How to conduct AMR analysis
Matthijs S. Berends
-23 November 2019
+29 November 2019
AMR.RmdNote: values on this page will change with every website update since they are based on randomly created values and the page was written in R Markdown. However, the methodology remains unchanged. This page was generated on 23 November 2019.
+Note: values on this page will change with every website update since they are based on randomly created values and the page was written in R Markdown. However, the methodology remains unchanged. This page was generated on 29 November 2019.
Introduction
@@ -212,21 +212,21 @@So, we can draw at least two conclusions immediately. From a data scientists perspective, the data looks clean: only values M and F. From a researchers perspective: there are slightly more men. Nothing we didn’t already know.
The data is already quite clean, but we still need to transform some variables. The bacteria column now consists of text, and we want to add more variables based on microbial IDs later on. So, we will transform this column to valid IDs. The mutate() function of the dplyr package makes this really easy:
data <- data %>%
@@ -422,8 +422,8 @@
# Other rules by this AMR package
# Non-EUCAST: inherit amoxicillin results for unavailable ampicillin (no changes)
# Non-EUCAST: inherit ampicillin results for unavailable amoxicillin (no changes)
-# Non-EUCAST: set amoxicillin/clav acid = S where ampicillin = S (2,952 values changed)
-# Non-EUCAST: set ampicillin = R where amoxicillin/clav acid = R (155 values changed)
+# Non-EUCAST: set amoxicillin/clav acid = S where ampicillin = S (2,987 values changed)
+# Non-EUCAST: set ampicillin = R where amoxicillin/clav acid = R (122 values changed)
# Non-EUCAST: set piperacillin = R where piperacillin/tazobactam = R (no changes)
# Non-EUCAST: set piperacillin/tazobactam = S where piperacillin = S (no changes)
# Non-EUCAST: set trimethoprim = R where trimethoprim/sulfa = R (no changes)
@@ -448,31 +448,31 @@
# Pasteurella multocida (no changes)
# Staphylococcus (no changes)
# Streptococcus groups A, B, C, G (no changes)
-# Streptococcus pneumoniae (984 values changed)
+# Streptococcus pneumoniae (1,064 values changed)
# Viridans group streptococci (no changes)
#
# EUCAST Expert Rules, Intrinsic Resistance and Exceptional Phenotypes (v3.1, 2016)
-# Table 01: Intrinsic resistance in Enterobacteriaceae (1,309 values changed)
+# Table 01: Intrinsic resistance in Enterobacteriaceae (1,300 values changed)
# Table 02: Intrinsic resistance in non-fermentative Gram-negative bacteria (no changes)
# Table 03: Intrinsic resistance in other Gram-negative bacteria (no changes)
-# Table 04: Intrinsic resistance in Gram-positive bacteria (2,719 values changed)
+# Table 04: Intrinsic resistance in Gram-positive bacteria (2,722 values changed)
# Table 08: Interpretive rules for B-lactam agents and Gram-positive cocci (no changes)
# Table 09: Interpretive rules for B-lactam agents and Gram-negative rods (no changes)
# Table 11: Interpretive rules for macrolides, lincosamides, and streptogramins (no changes)
# Table 12: Interpretive rules for aminoglycosides (no changes)
# Table 13: Interpretive rules for quinolones (no changes)
#
-# --------------------------------------------------------------------------
-# EUCAST rules affected 6,464 out of 20,000 rows, making a total of 8,119 edits
+# -------------------------------------------------------------------------------
+# EUCAST rules affected 6,539 out of 20,000 rows, making a total of 8,195 edits
# => added 0 test results
#
-# => changed 8,119 test results
-# - 109 test results changed from S to I
