-
Generate an antibiogram, and communicate the results in plots or tables. These functions follow the logic of Klinker et al. and Barbieri et al. (see Source), and allow reporting in e.g. R Markdown and Quarto as well.
+
Create detailed antibiograms with options for traditional, combination, syndromic, and Bayesian WISCA methods. Based on the approaches of Klinker et al., Barbieri et al., and the Bayesian WISCA model (Weighted-Incidence Syndromic Combination Antibiogram) by Bielicki et al., this function provides flexible output formats including plots and tables, ideal for integration with R Markdown and Quarto reports.
@@ -63,11 +63,12 @@
x,
antibiotics = where(is.sir),
mo_transform = "shortname",
-
ab_transform = NULL,
+
ab_transform = "name",
syndromic_group = NULL,
-
add_total_n = TRUE,
+
add_total_n = FALSE,
only_all_tested = FALSE,
digits = 0,
+
formatting_type = getOption("AMR_antibiogram_formatting_type", 10),
col_mo = NULL,
language = get_AMR_locale(),
minimum = 30,
@@ -94,7 +95,8 @@
@@ -111,11 +113,11 @@
mo_transform
-
a character to transform microorganism input - must be "name", "shortname", "gramstain", or one of the column names of the microorganisms data set: "mo", "fullname", "status", "kingdom", "phylum", "class", "order", "family", "genus", "species", "subspecies", "rank", "ref", "oxygen_tolerance", "source", "lpsn", "lpsn_parent", "lpsn_renamed_to", "mycobank", "mycobank_parent", "mycobank_renamed_to", "gbif", "gbif_parent", "gbif_renamed_to", "prevalence", or "snomed". Can also be NULL to not transform the input.
+
a character to transform microorganism input - must be "name", "shortname" (default), "gramstain", or one of the column names of the microorganisms data set: "mo", "fullname", "status", "kingdom", "phylum", "class", "order", "family", "genus", "species", "subspecies", "rank", "ref", "oxygen_tolerance", "source", "lpsn", "lpsn_parent", "lpsn_renamed_to", "mycobank", "mycobank_parent", "mycobank_renamed_to", "gbif", "gbif_parent", "gbif_renamed_to", "prevalence", or "snomed". Can also be NULL to not transform the input.
ab_transform
-
a character to transform antibiotic input - must be one of the column names of the antibiotics data set: "ab", "cid", "name", "group", "atc", "atc_group1", "atc_group2", "abbreviations", "synonyms", "oral_ddd", "oral_units", "iv_ddd", "iv_units", or "loinc". Can also be NULL to not transform the input.
+
a character to transform antibiotic input - must be one of the column names of the antibiotics data set (defaults to "name"): "ab", "cid", "name", "group", "atc", "atc_group1", "atc_group2", "abbreviations", "synonyms", "oral_ddd", "oral_units", "iv_ddd", "iv_units", or "loinc". Can also be NULL to not transform the input.
syndromic_group
@@ -131,7 +133,11 @@
digits
-
number of digits to use for rounding
+
number of digits to use for rounding the susceptibility percentage
+
+
+
formatting_type
+
numeric value (1–12) indicating how the 'cells' of the antibiogram table should be formatted. See Details > Formatting Type for a list of options.
col_mo
@@ -177,13 +183,33 @@
Details
This function returns a table with values between 0 and 100 for susceptibility, not resistance.
-
Remember that you should filter your data to let it contain only first isolates! This is needed to exclude duplicates and to reduce selection bias. Use first_isolate() to determine them in your data set with one of the four available algorithms.
-
All types of antibiograms as listed below can be plotted (using ggplot2::autoplot() or base R plot()/barplot()). The antibiogram object can also be used directly in R Markdown / Quarto (i.e., knitr) for reports. In this case, knitr::kable() will be applied automatically and microorganism names will even be printed in italics at default (see argument italicise). You can also use functions from specific 'table reporting' packages to transform the output of antibiogram() to your needs, e.g. with flextable::as_flextable() or gt::gt().
