@@ -211,7 +211,7 @@ my_data_with_all_these_columns %>%
The amr_class() function can be used to filter/select on a manually defined antimicrobial class. It searches for results in the antimicrobials data set within the columns group, atc_group1 and atc_group2.
The administrable_per_os() and administrable_iv() functions also rely on the antimicrobials data set - antimicrobials will be matched where a DDD (defined daily dose) for resp. oral and IV treatment is available in the antimicrobials data set.
The amr_selector() function can be used to internally filter the antimicrobials data set on any results, see Examples . It allows for filtering on a (part of) a certain name, and/or a group name or even a minimum of DDDs for oral treatment. This function yields the highest flexibility, but is also the least user-friendly, since it requires a hard-coded filter to set.
-The not_intrinsic_resistant() function can be used to only select antimicrobials that pose no intrinsic resistance for the microorganisms in the data set. For example, if a data set contains only microorganism codes or names of E. coli and K. pneumoniae and contains a column "vancomycin", this column will be removed (or rather, unselected) using this function. It currently applies 'EUCAST Expected Resistant Phenotypes' v1.2 (2023) to determine intrinsic resistance, using the eucast_rules() function internally. Because of this determination, this function is quite slow in terms of performance.
+The not_intrinsic_resistant() function can be used to only select antimicrobials that pose no intrinsic resistance for the microorganisms in the data set. For example, if a data set contains only microorganism codes or names of E. coli and K. pneumoniae and contains a column "vancomycin", this column will be removed (or rather, unselected) using this function. It currently applies 'EUCAST Expected Resistant Phenotypes' v1.2 (2023) to determine intrinsic resistance, using the eucast_rules() function internally. Because of this determination, this function is quite slow in terms of performance.
Full list of supported (antimicrobial) classes
@@ -247,9 +247,9 @@ my_data_with_all_these_columns %>%
rifamycins() can select: rifabutin (RIB), rifampicin (RIF), rifampicin/ethambutol/isoniazid (REI), rifampicin/isoniazid (RFI), rifampicin/pyrazinamide/ethambutol/isoniazid (RPEI), rifampicin/pyrazinamide/isoniazid (RPI), rifamycin (RFM), and rifapentine (RFP)
spiropyrimidinetriones() can select: zoliflodacin (ZFD)
streptogramins() can select: pristinamycin (PRI) and quinupristin/dalfopristin (QDA)
-
sulfonamides() can select: brodimoprim (BDP), sulfadiazine (SDI), sulfadiazine/tetroxoprim (SLT), sulfadimethoxine (SUD), sulfadimidine (SDM), sulfafurazole (SLF), sulfaisodimidine (SLF1), sulfalene (SLF2), sulfamazone (SZO), sulfamerazine (SLF3), sulfamethizole (SLF4), sulfamethoxazole (SMX), sulfamethoxypyridazine (SLF5), sulfametomidine (SLF6), sulfametoxydiazine (SLF7), sulfamoxole (SLF8), sulfanilamide (SLF9), sulfaperin (SLF10), sulfaphenazole (SLF11), sulfapyridine (SLF12), sulfathiazole (SUT), and sulfathiourea (SLF13)
-
tetracyclines() can select: cetocycline (CTO), chlortetracycline (CTE), clomocycline (CLM1), demeclocycline (DEM), doxycycline (DOX), eravacycline (ERV), lymecycline (LYM), metacycline (MTC), minocycline (MNO), omadacycline (OMC), oxytetracycline (OXY), penimepicycline (PNM1), rolitetracycline (RLT), sarecycline (SRC), tetracycline (TCY), tetracycline screening test (TCY-S), and tigecycline (TGC)
-
trimethoprims() can select: brodimoprim (BDP), sulfadiazine (SDI), sulfadiazine/tetroxoprim (SLT), sulfadiazine/trimethoprim (SLT1), sulfadimethoxine (SUD), sulfadimidine (SDM), sulfadimidine/trimethoprim (SLT2), sulfafurazole (SLF), sulfaisodimidine (SLF1), sulfalene (SLF2), sulfamazone (SZO), sulfamerazine (SLF3), sulfamerazine/trimethoprim (SLT3), sulfamethizole (SLF4), sulfamethoxazole (SMX), sulfamethoxypyridazine (SLF5), sulfametomidine (SLF6), sulfametoxydiazine (SLF7), sulfametrole/trimethoprim (SLT4), sulfamoxole (SLF8), sulfamoxole/trimethoprim (SLT5), sulfanilamide (SLF9), sulfaperin (SLF10), sulfaphenazole (SLF11), sulfapyridine (SLF12), sulfathiazole (SUT), sulfathiourea (SLF13), trimethoprim (TMP), and trimethoprim/sulfamethoxazole (SXT)
+
sulfonamides() can select: isoniazid/sulfamethoxazole/trimethoprim/pyridoxine (IST), ormetroprim/sulfamethoxazole (ORS), sulfachlorpyridazine (SUP), sulfadiazine (SDI), sulfadiazine/tetroxoprim (SLT), sulfadiazine/trimethoprim (SLT1), sulfadimethoxine (SUD), sulfadimidine (SDM), sulfadimidine/trimethoprim (SLT2), sulfafurazole (SLF), sulfaisodimidine (SLF1), sulfalene (SLF2), sulfamazone (SZO), sulfamerazine (SLF3), sulfamerazine/trimethoprim (SLT3), sulfamethazine (SUM), sulfamethizole (SLF4), sulfamethoxazole (SMX), sulfamethoxypyridazine (SLF5), sulfametomidine (SLF6), sulfametoxydiazine (SLF7), sulfametrole/trimethoprim (SLT4), sulfamoxole (SLF8), sulfamoxole/trimethoprim (SLT5), sulfanilamide (SLF9), sulfaperin (SLF10), sulfaphenazole (SLF11), sulfapyridine (SLF12), sulfasuccinamide (SNA), sulfathiazole (SUT), sulfathiourea (SLF13), sulfisoxazole (SOX), sulfonamide (SSS), and trimethoprim/sulfamethoxazole (SXT)
+
tetracyclines() can select: cetocycline (CTO), chlortetracycline (CTE), clomocycline (CLM1), demeclocycline (DEM), doxycycline (DOX), eravacycline (ERV), lymecycline (LYM), metacycline (MTC), minocycline (MNO), omadacycline (OMC), oxytetracycline (OXY), penimepicycline (PNM1), rolitetracycline (RLT), sarecycline (SRC), tetracycline (TCY), tetracycline screening test (TCY-S), tetracycline/oleandomycin (TOL), and tigecycline (TGC)
+
trimethoprims() can select: brodimoprim (BDP), iclaprim (ICL), isoniazid/sulfamethoxazole/trimethoprim/pyridoxine (IST), ormetroprim/sulfamethoxazole (ORS), sulfadiazine (SDI), sulfadiazine/tetroxoprim (SLT), sulfadiazine/trimethoprim (SLT1), sulfadimethoxine (SUD), sulfadimidine (SDM), sulfadimidine/trimethoprim (SLT2), sulfafurazole (SLF), sulfaisodimidine (SLF1), sulfalene (SLF2), sulfamazone (SZO), sulfamerazine (SLF3), sulfamerazine/trimethoprim (SLT3), sulfamethizole (SLF4), sulfamethoxazole (SMX), sulfamethoxypyridazine (SLF5), sulfametomidine (SLF6), sulfametoxydiazine (SLF7), sulfametrole/trimethoprim (SLT4), sulfamoxole (SLF8), sulfamoxole/trimethoprim (SLT5), sulfanilamide (SLF9), sulfaperin (SLF10), sulfaphenazole (SLF11), sulfapyridine (SLF12), sulfathiazole (SUT), sulfathiourea (SLF13), trimethoprim (TMP), and trimethoprim/sulfamethoxazole (SXT)
ureidopenicillins() can select: azlocillin (AZL), mezlocillin (MEZ), piperacillin (PIP), and piperacillin/tazobactam (TZP)
diff --git a/reference/antimicrobial_selectors.md b/reference/antimicrobial_selectors.md
index b95862277..e1b6ea5eb 100644
--- a/reference/antimicrobial_selectors.md
+++ b/reference/antimicrobial_selectors.md
@@ -231,7 +231,7 @@ codes or names of *E. coli* and *K. pneumoniae* and contains a column
"vancomycin", this column will be removed (or rather, unselected) using
this function. It currently applies ['EUCAST Expected Resistant
Phenotypes'
-v1.2](https://www.eucast.org/expert_rules_and_expected_phenotypes)
+v1.2](https://www.eucast.org/bacteria/important-additional-information/expert-rules/)
(2023) to determine intrinsic resistance, using the
[`eucast_rules()`](https://amr-for-r.org/reference/eucast_rules.md)
function internally. Because of this determination, this function is
@@ -559,14 +559,21 @@ quite slow in terms of performance.
pristinamycin (PRI) and quinupristin/dalfopristin (QDA)
- `sulfonamides()` can select:
- brodimoprim (BDP), sulfadiazine (SDI), sulfadiazine/tetroxoprim (SLT),
- sulfadimethoxine (SUD), sulfadimidine (SDM), sulfafurazole (SLF),
- sulfaisodimidine (SLF1), sulfalene (SLF2), sulfamazone (SZO),
- sulfamerazine (SLF3), sulfamethizole (SLF4), sulfamethoxazole (SMX),
+ isoniazid/sulfamethoxazole/trimethoprim/pyridoxine (IST),
+ ormetroprim/sulfamethoxazole (ORS), sulfachlorpyridazine (SUP),
+ sulfadiazine (SDI), sulfadiazine/tetroxoprim (SLT),
+ sulfadiazine/trimethoprim (SLT1), sulfadimethoxine (SUD),
+ sulfadimidine (SDM), sulfadimidine/trimethoprim (SLT2), sulfafurazole
+ (SLF), sulfaisodimidine (SLF1), sulfalene (SLF2), sulfamazone (SZO),
+ sulfamerazine (SLF3), sulfamerazine/trimethoprim (SLT3),
+ sulfamethazine (SUM), sulfamethizole (SLF4), sulfamethoxazole (SMX),
sulfamethoxypyridazine (SLF5), sulfametomidine (SLF6),
- sulfametoxydiazine (SLF7), sulfamoxole (SLF8), sulfanilamide (SLF9),
- sulfaperin (SLF10), sulfaphenazole (SLF11), sulfapyridine (SLF12),
- sulfathiazole (SUT), and sulfathiourea (SLF13)
+ sulfametoxydiazine (SLF7), sulfametrole/trimethoprim (SLT4),
+ sulfamoxole (SLF8), sulfamoxole/trimethoprim (SLT5), sulfanilamide
+ (SLF9), sulfaperin (SLF10), sulfaphenazole (SLF11), sulfapyridine
+ (SLF12), sulfasuccinamide (SNA), sulfathiazole (SUT), sulfathiourea
+ (SLF13), sulfisoxazole (SOX), sulfonamide (SSS), and
+ trimethoprim/sulfamethoxazole (SXT)
- `tetracyclines()` can select:
cetocycline (CTO), chlortetracycline (CTE), clomocycline (CLM1),
@@ -574,13 +581,16 @@ quite slow in terms of performance.
lymecycline (LYM), metacycline (MTC), minocycline (MNO), omadacycline
(OMC), oxytetracycline (OXY), penimepicycline (PNM1), rolitetracycline
(RLT), sarecycline (SRC), tetracycline (TCY), tetracycline screening
- test (TCY-S), and tigecycline (TGC)
+ test (TCY-S), tetracycline/oleandomycin (TOL), and tigecycline (TGC)
- `trimethoprims()` can select:
- brodimoprim (BDP), sulfadiazine (SDI), sulfadiazine/tetroxoprim (SLT),
- sulfadiazine/trimethoprim (SLT1), sulfadimethoxine (SUD),
- sulfadimidine (SDM), sulfadimidine/trimethoprim (SLT2), sulfafurazole
- (SLF), sulfaisodimidine (SLF1), sulfalene (SLF2), sulfamazone (SZO),
+ brodimoprim (BDP), iclaprim (ICL),
+ isoniazid/sulfamethoxazole/trimethoprim/pyridoxine (IST),
+ ormetroprim/sulfamethoxazole (ORS), sulfadiazine (SDI),
+ sulfadiazine/tetroxoprim (SLT), sulfadiazine/trimethoprim (SLT1),
+ sulfadimethoxine (SUD), sulfadimidine (SDM),
+ sulfadimidine/trimethoprim (SLT2), sulfafurazole (SLF),
+ sulfaisodimidine (SLF1), sulfalene (SLF2), sulfamazone (SZO),
sulfamerazine (SLF3), sulfamerazine/trimethoprim (SLT3),
sulfamethizole (SLF4), sulfamethoxazole (SMX), sulfamethoxypyridazine
(SLF5), sulfametomidine (SLF6), sulfametoxydiazine (SLF7),
diff --git a/reference/antimicrobials.html b/reference/antimicrobials.html
index dfb0c7dd5..e2a01198e 100644
--- a/reference/antimicrobials.html
+++ b/reference/antimicrobials.html
@@ -9,7 +9,7 @@ The antibiotics data set has been renamed to antimicrobials. The old name will b
AMR (for R)
-
3.0.1.9017
+
3.0.1.9018
diff --git a/reference/as.ab.html b/reference/as.ab.html
index 0502ecee3..650fccd8d 100644
--- a/reference/as.ab.html
+++ b/reference/as.ab.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9017
+ 3.0.1.9018
@@ -107,7 +107,7 @@
World Health Organization (WHO) Collaborating Centre for Drug Statistics Methodology: https://atcddd.fhi.no/atc_ddd_index/
-European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm
+European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: https://health.ec.europa.eu/documents/community-register/html/reg_hum_atc.htm
WHOCC
diff --git a/reference/as.av.md b/reference/as.av.md
index 112ed2da8..10e3b30ed 100644
--- a/reference/as.av.md
+++ b/reference/as.av.md
@@ -83,7 +83,7 @@ World Health Organization (WHO) Collaborating Centre for Drug Statistics
Methodology:
European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER:
-
+
## WHOCC
diff --git a/reference/as.disk.html b/reference/as.disk.html
index 15f560d65..9a98a3a91 100644
--- a/reference/as.disk.html
+++ b/reference/as.disk.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9017
+ 3.0.1.9018
diff --git a/reference/as.mic.html b/reference/as.mic.html
index 63715998d..1493e344f 100644
--- a/reference/as.mic.html
+++ b/reference/as.mic.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9017
+ 3.0.1.9018
diff --git a/reference/as.mo.html b/reference/as.mo.html
index 7dda019b2..a868483b2 100644
--- a/reference/as.mo.html
+++ b/reference/as.mo.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9017
+ 3.0.1.9018
diff --git a/reference/as.sir.html b/reference/as.sir.html
index d66cc0002..bcf8515da 100644
--- a/reference/as.sir.html
+++ b/reference/as.sir.html
@@ -9,7 +9,7 @@ Breakpoints are currently implemented from EUCAST 2011-2025 and CLSI 2011-2025,
AMR (for R)
- 3.0.1.9017
+ 3.0.1.9018
@@ -117,7 +117,7 @@ Breakpoints are currently implemented from EUCAST 2011-2025 and CLSI 2011-2025,
For interpretations of minimum inhibitory concentration (MIC) values and disk diffusion diameters:
CLSI M39: Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data , 2011-2025, Clinical and Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m39/ .