-# - 4,710 test results changed from S to R
-# - 1,166 test results changed from I to S
-# - 348 test results changed from I to R
-# - 1,786 test results changed from R to S
-# --------------------------------------------------------------------------
+# => changed 8,195 test results
+# - 125 test results changed from S to I
+# - 4,759 test results changed from S to R
+# - 1,206 test results changed from I to S
+# - 324 test results changed from I to R
+# - 1,781 test results changed from R to S
+# -------------------------------------------------------------------------------
#
# Use eucast_rules(..., verbose = TRUE) (on your original data) to get a data.frame with all specified edits instead.So only 28.3% is suitable for resistance analysis! We can now filter on it with the filter() function, also from the dplyr package:
So only 28.4% is suitable for resistance analysis! We can now filter on it with the filter() function, also from the dplyr package:
For future use, the above two syntaxes can be shortened with the filter_first_isolate() function:
First weighted isolates
-We made a slight twist to the CLSI algorithm, to take into account the antimicrobial susceptibility profile. Have a look at all isolates of patient T6, sorted on date:
+We made a slight twist to the CLSI algorithm, to take into account the antimicrobial susceptibility profile. Have a look at all isolates of patient N6, sorted on date:
| isolate | @@ -526,19 +526,19 @@|||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | -2010-02-28 | -T6 | +2010-02-23 | +N6 | B_ESCHR_COLI | -S | -S | -S | R | +S | +S | +S | TRUE |
| 2 | -2010-03-01 | -T6 | +2010-04-09 | +N6 | B_ESCHR_COLI | S | S | @@ -548,21 +548,21 @@||||||
| 3 | -2010-05-20 | -T6 | +2010-04-23 | +N6 | B_ESCHR_COLI | -S | -S | R | +R | +S | S | FALSE | |
| 4 | -2010-06-21 | -T6 | +2010-05-12 | +N6 | B_ESCHR_COLI | -S | +R | S | R | R | @@ -570,10 +570,10 @@|||
| 5 | -2010-09-24 | -T6 | +2010-06-27 | +N6 | B_ESCHR_COLI | -R | +I | S | S | S | @@ -581,52 +581,52 @@|||
| 6 | -2010-11-18 | -T6 | +2010-08-09 | +N6 | B_ESCHR_COLI | R | S | S | -R | +S | FALSE | ||
| 7 | -2011-02-07 | -T6 | +2010-09-01 | +N6 | B_ESCHR_COLI | +I | +S | R | S | -S | -S | FALSE | |
| 8 | -2011-03-09 | -T6 | +2010-10-18 | +N6 | B_ESCHR_COLI | S | S | -R | -S | -TRUE | -|||
| 9 | -2011-03-23 | -T6 | -B_ESCHR_COLI | -R | -S | S | S | FALSE | |||||
| 9 | +2011-01-18 | +N6 | +B_ESCHR_COLI | +R | +I | +R | +S | +FALSE | +|||||
| 10 | -2011-04-01 | -T6 | +2011-02-15 | +N6 | B_ESCHR_COLI | S | S | @@ -636,7 +636,7 @@
Only 2 isolates are marked as ‘first’ according to CLSI guideline. But when reviewing the antibiogram, it is obvious that some isolates are absolutely different strains and should be included too. This is why we weigh isolates, based on their antibiogram. The key_antibiotics() function adds a vector with 18 key antibiotics: 6 broad spectrum ones, 6 small spectrum for Gram negatives and 6 small spectrum for Gram positives. These can be defined by the user.
Only 1 isolates are marked as ‘first’ according to CLSI guideline. But when reviewing the antibiogram, it is obvious that some isolates are absolutely different strains and should be included too. This is why we weigh isolates, based on their antibiogram. The key_antibiotics() function adds a vector with 18 key antibiotics: 6 broad spectrum ones, 6 small spectrum for Gram negatives and 6 small spectrum for Gram positives. These can be defined by the user.
If a column exists with a name like ‘key(…)ab’ the first_isolate() function will automatically use it and determine the first weighted isolates. Mind the NOTEs in below output:
data <- data %>%
mutate(keyab = key_antibiotics(.)) %>%
@@ -647,7 +647,7 @@
# NOTE: Using column `patient_id` as input for `col_patient_id`.
# NOTE: Using column `keyab` as input for `col_keyantibiotics`. Use col_keyantibiotics = FALSE to prevent this.
# [Criterion] Inclusion based on key antibiotics, ignoring I
-# => Found 15,096 first weighted isolates (75.5% of total)| isolate | @@ -664,20 +664,20 @@||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | -2010-02-28 | -T6 | +2010-02-23 | +N6 | B_ESCHR_COLI | -S | -S | -S | R | +S | +S | +S | TRUE | TRUE |
| 2 | -2010-03-01 | -T6 | +2010-04-09 | +N6 | B_ESCHR_COLI | S | S | @@ -688,22 +688,22 @@|||||||
| 3 | -2010-05-20 | -T6 | +2010-04-23 | +N6 | B_ESCHR_COLI | -S | -S | R | +R | +S | S | FALSE | TRUE | |
| 4 | -2010-06-21 | -T6 | +2010-05-12 | +N6 | B_ESCHR_COLI | -S | +R | S | R | R | @@ -712,10 +712,10 @@||||
| 5 | -2010-09-24 | -T6 | +2010-06-27 | +N6 | B_ESCHR_COLI | -R | +I | S | S | S | @@ -724,56 +724,56 @@||||
| 6 | -2010-11-18 | -T6 | +2010-08-09 | +N6 | B_ESCHR_COLI | R | S | S | -R | +S | +FALSE | FALSE | -TRUE | |
| 7 | -2011-02-07 | -T6 | +2010-09-01 | +N6 | B_ESCHR_COLI | +I | +S | R | S | -S | -S | FALSE | TRUE | |
| 8 | -2011-03-09 | -T6 | +2010-10-18 | +N6 | B_ESCHR_COLI | S | S | -R | -S | -TRUE | -TRUE | -|||
| 9 | -2011-03-23 | -T6 | -B_ESCHR_COLI | -R | -S | S | S | FALSE | TRUE | |||||
| 9 | +2011-01-18 | +N6 | +B_ESCHR_COLI | +R | +I | +R | +S | +FALSE | +TRUE | +|||||
| 10 | -2011-04-01 | -T6 | +2011-02-15 | +N6 | B_ESCHR_COLI | S | S | @@ -784,11 +784,11 @@
Instead of 2, now 10 isolates are flagged. In total, 75.5% of all isolates are marked ‘first weighted’ - 47.1% more than when using the CLSI guideline. In real life, this novel algorithm will yield 5-10% more isolates than the classic CLSI guideline.
+Instead of 1, now 9 isolates are flagged. In total, 75.2% of all isolates are marked ‘first weighted’ - 46.7% more than when using the CLSI guideline. In real life, this novel algorithm will yield 5-10% more isolates than the classic CLSI guideline.
As with filter_first_isolate(), there’s a shortcut for this new algorithm too:
So we end up with 15,096 isolates for analysis.
+So we end up with 15,038 isolates for analysis.
We can remove unneeded columns:
@@ -813,44 +813,12 @@Time for the analysis!
@@ -927,7 +927,7 @@Frequency table
Class: character
-Length: 15,096 (of which NA: 0 = 0%)
+Length: 15,038 (of which NA: 0 = 0%)
Unique: 4
Shortest: 16
Longest: 24
The functions resistance() and susceptibility() can be used to calculate antimicrobial resistance or susceptibility. For more specific analyses, the functions proportion_S(), proportion_SI(), proportion_I(), proportion_IR() and proportion_R() can be used to determine the proportion of a specific antimicrobial outcome.
