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articles/AMR.md
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@@ -3,7 +3,7 @@
**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](https://rmarkdown.rstudio.com/). However, the
methodology remains unchanged. This page was generated on 25 April 2026.
methodology remains unchanged. This page was generated on 30 April 2026.
## Introduction
@@ -51,9 +51,9 @@ structure of your data generally look like this:
| date | patient_id | mo | AMX | CIP |
|:----------:|:----------:|:----------------:|:---:|:---:|
| 2026-04-25 | abcd | Escherichia coli | S | S |
| 2026-04-25 | abcd | Escherichia coli | S | R |
| 2026-04-25 | efgh | Escherichia coli | R | S |
| 2026-04-30 | abcd | Escherichia coli | S | S |
| 2026-04-30 | abcd | Escherichia coli | S | R |
| 2026-04-30 | efgh | Escherichia coli | R | S |
### Needed R packages
@@ -69,6 +69,7 @@ We will also use the `cleaner` package, that can be used for cleaning
data and creating frequency tables.
``` r
library(dplyr)
library(ggplot2)
library(AMR)
@@ -81,6 +82,7 @@ The `AMR` package contains a data set `example_isolates_unclean`, which
might look data that users have extracted from their laboratory systems:
``` r
example_isolates_unclean
#> # A tibble: 3,000 × 8
#> patient_id hospital date bacteria AMX AMC CIP GEN
@@ -119,6 +121,7 @@ still human readable. More importantly,
of input:
``` r
as.mo("Klebsiella pneumoniae")
#> Class <mo>
#> [1] B_KLBSL_PNMN
@@ -143,6 +146,7 @@ Gram-stain. They all start with `mo_` and they use
that still any arbitrary user input can be used:
``` r
mo_family("K. pneumoniae")
#> [1] "Enterobacteriaceae"
mo_genus("K. pneumoniae")
@@ -165,6 +169,7 @@ mo_snomed("K. pneumoniae")
Now we can thus clean our data:
``` r
our_data$bacteria <- as.mo(our_data$bacteria, info = TRUE)
#> ℹ Retrieved values from the `microorganisms.codes` data set for "ESCCOL",
#> "KLEPNE", "STAAUR", and "STRPNE".
@@ -177,6 +182,7 @@ Apparently, there was some uncertainty about the translation to
taxonomic codes. Let’s check this:
``` r
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()`.
@@ -231,6 +237,7 @@ diffusion values, read more about that on the
For now, we will just clean the SIR columns in our data using dplyr:
``` r
# method 1, be explicit about the columns:
our_data <- our_data %>%
mutate_at(vars(AMX:GEN), as.sir)
@@ -308,6 +315,7 @@ page.
The outcome of the function can easily be added to our data:
``` r
our_data <- our_data %>%
mutate(first = first_isolate(info = TRUE))
#> ℹ Determining first isolates using an episode length of 365 days
@@ -326,6 +334,7 @@ with the [`filter()`](https://dplyr.tidyverse.org/reference/filter.html)
function, also from the `dplyr` package:
``` r
our_data_1st <- our_data %>%
filter(first == TRUE)
```
@@ -333,6 +342,7 @@ our_data_1st <- our_data %>%
