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@@ -1,34 +1,76 @@
# Generate Traditional, Combination, Syndromic, or WISCA Antibiograms
# Generate Antibiograms (WISCA, Traditional, Combination, or Syndromic)
Create detailed antibiograms with options for traditional, combination,
syndromic, and Bayesian WISCA methods.
Generate antibiograms from antimicrobial susceptibility data, with
support for traditional, combination, syndromic, and WISCA
(Weighted-Incidence Syndromic Combination Antibiogram) methods.
Adhering to previously described approaches (see *Source*) and
especially the Bayesian WISCA model (Weighted-Incidence Syndromic
Combination Antibiogram) by Bielicki *et al.*, these functions provide
flexible output formats including plots and tables, ideal for
integration with R Markdown and Quarto reports.
**For empirical therapy guidance, WISCA is the recommended approach.**
When initiating empirical treatment, the causative pathogen is unknown,
and the clinically relevant question is: *"what is the probability that
this regimen will cover whatever pathogen turns out to cause the
infection?"* WISCA answers that question directly by weighting
susceptibility by pathogen incidence within a syndrome and providing
credible intervals via Bayesian Monte Carlo simulation. Traditional
antibiograms remain appropriate for tracking resistance per species for
surveillance purposes. See the section *Explaining WISCA* on this page
and the [WISCA vignette](https://amr-for-r.org/articles/WISCA.html) for
details.
All antibiogram types adhere to previously described approaches (see
*Source*), and the WISCA method implements the Bayesian decision model
by Bielicki *et al.* (2016,
[doi:10.1093/jac/dkv397](https://doi.org/10.1093/jac/dkv397) ). Output
formats include plots and tables, ideal for integration with R Markdown
and Quarto reports.
## Usage
``` r
antibiogram(x, antimicrobials = where(is.sir), mo_transform = "shortname",
ab_transform = "name", syndromic_group = NULL, add_total_n = FALSE,
only_all_tested = FALSE, digits = ifelse(wisca, 1, 0),
formatting_type = getOption("AMR_antibiogram_formatting_type",
ifelse(wisca, 14, 18)), col_mo = NULL, language = get_AMR_locale(),
minimum = 30, combine_SI = TRUE, sep = " + ", sort_columns = TRUE,
wisca = FALSE, simulations = 1000, conf_interval = 0.95,
interval_side = "two-tailed", info = interactive(), parallel = FALSE,
...)
wisca(x, antimicrobials = where(is.sir), ab_transform = "name",
syndromic_group = NULL, only_all_tested = FALSE, digits = 1,
wisca(
x,
antimicrobials = where(is.sir),
ab_transform = "name",
syndromic_group = NULL,
only_all_tested = FALSE,
digits = 1,
formatting_type = getOption("AMR_antibiogram_formatting_type", 14),
col_mo = NULL, language = get_AMR_locale(), combine_SI = TRUE,
sep = " + ", sort_columns = TRUE, simulations = 1000,
conf_interval = 0.95, interval_side = "two-tailed",
info = interactive(), parallel = FALSE, ...)
col_mo = NULL,
language = get_AMR_locale(),
combine_SI = TRUE,
sep = " + ",
sort_columns = TRUE,
simulations = 1000,
conf_interval = 0.95,
interval_side = "two-tailed",
info = interactive(),
parallel = FALSE,
...
)
antibiogram(
x,
antimicrobials = where(is.sir),
mo_transform = "shortname",
ab_transform = "name",
syndromic_group = NULL,
add_total_n = FALSE,
only_all_tested = FALSE,
digits = ifelse(wisca, 1, 0),
formatting_type = getOption("AMR_antibiogram_formatting_type", ifelse(wisca, 14, 18)),
col_mo = NULL,
language = get_AMR_locale(),
minimum = 30,
combine_SI = TRUE,
sep = " + ",
sort_columns = TRUE,
wisca = FALSE,
simulations = 1000,
conf_interval = 0.95,
interval_side = "two-tailed",
info = interactive(),
parallel = FALSE,
...
)
retrieve_wisca_parameters(wisca_model, ...)
@@ -36,44 +78,31 @@ retrieve_wisca_parameters(wisca_model, ...)
plot(x, ...)
# S3 method for class 'antibiogram'
autoplot(object, ...)
autoplot(
object,
geom = c("pointrange", "point", "col", "bar", "errorbar"),
ci = TRUE,
sort = TRUE,
flip = NULL,
caption = NULL,
...
)
wisca_plot(
wisca_model,
wisca_plot_type = c("susceptibility_incidence", "posterior_coverage"),
...
)
# S3 method for class 'antibiogram'
knit_print(x, italicise = TRUE,
na = getOption("knitr.kable.NA", default = ""), ...)
knit_print(
x,
italicise = TRUE,
na = getOption("knitr.kable.NA", default = ""),
...