As per the EUCAST guideline of 2019, we calculate resistance as the proportion of R (proportion_R(), equal to resistance()) and susceptibility as the proportion of S and I (proportion_SI(), equal to susceptibility()). These functions can be used on their own:
Or can be used in conjuction with group_by() and summarise(), both from the dplyr package:
data_1st %>%
group_by(hospital) %>%
@@ -995,19 +995,19 @@ Longest: 24
Hospital A
-0.4740061
+0.4688268
Hospital B
-0.4707669
+0.4681135
Hospital C
-0.4640934
+0.4541020
Hospital D
-0.4622766
+0.4743044
@@ -1025,23 +1025,23 @@ Longest: 24
Hospital A
-0.4740061
-4578
+0.4688268
+4475
Hospital B
-0.4707669
-5268
+0.4681135
+5253
Hospital C
-0.4640934
-2228
+0.4541020
+2255
Hospital D
-0.4622766
-3022
+0.4743044
+3055
@@ -1061,27 +1061,27 @@ Longest: 24
Escherichia
-0.9193042
-0.8966780
-0.9935914
+0.9244023
+0.8946173
+0.9929211
Klebsiella
-0.9285246
-0.8878689
-0.9940984
+0.9174726
+0.8891038
+0.9929078
Staphylococcus
-0.9166205
-0.9171745
-0.9919668
+0.9193941
+0.9248039
+0.9940492
Streptococcus
-0.6241901
+0.6070343
0.0000000
-0.6241901
+0.6070343
@@ -1129,7 +1129,7 @@ Longest: 24
geom_rsi(x = "genus") +
# split plots on antibiotic
facet_rsi(facet = "antibiotic") +
- # make R red, I yellow and S green
+ # set colours to the R/SI interpretations
scale_rsi_colours() +
# show percentages on y axis
scale_y_percent(breaks = 0:4 * 25) +
diff --git a/docs/articles/AMR_files/figure-html/plot 1-1.png b/docs/articles/AMR_files/figure-html/plot 1-1.png
index 32ddf375..ad242a4d 100644
Binary files a/docs/articles/AMR_files/figure-html/plot 1-1.png and b/docs/articles/AMR_files/figure-html/plot 1-1.png differ
diff --git a/docs/articles/AMR_files/figure-html/plot 3-1.png b/docs/articles/AMR_files/figure-html/plot 3-1.png
index 146d8308..6d92d711 100644
Binary files a/docs/articles/AMR_files/figure-html/plot 3-1.png and b/docs/articles/AMR_files/figure-html/plot 3-1.png differ
diff --git a/docs/articles/AMR_files/figure-html/plot 4-1.png b/docs/articles/AMR_files/figure-html/plot 4-1.png
index 462c348c..6560b52b 100644
Binary files a/docs/articles/AMR_files/figure-html/plot 4-1.png and b/docs/articles/AMR_files/figure-html/plot 4-1.png differ
diff --git a/docs/articles/AMR_files/figure-html/plot 5-1.png b/docs/articles/AMR_files/figure-html/plot 5-1.png
index 1c8dcc0b..2c2c9b73 100644
Binary files a/docs/articles/AMR_files/figure-html/plot 5-1.png and b/docs/articles/AMR_files/figure-html/plot 5-1.png differ
diff --git a/docs/articles/EUCAST.html b/docs/articles/EUCAST.html
index cfdb6474..7dc377a0 100644
--- a/docs/articles/EUCAST.html
+++ b/docs/articles/EUCAST.html
@@ -41,7 +41,7 @@
How to apply EUCAST rules
Matthijs S. Berends
-18 November 2019
+29 November 2019
EUCAST.RmdHow to determine multi-drug resistance (MDR)
Matthijs S. Berends
-23 November 2019
+29 November 2019
MDR.RmdFrequency table
Class: factor > ordered (numeric)
Length: 2,000 (of which NA: 289 = 14.45%)
@@ -307,19 +306,19 @@ Unique: 2
The data set now looks like this:
head(my_TB_data)
# rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin
-# 1 S R I S I S
-# 2 S I S S R R
-# 3 R I R R R I
-# 4 S S S R R R
-# 5 I S R S R I
-# 6 R S R R S R
+# 1 S S I R R S
+# 2 S S R R I S
+# 3 S S S I R I
+# 4 R R S R R S
+# 5 R R S R I S
+# 6 R R R R S R
# kanamycin
# 1 S
-# 2 S
-# 3 S
+# 2 I
+# 3 I
# 4 R
-# 5 S
-# 6 RWe can now add the interpretation of MDR-TB to our data set. You can use:
or its shortcut mdr_tb():
Create a frequency table of the results:
Frequency table
@@ -357,40 +356,40 @@ Unique: 5How to import data from SPSS / SAS / Stata
Matthijs S. Berends
-18 November 2019
+29 November 2019
SPSS.RmdHow to work with WHONET data
Matthijs S. Berends
-18 November 2019
+29 November 2019
WHONET.RmdNo errors or warnings, so all values are transformed succesfully.