For future use, the above two syntaxes can be shortened:
``` r
our_data_1st <- our_data %>%
filter_first_isolate()
```
@@ -340,6 +350,7 @@ our_data_1st <- our_data %>%
So we end up with 2 724 isolates for analysis. Now our data looks like:
``` r
our_data_1st
#> # A tibble: 2,724 × 9
#> patient_id hospital date bacteria AMX AMC CIP GEN first
@@ -366,6 +377,7 @@ 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:
``` r
summary(our_data_1st)
#> patient_id hospital date bacteria
#> Length :2724 Length :2724 Min. :2011-01-01 Class :mo
@@ -417,6 +429,7 @@ table with [`count()`](https://amr-for-r.org/reference/count.md) based
on the name of the microorganisms:
``` r
our_data %>%
count(mo_name(bacteria), sort = TRUE)
#> # A tibble: 4 × 2
@@ -445,6 +458,7 @@ columns based on the antibiotic class that your antibiotic results are
in:
``` r
our_data_1st %>%
select(date, aminoglycosides())
#> ℹ For `aminoglycosides()` using column GEN
@@ -590,6 +604,7 @@ function to create any of the above antibiogram types. For starters,
this is what the included `example_isolates` data set looks like:
``` r
example_isolates
#> # A tibble: 2,000 × 46
#> date patient age gender ward mo PEN OXA FLC AMX
@@ -622,6 +637,7 @@ function supports any (combination) of the previously mentioned
antibiotic class selectors:
``` r
antibiogram(example_isolates,
antibiotics = c(aminoglycosides(), carbapenems())
)
@@ -630,18 +646,18 @@ antibiogram(example_isolates,
#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM (meropenem)
```
| Pathogen | Amikacin | Gentamicin | Imipenem | Kanamycin | Meropenem | Tobramycin |
|:-----------------|:---------------------|:--------------------|:---------------------|:----------------|:---------------------|:--------------------|
| CoNS | 0% (0-8%,N=43) | 86% (82-90%,N=309) | 52% (37-67%,N=48) | 0% (0-8%,N=43) | 52% (37-67%,N=48) | 22% (12-35%,N=55) |
| *E. coli* | 100% (98-100%,N=171) | 98% (96-99%,N=460) | 100% (99-100%,N=422) | NA | 100% (99-100%,N=418) | 97% (96-99%,N=462) |
| *E. faecalis* | 0% (0-9%,N=39) | 0% (0-9%,N=39) | 100% (91-100%,N=38) | 0% (0-9%,N=39) | NA | 0% (0-9%,N=39) |
| *K. pneumoniae* | NA | 90% (79-96%,N=58) | 100% (93-100%,N=51) | NA | 100% (93-100%,N=53) | 90% (79-96%,N=58) |
| *P. aeruginosa* | NA | 100% (88-100%,N=30) | NA | 0% (0-12%,N=30) | NA | 100% (88-100%,N=30) |
| *P. mirabilis* | NA | 94% (80-99%,N=34) | 94% (79-99%,N=32) | NA | NA | 94% (80-99%,N=34) |
| *S. aureus* | NA | 99% (97-100%,N=233) | NA | NA | NA | 98% (92-100%,N=86) |
| *S. epidermidis* | 0% (0-8%,N=44) | 79% (71-85%,N=163) | NA | 0% (0-8%,N=44) | NA | 51% (40-61%,N=89) |
| *S. hominis* | NA | 92% (84-97%,N=80) | NA | NA | NA | 85% (74-93%,N=62) |
| *S. pneumoniae* | 0% (0-3%,N=117) | 0% (0-3%,N=117) | NA | 0% (0-3%,N=117) | NA | 0% (0-3%,N=117) |
| Pathogen | Amikacin | Gentamicin | Imipenem | Kanamycin | Meropenem | Tobramycin |