CLSI M100: Performance Standard for Antimicrobial Susceptibility Testing , 2011-2025, Clinical and Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m100/ .
CLSI VET01: Performance Standards for Antimicrobial Disk and Dilution Susceptibility Tests for Bacteria Isolated From Animals , 2019-2025, Clinical and Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/veterinary-medicine/documents/vet01/ .
-EUCAST Breakpoint tables for interpretation of MICs and zone diameters , 2011-2025, European Committee on Antimicrobial Susceptibility Testing (EUCAST). https://www.eucast.org/clinical_breakpoints .
+EUCAST Breakpoint tables for interpretation of MICs and zone diameters , 2011-2025, European Committee on Antimicrobial Susceptibility Testing (EUCAST). https://www.eucast.org/bacteria/clinical-breakpoints-and-interpretation/clinical-breakpoint-tables/ .
WHONET as a source for machine-reading the clinical breakpoints (read more here ), 1989-2025, WHO Collaborating Centre for Surveillance of Antimicrobial Resistance . https://whonet.org/ .
@@ -175,7 +175,7 @@ Breakpoints are currently implemented from EUCAST 2011-2025 and CLSI 2011-2025,
add_intrinsic_resistance
-
(only useful when using a EUCAST guideline) a logical to indicate whether intrinsic antibiotic resistance must also be considered for applicable bug-drug combinations, meaning that e.g. ampicillin will always return "R" in Klebsiella species. Determination is based on the intrinsic_resistant data set, that itself is based on 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3 (2021).
+
(only useful when using a EUCAST guideline) a logical to indicate whether intrinsic antibiotic resistance must also be considered for applicable bug-drug combinations, meaning that e.g. ampicillin will always return "R" in Klebsiella species. Determination is based on the intrinsic_resistant data set, that itself is based on 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3 (2021).
reference_data
@@ -321,7 +321,7 @@ Breakpoints are currently implemented from EUCAST 2011-2025 and CLSI 2011-2025,
Interpretation of SIR
-
In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories S, I, and R (https://www.eucast.org/newsiandr ).
+
In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories S, I, and R (https://www.eucast.org/bacteria/clinical-breakpoints-and-interpretation/definition-of-s-i-and-r/ ).
This AMR package follows insight; use susceptibility() (equal to proportion_SI() ) to determine antimicrobial susceptibility and count_susceptible() (equal to count_SI() ) to count susceptible isolates.
@@ -416,10 +416,10 @@ Breakpoints are currently implemented from EUCAST 2011-2025 and CLSI 2011-2025,
#> # A tibble: 4 × 18
#> datetime index method ab_given mo_given host_given input_given
#> <dttm> <int> <chr> <chr> <chr> <chr> <chr>
-
#> 1 2026-01-08 13:08:47 1 MIC amoxicillin Escherich… human 8
-
#> 2 2026-01-08 13:08:47 1 MIC cipro Escherich… human 0.256
-
#> 3 2026-01-08 13:08:48 1 DISK tobra Escherich… human 16
-
#> 4 2026-01-08 13:08:48 1 DISK genta Escherich… human 18
+
#> 1 2026-01-16 10:04:38 1 MIC amoxicillin Escherich… human 8
+
#> 2 2026-01-16 10:04:39 1 MIC cipro Escherich… human 0.256
+
#> 3 2026-01-16 10:04:39 1 DISK tobra Escherich… human 16
+
#> 4 2026-01-16 10:04:39 1 DISK genta Escherich… human 18
#> # ℹ 11 more variables: ab <ab>, mo <mo>, host <chr>, input <chr>,
#> # outcome <sir>, notes <chr>, guideline <chr>, ref_table <chr>, uti <lgl>,
#> # breakpoint_S_R <chr>, site <chr>
@@ -620,13 +620,13 @@ Breakpoints are currently implemented from EUCAST 2011-2025 and CLSI 2011-2025,
# For CLEANING existing SIR values -------------------------------------
as.sir ( c ( "S" , "SDD" , "I" , "R" , "NI" , "A" , "B" , "C" ) )
-
#> Warning: in `as.sir()` : 3 results in index '21' truncated (38%) that were invalid
-
#> antimicrobial interpretations: "A", "B", and "C"
+
#> Warning: in `as.sir()` : 3 results truncated (38%) that were invalid antimicrobial
+
#> interpretations: "A", "B", and "C"
#> Class 'sir'
#> [1] S SDD I R NI <NA> <NA> <NA>
as.sir ( "<= 0.002; S" ) # will return "S"
-
#> Warning: in `as.sir()` : 1 result in index '21' truncated (100%) that were invalid
-
#> antimicrobial interpretations: "<= 0.002; S"
+
#> Warning: in `as.sir()` : 1 result truncated (100%) that were invalid antimicrobial
+
#> interpretations: "<= 0.002; S"
#> Class 'sir'
#> [1] <NA>
diff --git a/reference/as.sir.md b/reference/as.sir.md
index 9bdb74d68..ebcdad1b4 100644
--- a/reference/as.sir.md
+++ b/reference/as.sir.md
@@ -93,7 +93,7 @@ disk diffusion diameters:
- **EUCAST Breakpoint tables for interpretation of MICs and zone
diameters**, 2011-2025, *European Committee on Antimicrobial
Susceptibility Testing* (EUCAST).
-
.
+ .
- **WHONET** as a source for machine-reading the clinical breakpoints
([read more
@@ -228,7 +228,7 @@ disk diffusion diameters:
[intrinsic_resistant](https://amr-for-r.org/reference/intrinsic_resistant.md)
data set, that itself is based on ['EUCAST Expert Rules' and 'EUCAST
Intrinsic Resistance and Unusual Phenotypes'
- v3.3](https://www.eucast.org/expert_rules_and_expected_phenotypes)
+ v3.3](https://www.eucast.org/bacteria/important-additional-information/expert-rules/)
(2021).
- reference_data:
@@ -537,7 +537,8 @@ base R's [`NA_character_`](https://rdrr.io/r/base/NA.html).
In 2019, the European Committee on Antimicrobial Susceptibility Testing
(EUCAST) has decided to change the definitions of susceptibility testing
-categories S, I, and R ().
+categories S, I, and R
+( ).
This AMR package follows insight; use
[`susceptibility()`](https://amr-for-r.org/reference/proportion.md)
@@ -650,10 +651,10 @@ sir_interpretation_history()
#> # A tibble: 4 × 18
#> datetime index method ab_given mo_given host_given input_given
#>
-#> 1 2026-01-08 13:08:47 1 MIC amoxicillin Escherich… human 8
-#> 2 2026-01-08 13:08:47 1 MIC cipro Escherich… human 0.256
-#> 3 2026-01-08 13:08:48 1 DISK tobra Escherich… human 16
-#> 4 2026-01-08 13:08:48 1 DISK genta Escherich… human 18
+#> 1 2026-01-16 10:04:38 1 MIC amoxicillin Escherich… human 8
+#> 2 2026-01-16 10:04:39 1 MIC cipro Escherich… human 0.256
+#> 3 2026-01-16 10:04:39 1 DISK tobra Escherich… human 16
+#> 4 2026-01-16 10:04:39 1 DISK genta Escherich… human 18
#> # ℹ 11 more variables: ab , mo , host , input ,
#> # outcome , notes , guideline , ref_table , uti ,
#> # breakpoint_S_R , site
@@ -854,13 +855,13 @@ as.sir(
# For CLEANING existing SIR values -------------------------------------
as.sir(c("S", "SDD", "I", "R", "NI", "A", "B", "C"))
-#> Warning: in `as.sir()`: 3 results in index '21' truncated (38%) that were invalid
-#> antimicrobial interpretations: "A", "B", and "C"
+#> Warning: in `as.sir()`: 3 results truncated (38%) that were invalid antimicrobial
+#> interpretations: "A", "B", and "C"
#> Class 'sir'
#> [1] S SDD I R NI
as.sir("<= 0.002; S") # will return "S"
-#> Warning: in `as.sir()`: 1 result in index '21' truncated (100%) that were invalid
-#> antimicrobial interpretations: "<= 0.002; S"
+#> Warning: in `as.sir()`: 1 result truncated (100%) that were invalid antimicrobial
+#> interpretations: "<= 0.002; S"
#> Class 'sir'
#> [1]
diff --git a/reference/atc_online.html b/reference/atc_online.html
index 288a310c8..318901705 100644
--- a/reference/atc_online.html
+++ b/reference/atc_online.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9017
+ 3.0.1.9018
diff --git a/reference/av_from_text.html b/reference/av_from_text.html
index 7e1563cd9..4105c0d3f 100644
--- a/reference/av_from_text.html
+++ b/reference/av_from_text.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9017
+ 3.0.1.9018
diff --git a/reference/av_property.html b/reference/av_property.html
index ed96ea799..7aa0e55c9 100644
--- a/reference/av_property.html
+++ b/reference/av_property.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9017
+ 3.0.1.9018
@@ -130,7 +130,7 @@
World Health Organization (WHO) Collaborating Centre for Drug Statistics Methodology: https://atcddd.fhi.no/atc_ddd_index/
-European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: https://ec.europa.eu/health/documents/community-register/html/reg_hum_atc.htm
+European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER: https://health.ec.europa.eu/documents/community-register/html/reg_hum_atc.htm
Download Our Reference Data
diff --git a/reference/av_property.md b/reference/av_property.md
index 65930855c..b445094df 100644
--- a/reference/av_property.md
+++ b/reference/av_property.md
@@ -104,7 +104,7 @@ World Health Organization (WHO) Collaborating Centre for Drug Statistics
Methodology:
European Commission Public Health PHARMACEUTICALS - COMMUNITY REGISTER:
-
+
## Download Our Reference Data
diff --git a/reference/availability.html b/reference/availability.html
index 35d464b2b..b0f79bef3 100644
--- a/reference/availability.html
+++ b/reference/availability.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9017
+ 3.0.1.9018
diff --git a/reference/bug_drug_combinations.html b/reference/bug_drug_combinations.html
index e14b86dd7..16f962ffe 100644
--- a/reference/bug_drug_combinations.html
+++ b/reference/bug_drug_combinations.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9017
+ 3.0.1.9018
diff --git a/reference/clinical_breakpoints.html b/reference/clinical_breakpoints.html
index bf051f12a..34467ad56 100644
--- a/reference/clinical_breakpoints.html
+++ b/reference/clinical_breakpoints.html
@@ -21,7 +21,7 @@ Use as.sir() to transform MICs or disks measurements to SIR values."> AMR (for R)
- 3.0.1.9017
+ 3.0.1.9018
diff --git a/reference/count.html b/reference/count.html
index 704ce6879..7480d87e3 100644
--- a/reference/count.html
+++ b/reference/count.html
@@ -9,7 +9,7 @@ count_resistant() should be used to count resistant isolates, count_susceptible(
AMR (for R)
- 3.0.1.9017
+ 3.0.1.9018
@@ -123,7 +123,7 @@ count_resistant() should be used to count resistant isolates, count_susceptible(
Interpretation of SIR
-In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories S, I, and R (https://www.eucast.org/newsiandr ).
+In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories S, I, and R (https://www.eucast.org/bacteria/clinical-breakpoints-and-interpretation/definition-of-s-i-and-r/ ).
This AMR package follows insight; use susceptibility() (equal to proportion_SI() ) to determine antimicrobial susceptibility and count_susceptible() (equal to count_SI()) to count susceptible isolates.
diff --git a/reference/count.md b/reference/count.md
index dcbb54ea8..a32ad963c 100644
--- a/reference/count.md
+++ b/reference/count.md
@@ -110,7 +110,8 @@ works exactly like `count_df()`, but adds the percentage of S, I and R.
In 2019, the European Committee on Antimicrobial Susceptibility Testing
(EUCAST) has decided to change the definitions of susceptibility testing
-categories S, I, and R (
).
+categories S, I, and R
+( ).
