 
#>  6 N3         A        2014-12-29 B_STRPT_PNMN R     R     R     S     TRUE 
#>  7 Q2         A        2019-09-22 B_ESCHR_COLI R     R     S     S     TRUE 
#>  8 X7         A        2011-03-20 B_ESCHR_COLI R     R     S     R     TRUE 
#>  9 C5         A        2015-08-30 B_KLBSL_PNMN R     R     S     R     TRUE 
#> 10 W9         A        2013-10-02 B_ESCHR_COLI R     R     S     S     TRUE 
#> # … with 461 more rows

# even works in base R (since R 3.0):
our_data_1st[all(betalactams() == "R"), ]
#> ℹ For betalactams() using columns 'AMX' (amoxicillin) and 'AMC'
#>   (amoxicillin/clavulanic acid)
#> # A tibble: 471 × 9
#>    patient_id hospital date       bacteria     AMX   AMC   CIP   GEN   first
#>    <chr>      <chr>    <date>     <mo>         <sir> <sir> <sir> <sir> <lgl>
#>  1 M7         A        2013-07-22 B_STRPT_PNMN R     R     S     S     TRUE 
#>  2 R10        A        2013-12-20 B_STPHY_AURS R     R     S     S     TRUE 
#>  3 R7         A        2015-10-25 B_STPHY_AURS R     R     S     S     TRUE 
#>  4 R8         A        2019-10-25 B_STPHY_AURS R     R     S     S     TRUE 
#>  5 I7         A        2015-08-19 B_ESCHR_COLI R     R     S     S     TRUE 
#>  6 N3         A        2014-12-29 B_STRPT_PNMN R     R     R     S     TRUE 
#>  7 Q2         A        2019-09-22 B_ESCHR_COLI R     R     S     S     TRUE 
#>  8 X7         A        2011-03-20 B_ESCHR_COLI R     R     S     R     TRUE 
#>  9 C5         A        2015-08-30 B_KLBSL_PNMN R     R     S     R     TRUE 
#> 10 W9         A        2013-10-02 B_ESCHR_COLI R     R     S     S     TRUE 
#> # … with 461 more rows

Generate antibiograms

This package comes with antibiogram(), a function that automatically generates traditional, combined, syndromic, and even weighted-incidence syndromic combination antibiograms (WISCA). For R Markdown (such as this page) it automatically prints in the right table format.

Below are some suggestions for how to generate the different antibiograms:

# traditional:
antibiogram(our_data_1st)
Pathogen (N min-max) AMC AMX CIP GEN
E. coli (1250-1250) 64 58 58 63
K. pneumoniae (316-316) 63 53 59 60
S. aureus (661-661) 64 57 57 63
S. pneumoniae (399-399) 64 56 60 66
antibiogram(our_data_1st,
            ab_transform = "name")
Pathogen (N min-max) Amoxicillin Amoxicillin/clavulanic acid Ciprofloxacin Gentamicin
E. coli (1250-1250) 58 64 58 63
K. pneumoniae (316-316) 53 63 59 60
S. aureus (661-661) 57 64 57 63
S. pneumoniae (399-399) 56 64 60 66
antibiogram(our_data_1st,
            ab_transform = "name",
            language = "es") # support for 20 languages
Patógeno (N min-max) Amoxicilina Amoxicilina/ácido clavulánico Ciprofloxacina Gentamicina
E. coli (1250-1250) 58 64 58 63
K. pneumoniae (316-316) 53 63 59 60
S. aureus (661-661) 57 64 57 63
S. pneumoniae (399-399) 56 64 60 66
# combined:
antibiogram(our_data_1st,
            antibiotics = c("AMC", "AMC+CIP", "AMC+GEN"))
Pathogen (N min-max) AMC AMC + CIP AMC + GEN
E. coli (1250-1250) 64 76 75
K. pneumoniae (316-316) 63 78 74
S. aureus (661-661) 64 77 75
S. pneumoniae (399-399) 64 77 76
# for a syndromic antibiogram, we must fake some clinical conditions:
our_data_1st$condition <- sample(c("Cardial", "Respiratory", "Rheumatic"),
                                 size = nrow(our_data_1st),
                                 replace = TRUE)