)
```
## Source
- Bielicki JA *et al.* (2016). **Selecting appropriate empirical
antibiotic regimens for paediatric bloodstream infections: application
of a Bayesian decision model to local and pooled antimicrobial
resistance surveillance data** *Journal of Antimicrobial Chemotherapy*
71(3); [doi:10.1093/jac/dkv397](https://doi.org/10.1093/jac/dkv397)
- Bielicki JA *et al.* (2020). **Evaluation of the coverage of 3
antibiotic regimens for neonatal sepsis in the hospital setting across
Asian countries** *JAMA Netw Open.* 3(2):e1921124;
[doi:10.1001/jamanetworkopen.2019.21124](https://doi.org/10.1001/jamanetworkopen.2019.21124)
- Klinker KP *et al.* (2021). **Antimicrobial stewardship and
antibiograms: importance of moving beyond traditional antibiograms**.
*Therapeutic Advances in Infectious Disease*, May
5;8:20499361211011373;
[doi:10.1177/20499361211011373](https://doi.org/10.1177/20499361211011373)
- Barbieri E *et al.* (2021). **Development of a Weighted-Incidence
Syndromic Combination Antibiogram (WISCA) to guide the choice of the
empiric antibiotic treatment for urinary tract infection in paediatric
patients: a Bayesian approach** *Antimicrobial Resistance & Infection
Control* May 1;10(1):74;
[doi:10.1186/s13756-021-00939-2](https://doi.org/10.1186/s13756-021-00939-2)
- **M39 Analysis and Presentation of Cumulative Antimicrobial
Susceptibility Test Data, 5th Edition**, 2022, *Clinical and
Laboratory Standards Institute (CLSI)*.
<https://clsi.org/standards/products/microbiology/documents/m39/>.
## Arguments
- x:
@@ -127,21 +156,6 @@ knit_print(x, italicise = TRUE,
- `carbapenems() + c("", aminoglycosides())`
- mo_transform:
A character to transform microorganism input - must be `"name"`,
`"shortname"` (default), `"gramstain"`, or one of the column names of
the
[microorganisms](https://amr-for-r.org/reference/microorganisms.md)
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 or `NA` to consider all
microorganisms 'unknown'.
- ab_transform:
A character to transform antimicrobial input - must be one of the
@@ -154,21 +168,10 @@ knit_print(x, italicise = TRUE,
- syndromic_group:
A column name of \`x\`, or values calculated to split rows of \`x\`,
e.g. by using \[ifelse()\] or
\[\`case_when()\`\]\[dplyr::case_when()\]. See \*Examples\*.
- add_total_n:
*(deprecated in favour of `formatting_type`)* A
[logical](https://rdrr.io/r/base/logical.html) to indicate whether
`n_tested` available numbers per pathogen should be added to the table
(default is `TRUE`). This will add the lowest and highest number of
available isolates per antimicrobial (e.g, if for *E. coli* 200
isolates are available for ciprofloxacin and 150 for amoxicillin, the
returned number will be "150-200"). This option is unavailable when
`wisca = TRUE`; in that case, use `retrieve_wisca_parameters()` to get
the parameters used for WISCA.
A column name of `x`, or values calculated to split rows of `x`, e.g.
by using [`ifelse()`](https://rdrr.io/r/base/ifelse.html) or
[`case_when()`](https://dplyr.tidyverse.org/reference/case-and-replace-when.html).
See *Examples*.
- only_all_tested:
@@ -200,13 +203,6 @@ knit_print(x, italicise = TRUE,
Language to translate text, which defaults to the system language (see
[`get_AMR_locale()`](https://amr-for-r.org/reference/translate.md)).
- minimum:
The minimum allowed number of available (tested) isolates. Any isolate
count lower than `minimum` will return `NA` with a warning. The
default number of `30` isolates is advised by the Clinical and
Laboratory Standards Institute (CLSI) as best practice, see *Source*.
- combine_SI:
A [logical](https://rdrr.io/r/base/logical.html) to indicate whether
@@ -223,15 +219,6 @@ knit_print(x, italicise = TRUE,
A [logical](https://rdrr.io/r/base/logical.html) to indicate whether
the antimicrobial columns must be sorted on name.
- wisca:
A [logical](https://rdrr.io/r/base/logical.html) to indicate whether a
Weighted-Incidence Syndromic Combination Antibiogram (WISCA) must be
generated (default is `FALSE`). This will use a Bayesian decision
model to estimate regimen coverage probabilities using [Monte Carlo
simulations](https://en.wikipedia.org/wiki/Monte_Carlo_method). Set
`simulations`, `conf_interval`, and `interval_side` to adjust.
- simulations:
(for WISCA) a numerical value to set the number of Monte Carlo
@@ -266,10 +253,57 @@ knit_print(x, italicise = TRUE,
- ...:
When used in [R Markdown or
Quarto](https://rdrr.io/pkg/knitr/man/kable.html): arguments passed on
to [`knitr::kable()`](https://rdrr.io/pkg/knitr/man/kable.html)
(otherwise, has no use).
Currently unused.
- mo_transform:
A character to transform microorganism input - must be `"name"`,
`"shortname"` (default), `"gramstain"`, or one of the column names of
the
[microorganisms](https://amr-for-r.org/reference/microorganisms.md)
data set: `"mo"`, `"fullname"`, `"status"`, `"domain"`, `"kingdom"`,
`"phylum"`, `"class"`, `"order"`, `"family"`, `"genus"`, `"species"`,
`"subspecies"`, `"rank"`, `"ref"`, `"oxygen_tolerance"`,
`"morphology"`, `"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 or `NA` to consider all microorganisms
'unknown'.