We also created a package dedicated to data cleaning and checking, called the cleaner package. It gets automatically installed with the AMR package. For its freq() function to create frequency tables, you don’t even need to load it yourself as it is available through the AMR package as well.
So let’s check our data, with a couple of frequency tables:
- +# our newly created `mo` variable, put in the mo_name() function
+data %>% freq(mo_name(mo), nmax = 10)Frequency table
-Class: mo (character)
+
Class: character
Length: 500 (of which NA: 0 = 0%)
Unique: 39
Gram-negative: 280 (56.00%)
-Gram-positive: 220 (44.00%)
-Nr of genera: 17
-Nr of species: 39
Shortest: 11
+Longest: 40
| @@ -248,7 +246,7 @@ Nr of species: 39 | |||||
|---|---|---|---|---|---|
| 1 | -B_ESCHR_COLI | +Escherichia coli | 245 | 49.0% | 245 | @@ -256,7 +254,7 @@ Nr of species: 39
| 2 | -B_STPHY_CONS | +Coagulase-negative Staphylococcus (CoNS) | 74 | 14.8% | 319 | @@ -264,7 +262,7 @@ Nr of species: 39
| 3 | -B_STPHY_EPDR | +Staphylococcus epidermidis | 38 | 7.6% | 357 | @@ -272,7 +270,7 @@ Nr of species: 39
| 4 | -B_STRPT_PNMN | +Streptococcus pneumoniae | 31 | 6.2% | 388 | @@ -280,7 +278,7 @@ Nr of species: 39
| 5 | -B_STPHY_HMNS | +Staphylococcus hominis | 21 | 4.2% | 409 | @@ -288,7 +286,7 @@ Nr of species: 39
| 6 | -B_PROTS_MRBL | +Proteus mirabilis | 9 | 1.8% | 418 | @@ -296,7 +294,7 @@ Nr of species: 39
| 7 | -B_ENTRC_FACM | +Enterococcus faecium | 8 | 1.6% | 426 | @@ -304,7 +302,7 @@ Nr of species: 39
| 8 | -B_STPHY_CPTS | +Staphylococcus capitis | 8 | 1.6% | 434 | @@ -312,7 +310,7 @@ Nr of species: 39
| 9 | -B_ENTRB_CLOC | +Enterobacter cloacae | 5 | 1.0% | 439 | @@ -320,7 +318,7 @@ Nr of species: 39
| 10 | -B_ENTRC_CLMB | +Enterococcus columbae | 4 | 0.8% | 443 | @@ -329,10 +327,9 @@ Nr of species: 39
(omitted 29 entries, n = 57 [11.40%])
-
-# our transformed antibiotic columns
-# amoxicillin/clavulanic acid (J01CR02) as an example
-data %>% freq(AMC_ND2)# our transformed antibiotic columns
+# amoxicillin/clavulanic acid (J01CR02) as an example
+data %>% freq(AMC_ND2)Frequency table
Class: factor > ordered > rsi (numeric)
Length: 500 (of which NA: 19 = 3.8%)
@@ -379,9 +376,12 @@ Unique: 3
A first glimpse at results
-An easy ggplot will already give a lot of information, using the included ggplot_rsi() function:
An easy ggplot will already give a lot of information, using the included ggplot_rsi() function:
data %>%
+ group_by(Country) %>%
+ select(Country, AMP_ND2, AMC_ED20, CAZ_ED10, CIP_ED5) %>%
+ ggplot_rsi(translate_ab = 'ab', facet = "Country", datalabels = FALSE)
Benchmarks
Matthijs S. Berends
-18 November 2019
+29 November 2019
benchmarks.Rmd
In the table above, all measurements are shown in milliseconds (thousands of seconds). A value of 5 milliseconds means it can determine 200 input values per second. It case of 100 milliseconds, this is only 10 input values per second. The second input is the only one that has to be looked up thoroughly. All the others are known codes (the first one is a WHONET code) or common laboratory codes, or common full organism names like the last one. Full organism names are always preferred.