+
Remember that you should filter your data to let it contain only first isolates! This is needed to exclude duplicates and to reduce selection bias. Use first_isolate() to determine them in your data set with one of the four available algorithms.
+
+
+
+
The formatting of the 'cells' of the table can be set with the argument formatting_type. In these examples, 5 is the susceptibility percentage, 15 the numerator, and 300 the denominator:
5
15
300
15/300
5 (300)
5% (300)
5 (N=300)
5% (N=300)
5 (15/300)
5% (15/300)
5 (N=15/300)
5% (N=15/300)
The default is 10, which can be set globally with the package option AMR_antibiogram_formatting_type, e.g. options(AMR_antibiogram_formatting_type = 5).
+
Set digits (defaults to 0) to alter the rounding of the susceptibility percentage.
+
+
+
Antibiogram Types
-
There are four antibiogram types, as proposed by Klinker et al. (2021, doi:10.1177/20499361211011373
-), and they are all supported by antibiogram():
Traditional Antibiogram
+There are four antibiogram types, as summarised by Klinker et al. (2021, doi:10.1177/20499361211011373
+), and they are all supported by antibiogram(). Use WISCA whenever possible, since it provides precise coverage estimates by accounting for pathogen incidence and antimicrobial susceptibility. See the section Why Use WISCA? on this page.
+The four antibiogram types:
Traditional Antibiogram
Case example: Susceptibility of Pseudomonas aeruginosa to piperacillin/tazobactam (TZP)
Code example:
Weighted-Incidence Syndromic Combination Antibiogram (WISCA)
+WISCA enhances empirical antibiotic selection by weighting the incidence of pathogens in specific clinical syndromes and combining them with their susceptibility data. It provides an estimation of regimen coverage by aggregating pathogen incidences and susceptibilities across potential causative organisms. See also the section Why Use WISCA? on this page.
Case example: Susceptibility of Pseudomonas aeruginosa to TZP among respiratory specimens (obtained among ICU patients only) for male patients age >=65 years with heart failure
Code example:
library(dplyr)
@@ -209,8 +236,15 @@
syndromic_group = ifelse(.$age >= 65 &
.$gender == "Male" &
.$condition == "Heart Disease",
- "Study Group", "Control Group"))
Note that for combination antibiograms, it is important to realise that susceptibility can be calculated in two ways, which can be set with the only_all_tested argument (default is FALSE). See this example for two antibiotics, Drug A and Drug B, about how antibiogram() works to calculate the %SI:
+ "Study Group", "Control Group"))
+
WISCA uses a sophisticated Bayesian decision model to combine both local and pooled antimicrobial resistance data. This approach not only evaluates local patterns but can also draw on multi-centre datasets to improve regimen accuracy, even in low-incidence infections like paediatric bloodstream infections (BSIs).
+
+
+
+
Inclusion in Combination Antibiogram and Syndromic Antibiogram
+
+
+
Note that for types 2 and 3 (Combination Antibiogram and Syndromic Antibiogram), it is important to realise that susceptibility can be calculated in two ways, which can be set with the only_all_tested argument (default is FALSE). See this example for two antibiotics, Drug A and Drug B, about how antibiogram() works to calculate the %SI:
--------------------------------------------------------------------
only_all_tested = FALSE only_all_tested = TRUE
----------------------- -----------------------
@@ -229,6 +263,26 @@
--------------------------------------------------------------------
+
+
Plotting
+
+
+
All types of antibiograms as listed above can be plotted (using ggplot2::autoplot() or base R's plot() and barplot()).
+
THe outcome of antibiogram() can also be used directly in R Markdown / Quarto (i.e., knitr) for reports. In this case, knitr::kable() will be applied automatically and microorganism names will even be printed in italics at default (see argument italicise).
+
You can also use functions from specific 'table reporting' packages to transform the output of antibiogram() to your needs, e.g. with flextable::as_flextable() or gt::gt().
