+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:
-
+
Or can be used in conjunction with group_by() and
summarise(), both from the dplyr package:
-
+
+
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 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)
+
+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:
+
+
+
+
+# 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