|:---|:---|:---|:---|:---|:---|:---|
| CoNS | 0% (0-8%,N=43) | 86% (82-90%,N=309) | 52% (37-67%,N=48) | 0% (0-8%,N=43) | 52% (37-67%,N=48) | 22% (12-35%,N=55) |
| *E. coli* | 100% (98-100%,N=171) | 98% (96-99%,N=460) | 100% (99-100%,N=422) | NA | 100% (99-100%,N=418) | 97% (96-99%,N=462) |
| *E. faecalis* | 0% (0-9%,N=39) | 0% (0-9%,N=39) | 100% (91-100%,N=38) | 0% (0-9%,N=39) | NA | 0% (0-9%,N=39) |
| *K. pneumoniae* | NA | 90% (79-96%,N=58) | 100% (93-100%,N=51) | NA | 100% (93-100%,N=53) | 90% (79-96%,N=58) |
| *P. aeruginosa* | NA | 100% (88-100%,N=30) | NA | 0% (0-12%,N=30) | NA | 100% (88-100%,N=30) |
| *P. mirabilis* | NA | 94% (80-99%,N=34) | 94% (79-99%,N=32) | NA | NA | 94% (80-99%,N=34) |
| *S. aureus* | NA | 99% (97-100%,N=233) | NA | NA | NA | 98% (92-100%,N=86) |
| *S. epidermidis* | 0% (0-8%,N=44) | 79% (71-85%,N=163) | NA | 0% (0-8%,N=44) | NA | 51% (40-61%,N=89) |
| *S. hominis* | NA | 92% (84-97%,N=80) | NA | NA | NA | 85% (74-93%,N=62) |
| *S. pneumoniae* | 0% (0-3%,N=117) | 0% (0-3%,N=117) | NA | 0% (0-3%,N=117) | NA | 0% (0-3%,N=117) |
Notice that the
[`antibiogram()`](https://amr-for-r.org/reference/antibiogram.md)
@@ -659,6 +675,7 @@ Ukrainian, Urdu, or Vietnamese. In this next example, we force the
language to be Spanish using the `language` argument:
``` r
antibiogram(example_isolates,
mo_transform = "gramstain",
antibiotics = aminoglycosides(),
@@ -669,10 +686,10 @@ antibiogram(example_isolates,
#> (amikacin), and KAN (kanamycin)
```
| Patógeno | Amikacina | Gentamicina | Kanamicina | Tobramicina |
|:--------------|:-------------------|:--------------------|:----------------|:-------------------|
| Gram negativo | 98% (96-99%,N=256) | 96% (95-98%,N=684) | 0% (0-10%,N=35) | 96% (94-97%,N=686) |
| Gram positivo | 0% (0-1%,N=436) | 63% (60-66%,N=1170) | 0% (0-1%,N=436) | 34% (31-38%,N=665) |
| Patógeno | Amikacina | Gentamicina | Kanamicina | Tobramicina |
|:---|:---|:---|:---|:---|
| Gram negativo | 98% (96-99%,N=256) | 96% (95-98%,N=684) | 0% (0-10%,N=35) | 96% (94-97%,N=686) |
| Gram positivo | 0% (0-1%,N=436) | 63% (60-66%,N=1170) | 0% (0-1%,N=436) | 34% (31-38%,N=665) |
#### Combined Antibiogram
@@ -680,6 +697,7 @@ To create a combined antibiogram, use antibiotic codes or names with a
plus `+` character like this:
``` r
combined_ab <- antibiogram(example_isolates,
antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
ab_transform = NULL
@@ -687,17 +705,17 @@ combined_ab <- antibiogram(example_isolates,
combined_ab
```
| Pathogen | TZP | TZP + GEN | TZP + TOB |
|:-----------------|:---------------------|:---------------------|:---------------------|
| CoNS | 30% (16-49%,N=33) | 97% (95-99%,N=274) | NA |
| *E. coli* | 94% (92-96%,N=416) | 100% (98-100%,N=459) | 99% (97-100%,N=461) |
| *K. pneumoniae* | 89% (77-96%,N=53) | 93% (83-98%,N=58) | 93% (83-98%,N=58) |