- add_total_n:
*(deprecated in favour of `formatting_type`)* A
[logical](https://rdrr.io/r/base/logical.html) to indicate whether
`n_tested` available numbers per pathogen should be added to the table
(default is `TRUE`). This will add the lowest and highest number of
available isolates per antimicrobial (e.g., if for *E. coli* 200
isolates are available for ciprofloxacin and 150 for amoxicillin, the
returned number will be "150-200"). This option is unavailable when
`wisca = TRUE`; in that case, use `retrieve_wisca_parameters()` to get
the parameters used for WISCA.
- minimum:
The minimum allowed number of available (tested) isolates. Any isolate
count lower than `minimum` will return `NA` with a warning. The
default number of `30` isolates is advised by the Clinical and
Laboratory Standards Institute (CLSI) as best practice, see *Source*.
- wisca:
A [logical](https://rdrr.io/r/base/logical.html) to indicate whether a
Weighted-Incidence Syndromic Combination Antibiogram (WISCA) must be
generated (default is `FALSE`). This will use a Bayesian decision
model to estimate regimen coverage probabilities using [Monte Carlo
simulations](https://en.wikipedia.org/wiki/Monte_Carlo_method). Per
[doi:10.1093/jac/dkv397](https://doi.org/10.1093/jac/dkv397) ,
susceptibility priors are \\\beta(0.5, 0.5)\\ (Jeffreys) and
intrinsically resistant pairs (based on
[intrinsic_resistant](https://amr-for-r.org/reference/intrinsic_resistant.md))
use \\\beta(1, 9999)\\.
Set `simulations`, `conf_interval`, and `interval_side` to adjust.
- wisca_model:
@@ -279,6 +313,42 @@ knit_print(x, italicise = TRUE,
An `antibiogram()` object.
- geom:
The plotting style for the point estimate. One of `"pointrange"`
(default), `"point"`, `"col"`/`"bar"`, or `"errorbar"`. `"pointrange"`
is recommended for coverage data: bars imply a meaningful baseline at
zero, which coverage estimates rarely have.
- ci:
Logical, whether to draw the credible/confidence interval. Defaults to
`TRUE`. Ignored (forced `TRUE`) when `geom = "pointrange"` or
`"errorbar"`, since the interval is intrinsic to those geoms.
- sort:
Logical, whether to order regimens by coverage. Defaults to `TRUE`.
When faceted (per pathogen) or grouped (syndromic), ordering is
applied within each panel/group.
- flip:
Logical, whether to draw regimens on the y-axis (horizontal). Defaults
to `NULL`, which flips automatically when any regimen label exceeds 20
characters (long combination names read poorly on the x-axis). Set
`TRUE`/`FALSE` to override.
- caption:
Text to show as caption, will explain non-inferiority for WISCA
models.
- wisca_plot_type:
Either `"susceptibility_incidence"` (default) or
`"posterior_coverage"`.
- italicise:
A [logical](https://rdrr.io/r/base/logical.html) to indicate whether
@@ -377,73 +447,37 @@ apparent from the susceptibility and its confidence level.
Set `digits` (defaults to `0`) to alter the rounding of the
susceptibility percentages.
### Antibiogram Types
### When to Use WISCA vs. Traditional Antibiograms
There are various antibiogram types, as summarised by Klinker *et al.*
(2021,
[doi:10.1177/20499361211011373](https://doi.org/10.1177/20499361211011373)
), and they are all supported by `antibiogram()`.
), and they are all supported by `antibiogram()`: traditional,
combination, syndromic, and WISCA.
For clinical coverage estimations, **use WISCA whenever possible**,
since it provides more precise coverage estimates by accounting for
pathogen incidence and antimicrobial susceptibility, as has been shown
by Bielicki *et al.* (2020,
[doi:10.1001/jamanetworkopen.2019.21124](https://doi.org/10.1001/jamanetworkopen.2019.21124)
). See the section *Explaining WISCA* on this page. Do note that WISCA
is pathogen-agnostic, meaning that the outcome is not stratied by
pathogen, but rather by syndrome.
**If your goal is to guide empirical therapy, use WISCA.** Traditional
antibiograms fragment susceptibility information by species, but at the
point of prescribing, the clinician does not know which species is
causing the infection. WISCA shifts the unit of analysis from the
isolate to the patient: it estimates the probability that a regimen will
cover the infection, given the local distribution of causative
pathogens. It evaluates combination regimens, weights by pathogen
incidence, and provides credible intervals that honestly communicate
uncertainty. Hebert *et al.* (2012) demonstrated this concretely for the
first time: ciprofloxacin showed 84% susceptibility against *E. coli* in
the traditional antibiogram, but WISCA coverage was only 62% for UTI and
37% for abdominal infections, because other species (including
intrinsically resistant enterococci) contribute substantially to these
syndromes. Note that WISCA is pathogen-agnostic: the outcome is not
stratified by species, but by syndrome.