+
+
+
+
+
Why Use WISCA?
+
+
+
WISCA is a powerful tool for guiding empirical antibiotic therapy because it provides precise coverage estimates by accounting for pathogen incidence and antimicrobial susceptibility. This is particularly important in empirical treatment, where the causative pathogen is often unknown at the outset. Traditional antibiograms do not reflect the weighted likelihood of specific pathogens based on clinical syndromes, which can lead to suboptimal treatment choices.
+
The Bayesian WISCA, as described by Bielicki et al. (2016), improves on earlier methods by handling uncertainties common in smaller datasets, such as low-incidence infections. This method offers a significant advantage by:
Pooling Data from Multiple Sources:
WISCA uses pooled data from multiple hospitals or surveillance sources to overcome limitations of small sample sizes at individual institutions, allowing for more confident selection of narrow-spectrum antibiotics or combinations.
Bayesian Framework:
The Bayesian decision tree model accounts for both local data and prior knowledge (such as inherent resistance patterns) to estimate regimen coverage. It allows for a more precise estimation of coverage, even in cases where susceptibility data is missing or incomplete.
Incorporating Pathogen and Regimen Uncertainty:
WISCA allows clinicians to see the likelihood that an empirical regimen will be effective against all relevant pathogens, taking into account uncertainties related to both pathogen prevalence and antimicrobial resistance. This leads to better-informed, data-driven clinical decisions.
Scenarios for Optimising Treatment:
For hospitals or settings with low-incidence infections, WISCA helps determine whether local data is sufficient or if pooling with external data is necessary. It also identifies statistically significant differences or similarities between antibiotic regimens, enabling clinicians to choose optimal therapies with greater confidence.
WISCA is essential in optimising empirical treatment by shifting away from broad-spectrum antibiotics, which are often overused in empirical settings. By offering precise estimates based on syndromic patterns and pooled data, WISCA supports antimicrobial stewardship by guiding more targeted therapy, reducing unnecessary broad-spectrum use, and combating the rise of antimicrobial resistance.
@@ -267,18 +321,18 @@
#> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin)
#> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem)
#> # An Antibiogram: 10 × 7
-
#> `Pathogen (N min-max)` AMK GEN IPM KAN MEM TOB
-
#> * <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
-
#> 1 CoNS (43-309) 0 86 52 0 52 22
-
#> 2 E. coli (0-462) 100 98 100 NA 100 97
-
#> 3 E. faecalis (0-39) 0 0 100 0 NA 0
-
#> 4 K. pneumoniae (0-58) NA 90 100 NA 100 90
-
#> 5 P. aeruginosa (17-30) NA 100 NA 0 NA 100
-
#> 6 P. mirabilis (0-34) NA 94 94 NA NA 94
-
#> 7 S. aureus (2-233) NA 99 NA NA NA 98