| *P. aeruginosa* | NA | 100% (88-100%,N=30) | 100% (88-100%,N=30) |
| *P. mirabilis* | NA | 100% (90-100%,N=34) | 100% (90-100%,N=34) |
| *S. aureus* | NA | 100% (98-100%,N=231) | 100% (96-100%,N=91) |
| *S. epidermidis* | NA | 100% (97-100%,N=128) | 100% (92-100%,N=46) |
| *S. hominis* | NA | 100% (95-100%,N=74) | 100% (93-100%,N=53) |
| *S. pneumoniae* | 100% (97-100%,N=112) | 100% (97-100%,N=112) | 100% (97-100%,N=112) |
| Pathogen | TZP | TZP + GEN | TZP + TOB |
|:---|:---|:---|:---|
| CoNS | 30% (16-49%,N=33) | 97% (95-99%,N=274) | NA |
| *E. coli* | 94% (92-96%,N=416) | 100% (98-100%,N=459) | 99% (97-100%,N=461) |
| *K. pneumoniae* | 89% (77-96%,N=53) | 93% (83-98%,N=58) | 93% (83-98%,N=58) |
| *P. aeruginosa* | NA | 100% (88-100%,N=30) | 100% (88-100%,N=30) |
| *P. mirabilis* | NA | 100% (90-100%,N=34) | 100% (90-100%,N=34) |
| *S. aureus* | NA | 100% (98-100%,N=231) | 100% (96-100%,N=91) |
| *S. epidermidis* | NA | 100% (97-100%,N=128) | 100% (92-100%,N=46) |
| *S. hominis* | NA | 100% (95-100%,N=74) | 100% (93-100%,N=53) |
| *S. pneumoniae* | 100% (97-100%,N=112) | 100% (97-100%,N=112) | 100% (97-100%,N=112) |
#### Syndromic Antibiogram
@@ -707,6 +725,7 @@ be used. This can be any column in the data, or e.g. an
on certain columns:
``` r
antibiogram(example_isolates,
antibiotics = c(aminoglycosides(), carbapenems()),
syndromic_group = "ward"
@@ -716,22 +735,22 @@ antibiogram(example_isolates,
#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM (meropenem)
```
| Syndromic Group | Pathogen | Amikacin | Gentamicin | Imipenem | Kanamycin | Meropenem | Tobramycin |
|:----------------|:-----------------|:---------------------|:--------------------|:---------------------|:----------------|:---------------------|:--------------------|
| Clinical | CoNS | NA | 89% (84-93%,N=205) | 57% (39-74%,N=35) | NA | 57% (39-74%,N=35) | 26% (12-45%,N=31) |
| ICU | CoNS | NA | 79% (68-88%,N=73) | NA | NA | NA | NA |
| Outpatient | CoNS | NA | 84% (66-95%,N=31) | NA | NA | NA | NA |
| Clinical | *E. coli* | 100% (97-100%,N=104) | 98% (96-99%,N=297) | 100% (99-100%,N=266) | NA | 100% (99-100%,N=276) | 98% (96-99%,N=299) |
| ICU | *E. coli* | 100% (93-100%,N=52) | 99% (95-100%,N=137) | 100% (97-100%,N=133) | NA | 100% (97-100%,N=118) | 96% (92-99%,N=137) |
| Clinical | *K. pneumoniae* | NA | 92% (81-98%,N=51) | 100% (92-100%,N=44) | NA | 100% (92-100%,N=46) | 92% (81-98%,N=51) |
| Clinical | *P. mirabilis* | NA | 100% (88-100%,N=30) | NA | NA | NA | 100% (88-100%,N=30) |
| Clinical | *S. aureus* | NA | 99% (95-100%,N=150) | NA | NA | NA | 97% (89-100%,N=63) |
| ICU | *S. aureus* | NA | 100% (95-100%,N=66) | NA | NA | NA | NA |
| Clinical | *S. epidermidis* | NA | 82% (72-90%,N=79) | NA | NA | NA | 55% (39-70%,N=44) |
| ICU | *S. epidermidis* | NA | 72% (60-82%,N=75) | NA | NA | NA | 41% (26-58%,N=41) |