1. **Traditional Antibiogram**
**Traditional, combination, and syndromic antibiograms remain
appropriate for AMR surveillance**, i.e., tracking resistance trends per
species over time. They are the right tool when the question is *"how
resistant is species X to drug Y in our setting?"* rather than *"what
regimen best covers this syndrome?"*.
Case example: Susceptibility of *Pseudomonas aeruginosa* to
piperacillin/tazobactam (TZP)
Code example:
antibiogram(your_data,
antimicrobials = "TZP")
2. **Combination Antibiogram**
Case example: Additional susceptibility of *Pseudomonas aeruginosa*
to TZP + tobramycin versus TZP alone
Code example:
antibiogram(your_data,
antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"))
3. **Syndromic Antibiogram**
Case example: Susceptibility of *Pseudomonas aeruginosa* to TZP
among respiratory specimens (obtained among ICU patients only)
Code example:
antibiogram(your_data,
antimicrobials = penicillins(),
syndromic_group = "ward")
4. **Weighted-Incidence Syndromic Combination Antibiogram (WISCA)**
WISCA can be applied to any antibiogram, see the section *Explaining
WISCA* on this page for more information.
Code example:
antibiogram(your_data,
antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"),
wisca = TRUE)
# this is equal to:
wisca(your_data,
antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"))
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 data
sets to improve regimen accuracy, even in low-incidence infections
like paediatric bloodstream infections (BSIs).
All four types are demonstrated in the *Examples* section below.
### Grouped tibbles
@@ -458,65 +492,6 @@ Code example:
group_by(has_sepsis, is_neonate, sex) %>%
wisca(antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"))
### Stepped Approach for Clinical Insight
In clinical practice, antimicrobial coverage decisions evolve as more
microbiological data becomes available. This theoretical stepped
approach ensures empirical coverage can continuously assessed to improve
patient outcomes:
1. **Initial Empirical Therapy (Admission / Pre-Culture Data)**
At admission, no pathogen information is available.
- Action: broad-spectrum coverage is based on local resistance
patterns and syndromic antibiograms. Using the pathogen-agnostic
yet incidence-weighted WISCA is preferred.
- Code example:
antibiogram(your_data,
antimicrobials = selected_regimens,
mo_transform = NA) # all pathogens set to `NA`
# preferred: use WISCA
wisca(your_data,
antimicrobials = selected_regimens)
2. **Refinement with Gram Stain Results**
When a blood culture becomes positive, the Gram stain provides an
initial and crucial first stratification (Gram-positive vs.
Gram-negative).
- Action: narrow coverage based on Gram stain-specific resistance
patterns.
- Code example:
antibiogram(your_data,
antimicrobials = selected_regimens,
mo_transform = "gramstain") # all pathogens set to Gram-pos/Gram-neg
3. **Definitive Therapy Based on Species Identification**
After cultivation of the pathogen, full pathogen identification
allows precise targeting of therapy.
- Action: adjust treatment to pathogen-specific antibiograms,
minimizing resistance risks.
- Code example:
antibiogram(your_data,
antimicrobials = selected_regimens,
mo_transform = "shortname") # all pathogens set to 'G. species', e.g., E. coli
By structuring antibiograms around this stepped approach, clinicians can
make data-driven adjustments at each stage, ensuring optimal empirical
and targeted therapy while reducing unnecessary broad-spectrum
antimicrobial use.
### Inclusion in Combination Antibiograms
Note that for combination antibiograms, it is important to realise that
@@ -567,14 +542,78 @@ transform the output of `antibiogram()` to your needs, e.g. with
## Explaining WISCA
WISCA (Weighted-Incidence Syndromic Combination Antibiogram) estimates
the probability of empirical coverage for combination regimens.
the probability that an empirical antimicrobial regimen will provide
adequate coverage for a given infection syndrome, before the causative
pathogen has been identified.
It weights susceptibility by pathogen prevalence within a clinical
syndrome and provides credible intervals around the expected coverage.
It does so by combining two quantities: the relative incidence of each
pathogen within the syndrome (modelled as a Dirichlet distribution) and
the susceptibility of each pathogen to the regimen (modelled as Beta
distributions). These are combined via Monte Carlo simulation to produce
a coverage estimate with a credible interval.
For more background, interpretation, and examples, see [the WISCA
**Prior distributions:** Pathogen incidence uses a non-informative
\\Dirichlet(1, 1, \ldots, 1)\\ prior. Susceptibility proportions use the
Jeffreys prior, \\\beta(0.5, 0.5)\\, except for pathogen-drug
combinations with known intrinsic resistance, which use a strongly
informative \\\beta(1, 9999)\\ prior that forces near-zero
susceptibility regardless of observed data. Intrinsic resistance is
determined using the
[intrinsic_resistant](https://amr-for-r.org/reference/intrinsic_resistant.md)
data set, which is based on ['EUCAST Expected Resistant Phenotypes'
v1.2](https://www.eucast.org/bacteria/important-additional-information/expert-rules/)
(2023).