-
#> 8 S. epidermidis (8-163) 0 79 NA 0 NA 51
-
#> 9 S. hominis (3-80) NA 92 NA NA NA 85
-
#> 10 S. pneumoniae (11-117) 0 0 NA 0 NA 0
+
#> Pathogen Amikacin Gentamicin Imipenem Kanamycin Meropenem Tobramycin
+
#> * <chr> <chr> <chr> <chr> <chr> <chr> <chr>
+
#> 1 CoNS 0% (0/43) 86% (267/… 52% (25… 0% (0/43) 52% (25/… 22% (12/5…
+
#> 2 E. coli 100% (171/… 98% (451/… 100% (4… NA 100% (41… 97% (450/…
+
#> 3 E. faecalis 0% (0/39) 0% (0/39) 100% (3… 0% (0/39) NA 0% (0/39)
+
#> 4 K. pneumoniae NA 90% (52/5… 100% (5… NA 100% (53… 90% (52/5…
+
#> 5 P. aeruginosa NA 100% (30/… NA 0% (0/30) NA 100% (30/…
+
#> 6 P. mirabilis NA 94% (32/3… 94% (30… NA NA 94% (32/3…
+
#> 7 S. aureus NA 99% (231/… NA NA NA 98% (84/8…
+
#> 8 S. epidermidis 0% (0/44) 79% (128/… NA 0% (0/44) NA 51% (45/8…
+
#> 9 S. hominis NA 92% (74/8… NA NA NA 85% (53/6…
+
#> 10 S. pneumoniae 0% (0/117) 0% (0/117) NA 0% (0/11… NA 0% (0/117)
#> # Use `plot()` or `ggplot2::autoplot()` to create a plot of this antibiogram,
#> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram
@@ -290,10 +344,10 @@
#> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB'
#> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin)
#> # An Antibiogram: 2 × 5
-
#> `Pathogen (N min-max)` J01GB01 J01GB03 J01GB04 J01GB06
-
#> * <chr> <dbl> <dbl> <dbl> <dbl>
-
#> 1 Gram-negative (35-686) 96 96 0 98
-
#> 2 Gram-positive (436-1170) 34 63 0 0
+
#> Pathogen J01GB01 J01GB03 J01GB04 J01GB06
+
#> * <chr> <chr> <chr> <chr> <chr>
+
#> 1 Gram-negative 96% (658/686) 96% (659/684) 0% (0/35) 98% (251/256)
+
#> 2 Gram-positive 34% (228/665) 63% (740/1170) 0% (0/436) 0% (0/436)
#> # Use `plot()` or `ggplot2::autoplot()` to create a plot of this antibiogram,
#> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram
@@ -304,13 +358,13 @@
)
#> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem)
#> # An Antibiogram: 5 × 3
-
#> `Pathogen (N min-max)` Imipenem Meropenem
-
#> * <chr> <dbl> <dbl>
-
#> 1 Coagulase-negative Staphylococcus (CoNS) (48-48) 52 52
-
#> 2 Enterococcus faecalis (0-38) 100 NA
-
#> 3 Escherichia coli (418-422) 100 100
-
#> 4 Klebsiella pneumoniae (51-53) 100 100
-
#> 5 Proteus mirabilis (27-32) 94 NA
+
#> Pathogen Imipenem Meropenem
+
#> * <chr> <chr> <chr>
+
#> 1 Coagulase-negative Staphylococcus (CoNS) 52% (25/48) 52% (25/48)
+
#> 2 Enterococcus faecalis 100% (38/38) NA
+
#> 3 Escherichia coli 100% (422/422) 100% (418/418)
+
#> 4 Klebsiella pneumoniae 100% (51/51) 100% (53/53)
+
#> 5 Proteus mirabilis 94% (30/32) NA
#> # Use `plot()` or `ggplot2::autoplot()` to create a plot of this antibiogram,
#> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram
@@ -323,10 +377,13 @@
mo_transform = "gramstain"
)
#> # An Antibiogram: 2 × 4
-
#> `Pathogen (N min-max)` TZP `TZP + GEN` `TZP + TOB`
-
#> * <chr> <dbl> <dbl> <dbl>
-
#> 1 Gram-negative (641-693) 88 99 98
-
#> 2 Gram-positive (345-1044) 86 98 95
+
#> Pathogen Piperacillin/tazobac…¹ Piperacillin/tazobac…² Piperacillin/tazobac…³