This AMR package follows insight; use
[`susceptibility()`](https://amr-for-r.org/reference/proportion.md)
diff --git a/reference/custom_eucast_rules.html b/reference/custom_eucast_rules.html
index bfb5c4c92..8a07d379c 100644
--- a/reference/custom_eucast_rules.html
+++ b/reference/custom_eucast_rules.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9017
+ 3.0.1.9018
@@ -180,10 +180,10 @@
rifamycins (rifabutin, rifampicin, rifampicin/ethambutol/isoniazid, rifampicin/isoniazid, rifampicin/pyrazinamide/ethambutol/isoniazid, rifampicin/pyrazinamide/isoniazid, rifamycin, and rifapentine)
spiropyrimidinetriones (zoliflodacin)
streptogramins (pristinamycin and quinupristin/dalfopristin)
-sulfonamides (brodimoprim, sulfadiazine, sulfadiazine/tetroxoprim, sulfadimethoxine, sulfadimidine, sulfafurazole, sulfaisodimidine, sulfalene, sulfamazone, sulfamerazine, sulfamethizole, sulfamethoxazole, sulfamethoxypyridazine, sulfametomidine, sulfametoxydiazine, sulfamoxole, sulfanilamide, sulfaperin, sulfaphenazole, sulfapyridine, sulfathiazole, and sulfathiourea)
-tetracyclines (cetocycline, chlortetracycline, clomocycline, demeclocycline, doxycycline, eravacycline, lymecycline, metacycline, minocycline, omadacycline, oxytetracycline, penimepicycline, rolitetracycline, sarecycline, tetracycline, tetracycline screening test, and tigecycline)
-tetracyclines_except_tgc (cetocycline, chlortetracycline, clomocycline, demeclocycline, doxycycline, eravacycline, lymecycline, metacycline, minocycline, omadacycline, oxytetracycline, penimepicycline, rolitetracycline, sarecycline, tetracycline, and tetracycline screening test)
-trimethoprims (brodimoprim, sulfadiazine, sulfadiazine/tetroxoprim, sulfadiazine/trimethoprim, sulfadimethoxine, sulfadimidine, sulfadimidine/trimethoprim, sulfafurazole, sulfaisodimidine, sulfalene, sulfamazone, sulfamerazine, sulfamerazine/trimethoprim, sulfamethizole, sulfamethoxazole, sulfamethoxypyridazine, sulfametomidine, sulfametoxydiazine, sulfametrole/trimethoprim, sulfamoxole, sulfamoxole/trimethoprim, sulfanilamide, sulfaperin, sulfaphenazole, sulfapyridine, sulfathiazole, sulfathiourea, trimethoprim, and trimethoprim/sulfamethoxazole)
+sulfonamides (isoniazid/sulfamethoxazole/trimethoprim/pyridoxine, ormetroprim/sulfamethoxazole, sulfachlorpyridazine, sulfadiazine, sulfadiazine/tetroxoprim, sulfadiazine/trimethoprim, sulfadimethoxine, sulfadimidine, sulfadimidine/trimethoprim, sulfafurazole, sulfaisodimidine, sulfalene, sulfamazone, sulfamerazine, sulfamerazine/trimethoprim, sulfamethazine, sulfamethizole, sulfamethoxazole, sulfamethoxypyridazine, sulfametomidine, sulfametoxydiazine, sulfametrole/trimethoprim, sulfamoxole, sulfamoxole/trimethoprim, sulfanilamide, sulfaperin, sulfaphenazole, sulfapyridine, sulfasuccinamide, sulfathiazole, sulfathiourea, sulfisoxazole, sulfonamide, and trimethoprim/sulfamethoxazole)
+tetracyclines (cetocycline, chlortetracycline, clomocycline, demeclocycline, doxycycline, eravacycline, lymecycline, metacycline, minocycline, omadacycline, oxytetracycline, penimepicycline, rolitetracycline, sarecycline, tetracycline, tetracycline screening test, tetracycline/oleandomycin, and tigecycline)
+tetracyclines_except_tgc (cetocycline, chlortetracycline, clomocycline, demeclocycline, doxycycline, eravacycline, lymecycline, metacycline, minocycline, omadacycline, oxytetracycline, penimepicycline, rolitetracycline, sarecycline, tetracycline, tetracycline screening test, and tetracycline/oleandomycin)
+trimethoprims (brodimoprim, iclaprim, isoniazid/sulfamethoxazole/trimethoprim/pyridoxine, ormetroprim/sulfamethoxazole, sulfadiazine, sulfadiazine/tetroxoprim, sulfadiazine/trimethoprim, sulfadimethoxine, sulfadimidine, sulfadimidine/trimethoprim, sulfafurazole, sulfaisodimidine, sulfalene, sulfamazone, sulfamerazine, sulfamerazine/trimethoprim, sulfamethizole, sulfamethoxazole, sulfamethoxypyridazine, sulfametomidine, sulfametoxydiazine, sulfametrole/trimethoprim, sulfamoxole, sulfamoxole/trimethoprim, sulfanilamide, sulfaperin, sulfaphenazole, sulfapyridine, sulfathiazole, sulfathiourea, trimethoprim, and trimethoprim/sulfamethoxazole)
ureidopenicillins (azlocillin, mezlocillin, piperacillin, and piperacillin/tazobactam)
diff --git a/reference/custom_eucast_rules.md b/reference/custom_eucast_rules.md
index 0ca75dddc..334eb1b1d 100644
--- a/reference/custom_eucast_rules.md
+++ b/reference/custom_eucast_rules.md
@@ -431,28 +431,36 @@ These 38 antimicrobial groups are allowed in the rules
(pristinamycin and quinupristin/dalfopristin)
- sulfonamides
- (brodimoprim, sulfadiazine, sulfadiazine/tetroxoprim,
- sulfadimethoxine, sulfadimidine, sulfafurazole, sulfaisodimidine,
- sulfalene, sulfamazone, sulfamerazine, sulfamethizole,
+ (isoniazid/sulfamethoxazole/trimethoprim/pyridoxine,
+ ormetroprim/sulfamethoxazole, sulfachlorpyridazine, sulfadiazine,
+ sulfadiazine/tetroxoprim, sulfadiazine/trimethoprim, sulfadimethoxine,
+ sulfadimidine, sulfadimidine/trimethoprim, sulfafurazole,
+ sulfaisodimidine, sulfalene, sulfamazone, sulfamerazine,
+ sulfamerazine/trimethoprim, sulfamethazine, sulfamethizole,
sulfamethoxazole, sulfamethoxypyridazine, sulfametomidine,
- sulfametoxydiazine, sulfamoxole, sulfanilamide, sulfaperin,
- sulfaphenazole, sulfapyridine, sulfathiazole, and sulfathiourea)
+ sulfametoxydiazine, sulfametrole/trimethoprim, sulfamoxole,
+ sulfamoxole/trimethoprim, sulfanilamide, sulfaperin, sulfaphenazole,
+ sulfapyridine, sulfasuccinamide, sulfathiazole, sulfathiourea,
+ sulfisoxazole, sulfonamide, and trimethoprim/sulfamethoxazole)
- tetracyclines
(cetocycline, chlortetracycline, clomocycline, demeclocycline,
doxycycline, eravacycline, lymecycline, metacycline, minocycline,
omadacycline, oxytetracycline, penimepicycline, rolitetracycline,
- sarecycline, tetracycline, tetracycline screening test, and
- tigecycline)
+ sarecycline, tetracycline, tetracycline screening test,
+ tetracycline/oleandomycin, and tigecycline)
- tetracyclines_except_tgc
(cetocycline, chlortetracycline, clomocycline, demeclocycline,
doxycycline, eravacycline, lymecycline, metacycline, minocycline,
omadacycline, oxytetracycline, penimepicycline, rolitetracycline,
- sarecycline, tetracycline, and tetracycline screening test)
+ sarecycline, tetracycline, tetracycline screening test, and
+ tetracycline/oleandomycin)
- trimethoprims
- (brodimoprim, sulfadiazine, sulfadiazine/tetroxoprim,
+ (brodimoprim, iclaprim,
+ isoniazid/sulfamethoxazole/trimethoprim/pyridoxine,
+ ormetroprim/sulfamethoxazole, sulfadiazine, sulfadiazine/tetroxoprim,
sulfadiazine/trimethoprim, sulfadimethoxine, sulfadimidine,
sulfadimidine/trimethoprim, sulfafurazole, sulfaisodimidine,
sulfalene, sulfamazone, sulfamerazine, sulfamerazine/trimethoprim,
diff --git a/reference/custom_mdro_guideline.html b/reference/custom_mdro_guideline.html
index c88f29208..ffabffdd4 100644
--- a/reference/custom_mdro_guideline.html
+++ b/reference/custom_mdro_guideline.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9017
+ 3.0.1.9018
@@ -174,9 +174,9 @@
rifamycins() can select: rifabutin, rifampicin, rifampicin/ethambutol/isoniazid, rifampicin/isoniazid, rifampicin/pyrazinamide/ethambutol/isoniazid, rifampicin/pyrazinamide/isoniazid, rifamycin, and rifapentine
spiropyrimidinetriones() can select: zoliflodacin
streptogramins() can select: pristinamycin and quinupristin/dalfopristin
-sulfonamides() can select: brodimoprim, sulfadiazine, sulfadiazine/tetroxoprim, sulfadimethoxine, sulfadimidine, sulfafurazole, sulfaisodimidine, sulfalene, sulfamazone, sulfamerazine, sulfamethizole, sulfamethoxazole, sulfamethoxypyridazine, sulfametomidine, sulfametoxydiazine, sulfamoxole, sulfanilamide, sulfaperin, sulfaphenazole, sulfapyridine, sulfathiazole, and sulfathiourea
-tetracyclines() can select: cetocycline, chlortetracycline, clomocycline, demeclocycline, doxycycline, eravacycline, lymecycline, metacycline, minocycline, omadacycline, oxytetracycline, penimepicycline, rolitetracycline, sarecycline, tetracycline, tetracycline screening test, and tigecycline
-trimethoprims() can select: brodimoprim, sulfadiazine, sulfadiazine/tetroxoprim, sulfadiazine/trimethoprim, sulfadimethoxine, sulfadimidine, sulfadimidine/trimethoprim, sulfafurazole, sulfaisodimidine, sulfalene, sulfamazone, sulfamerazine, sulfamerazine/trimethoprim, sulfamethizole, sulfamethoxazole, sulfamethoxypyridazine, sulfametomidine, sulfametoxydiazine, sulfametrole/trimethoprim, sulfamoxole, sulfamoxole/trimethoprim, sulfanilamide, sulfaperin, sulfaphenazole, sulfapyridine, sulfathiazole, sulfathiourea, trimethoprim, and trimethoprim/sulfamethoxazole
+sulfonamides() can select: isoniazid/sulfamethoxazole/trimethoprim/pyridoxine, ormetroprim/sulfamethoxazole, sulfachlorpyridazine, sulfadiazine, sulfadiazine/tetroxoprim, sulfadiazine/trimethoprim, sulfadimethoxine, sulfadimidine, sulfadimidine/trimethoprim, sulfafurazole, sulfaisodimidine, sulfalene, sulfamazone, sulfamerazine, sulfamerazine/trimethoprim, sulfamethazine, sulfamethizole, sulfamethoxazole, sulfamethoxypyridazine, sulfametomidine, sulfametoxydiazine, sulfametrole/trimethoprim, sulfamoxole, sulfamoxole/trimethoprim, sulfanilamide, sulfaperin, sulfaphenazole, sulfapyridine, sulfasuccinamide, sulfathiazole, sulfathiourea, sulfisoxazole, sulfonamide, and trimethoprim/sulfamethoxazole
+tetracyclines() can select: cetocycline, chlortetracycline, clomocycline, demeclocycline, doxycycline, eravacycline, lymecycline, metacycline, minocycline, omadacycline, oxytetracycline, penimepicycline, rolitetracycline, sarecycline, tetracycline, tetracycline screening test, tetracycline/oleandomycin, and tigecycline
+trimethoprims() can select: brodimoprim, iclaprim, isoniazid/sulfamethoxazole/trimethoprim/pyridoxine, ormetroprim/sulfamethoxazole, sulfadiazine, sulfadiazine/tetroxoprim, sulfadiazine/trimethoprim, sulfadimethoxine, sulfadimidine, sulfadimidine/trimethoprim, sulfafurazole, sulfaisodimidine, sulfalene, sulfamazone, sulfamerazine, sulfamerazine/trimethoprim, sulfamethizole, sulfamethoxazole, sulfamethoxypyridazine, sulfametomidine, sulfametoxydiazine, sulfametrole/trimethoprim, sulfamoxole, sulfamoxole/trimethoprim, sulfanilamide, sulfaperin, sulfaphenazole, sulfapyridine, sulfathiazole, sulfathiourea, trimethoprim, and trimethoprim/sulfamethoxazole
ureidopenicillins() can select: azlocillin, mezlocillin, piperacillin, and piperacillin/tazobactam
@@ -243,14 +243,6 @@
#> Results will be of class 'factor', with ordered levels: Negative < Custom MDRO 1 < Custom MDRO 2
out <- mdro ( example_isolates , guideline = my_guideline )
-#> ℹ Column ' esbl ' is SIR eligible (despite only having empty values), since
-#> it seems to be tazobactam (TAZ)
-#> ℹ Column ' mecC ' is SIR eligible (despite only having empty values), since
-#> it seems to be mecillinam (MEC)
-#> ℹ Column ' vanA ' is SIR eligible (despite only having empty values), since
-#> it seems to be lenampicillin (LEN)
-#> ℹ Column ' vanB ' is SIR eligible (despite only having empty values), since
-#> it seems to be metronidazole (MTR)
#> ℹ For `cephalosporins_2nd()` using columns ' CXM ' (cefuroxime) and ' FOX '
#> (cefoxitin)
#> ℹ Assuming a filter on all 2 cephalosporins_2nd. Wrap around `all()` or
diff --git a/reference/custom_mdro_guideline.md b/reference/custom_mdro_guideline.md
index cd143a14b..f6065fcb5 100644
--- a/reference/custom_mdro_guideline.md
+++ b/reference/custom_mdro_guideline.md
@@ -413,24 +413,31 @@ All 38 antimicrobial selectors are supported for use in the rules:
- [`sulfonamides()`](https://amr-for-r.org/reference/antimicrobial_selectors.md)
can select:
- brodimoprim, sulfadiazine, sulfadiazine/tetroxoprim, sulfadimethoxine,
- sulfadimidine, sulfafurazole, sulfaisodimidine, sulfalene,
- sulfamazone, sulfamerazine, sulfamethizole, sulfamethoxazole,
- sulfamethoxypyridazine, sulfametomidine, sulfametoxydiazine,
- sulfamoxole, sulfanilamide, sulfaperin, sulfaphenazole, sulfapyridine,
- sulfathiazole, and sulfathiourea
+ isoniazid/sulfamethoxazole/trimethoprim/pyridoxine,
+ ormetroprim/sulfamethoxazole, sulfachlorpyridazine, sulfadiazine,
+ sulfadiazine/tetroxoprim, sulfadiazine/trimethoprim, sulfadimethoxine,
+ sulfadimidine, sulfadimidine/trimethoprim, sulfafurazole,
+ sulfaisodimidine, sulfalene, sulfamazone, sulfamerazine,
+ sulfamerazine/trimethoprim, sulfamethazine, sulfamethizole,
+ sulfamethoxazole, sulfamethoxypyridazine, sulfametomidine,
+ sulfametoxydiazine, sulfametrole/trimethoprim, sulfamoxole,