# syndromic:
antibiogram(our_data_1st,
            syndromic_group = "condition")
Syndromic Group Pathogen (N min-max) AMC AMX CIP GEN
Cardial E. coli (431-431) 63 54 57 63
Respiratory E. coli (396-396) 64 61 58 63
Rheumatic E. coli (423-423) 65 60 58 63
Cardial K. pneumoniae (105-105) 63 54 59 61
Respiratory K. pneumoniae (110-110) 60 54 55 63
Rheumatic K. pneumoniae (101-101) 66 50 62 56
Cardial S. aureus (225-225) 66 57 53 61
Respiratory S. aureus (217-217) 60 55 61 64
Rheumatic S. aureus (219-219) 67 58 58 64
Cardial S. pneumoniae (152-152) 67 57 64 63
Respiratory S. pneumoniae (122-122) 67 57 64 67
Rheumatic S. pneumoniae (125-125) 57 53 50 67
antibiogram(our_data_1st,
            # you can use AB selectors here as well:
            antibiotics = c(penicillins(), aminoglycosides()),
            syndromic_group = "condition",
            mo_transform = "gramstain")
#> ℹ For penicillins() using columns 'AMX' (amoxicillin) and 'AMC'
#>   (amoxicillin/clavulanic acid)
#> ℹ For aminoglycosides() using column 'GEN' (gentamicin)
Syndromic Group Pathogen (N min-max) AMC AMX GEN
Cardial Gram-negative (536-536) 63 54 62
Respiratory Gram-negative (506-506) 63 59 63
Rheumatic Gram-negative (524-524) 65 58 62
Cardial Gram-positive (377-377) 67 57 62
Respiratory Gram-positive (339-339) 63 56 65
Rheumatic Gram-positive (344-344) 63 56 65
# WISCA: 
# (we lack some details, but it could contain a filter on e.g. >65 year-old males)
wisca <- antibiogram(our_data_1st,
                     antibiotics = c("AMC", "AMC+CIP", "AMC+GEN"),
                     syndromic_group = "condition",
                     mo_transform = "gramstain")
wisca
Syndromic Group Pathogen (N min-max) AMC AMC + CIP AMC + GEN
Cardial Gram-negative (536-536) 63 75 75
Respiratory Gram-negative (506-506) 63 77 75
Rheumatic Gram-negative (524-524) 65 76 75
Cardial Gram-positive (377-377) 67 78 75
Respiratory Gram-positive (339-339) 63 77 76
Rheumatic Gram-positive (344-344) 63 77 76

Antibiograms can be plotted using autoplot() from the ggplot2 packages, since this package provides an extension to that function:

autoplot(wisca)

To calculate antimicrobial resistance in a more sensible way, also by correcting for too few results, we use the resistance() and susceptibility() functions.

Resistance percentages

The functions resistance() and susceptibility() can be used to calculate antimicrobial resistance or susceptibility. For more specific analyses, the functions proportion_S(), proportion_SI(), proportion_I(), proportion_IR() and proportion_R() can be used to determine the proportion of a specific antimicrobial outcome.

All these functions contain a minimum argument, denoting the minimum required number of test results for returning a value. These functions will otherwise return NA. The default is minimum = 30, following the CLSI M39-A4 guideline for applying microbial epidemiology.

As per the EUCAST guideline of 2019, we calculate resistance as the proportion of R (proportion_R(), equal to resistance()) and susceptibility as the proportion of S and I (proportion_SI(), equal to susceptibility()). These functions can be used on their own:

our_data_1st %>% resistance(AMX)
#> [1] 0.4318355

Or can be used in conjunction with group_by() and summarise(), both from the dplyr package:

our_data_1st %>%
  group_by(hospital) %>%
  summarise(amoxicillin = resistance(AMX))
#> # A tibble: 3 × 2
#>   hospital amoxicillin
#>   <chr>          <dbl>
#> 1 A              0.343
#> 2 B              0.569
#> 3 C              0.375

Author: Dr. Matthijs Berends, 26th Feb 2023