+
#> * <chr> <chr> <chr> <chr>
+
#> 1 Gram-neg… 88% (565/641) 99% (681/691) 98% (679/693)
+
#> 2 Gram-pos… 86% (296/345) 98% (1018/1044) 95% (524/550)
+
#> # ℹ abbreviated names: ¹`Piperacillin/tazobactam`,
+
#> # ²`Piperacillin/tazobactam + Gentamicin`,
+
#> # ³`Piperacillin/tazobactam + Tobramycin`
#> # Use `plot()` or `ggplot2::autoplot()` to create a plot of this antibiogram,
#> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram
@@ -338,10 +395,10 @@
sep = " & "
)
#> # An Antibiogram: 2 × 3
-
#> `Pathogen (N min-max)` Ciprofloxacin `Ciprofloxacin & Gentamicin`
-
#> * <chr> <dbl> <dbl>
-
#> 1 Gram-negative (684-694) 91 99
-
#> 2 Gram-positive (724-847) 77 93
+
#> Pathogen Ciprofloxacin `Ciprofloxacin & Gentamicin`
+
#> * <chr> <chr> <chr>
+
#> 1 Gram-negative 91% (621/684) 99% (684/694)
+
#> 2 Gram-positive 77% (560/724) 93% (784/847)
#> # Use `plot()` or `ggplot2::autoplot()` to create a plot of this antibiogram,
#> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram
@@ -357,22 +414,23 @@
#> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin)
#> ℹ For carbapenems() using columns 'IPM' (imipenem) and 'MEM' (meropenem)
#> # An Antibiogram: 14 × 8
-
#> `Syndromic Group` `Pathogen (N min-max)` AMK GEN IPM KAN MEM TOB
-
#> * <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
-
#> 1 Clinical CoNS (23-205) NA 89 57 NA 57 26
-
#> 2 ICU CoNS (10-73) NA 79 NA NA NA NA
-
#> 3 Outpatient CoNS (3-31) NA 84 NA NA NA NA
-
#> 4 Clinical E. coli (0-299) 100 98 100 NA 100 98
-
#> 5 ICU E. coli (0-137) 100 99 100 NA 100 96
-
#> 6 Clinical K. pneumoniae (0-51) NA 92 100 NA 100 92
-
#> 7 Clinical P. mirabilis (0-30) NA 100 NA NA NA 100
-
#> 8 Clinical S. aureus (2-150) NA 99 NA NA NA 97
-
#> 9 ICU S. aureus (0-66) NA 100 NA NA NA NA
-
#> 10 Clinical S. epidermidis (4-79) NA 82 NA NA NA 55
-
#> 11 ICU S. epidermidis (4-75) NA 72 NA NA NA 41
-
#> 12 Clinical S. hominis (1-45) NA 96 NA NA NA 94
-
#> 13 Clinical S. pneumoniae (5-78) 0 0 NA 0 NA 0
-
#> 14 ICU S. pneumoniae (5-30) 0 0 NA 0 NA 0
+
#> `Syndromic Group` Pathogen Amikacin Gentamicin Imipenem Kanamycin Meropenem
+
#> * <chr> <chr> <chr> <chr> <chr> <chr> <chr>
+
#> 1 Clinical CoNS NA 89% (183/… 57% (20… NA 57% (20/…
+
#> 2 ICU CoNS NA 79% (58/7… NA NA NA
+
#> 3 Outpatient CoNS NA 84% (26/3… NA NA NA
+
#> 4 Clinical E. coli 100% (1… 98% (291/… 100% (2… NA 100% (27…
+
#> 5 ICU E. coli 100% (5… 99% (135/… 100% (1… NA 100% (11…
+
#> 6 Clinical K. pneumo… NA 92% (47/5… 100% (4… NA 100% (46…
+
#> 7 Clinical P. mirabi… NA 100% (30/… NA NA NA
+
#> 8 Clinical S. aureus NA 99% (148/… NA NA NA
+
#> 9 ICU S. aureus NA 100% (66/… NA NA NA
+
#> 10 Clinical S. epider… NA 82% (65/7… NA NA NA
+
#> 11 ICU S. epider… NA 72% (54/7… NA NA NA
+
#> 12 Clinical S. hominis NA 96% (43/4… NA NA NA
+
#> 13 Clinical S. pneumo… 0% (0/7… 0% (0/78) NA 0% (0/78) NA
+
#> 14 ICU S. pneumo… 0% (0/3… 0% (0/30) NA 0% (0/30) NA
+
#> # ℹ 1 more variable: Tobramycin <chr>