To achieve this speed, the as.mo function also takes into account the prevalence of human pathogenic microorganisms. The downside is of course that less prevalent microorganisms will be determined less fast. See this example for the ID of Methanosarcina semesiae (B_MTHNSR_SEMS), a bug probably never found before in humans:
That takes 13.8 times as much time on average. A value of 100 milliseconds means it can only determine ~10 different input values per second. We can conclude that looking up arbitrary codes of less prevalent microorganisms is the worst way to go, in terms of calculation performance. Full names (like Methanosarcina semesiae) are almost fast - these are the most probable input from most data sets.
+# expr min lq mean median uq max +# as.mo("metsem") 1501.0 1506.00 1665.0 1569.00 1788.00 2141.00 +# as.mo("METSEM") 1586.0 1610.00 1727.0 1675.00 1793.00 2002.00 +# as.mo("M. semesiae") 2266.0 2335.00 2587.0 2418.00 2524.00 3676.00 +# as.mo("M. semesiae") 2143.0 2252.00 2334.0 2331.00 2396.00 2540.00 +# as.mo("Methanosarcina semesiae") 34.8 35.74 46.3 39.64 62.28 65.83 +# neval +# 10 +# 10 +# 10 +# 10 +# 10 +That takes 15.6 times as much time on average. A value of 100 milliseconds means it can only determine ~10 different input values per second. We can conclude that looking up arbitrary codes of less prevalent microorganisms is the worst way to go, in terms of calculation performance. Full names (like Methanosarcina semesiae) are almost fast - these are the most probable input from most data sets.
In the figure below, we compare Escherichia coli (which is very common) with Prevotella brevis (which is moderately common) and with Methanosarcina semesiae (which is uncommon):
In reality, the as.mo() functions learns from its own output to speed up determinations for next times. In above figure, this effect was disabled to show the difference with the boxplot below - when you would use as.mo() yourself:
So transforming 500,000 values (!!) of 50 unique values only takes 0.69 seconds (686 ms). You only lose time on your unique input values.
+# mo_name(x) 655 668 715 717 751 797 10 +So transforming 500,000 values (!!) of 50 unique values only takes 0.72 seconds (717 ms). You only lose time on your unique input values.
@@ -309,10 +309,10 @@ times = 10) print(run_it, unit = "ms", signif = 3) # Unit: milliseconds -# expr min lq mean median uq max neval -# A 6.350 6.420 6.880 6.530 7.180 8.56 10 -# B 24.600 25.100 31.300 25.700 27.900 62.80 10 -# C 0.798 0.867 0.914 0.892 0.931 1.14 10
So going from mo_name("Staphylococcus aureus") to "Staphylococcus aureus" takes 0.0009 seconds - it doesn’t even start calculating if the result would be the same as the expected resulting value. That goes for all helper functions:
run_it <- microbenchmark(A = mo_species("aureus"),
B = mo_genus("Staphylococcus"),
@@ -326,14 +326,14 @@
print(run_it, unit = "ms", signif = 3)
# Unit: milliseconds
# expr min lq mean median uq max neval
-# A 0.446 0.459 0.471 0.475 0.481 0.485 10
-# B 0.502 0.506 0.518 0.513 0.521 0.568 10
-# C 0.774 0.802 0.811 0.815 0.823 0.838 10
-# D 0.505 0.511 0.532 0.515 0.534 0.627 10
-# E 0.467 0.469 0.495 0.478 0.481 0.676 10
-# F 0.457 0.467 0.478 0.479 0.484 0.509 10
-# G 0.452 0.460 0.467 0.471 0.472 0.478 10
-# H 0.460 0.469 0.473 0.473 0.480 0.486 10Of course, when running mo_phylum("Firmicutes") the function has zero knowledge about the actual microorganism, namely S. aureus. But since the result would be "Firmicutes" too, there is no point in calculating the result. And because this package ‘knows’ all phyla of all known bacteria (according to the Catalogue of Life), it can just return the initial value immediately.
