To create a traditional antibiogram, simply state which antibiotics
-should be used. The antibiotics argument in the
-antibiogram() function supports any (combination) of the
-previously mentioned antibiotic class selectors:
+If you need per-species susceptibility rates, e.g., for AMR
+surveillance reports, the traditional antibiogram remains the right
+tool. It reports the proportion of susceptible isolates per species per
+antibiotic:
+
antibiogram(example_isolates,
antibiotics = c(aminoglycosides(), carbapenems())
)
@@ -821,7 +979,7 @@ Greek, Hindi, Indonesian, Italian, Japanese, Korean, Norwegian, Polish,
Portuguese, Romanian, Russian, Spanish, Swahili, Swedish, Turkish,
Ukrainian, Urdu, or Vietnamese. In this next example, we force the
language to be Spanish using the language argument:
-
+
antibiogram(example_isolates,
mo_transform = "gramstain",
antibiotics = aminoglycosides(),
@@ -864,11 +1022,13 @@ language to be Spanish using the language argument:
-
Combined Antibiogram
+Combination Antibiogram
-
To create a combined antibiogram, use antibiotic codes or names with
-a plus + character like this:
-
+A combination antibiogram shows how much additional susceptibility a
+second agent adds for a given species. This is useful for surveillance
+of combination regimens, but note that it is still species-stratified
+and does not account for pathogen incidence in the syndrome:
+
combined_ab <- antibiogram(example_isolates,
antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
ab_transform = NULL
@@ -948,10 +1108,13 @@ a plus + character like this:
Syndromic Antibiogram
-
To create a syndromic antibiogram, the syndromic_group
-argument must be used. This can be any column in the data, or e.g. an
-ifelse() with calculations based on certain columns:
-
+A syndromic antibiogram stratifies per-species susceptibility by
+clinical context (ward, specimen type, etc.). It adds clinical context
+to the traditional antibiogram but is still species-level, without
+incidence weighting or uncertainty quantification. For surveillance by
+setting this is fine; for empirical therapy guidance, WISCA is
+preferred:
+
antibiogram(example_isolates,
antibiotics = c(aminoglycosides(), carbapenems()),
syndromic_group = "ward"
@@ -1125,511 +1288,14 @@ argument must be used. This can be any column in the data, or e.g. an
-
Weighted-Incidence Syndromic Combination Antibiogram (WISCA)
-
-
To create a Weighted-Incidence Syndromic Combination
-Antibiogram (WISCA), simply set wisca = TRUE in
-the antibiogram() function, or use the dedicated
-wisca() function. Unlike traditional antibiograms, WISCA
-provides syndrome-based susceptibility estimates, weighted by pathogen
-incidence and antimicrobial susceptibility patterns.
-
-example_isolates %>%
- wisca(
- antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
- minimum = 10
- ) # Recommended threshold: ≥30
-
-
-
-
-
-
-
-
-| 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 low-incidence infections. It is
-pathogen-agnostic, meaning results are syndrome-based
-rather than stratified by microorganism.
-
For reliable results, ensure your data includes only first
-isolates (use first_isolate()) and consider
-filtering for the top n species (use
-top_n_microorganisms()), as WISCA outcomes are most
-meaningful when based on robust incidence estimates.
