We can now add the interpretation of MDR-TB to our data set. You can
use:
@@ -438,40 +438,40 @@ Unique: 5
| 1 |
Mono-resistant |
-3220 |
-64.40% |
-3220 |
-64.40% |
+3214 |
+64.28% |
+3214 |
+64.28% |
| 2 |
Negative |
-941 |
-18.82% |
-4161 |
-83.22% |
+1018 |
+20.36% |
+4232 |
+84.64% |
| 3 |
Multi-drug-resistant |
-474 |
-9.48% |
-4635 |
-92.70% |
+439 |
+8.78% |
+4671 |
+93.42% |
| 4 |
Poly-resistant |
-238 |
-4.76% |
-4873 |
-97.46% |
+228 |
+4.56% |
+4899 |
+97.98% |
| 5 |
Extensively drug-resistant |
-127 |
-2.54% |
+101 |
+2.02% |
5000 |
100.00% |
diff --git a/articles/PCA.html b/articles/PCA.html
index 02b811f0..8d28f45e 100644
--- a/articles/PCA.html
+++ b/articles/PCA.html
@@ -38,7 +38,7 @@
AMR (for R)
- 1.8.2.9121
+ 1.8.2.9122
@@ -277,10 +271,14 @@
Weighted-Incidence Syndromic Combination Antibiogram (WISCA)
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:
-antibiogram(your_data,
- antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
- syndromic_group = ifelse(your_data$age >= 65 & your_data$gender == "Male",
- "Group 1", "Group 2"))
+
library(dplyr)
+your_data %>%
+ filter(ward == "ICU" & specimen_type == "Respiratory") %>%
+ antibiogram(antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
+ syndromic_group = ifelse(.$age >= 65 &
+ .$gender == "Male" &
+ .$condition == "Heart Disease",
+ "Study Group", "Control Group"))
All types of antibiograms can be generated with the functions as described on this page, and can be plotted (using ggplot2::autoplot() or base R plot()/barplot()) or printed into R Markdown / Quarto formats for reports. Use functions from specific 'table reporting' packages to transform the output of antibiogram() to your needs, e.g. flextable::as_flextable() or gt::gt().
Note that for combination antibiograms, it is important to realise that susceptibility can be calculated in two ways, which can be set with the only_all_tested argument (defaults to FALSE). See this example for two antibiotics, Drug A and Drug B, about how antibiogram() works to calculate the %SI:
--------------------------------------------------------------------
@@ -467,11 +465,11 @@
# Weighted-incidence syndromic combination antibiogram (WISCA) ---------
-# the data set could contain a filter for e.g. respiratory specimens
+# the data set could contain a filter for e.g. respiratory specimens/ICU
antibiogram(example_isolates,
antibiotics = c("AMC", "AMC+CIP", "TZP", "TZP+TOB"),
mo_transform = "gramstain",
- minimum = 10, # this should be >= 30, but now just as example
+ minimum = 10, # this should be >=30, but now just as example
syndromic_group = ifelse(example_isolates$age >= 65 &
example_isolates$gender == "M",
"WISCA Group 1", "WISCA Group 2"
diff --git a/reference/antibiotic_class_selectors.html b/reference/antibiotic_class_selectors.html
index def07545..a512ced9 100644
--- a/reference/antibiotic_class_selectors.html
+++ b/reference/antibiotic_class_selectors.html
@@ -10,7 +10,7 @@
AMR (for R)
- 1.8.2.9121
+ 1.8.2.9122
diff --git a/reference/ggplot_pca.html b/reference/ggplot_pca.html
index 4319c2af..09df18fb 100644
--- a/reference/ggplot_pca.html
+++ b/reference/ggplot_pca.html
@@ -10,7 +10,7 @@
AMR (for R)
-
1.8.2.9121
+
1.8.2.9122