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@@ -48,7 +48,7 @@ in scientific research.
After installing this package, R knows [**~97 000 distinct microbial
species**](https://amr-for-r.org/reference/microorganisms.md) (updated
mei 2026) and all [**~620 antimicrobial and antiviral
May 2026) and all [**~620 antimicrobial and antiviral
drugs**](https://amr-for-r.org/reference/antimicrobials.md) by name and
code (including ATC, EARS-Net, ASIARS-Net, PubChem, LOINC and SNOMED
CT), and knows all about valid SIR and MIC values. The integral clinical
@@ -114,13 +114,11 @@ example_isolates %>%
#> ℹ Using column mo as input for `mo_fullname()`
#> ℹ Using column mo as input for `mo_is_gram_negative()`
#> ℹ Using column mo as input for `mo_is_intrinsic_resistant()`
#> ℹ Determining intrinsic resistance based on 'EUCAST Expected
#> Resistant Phenotypes' v1.2 (2023). This note will be shown
#> once per session.
#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB
#> (tobramycin), AMK (amikacin), and KAN (kanamycin)
#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM
#> (meropenem)
#> ℹ Determining intrinsic resistance based on 'EUCAST Expected Resistant Phenotypes' v1.2 (2023).
#> This note will be shown once per session.
#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK (amikacin), and KAN
#> (kanamycin)
#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM (meropenem)
#> # A tibble: 35 × 7
#> bacteria GEN TOB AMK KAN IPM MEM
#> <chr> <sir> <sir> <sir> <sir> <sir> <sir>
@@ -179,7 +177,7 @@ wisca(example_isolates,
| Piperacillin/tazobactam | Piperacillin/tazobactam + Gentamicin | Piperacillin/tazobactam + Tobramycin |
|:---|:---|:---|
| 70.1% (65.1-75.4%) | 93.6% (92.1-95%) | 89.8% (87.3-92.4%) |
| 70% (64.8-75.2%) | 93.6% (92-95.1%) | 89.9% (87.1-92.5%) |
WISCA supports stratification by any clinical variable, so you can
generate syndrome-specific or ward-specific coverage estimates:
@@ -195,9 +193,9 @@ wisca(example_isolates,
| Syndromic Group | Piperacillin/tazobactam | Piperacillin/tazobactam + Gentamicin | Piperacillin/tazobactam + Tobramycin |
|:---|:---|:---|:---|
| Clinical | 74.4% (68.2-79.9%) | 93.6% (91.9-95.1%) | 90.4% (86.9-93.3%) |
| ICU | 57% (48.6-65.9%) | 86.8% (83.4-89.8%) | 82.9% (77.5-87.1%) |
| Outpatient | 57.5% (45.9-69.3%) | 76.6% (70.6-82.3%) | 67.9% (57.6-77.2%) |
| Clinical | 74.6% (69.3-80.3%) | 93.6% (92.1-95%) | 90.4% (87-93.2%) |
| ICU | 56.9% (48.2-66.3%) | 86.7% (83.4-89.7%) | 82.9% (78.1-87.3%) |
| Outpatient | 57.3% (45.8-69.1%) | 76.6% (70.6-81.9%) | 67.9% (58-76.9%) |
**For AMR surveillance**, traditional antibiograms remain the right tool
for tracking resistance per species over time:
@@ -207,8 +205,7 @@ for tracking resistance per species over time:
antibiogram(example_isolates,
mo_transform = "gramstain",
antimicrobials = c("AMC", carbapenems(), "TZP"))
#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM
#> (meropenem)
#> ℹ For `carbapenems()` using columns IPM (imipenem) and MEM (meropenem)
```
| Pathogen | Amoxicillin/clavulanic acid | Imipenem | Meropenem | Piperacillin/tazobactam |
@@ -329,16 +326,15 @@ out <- example_isolates %>%
# calculate AMR using resistance(), over all aminoglycosides and polymyxins:
summarise(across(c(aminoglycosides(), polymyxins()),
resistance))
#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB
#> (tobramycin), AMK (amikacin), and KAN (kanamycin)
#> ℹ For `aminoglycosides()` using columns GEN (gentamicin), TOB (tobramycin), AMK (amikacin), and KAN
#> (kanamycin)
#> ℹ For `polymyxins()` using column COL (colistin)
#> Warning: There was 1 warning in `summarise()`.
#> ℹ In argument: `across(c(aminoglycosides(), polymyxins()),
#> resistance)`.
#> ℹ In argument: `across(c(aminoglycosides(), polymyxins()), resistance)`.
#> ℹ In group 3: `ward = "Outpatient"`.
#> Caused by warning:
#> ! Introducing NA: only 23 results available for KAN in group:
#> ward = "Outpatient" (whilst `minimum = 30`).
#> ! Introducing NA: only 23 results available for KAN in group: ward = "Outpatient" (whilst `minimum =
#> 30`).
out
#> # A tibble: 3 × 6
#> ward GEN TOB AMK KAN COL