-
For patient- or syndrome-specific WISCA, run the
-function on a grouped tibble, i.e., using
-group_by() first:
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-| 0-24 |
-F |
-45.4% (15.4-79%) |
-50.1% (20.5-77.6%) |
-69% (44.5-88.5%) |
-50.4% (20.6-77.3%) |
-41.9% (18.1-65.6%) |
-36.1% (12.3-64.3%) |
-NA |
-NA |
-63.9% (34.6-87.6%) |
-56.7% (25.9-85.8%) |
-51.5% (25.6-74.4%) |
-63.4% (32-88.1%) |
-70.4% (45.4-89.1%) |
-54% (22.3-85.3%) |
-69.8% (45.9-88.9%) |
-39.3% (17.7-64.6%) |
-45.3% (18.1-75.9%) |
-50.1% (21.5-80.5%) |
-41.7% (19.1-67.6%) |
-55.8% (23.7-83.3%) |
-63.5% (32.6-89.4%) |
-69.3% (44.6-88.3%) |
-63.6% (36.1-88.2%) |
-45.5% (15.7-77.7%) |
-43.3% (17.8-71.2%) |
-55.9% (24.3-82.2%) |
-NA |
-NA |
-56.5% (24.3-85%) |
-56.8% (30.9-82.3%) |
-50.5% (19.4-80.8%) |
-56.9% (26.7-85%) |
-42.3% (18.3-68.8%) |
-40.2% (17.6-67.7%) |
-49.8% (20-79.3%) |
-56.1% (22-85.4%) |
-64.5% (39.6-85.5%) |
-69.7% (42.3-90.4%) |
-75.4% (52.1-91.7%) |
-48.5% (24.3-72.6%) |
-
-
-| 0-24 |
-M |
-41.9% (15.2-72.5%) |
-49.4% (23.3-75.5%) |
-73.8% (51.8-90.1%) |
-49.3% (22.7-76%) |
-63.4% (40.7-83.5%) |
-41.8% (20.4-64.8%) |
-56.8% (25.2-83.5%) |
-58.2% (29.1-85.8%) |
-59.7% (29.1-87.4%) |
-59.3% (29.1-86.6%) |
-24.9% (8.9-47.3%) |
-58.5% (28-86.5%) |
-72.1% (47.9-90.5%) |
-NA |
-77.2% (53-93.2%) |
-61.6% (36.2-83.6%) |
-25.5% (8.7-46.1%) |
-69.4% (44.6-89.4%) |
-63.4% (41.8-82.7%) |
-64% (37.6-85.6%) |
-NA |
-63.5% (40.9-83.1%) |
-58.7% (27.6-86.5%) |
-41.8% (13.6-71.2%) |
-48.3% (17.9-78%) |
-59.2% (27.4-86.4%) |
-NA |
-NA |
-NA |
-53% (21.3-83.7%) |
-57.2% (24.6-84.6%) |
-59.9% (29.7-85.6%) |
-48.2% (16.1-80.4%) |
-48.4% (17.4-79.8%) |
-68% (43.5-87.3%) |
-65.7% (36-89.2%) |
-44.3% (17.2-73.4%) |
-69.4% (46.9-87.8%) |
-74% (50.8-90.9%) |
-75.3% (52.4-92.2%) |
-
-
-| 25-49 |
-F |
-46.8% (26.7-65.6%) |
-39% (26.3-52.9%) |
-73.8% (63.5-82.6%) |
-39.3% (27.2-54.4%) |
-54.8% (44.9-64.8%) |
-36.5% (26.3-47.1%) |
-66.4% (46.1-85%) |
-69.2% (49.1-86.2%) |
-70.2% (50.5-86.2%) |
-68.1% (48.6-85.4%) |
-27.9% (19.2-37.9%) |
-70.1% (50.5-87.1%) |
-71.4% (61.7-80.4%) |
-58.2% (35.3-79.9%) |
-85.5% (74.1-94.2%) |
-67.1% (55.8-77.4%) |
-25.8% (17-36%) |
-75.5% (61.2-88.2%) |
-54.9% (44.8-65.6%) |
-55.2% (37.8-72.5%) |
-60.9% (38.3-81.8%) |
-75.2% (65.7-83.5%) |
-69.9% (50.1-86.6%) |
-37.5% (17.7-57.9%) |
-50.7% (30.8-68.7%) |
-69.4% (48.6-86.7%) |
-NA |
-56.9% (36.2-77.7%) |
-53.3% (30.9-75.6%) |
-60.1% (38.1-81.8%) |