+ sulfamoxole/trimethoprim, sulfanilamide, sulfaperin, sulfaphenazole,
+ sulfapyridine, sulfasuccinamide, sulfathiazole, sulfathiourea,
+ sulfisoxazole, sulfonamide, and trimethoprim/sulfamethoxazole
- [`tetracyclines()`](https://amr-for-r.org/reference/antimicrobial_selectors.md)
can select:
cetocycline, chlortetracycline, clomocycline, demeclocycline,
doxycycline, eravacycline, lymecycline, metacycline, minocycline,
omadacycline, oxytetracycline, penimepicycline, rolitetracycline,
- sarecycline, tetracycline, tetracycline screening test, and
- tigecycline
+ sarecycline, tetracycline, tetracycline screening test,
+ tetracycline/oleandomycin, and tigecycline
- [`trimethoprims()`](https://amr-for-r.org/reference/antimicrobial_selectors.md)
can select:
- brodimoprim, sulfadiazine, sulfadiazine/tetroxoprim,
+ brodimoprim, iclaprim,
+ isoniazid/sulfamethoxazole/trimethoprim/pyridoxine,
+ ormetroprim/sulfamethoxazole, sulfadiazine, sulfadiazine/tetroxoprim,
sulfadiazine/trimethoprim, sulfadimethoxine, sulfadimidine,
sulfadimidine/trimethoprim, sulfafurazole, sulfaisodimidine,
sulfalene, sulfamazone, sulfamerazine, sulfamerazine/trimethoprim,
@@ -506,14 +513,6 @@ my_guideline
#> Results will be of class 'factor', with ordered levels: Negative < Custom MDRO 1 < Custom MDRO 2
out <- mdro(example_isolates, guideline = my_guideline)
-#> ℹ Column 'esbl' is SIR eligible (despite only having empty values), since
-#> it seems to be tazobactam (TAZ)
-#> ℹ Column 'mecC' is SIR eligible (despite only having empty values), since
-#> it seems to be mecillinam (MEC)
-#> ℹ Column 'vanA' is SIR eligible (despite only having empty values), since
-#> it seems to be lenampicillin (LEN)
-#> ℹ Column 'vanB' is SIR eligible (despite only having empty values), since
-#> it seems to be metronidazole (MTR)
#> ℹ For `cephalosporins_2nd()` using columns 'CXM' (cefuroxime) and 'FOX'
#> (cefoxitin)
#> ℹ Assuming a filter on all 2 cephalosporins_2nd. Wrap around `all()` or
diff --git a/reference/dosage.html b/reference/dosage.html
index 114fb5b5f..36a5a47bd 100644
--- a/reference/dosage.html
+++ b/reference/dosage.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9017
+ 3.0.1.9018
diff --git a/reference/esbl_isolates.html b/reference/esbl_isolates.html
index d4c737a51..1f7e90f8f 100644
--- a/reference/esbl_isolates.html
+++ b/reference/esbl_isolates.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9017
+ 3.0.1.9018
diff --git a/reference/eucast_rules.html b/reference/eucast_rules.html
index e3625a6f3..63660314a 100644
--- a/reference/eucast_rules.html
+++ b/reference/eucast_rules.html
@@ -9,7 +9,7 @@ To improve the interpretation of the antibiogram before EUCAST rules are applied
AMR (for R)
- 3.0.1.9017
+ 3.0.1.9018
diff --git a/reference/example_isolates.html b/reference/example_isolates.html
index c0c0ce870..96cc0dbee 100644
--- a/reference/example_isolates.html
+++ b/reference/example_isolates.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9017
+ 3.0.1.9018
diff --git a/reference/example_isolates_unclean.html b/reference/example_isolates_unclean.html
index 1b704ed38..9e8167280 100644
--- a/reference/example_isolates_unclean.html
+++ b/reference/example_isolates_unclean.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9017
+ 3.0.1.9018
diff --git a/reference/export_ncbi_biosample.html b/reference/export_ncbi_biosample.html
index 9f6154292..29a18f5d9 100644
--- a/reference/export_ncbi_biosample.html
+++ b/reference/export_ncbi_biosample.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9017
+ 3.0.1.9018
diff --git a/reference/first_isolate.html b/reference/first_isolate.html
index 861f5710f..28c242739 100644
--- a/reference/first_isolate.html
+++ b/reference/first_isolate.html
@@ -9,7 +9,7 @@
AMR (for R)
- 3.0.1.9017
+ 3.0.1.9018
diff --git a/reference/g.test.html b/reference/g.test.html
index 6dbabb4e6..e10e63434 100644
--- a/reference/g.test.html
+++ b/reference/g.test.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9017
+ 3.0.1.9018
diff --git a/reference/get_episode.html b/reference/get_episode.html
index d046a4ea6..b94077182 100644
--- a/reference/get_episode.html
+++ b/reference/get_episode.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9017
+ 3.0.1.9018
diff --git a/reference/ggplot_pca.html b/reference/ggplot_pca.html
index a8a927732..b18ceb269 100644
--- a/reference/ggplot_pca.html
+++ b/reference/ggplot_pca.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9017
+ 3.0.1.9018
diff --git a/reference/ggplot_sir.html b/reference/ggplot_sir.html
index 1acff8aea..839615c4f 100644
--- a/reference/ggplot_sir.html
+++ b/reference/ggplot_sir.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9017
+ 3.0.1.9018
diff --git a/reference/guess_ab_col.html b/reference/guess_ab_col.html
index b66483fb8..6037da6f8 100644
--- a/reference/guess_ab_col.html
+++ b/reference/guess_ab_col.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9017
+ 3.0.1.9018
diff --git a/reference/index.html b/reference/index.html
index 3ab28c86a..eee3d47d9 100644
--- a/reference/index.html
+++ b/reference/index.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9017
+ 3.0.1.9018
diff --git a/reference/intrinsic_resistant.html b/reference/intrinsic_resistant.html
index 6a312814c..49fbada1b 100644
--- a/reference/intrinsic_resistant.html
+++ b/reference/intrinsic_resistant.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9017
+ 3.0.1.9018
@@ -65,7 +65,7 @@
diff --git a/reference/intrinsic_resistant.md b/reference/intrinsic_resistant.md
index d4d946a61..6cbb39817 100644
--- a/reference/intrinsic_resistant.md
+++ b/reference/intrinsic_resistant.md
@@ -33,7 +33,7 @@ A [tibble](https://tibble.tidyverse.org/reference/tibble.html) with 271
This data set is currently based on ['EUCAST Expected Resistant
Phenotypes'
-v1.2](https://www.eucast.org/expert_rules_and_expected_phenotypes)
+v1.2](https://www.eucast.org/bacteria/important-additional-information/expert-rules/)
(2023).
This data set is internally used by:
diff --git a/reference/italicise_taxonomy.html b/reference/italicise_taxonomy.html
index 987a0d11c..05950bf64 100644
--- a/reference/italicise_taxonomy.html
+++ b/reference/italicise_taxonomy.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9017
+ 3.0.1.9018
diff --git a/reference/join.html b/reference/join.html
index eaf56c155..c2a5f6acc 100644
--- a/reference/join.html
+++ b/reference/join.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9017
+ 3.0.1.9018
diff --git a/reference/key_antimicrobials.html b/reference/key_antimicrobials.html
index 1bcfb9d5c..338d1d0c4 100644
--- a/reference/key_antimicrobials.html
+++ b/reference/key_antimicrobials.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9017
+ 3.0.1.9018
diff --git a/reference/kurtosis.html b/reference/kurtosis.html
index e793ed24e..94eb2f883 100644
--- a/reference/kurtosis.html
+++ b/reference/kurtosis.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9017
+ 3.0.1.9018
diff --git a/reference/like.html b/reference/like.html
index ca4dfa706..b28a3ee62 100644
--- a/reference/like.html
+++ b/reference/like.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9017
+ 3.0.1.9018
diff --git a/reference/mdro.html b/reference/mdro.html
index b559b06d3..40a652750 100644
--- a/reference/mdro.html
+++ b/reference/mdro.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9017
+ 3.0.1.9018
@@ -194,7 +194,7 @@ Ordered Interpretation of SIR
-In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories S, I, and R (https://www.eucast.org/newsiandr ).
+In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories S, I, and R (https://www.eucast.org/bacteria/clinical-breakpoints-and-interpretation/definition-of-s-i-and-r/ ).
This AMR package follows insight; use susceptibility() (equal to proportion_SI() ) to determine antimicrobial susceptibility and count_susceptible() (equal to count_SI() ) to count susceptible isolates.
diff --git a/reference/mdro.md b/reference/mdro.md
index 516004595..6d56957ac 100644
--- a/reference/mdro.md
+++ b/reference/mdro.md
@@ -269,7 +269,8 @@ function.
In 2019, the European Committee on Antimicrobial Susceptibility Testing
(EUCAST) has decided to change the definitions of susceptibility testing
-categories S, I, and R (
).
+categories S, I, and R
+( ).
This AMR package follows insight; use
[`susceptibility()`](https://amr-for-r.org/reference/proportion.md)
diff --git a/reference/mean_amr_distance.html b/reference/mean_amr_distance.html
index 00a862aa2..a322cc5a0 100644
--- a/reference/mean_amr_distance.html
+++ b/reference/mean_amr_distance.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9017
+ 3.0.1.9018
diff --git a/reference/microorganisms.codes.html b/reference/microorganisms.codes.html
index 40750d9d2..621fd5a4b 100644
--- a/reference/microorganisms.codes.html
+++ b/reference/microorganisms.codes.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9017
+ 3.0.1.9018
diff --git a/reference/microorganisms.groups.html b/reference/microorganisms.groups.html
index d9e0e9597..2e8a35c34 100644
--- a/reference/microorganisms.groups.html
+++ b/reference/microorganisms.groups.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9017
+ 3.0.1.9018
diff --git a/reference/microorganisms.html b/reference/microorganisms.html
index 7bb6108b8..c2c459e98 100644
--- a/reference/microorganisms.html
+++ b/reference/microorganisms.html
@@ -9,7 +9,7 @@ This data set is carefully crafted, yet made 100% reproducible from public and a
AMR (for R)
- 3.0.1.9017
+ 3.0.1.9018
diff --git a/reference/mo_matching_score.html b/reference/mo_matching_score.html
index 02c9f6287..59ec688e1 100644
--- a/reference/mo_matching_score.html
+++ b/reference/mo_matching_score.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9017
+ 3.0.1.9018
diff --git a/reference/mo_property.html b/reference/mo_property.html
index 206e72c7e..2a597957d 100644
--- a/reference/mo_property.html
+++ b/reference/mo_property.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9017
+ 3.0.1.9018
@@ -220,7 +220,7 @@
). This function returns a factor with the levels Pathogenic , Potentially pathogenic , Non-pathogenic , and Unknown .
Determination of the Gram stain (mo_gramstain()) will be based on the taxonomic kingdom and phylum. Originally, Cavalier-Smith defined the so-called subkingdoms Negibacteria and Posibacteria (2002, PMID 11837318 ), and only considered these phyla as Posibacteria: Actinobacteria, Chloroflexi, Firmicutes, and Tenericutes. These phyla were later renamed to Actinomycetota, Chloroflexota, Bacillota, and Mycoplasmatota (2021, PMID 34694987 ). Bacteria in these phyla are considered Gram-positive in this AMR package, except for members of the class Negativicutes (within phylum Bacillota) which are Gram-negative. All other bacteria are considered Gram-negative. Species outside the kingdom of Bacteria will return a value NA. Functions mo_is_gram_negative() and mo_is_gram_positive() always return TRUE or FALSE (or NA when the input is NA or the MO code is UNKNOWN), thus always return FALSE for species outside the taxonomic kingdom of Bacteria.
Determination of yeasts (mo_is_yeast()) will be based on the taxonomic kingdom and class. Budding yeasts are yeasts that reproduce asexually through a process called budding, where a new cell develops from a small protrusion on the parent cell. Taxonomically, these are members of the phylum Ascomycota, class Saccharomycetes (also called Hemiascomycetes) or Pichiomycetes. True yeasts quite specifically refers to yeasts in the underlying order Saccharomycetales (such as Saccharomyces cerevisiae ). Thus, for all microorganisms that are member of the taxonomic class Saccharomycetes or Pichiomycetes, the function will return TRUE. It returns FALSE otherwise (or NA when the input is NA or the MO code is UNKNOWN).
-Determination of intrinsic resistance (mo_is_intrinsic_resistant()) will be based on the intrinsic_resistant data set, which is based on 'EUCAST Expected Resistant Phenotypes' v1.2 (2023). The mo_is_intrinsic_resistant() function can be vectorised over both argument x (input for microorganisms) and ab (input for antimicrobials).
+Determination of intrinsic resistance (mo_is_intrinsic_resistant()) will be based on the intrinsic_resistant data set, which is based on 'EUCAST Expected Resistant Phenotypes' v1.2 (2023). The mo_is_intrinsic_resistant() function can be vectorised over both argument x (input for microorganisms) and ab (input for antimicrobials).