**Interpreting the output:** Overlapping credible intervals between
regimens indicate no significant difference in coverage; if a
narrower-spectrum regimen overlaps with a broader one, the
narrower-spectrum option may be preferred on stewardship grounds.
Non-overlapping intervals indicate a clinically meaningful difference.
For small sample sizes, consider pooling data from multiple sites to
improve precision, provided pathogen distributions are sufficiently
similar (Bielicki *et al.*, 2016).
For the full mathematical derivation and worked examples, see the [WISCA
vignette](https://amr-for-r.org/articles/WISCA.html).
## References
- Hebert C *et al.* (2012). **Demonstration of the weighted-incidence
syndromic combination antibiogram: an empiric prescribing decision
aid.** *Infection Control & Hospital Epidemiology* 33(4):381-388;
[doi:10.1086/664768](https://doi.org/10.1086/664768)
- Bielicki JA *et al.* (2016). **Selecting appropriate empirical
antibiotic regimens for paediatric bloodstream infections: application
of a Bayesian decision model to local and pooled antimicrobial
resistance surveillance data.** *Journal of Antimicrobial
Chemotherapy* 71(3):794-802;
[doi:10.1093/jac/dkv397](https://doi.org/10.1093/jac/dkv397)
- Cook A *et al.* (2022). **Improving empiric antibiotic prescribing in
pediatric bloodstream infections: a potential application of
weighted-incidence syndromic combination antibiograms (WISCA).**
*Expert Review of Anti-infective Therapy* 20(3):445-456;
[doi:10.1080/14787210.2021.1967145](https://doi.org/10.1080/14787210.2021.1967145)
- Klinker KP *et al.* (2021). **Antimicrobial stewardship and
antibiograms: importance of moving beyond traditional antibiograms.**
*Therapeutic Advances in Infectious Disease*, May
5;8:20499361211011373;
[doi:10.1177/20499361211011373](https://doi.org/10.1177/20499361211011373)
- Barbieri E *et al.* (2021). **Development of a Weighted-Incidence
Syndromic Combination Antibiogram (WISCA) to guide the choice of the
empiric antibiotic treatment for urinary tract infection in paediatric
patients: a Bayesian approach.** *Antimicrobial Resistance & Infection
Control* May 1;10(1):74;
[doi:10.1186/s13756-021-00939-2](https://doi.org/10.1186/s13756-021-00939-2)
- **M39 Analysis and Presentation of Cumulative Antimicrobial
Susceptibility Test Data, 5th Edition**, 2022, *Clinical and
Laboratory Standards Institute (CLSI)*.
<https://clsi.org/standards/products/microbiology/documents/m39/>.
## Author
Implementation: Dr. Larisse Bolton and Dr. Matthijs Berends
@@ -607,7 +646,105 @@ example_isolates
#> # IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …
# \donttest{
# Traditional antibiogram ----------------------------------------------
# WISCA antibiogram (recommended for empirical therapy) -----------------
# basic WISCA: empirical coverage per regimen, weighted by pathogen
# incidence, with 95% credible intervals
wisca(example_isolates,
antimicrobials = c("AMC", "AMC+CIP", "AMC+GEN")
)
#> Warning: invalid microorganism code, NA generated
#> # A tibble: 1 × 3
#> # Type: Weighted-Incidence Syndromic Combination Antibiogram (WISCA)
#> # Cred. interval: 95%
#> # Simulations: 1000 per stratum
#> `Amoxicillin/clavulanic acid` Amoxicillin/clavulanic …¹ Amoxicillin/clavulan…²
#> <chr> <chr> <chr>
#> 1 74.2% (72.1-76.1%) 88.8% (87.2-90.3%) 90.8% (89.3-92.1%)
#> # ℹ abbreviated names: ¹​`Amoxicillin/clavulanic acid + Ciprofloxacin`,
#> # ²​`Amoxicillin/clavulanic acid + Gentamicin`
#> # Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram,
#> # and use `wisca_plot()` to assess the simulation outcomes.
#> # Or, use it directly in R Markdown or Quarto, see `antibiogram()`.
# equivalent using antibiogram():
antibiogram(example_isolates,
antimicrobials = c("AMC", "AMC+CIP", "AMC+GEN"),
wisca = TRUE
)
#> Warning: invalid microorganism code, NA generated
#> # A tibble: 1 × 3
#> # Type: Weighted-Incidence Syndromic Combination Antibiogram (WISCA)
#> # Cred. interval: 95%
#> # Simulations: 1000 per stratum
#> `Amoxicillin/clavulanic acid` Amoxicillin/clavulanic …¹ Amoxicillin/clavulan…²
#> <chr> <chr> <chr>
#> 1 74.2% (72.2-76.1%) 88.8% (87.1-90.4%) 90.8% (89.4-92.2%)
#> # ℹ abbreviated names: ¹​`Amoxicillin/clavulanic acid + Ciprofloxacin`,
#> # ²​`Amoxicillin/clavulanic acid + Gentamicin`
#> # Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram,
#> # and use `wisca_plot()` to assess the simulation outcomes.
#> # Or, use it directly in R Markdown or Quarto, see `antibiogram()`.