-64.3% (43.2-83.6%) |
-66% (45.7-85.2%) |
-50.1% (30-69.6%) |
-38.8% (19.6-58.9%) |
-75.8% (61.6-88.1%) |
-73.3% (56.6-89.5%) |
-62.7% (47.6-77.1%) |
-70.4% (58.7-80.2%) |
-90% (82.9-95.4%) |
-71.6% (61.7-80.4%) |
-
-
-| 25-49 |
-M |
-49.8% (24.2-75.8%) |
-16.5% (8.1-27.4%) |
-72.4% (60.5-83.5%) |
-16.6% (7.7-28.2%) |
-55.9% (43.6-67.9%) |
-24.9% (14.7-37.6%) |
-60.3% (33.2-82.4%) |
-55.3% (27.6-81.8%) |
-55.9% (29.7-81.1%) |
-56.2% (27.7-82.1%) |
-22.2% (12.7-33.9%) |
-55.6% (29.1-81.8%) |
-73.7% (62.6-83.8%) |
-52.9% (25.2-79.6%) |
-67.1% (53-79.8%) |
-57.8% (43.5-71.8%) |
-22.3% (12.6-33.6%) |
-73% (57.8-85.5%) |
-55.8% (43-68.2%) |
-66.5% (51.6-79.4%) |
-63.1% (40.3-84.5%) |
-83.9% (74.5-91.7%) |
-56.4% (28.4-84%) |
-45.4% (18.9-73.8%) |
-59.4% (37.6-77.9%) |
-56.3% (28.8-81.1%) |
-NA |
-52.8% (24.7-78.7%) |
-64.2% (40.2-84.5%) |
-62.9% (37.7-85.1%) |
-60.5% (37.1-80.7%) |
-55.8% (29.4-82.9%) |
-65.4% (48.7-80.8%) |
-54.5% (31.7-73.7%) |
-72.8% (58.7-84.8%) |
-84.8% (72.4-93.6%) |
-66.7% (44.5-84.1%) |
-71.4% (58.9-82.6%) |
-86.6% (77.9-93.7%) |
-77.1% (65.5-87.1%) |
-
-
-| 50-74 |
-F |
-44.8% (35.8-54.1%) |
-30.1% (24.9-35.3%) |
-74.1% (69.2-78.7%) |
-30% (24.6-35.4%) |
-41.9% (36.5-47.3%) |
-23.5% (18.6-29%) |
-73.1% (62-82.9%) |
-76.6% (66.1-86%) |
-74.8% (64.9-84.5%) |
-74.6% (64.2-83.3%) |
-37.5% (32.3-43.4%) |
-74.8% (64.4-83.8%) |
-74.5% (69.7-78.9%) |
-61.2% (40.3-82.4%) |
-79.4% (73-85%) |
-44.9% (38.7-51%) |
-37.8% (32.7-43.3%) |
-63.8% (47.6-80.1%) |
-41.7% (36.6-46.9%) |
-58.1% (40-75.1%) |
-65.2% (53.5-76.6%) |
-78.7% (73.8-83.2%) |
-80.6% (70.3-90%) |
-28.1% (10.1-46.6%) |
-53.2% (42.9-62.4%) |
-79.3% (68.7-88.6%) |
-NA |
-49.5% (37.5-61.8%) |
-67.8% (48.5-86%) |
-75.1% (63.3-86.3%) |
-56.6% (37.8-74.2%) |
-67.7% (56.4-79.6%) |
-50.6% (40.9-59.1%) |
-41.3% (31.5-50.4%) |
-59% (48.3-74.5%) |
-87.7% (80.4-94.1%) |
-62.2% (55.4-68.4%) |
-55.5% (49.8-61.1%) |
-68% (62.7-73.3%) |
-60.9% (55.8-66.1%) |
-
-
-| 50-74 |
-M |
-38.8% (30.6-48.6%) |
-34.6% (29.1-40.3%) |
-75% (70-79.5%) |
-34.7% (29.2-40.5%) |
-43.4% (37.8-48.5%) |
-21% (16.5-26.4%) |
-64.3% (54.1-74.1%) |
-65.9% (56.5-75.4%) |
-67.3% (58.3-77%) |
-65.9% (56.1-75.9%) |
-32.9% (27.6-38%) |
-67.3% (57.4-76.8%) |
-74.1% (69.2-78.8%) |
-63.5% (42.4-83%) |
-76.9% (71.6-81.9%) |
-47.3% (40.9-53.8%) |
-30.8% (26.1-36.1%) |
-68.5% (53.5-81.9%) |
-43.4% (37.7-48.8%) |
-58.1% (42.4-73.2%) |
-68.1% (53.5-82.2%) |