Determination of bacterial oxygen tolerance (mo_oxygen_tolerance()) will be based on BacDive, see Source . The function mo_is_anaerobic() only returns TRUE if the oxygen tolerance is "anaerobe", indicting an obligate anaerobic species or genus. It always returns FALSE for species outside the taxonomic kingdom of Bacteria.
The function mo_url() will return the direct URL to the online database entry, which also shows the scientific reference of the concerned species. This MycoBank URL will be used for fungi wherever available , this LPSN URL for bacteria wherever available, and this GBIF link otherwise.
SNOMED codes (mo_snomed()) was last updated on July 16th, 2024. See Source and the microorganisms data set for more info.
diff --git a/reference/mo_property.md b/reference/mo_property.md
index d2226769f..73e0c5a55 100644
--- a/reference/mo_property.md
+++ b/reference/mo_property.md
@@ -277,7 +277,7 @@ Determination of intrinsic resistance (`mo_is_intrinsic_resistant()`)
will be based on the
[intrinsic_resistant](https://amr-for-r.org/reference/intrinsic_resistant.md)
data set, which is based on ['EUCAST Expected Resistant Phenotypes'
-v1.2](https://www.eucast.org/expert_rules_and_expected_phenotypes)
+v1.2](https://www.eucast.org/bacteria/important-additional-information/expert-rules/)
(2023). The `mo_is_intrinsic_resistant()` function can be vectorised
over both argument `x` (input for microorganisms) and `ab` (input for
antimicrobials).
diff --git a/reference/mo_source.html b/reference/mo_source.html
index 08759f868..65601be25 100644
--- a/reference/mo_source.html
+++ b/reference/mo_source.html
@@ -9,7 +9,7 @@ This is the fastest way to have your organisation (or analysis) specific codes p
AMR (for R)
- 3.0.1.9017
+ 3.0.1.9018
diff --git a/reference/pca.html b/reference/pca.html
index ac6e07590..c481dd8dc 100644
--- a/reference/pca.html
+++ b/reference/pca.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9017
+ 3.0.1.9018
diff --git a/reference/plot.html b/reference/plot.html
index badef4222..a224a3a59 100644
--- a/reference/plot.html
+++ b/reference/plot.html
@@ -9,7 +9,7 @@ Especially the scale_*_mic() functions are relevant wrappers to plot MIC values
AMR (for R)
- 3.0.1.9017
+ 3.0.1.9018
diff --git a/reference/proportion.html b/reference/proportion.html
index 26d357421..fca4e6c1a 100644
--- a/reference/proportion.html
+++ b/reference/proportion.html
@@ -9,7 +9,7 @@ resistance() should be used to calculate resistance, susceptibility() should be
AMR (for R)
- 3.0.1.9017
+ 3.0.1.9018
@@ -194,7 +194,7 @@ resistance() should be used to calculate resistance, susceptibility() should be
Interpretation of SIR
-In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories S, I, and R (https://www.eucast.org/newsiandr ).
+In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories S, I, and R (https://www.eucast.org/bacteria/clinical-breakpoints-and-interpretation/definition-of-s-i-and-r/ ).
This AMR package follows insight; use susceptibility() (equal to proportion_SI()) to determine antimicrobial susceptibility and count_susceptible() (equal to count_SI() ) to count susceptible isolates.
diff --git a/reference/proportion.md b/reference/proportion.md
index 15e501625..4101c0a59 100644
--- a/reference/proportion.md
+++ b/reference/proportion.md
@@ -236,7 +236,8 @@ input.
In 2019, the European Committee on Antimicrobial Susceptibility Testing
(EUCAST) has decided to change the definitions of susceptibility testing
-categories S, I, and R (
).
+categories S, I, and R
+( ).
This AMR package follows insight; use `susceptibility()` (equal to
`proportion_SI()`) to determine antimicrobial susceptibility and
diff --git a/reference/random.html b/reference/random.html
index df2c7d68f..e64a73dfe 100644
--- a/reference/random.html
+++ b/reference/random.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9017
+ 3.0.1.9018
diff --git a/reference/resistance_predict.html b/reference/resistance_predict.html
index 7bc6ef373..b8bac6209 100644
--- a/reference/resistance_predict.html
+++ b/reference/resistance_predict.html
@@ -9,7 +9,7 @@ NOTE: These functions are deprecated and will be removed in a future version. Us
AMR (for R)
- 3.0.1.9017
+ 3.0.1.9018
@@ -163,7 +163,7 @@ NOTE: These functions are deprecated and will be removed in a future version. Us
Interpretation of SIR
-In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories S, I, and R (https://www.eucast.org/newsiandr ).
+In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories S, I, and R (https://www.eucast.org/bacteria/clinical-breakpoints-and-interpretation/definition-of-s-i-and-r/ ).
This AMR package follows insight; use susceptibility() (equal to proportion_SI() ) to determine antimicrobial susceptibility and count_susceptible() (equal to count_SI() ) to count susceptible isolates.
diff --git a/reference/resistance_predict.md b/reference/resistance_predict.md
index e5218210e..f0183cbe5 100644
--- a/reference/resistance_predict.md
+++ b/reference/resistance_predict.md
@@ -163,7 +163,8 @@ Valid options for the statistical model (argument `model`) are:
In 2019, the European Committee on Antimicrobial Susceptibility Testing
(EUCAST) has decided to change the definitions of susceptibility testing
-categories S, I, and R (
).
+categories S, I, and R
+( ).
This AMR package follows insight; use
[`susceptibility()`](https://amr-for-r.org/reference/proportion.md)
diff --git a/reference/skewness.html b/reference/skewness.html
index 6a4c85c1a..aa6e83069 100644
--- a/reference/skewness.html
+++ b/reference/skewness.html
@@ -9,7 +9,7 @@ When negative ('left-skewed'): the left tail is longer; the mass of the distribu
AMR (for R)
- 3.0.1.9017
+ 3.0.1.9018
diff --git a/reference/top_n_microorganisms.html b/reference/top_n_microorganisms.html
index a50104813..e55e6beb9 100644
--- a/reference/top_n_microorganisms.html
+++ b/reference/top_n_microorganisms.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9017
+ 3.0.1.9018
diff --git a/reference/translate.html b/reference/translate.html
index 43e19d891..f7de3e17e 100644
--- a/reference/translate.html
+++ b/reference/translate.html
@@ -7,7 +7,7 @@
AMR (for R)
- 3.0.1.9017
+ 3.0.1.9018
diff --git a/search.json b/search.json
index de5b37e14..562b5d911 100644
--- a/search.json
+++ b/search.json
@@ -1 +1 @@
-[{"path":"https://amr-for-r.org/articles/AMR.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Conduct AMR data analysis","text":"Conducting AMR data analysis unfortunately requires -depth knowledge different scientific fields, makes hard right. least, requires: Good questions (always start !) reliable data thorough understanding (clinical) epidemiology, understand clinical epidemiological relevance possible bias results thorough understanding (clinical) microbiology/infectious diseases, understand microorganisms causal infections implications pharmaceutical treatment, well understanding intrinsic acquired microbial resistance Experience data analysis microbiological tests results, understand determination limitations MIC values interpretations SIR values Availability biological taxonomy microorganisms probably normalisation factors pharmaceuticals, defined daily doses (DDD) Available (inter-)national guidelines, profound methods apply course, instantly provide knowledge experience. AMR package, aimed providing (1) tools simplify antimicrobial resistance data cleaning, transformation analysis, (2) methods easily incorporate international guidelines (3) scientifically reliable reference data, including requirements mentioned . AMR package enables standardised reproducible AMR data analysis, application evidence-based rules, determination first isolates, translation various codes microorganisms antimicrobial drugs, determination (multi-drug) resistant microorganisms, calculation antimicrobial resistance, prevalence future trends.","code":""},{"path":"https://amr-for-r.org/articles/AMR.html","id":"preparation","dir":"Articles","previous_headings":"","what":"Preparation","title":"Conduct AMR data analysis","text":"tutorial, create fake demonstration data work . can skip Cleaning data already data ready. start analysis, try make structure data generally look like :","code":""},{"path":"https://amr-for-r.org/articles/AMR.html","id":"needed-r-packages","dir":"Articles","previous_headings":"Preparation","what":"Needed R packages","title":"Conduct AMR data analysis","text":"many uses R, need additional packages AMR data analysis. package works closely together tidyverse packages dplyr ggplot2 RStudio. tidyverse tremendously improves way conduct data science - allows natural way writing syntaxes creating beautiful plots R. also use cleaner package, can used cleaning data creating frequency tables. AMR package contains data set example_isolates_unclean, might look data users extracted laboratory systems: AMR data analysis, like microorganism column contain valid, --date taxonomy, antibiotic columns cleaned SIR values well.","code":"library(dplyr) library(ggplot2) library(AMR) # (if not yet installed, install with:) # install.packages(c(\"dplyr\", \"ggplot2\", \"AMR\")) example_isolates_unclean #> # A tibble: 3,000 × 8 #> patient_id hospital date bacteria AMX AMC CIP GEN #> #> 1 J3 A 2012-11-21 E. coli R I S S #> 2 R7 A 2018-04-03 K. pneumoniae R I S S #> 3 P3 A 2014-09-19 E. coli R S S S #> 4 P10 A 2015-12-10 E. coli S I S S #> 5 B7 A 2015-03-02 E. coli S S S S #> 6 W3 A 2018-03-31 S. aureus R S R S #> 7 J8 A 2016-06-14 E. coli R S S S #> 8 M3 A 2015-10-25 E. coli R S S S #> 9 J3 A 2019-06-19 E. coli S S S S #> 10 G6 A 2015-04-27 S. aureus S S S S #> # ℹ 2,990 more rows # we will use 'our_data' as the data set name for this tutorial our_data <- example_isolates_unclean"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"taxonomy-of-microorganisms","dir":"Articles","previous_headings":"Preparation","what":"Taxonomy of microorganisms","title":"Conduct AMR data analysis","text":".mo(), users can transform arbitrary microorganism names codes current taxonomy. AMR package contains --date taxonomic data. specific, currently included data retrieved 24 Jun 2024. codes AMR packages come .mo() short, still human readable. importantly, .mo() supports kinds input: first character codes denote taxonomic kingdom, Bacteria (B), Fungi (F), Protozoa (P). AMR package also contain functions directly retrieve taxonomic properties, name, genus, species, family, order, even Gram-stain. start mo_ use .mo() internally, still arbitrary user input can used: Now can thus clean data: Apparently, uncertainty translation taxonomic codes. Let’s check : ’s good.","code":"as.mo(\"Klebsiella pneumoniae\") #> Class 'mo' #> [1] B_KLBSL_PNMN as.mo(\"K. pneumoniae\") #> Class 'mo' #> [1] B_KLBSL_PNMN as.mo(\"KLEPNE\") #> Class 'mo' #> [1] B_KLBSL_PNMN as.mo(\"KLPN\") #> Class 'mo' #> [1] B_KLBSL_PNMN mo_family(\"K. pneumoniae\") #> [1] \"Enterobacteriaceae\" mo_genus(\"K. pneumoniae\") #> [1] \"Klebsiella\" mo_species(\"K. pneumoniae\") #> [1] \"pneumoniae\" mo_gramstain(\"Klebsiella pneumoniae\") #> [1] \"Gram-negative\" mo_ref(\"K. pneumoniae\") #> [1] \"Trevisan, 1887\" mo_snomed(\"K. pneumoniae\") #> [[1]] #> [1] \"1098101000112102\" \"446870005\" \"1098201000112108\" \"409801009\" #> [5] \"56415008\" \"714315002\" \"713926009\" our_data$bacteria <- as.mo(our_data$bacteria, info = TRUE) #> ℹ Retrieved values from the `microorganisms.codes` data set for \"ESCCOL\", #> \"KLEPNE\", \"STAAUR\", and \"STRPNE\". #> ℹ Microorganism translation was uncertain for four microorganisms. Run #> `mo_uncertainties()` to review these uncertainties, or use #> `add_custom_microorganisms()` to add custom entries. mo_uncertainties() #> Matching scores are based on the resemblance between the input and the full #> taxonomic name, and the pathogenicity in humans. See `?mo_matching_score`. #> Colour keys: 0.000-0.549 0.550-0.649 0.650-0.749 0.750-1.000 #> #> -------------------------------------------------------------------------------- #> \"E. coli\" -> Escherichia coli (B_ESCHR_COLI, 0.688) #> Also matched: Enterococcus crotali (0.650), Escherichia coli coli #> (0.643), Escherichia coli expressing (0.611), Enterobacter cowanii #> (0.600), Enterococcus columbae (0.595), Enterococcus camelliae (0.591), #> Enterococcus casseliflavus (0.577), Enterobacter cloacae cloacae #> (0.571), Enterobacter cloacae complex (0.571), and Enterobacter cloacae #> dissolvens (0.565) #> -------------------------------------------------------------------------------- #> \"K. pneumoniae\" -> Klebsiella pneumoniae (B_KLBSL_PNMN, 0.786) #> Also matched: Klebsiella pneumoniae complex (0.707), Klebsiella #> pneumoniae ozaenae (0.707), Klebsiella pneumoniae pneumoniae (0.688), #> Klebsiella pneumoniae rhinoscleromatis (0.658), Klebsiella pasteurii #> (0.500), Klebsiella planticola (0.500), Kingella potus (0.400), #> Kluyveromyces pseudotropicale (0.386), Kluyveromyces pseudotropicalis #> (0.363), and Kosakonia pseudosacchari (0.361) #> -------------------------------------------------------------------------------- #> \"S. aureus\" -> Staphylococcus aureus (B_STPHY_AURS, 0.690) #> Also matched: Staphylococcus aureus aureus (0.643), Staphylococcus #> argenteus (0.625), Staphylococcus aureus anaerobius (0.625), #> Staphylococcus auricularis (0.615), Salmonella Aurelianis (0.595), #> Salmonella Aarhus (0.588), Salmonella Amounderness (0.587), #> Staphylococcus argensis (0.587), Streptococcus australis (0.587), and #> Salmonella choleraesuis arizonae (0.562) #> -------------------------------------------------------------------------------- #> \"S. pneumoniae\" -> Streptococcus pneumoniae (B_STRPT_PNMN, 0.750) #> Also matched: Streptococcus pseudopneumoniae (0.700), Streptococcus #> phocae salmonis (0.552), Serratia proteamaculans quinovora (0.545), #> Streptococcus pseudoporcinus (0.536), Staphylococcus piscifermentans #> (0.533), Staphylococcus pseudintermedius (0.532), Serratia #> proteamaculans proteamaculans (0.526), Streptococcus gallolyticus #> pasteurianus (0.526), Salmonella Portanigra (0.524), and Streptococcus #> periodonticum (0.519) #> #> Only the first 10 other matches of each record are shown. Run #> `print(mo_uncertainties(), n = ...)