1
0
mirror of https://github.com/msberends/AMR.git synced 2025-07-10 14:21:48 +02:00

(v2.1.1.9155) new mic_p50() and mic_p90() - updated AMR intro

This commit is contained in:
2025-02-23 11:18:08 +01:00
parent 226d10f546
commit aa8f6af185
24 changed files with 481 additions and 99 deletions

View File

@ -288,8 +288,10 @@ antibiogram(example_isolates,
To create a combined antibiogram, use antibiotic codes or names with a plus `+` character like this:
```{r comb}
antibiogram(example_isolates,
antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"))
combined_ab <- antibiogram(example_isolates,
antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
ab_transform = NULL)
combined_ab
```
### Syndromic Antibiogram
@ -304,17 +306,26 @@ antibiogram(example_isolates,
### Weighted-Incidence Syndromic Combination Antibiogram (WISCA)
To create a WISCA, you must state combination therapy in the `antibiotics` argument (similar to the Combination Antibiogram), define a syndromic group with the `syndromic_group` argument (similar to the Syndromic Antibiogram) in which cases are predefined based on clinical or demographic characteristics (e.g., endocarditis in 75+ females). This next example is a simplification without clinical characteristics, but just gives an idea of how a WISCA can be created:
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.
```{r wisca}
wisca <- 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
syndromic_group = ifelse(example_isolates$age >= 65 &
example_isolates$gender == "M",
"WISCA Group 1", "WISCA Group 2"))
wisca
example_isolates %>%
wisca(antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
minimum = 10) # Recommended threshold: ≥30
```
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:
```{r wisca_grouped}
example_isolates %>%
top_n_microorganisms(n = 10) %>%
group_by(age_group = age_groups(age, c(25, 50, 75)),
gender) %>%
wisca(antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"))
```
### Plotting antibiograms
@ -322,7 +333,7 @@ wisca
Antibiograms can be plotted using `autoplot()` from the `ggplot2` packages, since this `AMR` package provides an extension to that function:
```{r}
autoplot(wisca)
autoplot(combined_ab)
```
To calculate antimicrobial resistance in a more sensible way, also by correcting for too few results, we use the `resistance()` and `susceptibility()` functions.
@ -347,6 +358,51 @@ our_data_1st %>%
summarise(amoxicillin = resistance(AMX))
```
## Interpreting MIC and Disk Diffusion Values
Minimal inhibitory concentration (MIC) values and disk diffusion diameters can be interpreted into clinical breakpoints (SIR) using `as.sir()`. Heres an example with randomly generated MIC values for *Klebsiella pneumoniae* and ciprofloxacin:
```{r mic_interpretation}
set.seed(123)
mic_values <- random_mic(100)
sir_values <- as.sir(mic_values, mo = "K. pneumoniae", ab = "cipro", guideline = "EUCAST 2024")
my_data <- tibble(MIC = mic_values, SIR = sir_values)
my_data
```
This allows direct interpretation according to EUCAST or CLSI breakpoints, facilitating automated AMR data processing.
## Plotting MIC and SIR Interpretations
We can visualise MIC distributions and their SIR interpretations using `ggplot2`, using the new `scale_y_mic()` for the y-axis and `scale_colour_sir()` to colour-code SIR categories.
```{r mic_plot}
# add a group
my_data$group <- rep(c("A", "B", "C", "D"), each = 25)
ggplot(my_data,
aes(x = group, y = MIC, colour = SIR)) +
geom_jitter(width = 0.2, size = 2) +
geom_boxplot(fill = NA, colour = "grey40") +
scale_y_mic() +
scale_colour_sir() +
labs(title = "MIC Distribution and SIR Interpretation",
x = "Sample Groups",
y = "MIC (mg/L)")
```
This plot provides an intuitive way to assess susceptibility patterns across different groups while incorporating clinical breakpoints.
For a more straightforward and less manual approach, `ggplot2`'s function `autoplot()` has been extended by this package to directly plot MIC and disk diffusion values:
```{r autoplot}
autoplot(mic_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, 26th Feb 2023*
*Author: Dr. Matthijs Berends, 23rd Feb 2025*