-79.1% (74.4-83.1%) |
-69% (59.7-78.3%) |
-24.8% (9.5-40.5%) |
-49.7% (35-63.2%) |
-68.1% (58.1-77.6%) |
-53.8% (32-75%) |
-51.7% (36.1-67.3%) |
-68.8% (51.1-85.7%) |
-70.2% (54.7-85.3%) |
-53.2% (37.5-68.7%) |
-66.5% (55-76.8%) |
-56.2% (45.8-65.4%) |
-44% (30.3-57.5%) |
-71.9% (58.2-82.2%) |
-86.8% (77.3-93.6%) |
-54.1% (46.9-61.4%) |
-67.1% (61.5-72.5%) |
-81% (76.4-85.2%) |
-66.3% (61-71.2%) |
-
-
-| 75+ |
-F |
-51.4% (41.7-62%) |
-30.9% (26.2-36.5%) |
-74.4% (70.3-78.6%) |
-30.9% (25.7-36.1%) |
-36.6% (32-41.6%) |
-20.7% (16.2-25.4%) |
-73.6% (63.6-82.5%) |
-79.1% (70.6-86.8%) |
-78.6% (69.9-86.3%) |
-76% (67.5-83.7%) |
-43.1% (38.6-48%) |
-78.9% (70.5-86.4%) |
-77% (72.6-81.3%) |
-63.2% (43.2-84.1%) |
-77.7% (72.1-83.2%) |
-41.2% (36-46.4%) |
-39.1% (34.2-44.4%) |
-63.7% (46.3-80.6%) |
-36.5% (31.9-41.2%) |
-57.1% (39.8-76%) |
-65.8% (57.2-73.5%) |
-84.6% (80.6-88%) |
-81.9% (73.7-89.5%) |
-33.3% (13.7-53%) |
-49.6% (42.3-56.1%) |
-81.3% (73.2-88.9%) |
-55.9% (33.5-76.5%) |
-41% (31.4-51.8%) |
-63.7% (43.8-82.3%) |
-77.8% (66-87.4%) |
-56.3% (37.3-75.1%) |
-71.8% (62-82.2%) |
-48.3% (41.5-54.9%) |
-43.3% (36.2-50.7%) |
-63% (45.3-80.3%) |
-85.9% (79.9-90.9%) |
-70.4% (64.1-76.8%) |
-60.4% (55.1-65.8%) |
-77.6% (73.4-82.1%) |
-55.3% (50.4-60.1%) |
-
-
-| 75+ |
-M |
-52.6% (43.3-62.6%) |
-33% (28.1-38%) |
-77.4% (73.3-81.5%) |
-33% (28.2-38.2%) |
-36.8% (32.3-41.8%) |
-17.9% (12.6-23.2%) |
-64.4% (55.4-73.3%) |
-71.2% (63.1-79.1%) |
-67.9% (59.5-75.8%) |
-65.3% (56.3-73.6%) |
-42.6% (37.8-47.4%) |
-68.2% (59.7-76.4%) |
-75.1% (70.9-79.2%) |
-64.1% (45.8-81.8%) |
-77.6% (72-82.6%) |
-41% (36-46.4%) |
-39.9% (35.1-44.5%) |
-62.1% (46-78.8%) |
-36.9% (32.4-41.4%) |
-59.7% (43.4-76.6%) |
-64.7% (56.6-73.6%) |
-83% (79.4-86.7%) |
-75.7% (66.6-83%) |
-31.6% (12.1-51.7%) |
-51.8% (44.9-58%) |
-74.2% (65.8-82.7%) |
-NA |
-52.2% (41.5-60.8%) |
-69.3% (50.5-86.4%) |
-72.2% (58.7-83.4%) |
-59.3% (41.6-76.7%) |
-73.1% (64.3-81.4%) |
-49.9% (42.4-56.9%) |
-46.3% (38.2-53.1%) |
-59.7% (44.2-75.7%) |
-86.8% (81.4-91.3%) |
-72% (66.3-77.6%) |
-55.8% (50.3-61.1%) |
-73.3% (68.9-77.6%) |
-57% (52.2-61.6%) |
-
-
-
-
Plotting antibiograms
-
Antibiograms can be plotted using autoplot() from the
-ggplot2 packages, since this AMR package
-provides an extension to that function:
-
+
All antibiogram types, including WISCA, can be plotted using
+autoplot() from the ggplot2 package, since