#> # Use `plot()` or `ggplot2::autoplot()` to create a plot of this antibiogram,
#> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram
@@ -393,10 +451,10 @@
#> ℹ For aminoglycosides() using columns 'GEN' (gentamicin), 'TOB'
#> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin)
#> # An Antibiogram: 2 × 5
-
#> `Grupo sindrómico` `Patógeno (N min-max)` Amikacina Gentamicina Tobramicina
-
#> * <chr> <chr> <dbl> <dbl> <dbl>
-
#> 1 No UCI E. coli (0-325) 100 98 98
-
#> 2 UCI E. coli (0-137) 100 99 96
+
#> `Grupo sindrómico` Patógeno Amikacina Gentamicina Tobramicina
+
#> * <chr> <chr> <chr> <chr> <chr>
+
#> 1 No UCI E. coli 100% (119/119) 98% (316/323) 98% (318/325)
+
#> 2 UCI E. coli 100% (52/52) 99% (135/137) 96% (132/137)
#> # Use `plot()` or `ggplot2::autoplot()` to create a plot of this antibiogram,
#> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram
@@ -414,12 +472,16 @@
)
)
#> # An Antibiogram: 4 × 6
-
#> `Syndromic Group` `Pathogen (N min-max)` AMC `AMC + CIP` TZP `TZP + TOB`
-
#> * <chr> <chr> <dbl> <dbl> <dbl> <dbl>
-
#> 1 WISCA Group 1 Gram-negative (261-285) 76 95 89 99
-
#> 2 WISCA Group 2 Gram-negative (380-442) 76 98 88 98
-
#> 3 WISCA Group 1 Gram-positive (123-406) 76 89 81 95
-
#> 4 WISCA Group 2 Gram-positive (222-732) 76 89 88 95
+
#> `Syndromic Group` Pathogen Amoxicillin/clavulani…¹ Amoxicillin/clavulan…²
+
#> * <chr> <chr> <chr> <chr>
+
#> 1 WISCA Group 1 Gram-negative 76% (216/285) 95% (270/284)
+
#> 2 WISCA Group 2 Gram-negative 76% (336/441) 98% (432/442)
+
#> 3 WISCA Group 1 Gram-positive 76% (310/406) 89% (347/392)
+
#> 4 WISCA Group 2 Gram-positive 76% (556/732) 89% (617/695)
+
#> # ℹ abbreviated names: ¹`Amoxicillin/clavulanic acid`,
+
#> # ²`Amoxicillin/clavulanic acid + Ciprofloxacin`
+
#> # ℹ 2 more variables: `Piperacillin/tazobactam` <chr>,
+
#> # `Piperacillin/tazobactam + Tobramycin` <chr>
#> # Use `plot()` or `ggplot2::autoplot()` to create a plot of this antibiogram,
#> # or use it directly in R Markdown or https://quarto.org, see ?antibiogram
@@ -439,12 +501,12 @@
}
#>
#>
-
#> |Pathogen (N) | Piperacillin/tazobactam|
-
#> |:---------------------|-----------------------:|
-
#> |CoNS (33) | 30|
-
#> |*E. coli* (416) | 94|
-
#> |*K. pneumoniae* (53) | 89|
-
#> |*S. pneumoniae* (112) | 100|
+
#> |Pathogen |Piperacillin/tazobactam |
+
#> |:---------------|:-----------------------|
+
#> |CoNS |30% (10/33) |
+
#> |*E. coli* |94% (393/416) |
+
#> |*K. pneumoniae* |89% (47/53) |
+
#> |*S. pneumoniae* |100% (112/112) |
# Generate plots with ggplot2 or base R --------------------------------
diff --git a/reference/antibiotic_class_selectors.html b/reference/antibiotic_class_selectors.html
index a655ad82..04738b7d 100644
--- a/reference/antibiotic_class_selectors.html
+++ b/reference/antibiotic_class_selectors.html
@@ -9,7 +9,7 @@ In short, if you have a column name that resembles an antimicrobial drug, it wil
AMR (for R)
-
2.1.1.9077
+
2.1.1.9078