| Clinical | *S. hominis* | NA | 96% (85-99%,N=45) | NA | NA | NA | 94% (79-99%,N=31) |
| Clinical | *S. pneumoniae* | 0% (0-5%,N=78) | 0% (0-5%,N=78) | NA | 0% (0-5%,N=78) | NA | 0% (0-5%,N=78) |
| ICU | *S. pneumoniae* | 0% (0-12%,N=30) | 0% (0-12%,N=30) | NA | 0% (0-12%,N=30) | NA | 0% (0-12%,N=30) |
| Syndromic Group | Pathogen | Amikacin | Gentamicin | Imipenem | Kanamycin | Meropenem | Tobramycin |
|:---|:---|:---|:---|:---|:---|:---|:---|
| Clinical | CoNS | NA | 89% (84-93%,N=205) | 57% (39-74%,N=35) | NA | 57% (39-74%,N=35) | 26% (12-45%,N=31) |
| ICU | CoNS | NA | 79% (68-88%,N=73) | NA | NA | NA | NA |
| Outpatient | CoNS | NA | 84% (66-95%,N=31) | NA | NA | NA | NA |
| Clinical | *E. coli* | 100% (97-100%,N=104) | 98% (96-99%,N=297) | 100% (99-100%,N=266) | NA | 100% (99-100%,N=276) | 98% (96-99%,N=299) |
| ICU | *E. coli* | 100% (93-100%,N=52) | 99% (95-100%,N=137) | 100% (97-100%,N=133) | NA | 100% (97-100%,N=118) | 96% (92-99%,N=137) |
| Clinical | *K. pneumoniae* | NA | 92% (81-98%,N=51) | 100% (92-100%,N=44) | NA | 100% (92-100%,N=46) | 92% (81-98%,N=51) |
| Clinical | *P. mirabilis* | NA | 100% (88-100%,N=30) | NA | NA | NA | 100% (88-100%,N=30) |
| Clinical | *S. aureus* | NA | 99% (95-100%,N=150) | NA | NA | NA | 97% (89-100%,N=63) |
| ICU | *S. aureus* | NA | 100% (95-100%,N=66) | NA | NA | NA | NA |
| Clinical | *S. epidermidis* | NA | 82% (72-90%,N=79) | NA | NA | NA | 55% (39-70%,N=44) |
| ICU | *S. epidermidis* | NA | 72% (60-82%,N=75) | NA | NA | NA | 41% (26-58%,N=41) |
| Clinical | *S. hominis* | NA | 96% (85-99%,N=45) | NA | NA | NA | 94% (79-99%,N=31) |
| Clinical | *S. pneumoniae* | 0% (0-5%,N=78) | 0% (0-5%,N=78) | NA | 0% (0-5%,N=78) | NA | 0% (0-5%,N=78) |
| ICU | *S. pneumoniae* | 0% (0-12%,N=30) | 0% (0-12%,N=30) | NA | 0% (0-12%,N=30) | NA | 0% (0-12%,N=30) |
#### Weighted-Incidence Syndromic Combination Antibiogram (WISCA)
@@ -745,6 +764,7 @@ susceptibility estimates, weighted by pathogen incidence and
antimicrobial susceptibility patterns.
``` r
example_isolates %>%
wisca(
antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
@@ -753,8 +773,8 @@ example_isolates %>%
```
| Piperacillin/tazobactam | Piperacillin/tazobactam + Gentamicin | Piperacillin/tazobactam + Tobramycin |
|:------------------------|:-------------------------------------|:-------------------------------------|
| 69.4% (64.3-74.3%) | 92.6% (91.1-93.9%) | 88.7% (85.8-91.2%) |
|:---|:---|:---|
| 69.4% (64.3-74.3%) | 92.6% (91.1-93.9%) | 88.7% (85.8-91.2%) |
WISCA uses a **Bayesian decision model** to integrate data from multiple
pathogens, improving empirical therapy guidance, especially for
@@ -775,6 +795,7 @@ grouped `tibble`, i.e., using
first:
``` r
example_isolates %>%
top_n_microorganisms(n = 10) %>%
group_by(
@@ -785,15 +806,15 @@ example_isolates %>%