# stratified by syndrome or clinical group
out <- wisca(example_isolates,
antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"),
syndromic_group = "ward"
)
#> Warning: invalid microorganism code, NA generated
out
#> # A tibble: 3 × 4
#> # Type: Weighted-Incidence Syndromic Combination Antibiogram (WISCA)
#> # Cred. interval: 95%
#> # Simulations: 1000 per stratum
#> `Syndromic Group` `Piperacillin/tazobactam` Piperacillin/tazobactam + Gentam…¹
#> <chr> <chr> <chr>
#> 1 Clinical 74.5% (68.8-79.8%) 93.6% (91.9-95.1%)
#> 2 ICU 57.1% (48.2-65.9%) 86.7% (83.3-89.9%)
#> 3 Outpatient 57.5% (46-68.7%) 76.5% (70.6-82.2%)
#> # ℹ abbreviated name: ¹​`Piperacillin/tazobactam + Gentamicin`
#> # ℹ 1 more variable: `Piperacillin/tazobactam + Tobramycin` <chr>
#> # Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram,
#> # and use `wisca_plot()` to assess the simulation outcomes.
#> # Or, use it directly in R Markdown or Quarto, see `antibiogram()`.
wisca_plot(out)
# stratified using grouped tibbles (e.g. by age and gender)
if (requireNamespace("dplyr")) {
library(dplyr)
example_isolates %>%
top_n_microorganisms(n = 10) %>%
group_by(
age_group = age_groups(age, c(25, 50, 75)),
gender) %>%
wisca(antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"))
}
#> ℹ Using column mo as input for `col_mo`.
#> Warning: Number of tested isolates should exceed 30 for each regimen (and group). WISCA
#> coverage estimates might be inaccurate.
#> # A tibble: 8 × 5
#> # Type: Weighted-Incidence Syndromic Combination Antibiogram (WISCA)
#> # Cred. interval: 95%
#> # Simulations: 1000 per stratum
#> age_group gender `Piperacillin/tazobactam` Piperacillin/tazobactam + Gentami…¹
#> <chr> <chr> <chr> <chr>
#> 1 0-24 F 57.7% (29.5-82.6%) 70.5% (45.9-89.1%)
#> 2 0-24 M 59.1% (33-84.2%) 76.1% (55.7-90.6%)
#> 3 25-49 F 67.4% (43.3-90.5%) 93.8% (87.8-97.9%)
#> 4 25-49 M 56.8% (27.5-86.5%) 90.9% (82.4-96.8%)
#> 5 50-74 F 68% (53.3-82.3%) 96.9% (94.7-98.5%)
#> 6 50-74 M 67.1% (56.5-77.5%) 96.8% (94.2-98.8%)
#> 7 75+ F 73.3% (62.9-83.6%) 97.7% (96-98.9%)
#> 8 75+ M 74% (64.2-83.1%) 97.9% (96.1-99.1%)
#> # ℹ abbreviated name: ¹​`Piperacillin/tazobactam + Gentamicin`
#> # ℹ 1 more variable: `Piperacillin/tazobactam + Tobramycin` <chr>
#> # Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram,
#> # and use `wisca_plot()` to assess the simulation outcomes.
#> # Or, use it directly in R Markdown or Quarto, see `antibiogram()`.
# Traditional antibiogram (for AMR surveillance) ------------------------
antibiogram(example_isolates,
antimicrobials = c(aminoglycosides(), carbapenems())
@@ -615,8 +752,9 @@ antibiogram(example_isolates,
#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK
#> (amikacin), and KAN (kanamycin)
#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM (meropenem)
#> # An antibiogram: 10 × 7
#> # Type: Non-WISCA with 95% CI
#> # A tibble: 10 × 7
#> # Type: Traditional Antibiogram
#> # Conf. interval: 95%
#> Pathogen Amikacin Gentamicin Imipenem Kanamycin Meropenem Tobramycin
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 CoNS 0% (0-8%,N… 86% (82-9… 52% (37… 0% (0-8%… 52% (37-… 22% (12-3…
@@ -630,7 +768,7 @@ antibiogram(example_isolates,
#> 9 S. hominis NA 92% (84-9… NA NA NA 85% (74-9…
#> 10 S. pneumoniae 0% (0-3%,N… 0% (0-3%,… NA 0% (0-3%… NA 0% (0-3%,…
#> # Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram,
#> # or use it directly in R Markdown or Quarto, see ?antibiogram
#> # or use it directly in R Markdown or Quarto, see `antibiogram()`.
