diff --git a/404.html b/404.html index a4e1e4f46..e40db9e09 100644 --- a/404.html +++ b/404.html @@ -32,7 +32,7 @@ AMR (for R) - 2.1.1.9154 + 2.1.1.9156
+generated on 23 February 2025.language
argument:
To create a combined antibiogram, use antibiotic codes or names with
a plus +
character like this:
-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
Pathogen | -Piperacillin/tazobactam | -Piperacillin/tazobactam + Gentamicin | -Piperacillin/tazobactam + Tobramycin | +TZP | +TZP + GEN | +TZP + TOB | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Syndromic Group | -Pathogen | -Amoxicillin/clavulanic acid | -Amoxicillin/clavulanic acid + Ciprofloxacin | Piperacillin/tazobactam | +Piperacillin/tazobactam + Gentamicin | Piperacillin/tazobactam + Tobramycin |
---|---|---|---|---|---|---|
73.7% (65.8-81.1%) | +97% (94.2-98.7%) | +92.4% (85.9-96.6%) | +
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:
+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"))
age_group | +gender | +Piperacillin/tazobactam + Gentamicin | +Piperacillin/tazobactam + Tobramycin | +Piperacillin/tazobactam | +||||||
---|---|---|---|---|---|---|---|---|---|---|
WISCA Group 1 | -Gram-negative | -76% (70-81%) | -95% (92-97%) | -89% (84-92%) | -99% (96-100%) | +25-49 | +F | +94.4% (79.6-99.9%) | +94.5% (79.6-99.8%) | +|
WISCA Group 2 | -Gram-negative | -76% (72-80%) | -98% (96-99%) | -88% (84-91%) | -98% (96-99%) | +25-49 | +M | +96.1% (85.4-99.9%) | ++ | |
WISCA Group 1 | -Gram-positive | -76% (72-80%) | -89% (85-92%) | -81% (73-88%) | -95% (91-98%) | +50-74 | +F | +97.1% (91.2-99.7%) | +97.7% (91.9-100%) | +93.6% (87.4-97.5%) |
WISCA Group 2 | -Gram-positive | -76% (73-79%) | -89% (86-91%) | -88% (83-92%) | -95% (92-97%) | +50-74 | +M | +96.7% (90.7-99.5%) | +97% (91-99.8%) | +92.5% (84.3-97.4%) | +
75+ | +F | +97.6% (91.7-99.9%) | +95.7% (88.2-99.2%) | +92.1% (85.8-96.4%) | +||||||
75+ | +M | +97.2% (91.5-99.7%) | +97.1% (89.6-99.9%) | +96.5% (91.3-99.5%) |
Antibiograms can be plotted using autoplot()
from the
ggplot2
packages, since this AMR
package
provides an extension to that function:
-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
@@ -1217,12 +1250,12 @@ proportion of R (proportion_R()
I (
proportion_SI()
, equal to
susceptibility()
). These functions can be used on their
own:
+our_data_1st %>% resistance(AMX) #> [1] 0.4203377
Or can be used in conjunction with
-group_by()
andsummarise()
, both from thedplyr
package:+++our_data_1st %>% group_by(hospital) %>% summarise(amoxicillin = resistance(AMX)) @@ -1232,8 +1265,81 @@ own: #> 1 A 0.340 #> 2 B 0.551 #> 3 C 0.370
++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()
. 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") +#> +#> ℹ Run sir_interpretation_history() afterwards to retrieve a logbook with +#> all the details of the breakpoint interpretations. +#> +#> Interpreting MIC values: 'cipro' (CIP, ciprofloxacin), EUCAST 2024... NOTE +#> • Multiple breakpoints available for ciprofloxacin (CIP) in Klebsiella pneumoniae - assuming body site 'Non-meningitis'. + +my_data <- tibble(MIC = mic_values, SIR = sir_values) +my_data +#> # A tibble: 100 × 2 +#> MIC SIR +#> <mic> <sir> +#> 1 16.000 R +#> 2 0.005 S +#> 3 1.000 R +#> 4 >=256.000 R +#> 5 2.000 R +#> 6 0.025 S +#> 7 16.000 R +#> 8 0.025 S +#> 9 0.500 I +#> 10 0.005 S +#> # ℹ 90 more rows
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 newscale_y_mic()
for +the y-axis andscale_colour_sir()
to colour-code SIR +categories.++# 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 functionautoplot()
has been +extended by this package to directly plot MIC and disk diffusion +values:++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