` to view more entries, or save #> `mo_uncertainties()` to an object."},{"path":"https://amr-for-r.org/articles/AMR.html","id":"antibiotic-results","dir":"Articles","previous_headings":"Preparation","what":"Antibiotic results","title":"Conduct AMR data analysis","text":"column antibiotic test results must also cleaned. AMR package comes three new data types work test results: mic minimal inhibitory concentrations (MIC), disk disk diffusion diameters, sir SIR data interpreted already. package can also determine SIR values based MIC disk diffusion values, read .sir() page. now, just clean SIR columns data using dplyr: basically cleaning, time start data inclusion.","code":"# method 1, be explicit about the columns: our_data <- our_data %>% mutate_at(vars(AMX:GEN), as.sir) # method 2, let the AMR package determine the eligible columns our_data <- our_data %>% mutate_if(is_sir_eligible, as.sir) # result: our_data #> # A tibble: 3,000 × 8 #> patient_id hospital date bacteria AMX AMC CIP GEN #> #> 1 J3 A 2012-11-21 B_ESCHR_COLI R I S S #> 2 R7 A 2018-04-03 B_KLBSL_PNMN R I S S #> 3 P3 A 2014-09-19 B_ESCHR_COLI R S S S #> 4 P10 A 2015-12-10 B_ESCHR_COLI S I S S #> 5 B7 A 2015-03-02 B_ESCHR_COLI S S S S #> 6 W3 A 2018-03-31 B_STPHY_AURS R S R S #> 7 J8 A 2016-06-14 B_ESCHR_COLI R S S S #> 8 M3 A 2015-10-25 B_ESCHR_COLI R S S S #> 9 J3 A 2019-06-19 B_ESCHR_COLI S S S S #> 10 G6 A 2015-04-27 B_STPHY_AURS S S S S #> # ℹ 2,990 more rows"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"first-isolates","dir":"Articles","previous_headings":"Preparation","what":"First isolates","title":"Conduct AMR data analysis","text":"need know isolates can actually use analysis without repetition bias. conduct analysis antimicrobial resistance, must include first isolate every patient per episode (Hindler et al., Clin Infect Dis. 2007). , easily get overestimate underestimate resistance antibiotic. Imagine patient admitted MRSA found 5 different blood cultures following weeks (yes, countries like Netherlands blood drawing policies). resistance percentage oxacillin isolates overestimated, included MRSA . clearly selection bias. Clinical Laboratory Standards Institute (CLSI) appoints follows: (…) preparing cumulative antibiogram guide clinical decisions empirical antimicrobial therapy initial infections, first isolate given species per patient, per analysis period (eg, one year) included, irrespective body site, antimicrobial susceptibility profile, phenotypical characteristics (eg, biotype). first isolate easily identified, cumulative antimicrobial susceptibility test data prepared using first isolate generally comparable cumulative antimicrobial susceptibility test data calculated methods, providing duplicate isolates excluded. M39-A4 Analysis Presentation Cumulative Antimicrobial Susceptibility Test Data, 4th Edition. CLSI, 2014. Chapter 6.4 AMR package includes methodology first_isolate() function able apply four different methods defined Hindler et al. 2007: phenotype-based, episode-based, patient-based, isolate-based. right method depends goals analysis, default phenotype-based method case method properly correct duplicate isolates. Read methods first_isolate() page. outcome function can easily added data: 91% suitable resistance analysis! can now filter filter() function, also dplyr package: future use, two syntaxes can shortened: end 2 730 isolates analysis. Now data looks like: Time analysis.","code":"our_data <- our_data %>% mutate(first = first_isolate(info = TRUE)) #> ℹ Determining first isolates using an episode length of 365 days #> ℹ Using column 'bacteria' as input for `col_mo`. #> ℹ Using column 'date' as input for `col_date`. #> ℹ Using column 'patient_id' as input for `col_patient_id`. #> ℹ Basing inclusion on all antimicrobial results, using a points threshold #> of 2 #> => Found 2,730 'phenotype-based' first isolates (91.0% of total where a #> microbial ID was available) our_data_1st <- our_data %>% filter(first == TRUE) our_data_1st <- our_data %>% filter_first_isolate() our_data_1st #> # A tibble: 2,730 × 9 #> patient_id hospital date bacteria AMX AMC CIP GEN first #> #> 1 J3 A 2012-11-21 B_ESCHR_COLI R I S S TRUE #> 2 R7 A 2018-04-03 B_KLBSL_PNMN R I S S TRUE #> 3 P3 A 2014-09-19 B_ESCHR_COLI R S S S TRUE #> 4 P10 A 2015-12-10 B_ESCHR_COLI S I S S TRUE #> 5 B7 A 2015-03-02 B_ESCHR_COLI S S S S TRUE #> 6 W3 A 2018-03-31 B_STPHY_AURS R S R S TRUE #> 7 M3 A 2015-10-25 B_ESCHR_COLI R S S S TRUE #> 8 J3 A 2019-06-19 B_ESCHR_COLI S S S S TRUE #> 9 G6 A 2015-04-27 B_STPHY_AURS S S S S TRUE #> 10 P4 A 2011-06-21 B_ESCHR_COLI S S S S TRUE #> # ℹ 2,720 more rows"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"analysing-the-data","dir":"Articles","previous_headings":"","what":"Analysing the data","title":"Conduct AMR data analysis","text":"base R summary() function gives good first impression, comes support new mo sir classes now data set:","code":"summary(our_data_1st) #> patient_id hospital date #> Length:2730 Length:2730 Min. :2011-01-01 #> Class :character Class :character 1st Qu.:2013-04-06 #> Mode :character Mode :character Median :2015-06-04 #> Mean :2015-06-09 #> 3rd Qu.:2017-08-14 #> Max. :2019-12-27 #> bacteria AMX AMC #> Class :mo Class:sir Class:sir #> :0 %S :40.1% (n=1071) %S :51.1% (n=1354) #> Unique:4 %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) #> #1 :B_ESCHR_COLI %I :17.0% (n=453) %I :12.7% (n=335) #> #2 :B_STPHY_AURS %R :42.9% (n=1147) %R :36.2% (n=959) #> #3 :B_STRPT_PNMN %NI : 0.0% (n=0) %NI : 0.0% (n=0) #> CIP GEN first #> Class:sir Class:sir Mode:logical #> %S :52.2% (n=1426) %S :60.7% (n=1656) TRUE:2730 #> %SDD : 0.0% (n=0) %SDD : 0.0% (n=0) #> %I : 6.5% (n=178) %I : 3.0% (n=83) #> %R :41.2% (n=1126) %R :36.3% (n=991) #> %NI : 0.0% (n=0) %NI : 0.0% (n=0) glimpse(our_data_1st) #> Rows: 2,730 #> Columns: 9 #> $ patient_id \"J3\", \"R7\", \"P3\", \"P10\", \"B7\", \"W3\", \"M3\", \"J3\", \"G6\", \"P4\"… #> $ hospital \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\", \"A\",… #> $ date 2012-11-21, 2018-04-03, 2014-09-19, 2015-12-10, 2015-03-02… #> $ bacteria \"B_ESCHR_COLI\", \"B_KLBSL_PNMN\", \"B_ESCHR_COLI\", \"B_ESCHR_COL… #> $ AMX R, R, R, S, S, R, R, S, S, S, S, R, S, S, R, R, R, R, S, R,… #> $ AMC I, I, S, I, S, S, S, S, S, S, S, S, S, S, S, S, S, R, S, S,… #> $ CIP S, S, S, S, S, R, S, S, S, S, S, S, S, S, S, S, S, S, S, S,… #> $ GEN S, S, S, S, S, S, S, S, S, S, S, R, S, S, S, S, S, S, S, S,… #> $ first TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE,… # number of unique values per column: sapply(our_data_1st, n_distinct) #> patient_id hospital date bacteria AMX AMC CIP #> 260 3 1854 4 4 4 3 #> GEN first #> 3 1"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"availability-of-species","dir":"Articles","previous_headings":"Analysing the data","what":"Availability of species","title":"Conduct AMR data analysis","text":"just get idea species distributed, create frequency table count() based name microorganisms:","code":"our_data %>% count(mo_name(bacteria), sort = TRUE) #> # A tibble: 4 × 2 #> `mo_name(bacteria)` n #> #> 1 Escherichia coli 1518 #> 2 Staphylococcus aureus 730 #> 3 Streptococcus pneumoniae 426 #> 4 Klebsiella pneumoniae 326 our_data_1st %>% count(mo_name(bacteria), sort = TRUE) #> # A tibble: 4 × 2 #> `mo_name(bacteria)` n #> #> 1 Escherichia coli 1326 #> 2 Staphylococcus aureus 684 #> 3 Streptococcus pneumoniae 401 #> 4 Klebsiella pneumoniae 319"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"select-and-filter-with-antibiotic-selectors","dir":"Articles","previous_headings":"Analysing the data","what":"Select and filter with antibiotic selectors","title":"Conduct AMR data analysis","text":"Using -called antibiotic class selectors, can select filter columns based antibiotic class antibiotic results :","code":"our_data_1st %>% select(date, aminoglycosides()) #> ℹ For `aminoglycosides()` using column 'GEN' (gentamicin) #> # A tibble: 2,730 × 2 #> date GEN #> #> 1 2012-11-21 S #> 2 2018-04-03 S #> 3 2014-09-19 S #> 4 2015-12-10 S #> 5 2015-03-02 S #> 6 2018-03-31 S #> 7 2015-10-25 S #> 8 2019-06-19 S #> 9 2015-04-27 S #> 10 2011-06-21 S #> # ℹ 2,720 more rows our_data_1st %>% select(bacteria, betalactams()) #> ℹ For `betalactams()` using columns 'AMX' (amoxicillin) and 'AMC' #> (amoxicillin/clavulanic acid) #> # A tibble: 2,730 × 3 #> bacteria AMX AMC #> #> 1 B_ESCHR_COLI R I #> 2 B_KLBSL_PNMN R I #> 3 B_ESCHR_COLI R S #> 4 B_ESCHR_COLI S I #> 5 B_ESCHR_COLI S S #> 6 B_STPHY_AURS R S #> 7 B_ESCHR_COLI R S #> 8 B_ESCHR_COLI S S #> 9 B_STPHY_AURS S S #> 10 B_ESCHR_COLI S S #> # ℹ 2,720 more rows our_data_1st %>% select(bacteria, where(is.sir)) #> # A tibble: 2,730 × 5 #> bacteria AMX AMC CIP GEN #> #> 1 B_ESCHR_COLI R I S S #> 2 B_KLBSL_PNMN R I S S #> 3 B_ESCHR_COLI R S S S #> 4 B_ESCHR_COLI S I S S #> 5 B_ESCHR_COLI S S S S #> 6 B_STPHY_AURS R S R S #> 7 B_ESCHR_COLI R S S S #> 8 B_ESCHR_COLI S S S S #> 9 B_STPHY_AURS S S S S #> 10 B_ESCHR_COLI S S S S #> # ℹ 2,720 more rows # filtering using AB selectors is also possible: our_data_1st %>% filter(any(aminoglycosides() == \"R\")) #> ℹ For `aminoglycosides()` using column 'GEN' (gentamicin) #> # A tibble: 991 × 9 #> patient_id hospital date bacteria AMX AMC CIP GEN first #> #> 1 J5 A 2017-12-25 B_STRPT_PNMN R S S R TRUE #> 2 X1 A 2017-07-04 B_STPHY_AURS R S S R TRUE #> 3 B3 A 2016-07-24 B_ESCHR_COLI S S S R TRUE #> 4 V7 A 2012-04-03 B_ESCHR_COLI S S S R TRUE #> 5 C9 A 2017-03-23 B_ESCHR_COLI S S S R TRUE #> 6 R1 A 2018-06-10 B_STPHY_AURS S S S R TRUE #> 7 S2 A 2013-07-19 B_STRPT_PNMN S S S R TRUE #> 8 P5 A 2019-03-09 B_STPHY_AURS S S S R TRUE #> 9 Q8 A 2019-08-10 B_STPHY_AURS S S S R TRUE #> 10 K5 A 2013-03-15 B_STRPT_PNMN S S S R TRUE #> # ℹ 981 more rows our_data_1st %>% filter(all(betalactams() == \"R\")) #> ℹ For `betalactams()` using columns 'AMX' (amoxicillin) and 'AMC' #> (amoxicillin/clavulanic acid) #> # A tibble: 461 × 9 #> patient_id hospital date bacteria AMX AMC CIP GEN first #> #> 1 M7 A 2013-07-22 B_STRPT_PNMN R R S S TRUE #> 2 R10 A 2013-12-20 B_STPHY_AURS R R S S TRUE #> 3 R7 A 2015-10-25 B_STPHY_AURS R R S S TRUE #> 4 R8 A 2019-10-25 B_STPHY_AURS R R S S TRUE #> 5 B6 A 2016-11-20 B_ESCHR_COLI R R R R TRUE #> 6 I7 A 2015-08-19 B_ESCHR_COLI R R S S TRUE #> 7 N3 A 2014-12-29 B_STRPT_PNMN R R R S TRUE #> 8 Q2 A 2019-09-22 B_ESCHR_COLI R R S S TRUE #> 9 X7 A 2011-03-20 B_ESCHR_COLI R R S R TRUE #> 10 V1 A 2018-08-07 B_STPHY_AURS R R S S TRUE #> # ℹ 451 more rows # even works in base R (since R 3.0): our_data_1st[all(betalactams() == \"R\"), ] #> ℹ For `betalactams()` using columns 'AMX' (amoxicillin) and 'AMC' #> (amoxicillin/clavulanic acid) #> # A tibble: 461 × 9 #> patient_id hospital date bacteria AMX AMC CIP GEN first #> #> 1 M7 A 2013-07-22 B_STRPT_PNMN R R S S TRUE #> 2 R10 A 2013-12-20 B_STPHY_AURS R R S S TRUE #> 3 R7 A 2015-10-25 B_STPHY_AURS R R S S TRUE #> 4 R8 A 2019-10-25 B_STPHY_AURS R R S S TRUE #> 5 B6 A 2016-11-20 B_ESCHR_COLI R R R R TRUE #> 6 I7 A 2015-08-19 B_ESCHR_COLI R R S S TRUE #> 7 N3 A 2014-12-29 B_STRPT_PNMN R R R S TRUE #> 8 Q2 A 2019-09-22 B_ESCHR_COLI R R S S TRUE #> 9 X7 A 2011-03-20 B_ESCHR_COLI R R S R TRUE #> 10 V1 A 2018-08-07 B_STPHY_AURS R R S S TRUE #> # ℹ 451 more rows"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"generate-antibiograms","dir":"Articles","previous_headings":"Analysing the data","what":"Generate antibiograms","title":"Conduct AMR data analysis","text":"Since AMR v2.0 (March 2023), easy create different types antibiograms, support 20 different languages. four antibiogram types, proposed Klinker et al. (2021, DOI 10.1177/20499361211011373), supported new antibiogram() function: Traditional Antibiogram (TA) e.g, susceptibility Pseudomonas aeruginosa piperacillin/tazobactam (TZP) Combination Antibiogram (CA) e.g, sdditional susceptibility Pseudomonas aeruginosa TZP + tobramycin