```
| age_group | gender | Piperacillin/tazobactam | Piperacillin/tazobactam + Gentamicin | Piperacillin/tazobactam + Tobramycin |
|:----------|:-------|:------------------------|:-------------------------------------|:-------------------------------------|
| 0-24 | F | 56.6% (25.2-83.9%) | 73.6% (48-91.6%) | 68.6% (42.9-89.5%) |
| 0-24 | M | 60.3% (28.4-87.1%) | 79.7% (57.6-94.2%) | 60.1% (29.5-87.7%) |
| 25-49 | F | 66.6% (45.6-85.5%) | 91.7% (84.6-96.7%) | 83% (67.9-94%) |
| 25-49 | M | 56.4% (29.1-81.7%) | 89.2% (80.3-95.7%) | 72.4% (49.7-90%) |
| 50-74 | F | 67.8% (55.8-80.1%) | 95.6% (93.2-97.5%) | 88.1% (80.4-94.6%) |
| 50-74 | M | 66.2% (54.8-75.8%) | 95.2% (92.4-97.4%) | 84.4% (74.4-92.5%) |
| 75+ | F | 71.7% (61-81.7%) | 96.6% (94.4-98.2%) | 90.6% (84.6-95.3%) |
| 75+ | M | 72.9% (63.8-82%) | 96.6% (94.6-98.1%) | 92.8% (87.8-96.5%) |
|:---|:---|:---|:---|:---|
| 0-24 | F | 56.6% (25.2-83.9%) | 73.6% (48-91.6%) | 68.6% (42.9-89.5%) |
| 0-24 | M | 60.3% (28.4-87.1%) | 79.7% (57.6-94.2%) | 60.1% (29.5-87.7%) |
| 25-49 | F | 66.6% (45.6-85.5%) | 91.7% (84.6-96.7%) | 83% (67.9-94%) |
| 25-49 | M | 56.4% (29.1-81.7%) | 89.2% (80.3-95.7%) | 72.4% (49.7-90%) |
| 50-74 | F | 67.8% (55.8-80.1%) | 95.6% (93.2-97.5%) | 88.1% (80.4-94.6%) |
| 50-74 | M | 66.2% (54.8-75.8%) | 95.2% (92.4-97.4%) | 84.4% (74.4-92.5%) |
| 75+ | F | 71.7% (61-81.7%) | 96.6% (94.4-98.2%) | 90.6% (84.6-95.3%) |
| 75+ | M | 72.9% (63.8-82%) | 96.6% (94.6-98.1%) | 92.8% (87.8-96.5%) |
#### Plotting antibiograms
@@ -803,6 +824,7 @@ from the `ggplot2` packages, since this `AMR` package provides an
extension to that function:
``` r
autoplot(combined_ab)
```
@@ -847,6 +869,7 @@ equal to
These functions can be used on their own:
``` r
our_data_1st %>% resistance(AMX)
#> ℹ `resistance()` assumes the EUCAST guideline and thus considers the 'I'
#> category susceptible. Set the `guideline` argument or the `AMR_guideline`
@@ -861,6 +884,7 @@ Or can be used in conjunction with
both from the `dplyr` package:
``` r
our_data_1st %>%
group_by(hospital) %>%
summarise(amoxicillin = resistance(AMX))
@@ -881,6 +905,7 @@ example with randomly generated MIC values for *Klebsiella pneumoniae*
and ciprofloxacin:
``` r
set.seed(123)
mic_values <- random_mic(100)
sir_values <- as.sir(mic_values, mo = "K. pneumoniae", ab = "cipro", guideline = "EUCAST 2024")
@@ -916,6 +941,7 @@ y-axis and
colour-code SIR categories.
``` r
# add a group
my_data$group <- rep(c("A", "B", "C", "D"), each = 25)
@@ -946,6 +972,7 @@ has been extended by this package to directly plot MIC and disk
diffusion values:
``` r
autoplot(mic_values)
```
@@ -953,6 +980,7 @@ autoplot(mic_values)
``` r
# by providing `mo` and `ab`, colours will indicate the SIR interpretation:
autoplot(mic_values, mo = "K. pneumoniae", ab = "cipro", guideline = "EUCAST 2024")
```