antibiogram(example_isolates,
antimicrobials = aminoglycosides(),
@@ -639,43 +777,26 @@ antibiogram(example_isolates,
)
#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK
#> (amikacin), and KAN (kanamycin)
#> # An antibiogram: 2 × 5
#> # Type: Non-WISCA with 95% CI
#> # A tibble: 2 × 5
#> # Type: Traditional Antibiogram
#> # Conf. interval: 95%
#> Pathogen J01GB01 J01GB03 J01GB04 J01GB06
#> <chr> <chr> <chr> <chr> <chr>
#> 1 Gram-negative 96% (94-97%,N=686) 96% (95-98%,N=684) 0% (0-10%,N=35) 98% (96-…
#> 2 Gram-positive 34% (31-38%,N=665) 63% (60-66%,N=1170) 0% (0-1%,N=436) 0% (0-1%…
#> # Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram,
#> # or use it directly in R Markdown or Quarto, see ?antibiogram
antibiogram(example_isolates,
antimicrobials = carbapenems(),
ab_transform = "name",
mo_transform = "name"
)
#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM (meropenem)
#> # An antibiogram: 5 × 3
#> # Type: Non-WISCA with 95% CI
#> Pathogen Imipenem Meropenem
#> <chr> <chr> <chr>
#> 1 Coagulase-negative Staphylococcus (CoNS) 52% (37-67%,N=48) 52% (37-67%,N=4…
#> 2 Enterococcus faecalis 100% (91-100%,N=38) NA
#> 3 Escherichia coli 100% (99-100%,N=422) 100% (99-100%,N…
#> 4 Klebsiella pneumoniae 100% (93-100%,N=51) 100% (93-100%,N…
#> 5 Proteus mirabilis 94% (79-99%,N=32) NA
#> # Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram,
#> # or use it directly in R Markdown or Quarto, see ?antibiogram
#> # or use it directly in R Markdown or Quarto, see `antibiogram()`.
# Combined antibiogram -------------------------------------------------
# Combination antibiogram (for AMR surveillance) ------------------------
# combined antimicrobials yield higher empiric coverage
antibiogram(example_isolates,
antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"),
mo_transform = "gramstain"
)
#> # An antibiogram: 2 × 4
#> # Type: Non-WISCA with 95% CI
#> # A tibble: 2 × 4
#> # Type: Combination Antibiogram
#> # Conf. interval: 95%
#> Pathogen Piperacillin/tazobac…¹ Piperacillin/tazobac…² Piperacillin/tazobac…³
#> <chr> <chr> <chr> <chr>
#> 1 Gram-neg… 88% (85-91%,N=641) 99% (97-99%,N=691) 98% (97-99%,N=693)
@@ -684,7 +805,7 @@ antibiogram(example_isolates,
#> # ²​`Piperacillin/tazobactam + Gentamicin`,
#> # ³​`Piperacillin/tazobactam + Tobramycin`
#> # Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram,
#> # or use it directly in R Markdown or Quarto, see ?antibiogram
#> # or use it directly in R Markdown or Quarto, see `antibiogram()`.
# you can use any antimicrobial selector with `+` too:
antibiogram(example_isolates,
@@ -692,8 +813,9 @@ antibiogram(example_isolates,
mo_transform = "gramstain"
)
#> ℹ For `ureidopenicillins()` using column TZP (piperacillin/tazobactam)
#> # An antibiogram: 2 × 4
#> # Type: Non-WISCA with 95% CI
#> # A tibble: 2 × 4
#> # Type: Combination Antibiogram
#> # Conf. interval: 95%
#> Pathogen Piperacillin/tazobac…¹ Piperacillin/tazobac…² Piperacillin/tazobac…³
#> <chr> <chr> <chr> <chr>
#> 1 Gram-neg… 88% (85-91%,N=641) 99% (97-99%,N=691) 98% (97-99%,N=693)
@@ -702,7 +824,7 @@ antibiogram(example_isolates,
#> # ²​`Piperacillin/tazobactam + Gentamicin`,
#> # ³​`Piperacillin/tazobactam + Tobramycin`
#> # Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram,
#> # or use it directly in R Markdown or Quarto, see ?antibiogram
#> # or use it directly in R Markdown or Quarto, see `antibiogram()`.
# names of antimicrobials do not need to resemble columns exactly:
antibiogram(example_isolates,
@@ -711,19 +833,19 @@ antibiogram(example_isolates,
ab_transform = "name",
sep = " & "
)
#> # An antibiogram: 2 × 3
#> # Type: Non-WISCA with 95% CI
#> # A tibble: 2 × 3
#> # Type: Traditional Antibiogram
#> # Conf. interval: 95%
#> Pathogen Ciprofloxacin `Ciprofloxacin & Gentamicin`
#> <chr> <chr> <chr>
#> 1 Gram-negative 91% (88-93%,N=684) 99% (97-99%,N=694)
#> 2 Gram-positive 77% (74-80%,N=724) 93% (91-94%,N=847)
#> # Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram,
#> # or use it directly in R Markdown or Quarto, see ?antibiogram
#> # or use it directly in R Markdown or Quarto, see `antibiogram()`.