versus TZP alone Syndromic Antibiogram (SA) e.g, susceptibility Pseudomonas aeruginosa TZP among respiratory specimens (obtained among ICU patients ) Weighted-Incidence Syndromic Combination Antibiogram (WISCA) e.g, susceptibility Pseudomonas aeruginosa TZP among respiratory specimens (obtained among ICU patients ) male patients age >=65 years heart failure section, show use antibiogram() function create antibiogram types. starters, included example_isolates data set looks like:","code":"example_isolates #> # A tibble: 2,000 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA #> # ℹ 1,990 more rows #> # ℹ 36 more variables: AMC , AMP , TZP , CZO , FEP , #> # CXM , FOX , CTX , CAZ , CRO , GEN , #> # TOB , AMK , KAN , TMP , SXT , NIT , #> # FOS , LNZ , CIP , MFX , VAN , TEC , #> # TCY , TGC , DOX , ERY , CLI , AZM , #> # IPM , MEM , MTR , CHL , COL , MUP , …"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"traditional-antibiogram","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Traditional Antibiogram","title":"Conduct AMR data analysis","text":"create traditional antibiogram, simply state antibiotics used. antibiotics argument antibiogram() function supports (combination) previously mentioned antibiotic class selectors: Notice antibiogram() function automatically prints right format using Quarto R Markdown (page), even applies italics taxonomic names (using italicise_taxonomy() internally). also uses language OS either English, Arabic, Bengali, Chinese, Czech, Danish, Dutch, Finnish, French, German, Greek, Hindi, Indonesian, Italian, Japanese, Korean, Norwegian, Polish, Portuguese, Romanian, Russian, Spanish, Swahili, Swedish, Turkish, Ukrainian, Urdu, Vietnamese. next example, force language Spanish using language argument:","code":"antibiogram(example_isolates, antibiotics = c(aminoglycosides(), carbapenems())) #> ℹ For `aminoglycosides()` using columns 'GEN' (gentamicin), 'TOB' #> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ For `carbapenems()` using columns 'IPM' (imipenem) and 'MEM' (meropenem) antibiogram(example_isolates, mo_transform = \"gramstain\", antibiotics = aminoglycosides(), ab_transform = \"name\", language = \"es\") #> ℹ For `aminoglycosides()` using columns 'GEN' (gentamicin), 'TOB' #> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin)"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"combined-antibiogram","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Combined Antibiogram","title":"Conduct AMR data analysis","text":"create combined antibiogram, use antibiotic codes names plus + character like :","code":"combined_ab <- antibiogram(example_isolates, antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"), ab_transform = NULL) combined_ab"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"syndromic-antibiogram","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Syndromic Antibiogram","title":"Conduct AMR data analysis","text":"create syndromic antibiogram, syndromic_group argument must used. can column data, e.g. ifelse() calculations based certain columns:","code":"antibiogram(example_isolates, antibiotics = c(aminoglycosides(), carbapenems()), syndromic_group = \"ward\") #> ℹ For `aminoglycosides()` using columns 'GEN' (gentamicin), 'TOB' #> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ For `carbapenems()` using columns 'IPM' (imipenem) and 'MEM' (meropenem)"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"weighted-incidence-syndromic-combination-antibiogram-wisca","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Weighted-Incidence Syndromic Combination Antibiogram (WISCA)","title":"Conduct AMR data analysis","text":"create Weighted-Incidence Syndromic Combination Antibiogram (WISCA), simply set wisca = TRUE antibiogram() function, use dedicated wisca() function. Unlike traditional antibiograms, WISCA provides syndrome-based susceptibility estimates, weighted pathogen incidence antimicrobial susceptibility patterns. WISCA uses Bayesian decision model integrate data multiple pathogens, improving empirical therapy guidance, especially low-incidence infections. pathogen-agnostic, meaning results syndrome-based rather stratified microorganism. reliable results, ensure data includes first isolates (use first_isolate()) consider filtering top n species (use top_n_microorganisms()), WISCA outcomes meaningful based robust incidence estimates. patient- syndrome-specific WISCA, run function grouped tibble, .e., using group_by() first:","code":"example_isolates %>% wisca(antibiotics = c(\"TZP\", \"TZP+TOB\", \"TZP+GEN\"), minimum = 10) # Recommended threshold: ≥30 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\"))"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"plotting-antibiograms","dir":"Articles","previous_headings":"Analysing the data > Generate antibiograms","what":"Plotting antibiograms","title":"Conduct AMR data analysis","text":"Antibiograms can plotted using autoplot() ggplot2 packages, since AMR package provides extension function: calculate antimicrobial resistance sensible way, also correcting results, use resistance() susceptibility() functions.","code":"autoplot(combined_ab)"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"resistance-percentages","dir":"Articles","previous_headings":"Analysing the data","what":"Resistance percentages","title":"Conduct AMR data analysis","text":"functions resistance() susceptibility() can used calculate antimicrobial resistance susceptibility. specific analyses, functions proportion_S(), proportion_SI(), proportion_I(), proportion_IR() proportion_R() can used determine proportion specific antimicrobial outcome. functions contain minimum argument, denoting minimum required number test results returning value. functions otherwise return NA. default minimum = 30, following CLSI M39-A4 guideline applying microbial epidemiology. per EUCAST guideline 2019, calculate resistance proportion R (proportion_R(), equal resistance()) susceptibility proportion S (proportion_SI(), equal susceptibility()). functions can used : can used conjunction group_by() summarise(), dplyr package:","code":"our_data_1st %>% resistance(AMX) #> [1] 0.4294272 our_data_1st %>% group_by(hospital) %>% summarise(amoxicillin = resistance(AMX)) #> # A tibble: 3 × 2 #> hospital amoxicillin #> #> 1 A 0.341 #> 2 B 0.586 #> 3 C 0.370"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"interpreting-mic-and-disk-diffusion-values","dir":"Articles","previous_headings":"Analysing the data","what":"Interpreting MIC and Disk Diffusion Values","title":"Conduct AMR data analysis","text":"Minimal inhibitory concentration (MIC) values disk diffusion diameters can interpreted clinical breakpoints (SIR) using .sir(). ’s example randomly generated MIC values Klebsiella pneumoniae ciprofloxacin: allows direct interpretation according EUCAST CLSI breakpoints, facilitating automated AMR data processing.","code":"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 #> # A tibble: 100 × 2 #> MIC SIR #> #> 1 <=0.0001 S #> 2 0.0160 S #> 3 >=8.0000 R #> 4 0.0320 S #> 5 0.0080 S #> 6 64.0000 R #> 7 0.0080 S #> 8 0.1250 S #> 9 0.0320 S #> 10 0.0002 S #> # ℹ 90 more rows"},{"path":"https://amr-for-r.org/articles/AMR.html","id":"plotting-mic-and-sir-interpretations","dir":"Articles","previous_headings":"Analysing the data","what":"Plotting MIC and SIR Interpretations","title":"Conduct AMR data analysis","text":"can visualise MIC distributions SIR interpretations using ggplot2, using new scale_y_mic() y-axis scale_colour_sir() colour-code SIR categories. plot provides intuitive way assess susceptibility patterns across different groups incorporating clinical breakpoints. straightforward less manual approach, ggplot2’s function autoplot() extended package directly plot MIC disk diffusion values: Author: Dr. Matthijs Berends, 23rd Feb 2025","code":"# 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)\") 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\")"},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"AMR for Python","text":"AMR package R powerful tool antimicrobial resistance (AMR) analysis. provides extensive features handling microbial antimicrobial data. However, work primarily Python, now intuitive option available: AMR Python package. Python package wrapper around AMR R package. uses rpy2 package internally. Despite need R installed, Python users can now easily work AMR data directly Python code.","code":""},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"prerequisites","dir":"Articles","previous_headings":"","what":"Prerequisites","title":"AMR for Python","text":"package tested virtual environment (venv). can set environment running: can activate environment, venv ready work .","code":"# linux and macOS: python -m venv /path/to/new/virtual/environment # Windows: python -m venv C:\\path\\to\\new\\virtual\\environment"},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"install-amr","dir":"Articles","previous_headings":"","what":"Install AMR","title":"AMR for Python","text":"Since Python package available official Python Package Index, can just run: Make sure R installed. need install AMR R package, installed automatically. Linux: macOS (using Homebrew): Windows, visit CRAN download page download install R.","code":"pip install AMR # Ubuntu / Debian sudo apt install r-base # Fedora: sudo dnf install R # CentOS/RHEL sudo yum install R brew install r"},{"path":[]},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"cleaning-taxonomy","dir":"Articles","previous_headings":"Examples of Usage","what":"Cleaning Taxonomy","title":"AMR for Python","text":"’s example demonstrates clean microorganism drug names using AMR Python package:","code":"import pandas as pd import AMR # Sample data data = { \"MOs\": ['E. coli', 'ESCCOL', 'esco', 'Esche coli'], \"Drug\": ['Cipro', 'CIP', 'J01MA02', 'Ciproxin'] } df = pd.DataFrame(data) # Use AMR functions to clean microorganism and drug names df['MO_clean'] = AMR.mo_name(df['MOs']) df['Drug_clean'] = AMR.ab_name(df['Drug']) # Display the results print(df)"},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"explanation","dir":"Articles","previous_headings":"Examples of Usage > Cleaning Taxonomy","what":"Explanation","title":"AMR for Python","text":"mo_name: function standardises microorganism names. , different variations Escherichia coli (“E. coli”, “ESCCOL”, “esco”, “Esche coli”) converted correct, standardised form, “Escherichia coli”. ab_name: Similarly, function standardises antimicrobial names. different representations ciprofloxacin (e.g., “Cipro”, “CIP”, “J01MA02”, “Ciproxin”) converted standard name, “Ciprofloxacin”.","code":""},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"calculating-amr","dir":"Articles","previous_headings":"Examples of Usage","what":"Calculating AMR","title":"AMR for Python","text":"","code":"import AMR import pandas as pd df = AMR.example_isolates result = AMR.resistance(df[\"AMX\"]) print(result) [0.59555556]"},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"generating-antibiograms","dir":"Articles","previous_headings":"Examples of Usage","what":"Generating Antibiograms","title":"AMR for Python","text":"One core functions AMR package generating antibiogram, table summarises antimicrobial susceptibility bacterial isolates. ’s can generate antibiogram Python: example, generate antibiogram selecting various antibiotics.","code":"result2a = AMR.antibiogram(df[[\"mo\", \"AMX\", \"CIP\", \"TZP\"]]) print(result2a) result2b = AMR.antibiogram(df[[\"mo\", \"AMX\", \"CIP\", \"TZP\"]], mo_transform = \"gramstain\") print(result2b)"},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"taxonomic-data-sets-now-in-python","dir":"Articles","previous_headings":"Examples of Usage","what":"Taxonomic Data Sets Now in Python!","title":"AMR for Python","text":"Python user, might like important data sets AMR R package, microorganisms, antimicrobials, clinical_breakpoints, example_isolates, now available regular Python data frames:","code":"AMR.microorganisms AMR.antimicrobials"},{"path":"https://amr-for-r.org/articles/AMR_for_Python.html","id":"conclusion","dir":"Articles","previous_headings":"","what":"Conclusion","title":"AMR for Python","text":"AMR Python package, Python users can now effortlessly call R functions AMR R package. eliminates need complex rpy2 configurations provides clean, easy--use interface antimicrobial resistance analysis. examples provided demonstrate can applied typical workflows, standardising microorganism antimicrobial names calculating resistance. just running import AMR, users can seamlessly integrate robust features R AMR package Python workflows. Whether ’re cleaning data analysing resistance patterns, AMR Python package makes easy work AMR data Python.","code":""},{"path":"https://amr-for-r.org/articles/AMR_with_tidymodels.html","id":"example-1-using-antimicrobial-selectors","dir":"Articles","previous_headings":"","what":"Example 1: Using Antimicrobial Selectors","title":"AMR with tidymodels","text":"leveraging