+this AMR package provides an extension to that
+function:
+
To calculate antimicrobial resistance in a more sensible way, also by
correcting for too few results, we use the resistance() and
@@ -1657,7 +1323,7 @@ proportion of R (proportion_R()
I (proportion_SI(), equal to
susceptibility()). These functions can be used on their
own:
-
+
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`
@@ -1666,7 +1332,7 @@ own:
#> [1] 0.4203377
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))
@@ -1684,7 +1350,7 @@ own:
diameters can be interpreted into clinical breakpoints (SIR) using
as.sir(). Here’s an example with randomly generated MIC
values for Klebsiella pneumoniae and ciprofloxacin:
-
+
set.seed(123)
mic_values <- random_mic(100)
sir_values <- as.sir(mic_values, mo = "K. pneumoniae", ab = "cipro", guideline = "EUCAST 2024")
@@ -1715,7 +1381,7 @@ breakpoints, facilitating automated AMR data processing.
using ggplot2, using the new scale_y_mic() for
the y-axis and scale_colour_sir() to colour-code SIR
categories.
-
+
# add a group
my_data$group <- rep(c("A", "B", "C", "D"), each = 25)
@@ -1739,16 +1405,16 @@ across different groups while incorporating clinical breakpoints.
ggplot2’s function autoplot() has been
extended by this package to directly plot MIC and disk diffusion
values:
-
+
-
+
# by providing `mo` and `ab`, colours will indicate the SIR interpretation:
autoplot(mic_values, mo = "K. pneumoniae", ab = "cipro", guideline = "EUCAST 2024")
-Author: Dr. Matthijs Berends, 23rd Feb 2025
+Author: Dr. Matthijs Berends, 23rd June 2026
#> Groups (n=4, named as 'order'):
diff --git a/articles/PCA.md b/articles/PCA.md
index 79cec35ca..879ba066a 100644
--- a/articles/PCA.md
+++ b/articles/PCA.md
@@ -123,7 +123,7 @@ summary(pca_result)
#> [1] "Caryophanales" "Enterobacterales" "Lactobacillales" "Pseudomonadales"
#> Importance of components:
#> PC1 PC2 PC3 PC4 PC5 PC6 PC7
-#> Standard deviation 2.1539 1.6807 0.6138 0.33879 0.20808 0.03140 9.577e-17
+#> Standard deviation 2.1539 1.6807 0.6138 0.33879 0.20808 0.03140 1.232e-16
#> Proportion of Variance 0.5799 0.3531 0.0471 0.01435 0.00541 0.00012 0.000e+00
#> Cumulative Proportion 0.5799 0.9330 0.9801 0.99446 0.99988 1.00000 1.000e+00
```
diff --git a/articles/WHONET.html b/articles/WHONET.html
index deb9deb19..3d0a7b1b3 100644
--- a/articles/WHONET.html
+++ b/articles/WHONET.html
@@ -12,8 +12,8 @@
-
-
+
+
@@ -30,7 +30,7 @@
AMR (for R)
- 3.0.1.9057
+ 3.0.1.9061