# Syndromic antibiogram ------------------------------------------------
# Syndromic antibiogram (for AMR surveillance) --------------------------
# the data set could contain a filter for e.g. respiratory specimens
antibiogram(example_isolates,
antimicrobials = c(aminoglycosides(), carbapenems()),
syndromic_group = "ward"
@@ -731,8 +853,9 @@ antibiogram(example_isolates,
#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK
#> (amikacin), and KAN (kanamycin)
#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM (meropenem)
#> # An antibiogram: 14 × 8
#> # Type: Non-WISCA with 95% CI
#> # A tibble: 14 × 8
#> # Type: Syndromic Antibiogram
#> # Conf. interval: 95%
#> `Syndromic Group` Pathogen Amikacin Gentamicin Imipenem Kanamycin Meropenem
#> <chr> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 Clinical CoNS NA 89% (84-9… 57% (39… NA 57% (39-…
@@ -751,14 +874,12 @@ antibiogram(example_isolates,
#> 14 ICU S. pneumo… 0% (0-1… 0% (0-12%… NA 0% (0-12… NA
#> # ℹ 1 more variable: Tobramycin <chr>
#> # Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram,
#> # or use it directly in R Markdown or Quarto, see ?antibiogram
# now define a data set with only E. coli
ex1 <- example_isolates[which(mo_genus() == "Escherichia"), ]
#> ℹ Using column mo as input for `mo_genus()`
#> # or use it directly in R Markdown or Quarto, see `antibiogram()`.
# with a custom language, though this will be determined automatically
# (i.e., this table will be in Spanish on Spanish systems)
ex1 <- example_isolates[which(mo_genus() == "Escherichia"), ]
#> ℹ Using column mo as input for `mo_genus()`
antibiogram(ex1,
antimicrobials = aminoglycosides(),
ab_transform = "name",
@@ -769,45 +890,25 @@ antibiogram(ex1,
)
#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK
#> (amikacin), and KAN (kanamycin)
#> # An antibiogram: 2 × 5
#> # Type: Non-WISCA with 95% CI
#> # A tibble: 2 × 5
#> # Type: Syndromic Antibiogram
#> # Conf. interval: 95%
#> `Grupo sindrómico` Patógeno Amikacina Gentamicina Tobramicina
#> <chr> <chr> <chr> <chr> <chr>
#> 1 No UCI E. coli 100% (97-100%,N=119) 98% (96-99%,N=32… 98% (96-99…
#> 2 UCI E. coli 100% (93-100%,N=52) 99% (95-100%,N=1… 96% (92-99…
#> # Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram,
#> # or use it directly in R Markdown or Quarto, see ?antibiogram
# WISCA antibiogram ----------------------------------------------------
# WISCA are not stratified by species, but rather on syndromes
antibiogram(example_isolates,
antimicrobials = c("TZP", "TZP+TOB", "TZP+GEN"),
syndromic_group = "ward",
wisca = TRUE
)
#> # An antibiogram: 3 × 4
#> # Type: WISCA with 95% CI
#> `Syndromic Group` `Piperacillin/tazobactam` Piperacillin/tazobactam + Gentam…¹
#> <chr> <chr> <chr>
#> 1 Clinical 73.4% (68.3-78.6%) 92.3% (90.7-93.7%)
#> 2 ICU 57.4% (49.7-65.4%) 84.9% (82.1-87.6%)
#> 3 Outpatient 57% (47.4-66.7%) 74.6% (68.8-79.8%)
#> # ℹ abbreviated name: ¹​`Piperacillin/tazobactam + Gentamicin`
#> # ℹ 1 more variable: `Piperacillin/tazobactam + Tobramycin` <chr>
#> # Use `ggplot2::autoplot()` or base R `plot()` to create a plot of this antibiogram,
#> # or use it directly in R Markdown or Quarto, see ?antibiogram
#> # or use it directly in R Markdown or Quarto, see `antibiogram()`.
# Print the output for R Markdown / Quarto -----------------------------
ureido <- antibiogram(example_isolates,
ureido <- wisca(example_isolates,
antimicrobials = ureidopenicillins(),
syndromic_group = "ward",
wisca = TRUE
syndromic_group = "ward"
)
#> ℹ For `ureidopenicillins()` using column TZP (piperacillin/tazobactam)
#> Warning: invalid microorganism code, NA generated
# in an Rmd file, you would just need to return `ureido` in a chunk,
# but to be explicit here:
@@ -818,9 +919,9 @@ if (requireNamespace("knitr")) {
#>
#> |Syndromic Group |Piperacillin/tazobactam |
#> |:---------------|:-----------------------|
#> |Clinical |73.5% (68-79%) |
#> |ICU |57.7% (49.9-65.3%) |
#> |Outpatient |56.9% (46.5-66.8%) |
#> |Clinical |74.6% (68.9-80%) |
#> |ICU |57% (49.1-65.8%) |
#> |Outpatient |57.4% (45.6-68.4%) |
# Generate plots with ggplot2 or base R --------------------------------
@@ -829,11 +930,11 @@ ab1 <- antibiogram(example_isolates,
antimicrobials = c("AMC", "CIP", "TZP", "TZP+TOB"),
mo_transform = "gramstain"
)
ab2 <- antibiogram(example_isolates,
ab2 <- wisca(example_isolates,
antimicrobials = c("AMC", "CIP", "TZP", "TZP+TOB"),
mo_transform = "gramstain",
syndromic_group = "ward"
)
#> Warning: invalid microorganism code, NA generated
if (requireNamespace("ggplot2")) {
ggplot2::autoplot(ab1)
@@ -848,5 +949,6 @@ plot(ab1)
plot(ab2)
# }
```