power tidymodels AMR package, ’ll build reproducible machine learning workflow predict Gramstain microorganism two important antibiotic classes: aminoglycosides beta-lactams.","code":""},{"path":"https://amr-for-r.org/articles/AMR_with_tidymodels.html","id":"objective","dir":"Articles","previous_headings":"Example 1: Using Antimicrobial Selectors","what":"Objective","title":"AMR with tidymodels","text":"goal build predictive model using tidymodels framework determine Gramstain microorganism based microbial data. : Preprocess data using selector functions aminoglycosides() betalactams(). Define logistic regression model prediction. Use structured tidymodels workflow preprocess, train, evaluate model.","code":""},{"path":"https://amr-for-r.org/articles/AMR_with_tidymodels.html","id":"data-preparation","dir":"Articles","previous_headings":"Example 1: Using Antimicrobial Selectors","what":"Data Preparation","title":"AMR with tidymodels","text":"begin loading required libraries preparing example_isolates dataset AMR package. Prepare data: Explanation: aminoglycosides() betalactams() dynamically select columns antimicrobials classes. drop_na() ensures model receives complete cases training.","code":"# Load required libraries library(AMR) # For AMR data analysis library(tidymodels) # For machine learning workflows, and data manipulation (dplyr, tidyr, ...) # Your data could look like this: example_isolates #> # A tibble: 2,000 × 46 #> date patient age gender ward mo PEN OXA FLC AMX #> #> 1 2002-01-02 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 2 2002-01-03 A77334 65 F Clinical B_ESCHR_COLI R NA NA NA #> 3 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 4 2002-01-07 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 5 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 6 2002-01-13 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 7 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 8 2002-01-14 462729 78 M Clinical B_STPHY_AURS R NA S R #> 9 2002-01-16 067927 45 F ICU B_STPHY_EPDR R NA R NA #> 10 2002-01-17 858515 79 F ICU B_STPHY_EPDR R NA S NA #> # ℹ 1,990 more rows #> # ℹ 36 more variables: AMC , AMP , TZP , CZO , FEP , #> # CXM , FOX , CTX , CAZ , CRO , GEN , #> # TOB , AMK , KAN , TMP , SXT , NIT , #> # FOS , LNZ , CIP , MFX , VAN , TEC , #> # TCY , TGC , DOX , ERY , CLI , AZM , #> # IPM , MEM , MTR , CHL , COL , MUP , … # Select relevant columns for prediction data <- example_isolates %>% # select AB results dynamically select(mo, aminoglycosides(), betalactams()) %>% # replace NAs with NI (not-interpretable) mutate(across(where(is.sir), ~replace_na(.x, \"NI\")), # make factors of SIR columns across(where(is.sir), as.integer), # get Gramstain of microorganisms mo = as.factor(mo_gramstain(mo))) %>% # drop NAs - the ones without a Gramstain (fungi, etc.) drop_na() #> ℹ For `aminoglycosides()` using columns 'GEN' (gentamicin), 'TOB' #> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ For `betalactams()` using columns 'PEN' (benzylpenicillin), 'OXA' #> (oxacillin), 'FLC' (flucloxacillin), 'AMX' (amoxicillin), 'AMC' #> (amoxicillin/clavulanic acid), 'AMP' (ampicillin), 'TZP' #> (piperacillin/tazobactam), 'CZO' (cefazolin), 'FEP' (cefepime), 'CXM' #> (cefuroxime), 'FOX' (cefoxitin), 'CTX' (cefotaxime), 'CAZ' (ceftazidime), #> 'CRO' (ceftriaxone), 'IPM' (imipenem), and 'MEM' (meropenem)"},{"path":"https://amr-for-r.org/articles/AMR_with_tidymodels.html","id":"defining-the-workflow","dir":"Articles","previous_headings":"Example 1: Using Antimicrobial Selectors","what":"Defining the Workflow","title":"AMR with tidymodels","text":"now define tidymodels workflow, consists three steps: preprocessing, model specification, fitting.","code":""},{"path":"https://amr-for-r.org/articles/AMR_with_tidymodels.html","id":"preprocessing-with-a-recipe","dir":"Articles","previous_headings":"Example 1: Using Antimicrobial Selectors > Defining the Workflow","what":"1. Preprocessing with a Recipe","title":"AMR with tidymodels","text":"create recipe preprocess data modelling. recipe includes least one preprocessing operation, like step_corr(), necessary parameters can estimated training set using prep(): Explanation: recipe(mo ~ ., data = data) take mo column outcome columns predictors. step_corr() removes predictors (.e., antibiotic columns) higher correlation 90%. Notice recipe contains just antimicrobial selector functions - need define columns specifically. preparation (retrieved prep()) can see columns variables ‘AMX’ ‘CTX’ removed correlate much existing, variables.","code":"# Define the recipe for data preprocessing resistance_recipe <- recipe(mo ~ ., data = data) %>% step_corr(c(aminoglycosides(), betalactams()), threshold = 0.9) resistance_recipe #> #> ── Recipe ────────────────────────────────────────────────────────────────────── #> #> ── Inputs #> Number of variables by role #> outcome: 1 #> predictor: 20 #> #> ── Operations #> • Correlation filter on: c(aminoglycosides(), betalactams()) prep(resistance_recipe) #> ℹ For `aminoglycosides()` using columns 'GEN' (gentamicin), 'TOB' #> (tobramycin), 'AMK' (amikacin), and 'KAN' (kanamycin) #> ℹ For `betalactams()` using columns 'PEN' (benzylpenicillin), 'OXA' #> (oxacillin), 'FLC' (flucloxacillin), 'AMX' (amoxicillin), 'AMC' #> (amoxicillin/clavulanic acid), 'AMP' (ampicillin), 'TZP' #> (piperacillin/tazobactam), 'CZO' (cefazolin), 'FEP' (cefepime), 'CXM' #> (cefuroxime), 'FOX' (cefoxitin), 'CTX' (cefotaxime), 'CAZ' (ceftazidime), #> 'CRO' (ceftriaxone), 'IPM' (imipenem), and 'MEM' (meropenem) #> #> ── Recipe ────────────────────────────────────────────────────────────────────── #> #> ── Inputs #> Number of variables by role #> outcome: 1 #> predictor: 20 #> #> ── Training information #> Training data contained 1968 data points and no incomplete rows. #> #> ── Operations #> • Correlation filter on: AMX CTX | Trained"},{"path":"https://amr-for-r.org/articles/AMR_with_tidymodels.html","id":"specifying-the-model","dir":"Articles","previous_headings":"Example 1: Using Antimicrobial Selectors > Defining the Workflow","what":"2. Specifying the Model","title":"AMR with tidymodels","text":"define logistic regression model since resistance prediction binary classification task. Explanation: logistic_reg() sets logistic regression model. set_engine(\"glm\") specifies use R’s built-GLM engine.","code":"# Specify a logistic regression model logistic_model <- logistic_reg() %>% set_engine(\"glm\") # Use the Generalised Linear Model engine logistic_model #> Logistic Regression Model Specification (classification) #> #> Computational engine: glm"},{"path":"https://amr-for-r.org/articles/AMR_with_tidymodels.html","id":"building-the-workflow","dir":"Articles","previous_headings":"Example 1: Using Antimicrobial Selectors > Defining the Workflow","what":"3. Building the Workflow","title":"AMR with tidymodels","text":"bundle recipe model together workflow, organises entire modelling process.","code":"# Combine the recipe and model into a workflow resistance_workflow <- workflow() %>% add_recipe(resistance_recipe) %>% # Add the preprocessing recipe add_model(logistic_model) # Add the logistic regression model resistance_workflow #> ══ Workflow ════════════════════════════════════════════════════════════════════ #> Preprocessor: Recipe #> Model: logistic_reg() #> #> ── Preprocessor ──────────────────────────────────────────────────────────────── #> 1 Recipe Step #> #> • step_corr() #> #> ── Model ─────────────────────────────────────────────────────────────────────── #> Logistic Regression Model Specification (classification) #> #> Computational engine: glm"},{"path":"https://amr-for-r.org/articles/AMR_with_tidymodels.html","id":"training-and-evaluating-the-model","dir":"Articles","previous_headings":"Example 1: Using Antimicrobial Selectors","what":"Training and Evaluating the Model","title":"AMR with tidymodels","text":"train model, split data training testing sets. , fit workflow training set evaluate performance. Explanation: initial_split() splits data training testing sets. fit() trains workflow training set. Notice fit(), antimicrobial selector functions internally called . training, functions called since stored recipe. Next, evaluate model testing data. Explanation: predict() generates predictions testing set. metrics() computes evaluation metrics like accuracy kappa. appears can predict Gram stain 99.5% accuracy based AMR results aminoglycosides beta-lactam antibiotics. ROC curve looks like :","code":"# Split data into training and testing sets set.seed(123) # For reproducibility data_split <- initial_split(data, prop = 0.8) # 80% training, 20% testing training_data <- training(data_split) # Training set testing_data <- testing(data_split) # Testing set # Fit the workflow to the training data fitted_workflow <- resistance_workflow %>% fit(training_data) # Train the model # Make predictions on the testing set predictions <- fitted_workflow %>% predict(testing_data) # Generate predictions probabilities <- fitted_workflow %>% predict(testing_data, type = \"prob\") # Generate probabilities predictions <- predictions %>% bind_cols(probabilities) %>% bind_cols(testing_data) # Combine with true labels predictions #> # A tibble: 394 × 24 #> .pred_class `.pred_Gram-negative` `.pred_Gram-positive` mo GEN TOB #> #> 1 Gram-positive 1.07e- 1 8.93 e- 1 Gram-p… 5 5 #> 2 Gram-positive 3.17e- 8 1.000e+ 0 Gram-p… 5 1 #> 3 Gram-negative 9.99e- 1 1.42 e- 3 Gram-n… 5 5 #> 4 Gram-positive 2.22e-16 1 e+ 0 Gram-p… 5 5 #> 5 Gram-negative 9.46e- 1 5.42 e- 2 Gram-n… 5 5 #> 6 Gram-positive 1.07e- 1 8.93 e- 1 Gram-p… 5 5 #> 7 Gram-positive 2.22e-16 1 e+ 0 Gram-p… 1 5 #> 8 Gram-positive 2.22e-16 1 e+ 0 Gram-p… 4 4 #> 9 Gram-negative 1 e+ 0 2.22 e-16 Gram-n… 1 1 #> 10 Gram-positive 6.05e-11 1.000e+ 0 Gram-p… 4 4 #> # ℹ 384 more rows #> # ℹ 18 more variables: AMK , KAN , PEN , OXA , FLC , #> # AMX , AMC , AMP , TZP , CZO , FEP , #> # CXM , FOX , CTX , CAZ , CRO , IPM , MEM # Evaluate model performance metrics <- predictions %>% metrics(truth = mo, estimate = .pred_class) # Calculate performance metrics metrics #> # A tibble: 2 × 3 #> .metric .estimator .estimate #> #> 1 accuracy binary 0.995 #> 2 kap binary 0.989 # To assess some other model properties, you can make our own `metrics()` function our_metrics <- metric_set(accuracy, kap, ppv, npv) # add Positive Predictive Value and Negative Predictive Value metrics2 <- predictions %>% our_metrics(truth = mo, estimate = .pred_class) # run again on our `our_metrics()` function metrics2 #> # A tibble: 4 × 3 #> .metric .estimator .estimate #> #> 1 accuracy binary 0.995 #> 2 kap binary 0.989 #> 3 ppv binary 0.987 #> 4 npv binary 1 predictions %>% roc_curve(mo, `.pred_Gram-negative`) %>% autoplot()"},{"path":"https://amr-for-r.org/articles/AMR_with_tidymodels.html","id":"conclusion","dir":"Articles","previous_headings":"Example 1: Using Antimicrobial Selectors","what":"Conclusion","title":"AMR with tidymodels","text":"example, demonstrated build machine learning pipeline tidymodels framework AMR package. combining selector functions like aminoglycosides() betalactams() tidymodels, efficiently prepared data, trained model, evaluated performance. workflow extensible antimicrobial classes resistance patterns, empowering users analyse AMR data systematically reproducibly.","code":""},{"path":"https://amr-for-r.org/articles/AMR_with_tidymodels.html","id":"example-2-predicting-esbl-presence-using-raw-mics","dir":"Articles","previous_headings":"","what":"Example 2: Predicting ESBL Presence Using Raw MICs","title":"AMR with tidymodels","text":"second example, demonstrate use columns directly tidymodels workflows using AMR-specific recipe steps. includes transformation log2 scale using step_mic_log2(), prepares MIC values use classification models. approach idea formed basis publication DOI: 10.3389/fmicb.2025.1582703 model presence extended-spectrum beta-lactamases (ESBL) based MIC values.","code":""},{"path":"https://amr-for-r.org/articles/AMR_with_tidymodels.html","id":"objective-1","dir":"Articles","previous_headings":"Example 2: Predicting ESBL Presence Using Raw MICs","what":"Objective","title":"AMR with tidymodels","text":"goal : Use raw MIC values predict whether bacterial isolate produces ESBL. Apply AMR-aware preprocessing tidymodels recipe. Train classification model evaluate predictive performance.","code":""},{"path":"https://amr-for-r.org/articles/AMR_with_tidymodels.html","id":"data-preparation-1","dir":"Articles","previous_headings":"Example 2: Predicting ESBL Presence Using Raw MICs","what":"Data Preparation","title":"AMR with tidymodels","text":"use esbl_isolates dataset comes AMR package. Explanation: esbl_isolates: Contains MIC test results ESBL status isolate. mutate(esbl = ...): Converts target column ordered factor classification.","code":"# Load required libraries library(AMR) library(tidymodels) # View the esbl_isolates data set esbl_isolates #> # A tibble: 500 × 19 #> esbl genus AMC AMP TZP CXM FOX CTX CAZ GEN TOB TMP SXT #>