Currently supported are German, Dutch, Spanish, Italian, French and Portuguese.
diff --git a/docs/articles/benchmarks_files/figure-html/unnamed-chunk-11-1.png b/docs/articles/benchmarks_files/figure-html/unnamed-chunk-11-1.png index a0ab03d1..5702abb8 100644 Binary files a/docs/articles/benchmarks_files/figure-html/unnamed-chunk-11-1.png and b/docs/articles/benchmarks_files/figure-html/unnamed-chunk-11-1.png differ diff --git a/docs/articles/benchmarks_files/figure-html/unnamed-chunk-5-1.png b/docs/articles/benchmarks_files/figure-html/unnamed-chunk-5-1.png index 8189a9e9..e0d2422f 100644 Binary files a/docs/articles/benchmarks_files/figure-html/unnamed-chunk-5-1.png and b/docs/articles/benchmarks_files/figure-html/unnamed-chunk-5-1.png differ diff --git a/docs/articles/benchmarks_files/figure-html/unnamed-chunk-9-1.png b/docs/articles/benchmarks_files/figure-html/unnamed-chunk-9-1.png index 07b95fbd..6ee94219 100644 Binary files a/docs/articles/benchmarks_files/figure-html/unnamed-chunk-9-1.png and b/docs/articles/benchmarks_files/figure-html/unnamed-chunk-9-1.png differ diff --git a/docs/articles/index.html b/docs/articles/index.html index 1d94262f..7b5c5307 100644 --- a/docs/articles/index.html +++ b/docs/articles/index.html @@ -84,7 +84,7 @@ diff --git a/docs/articles/resistance_predict.html b/docs/articles/resistance_predict.html index 95212fe6..bf4224fb 100644 --- a/docs/articles/resistance_predict.html +++ b/docs/articles/resistance_predict.html @@ -41,7 +41,7 @@ @@ -187,7 +187,7 @@How to predict antimicrobial resistance
Matthijs S. Berends
-18 November 2019
+29 November 2019
resistance_predict.Rmd
@@ -1395,7 +1390,7 @@ Using as.mo(..., allow_uncertain = 3)
Contents
Contents
Interpret MIC values and disk diffusion diameters according to EUCAST or CLSI, or clean up existing RSI values. This transforms the input to a new class rsi, which is an ordered factor with levels S < I < R. Invalid antimicrobial interpretations will be translated as NA with a warning.
Interpret MIC values and disk diffusion diameters according to EUCAST or CLSI, or clean up existing R/SI values. This transforms the input to a new class rsi, which is an ordered factor with levels S < I < R. Invalid antimicrobial interpretations will be translated as NA with a warning.
as.rsi(x, ...) @@ -293,17 +293,19 @@Run
unique(AMR::rsi_translation$guideline)for a list of all supported guidelines.After using
as.rsi(), you can useeucast_rules()to (1) apply inferred susceptibility and resistance based on results of other antimicrobials and (2) apply intrinsic resistance based on taxonomic properties of a microorganism.The function
-is.rsi.eligible()returnsTRUEwhen a columns contains at most 5% invalid antimicrobial interpretations (not S and/or I and/or R), andFALSEotherwise. The threshold of 5% can be set with thethresholdparameter.Interpretation of S, I and R
+Interpretation of R and S/I
-In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories S, I and R as shown below (http://www.eucast.org/newsiandr/). Results of several consultations on the new definitions are available on the EUCAST website under "Consultations".
-
-
S - Susceptible, standard dosing regimen: A microorganism is categorised as "Susceptible, standard dosing regimen", when there is a high likelihood of therapeutic success using a standard dosing regimen of the agent.
-I - Susceptible, increased exposure: A microorganism is categorised as "Susceptible, Increased exposure" when there is a high likelihood of therapeutic success because exposure to the agent is increased by adjusting the dosing regimen or by its concentration at the site of infection.
-R - Resistant: A microorganism is categorised as "Resistant" when there is a high likelihood of therapeutic failure even when there is increased exposure.
+R = Resistant
+A microorganism is categorised as Resistant when there is a high likelihood of therapeutic failure even when there is increased exposure. Exposure is a function of how the mode of administration, dose, dosing interval, infusion time, as well as distribution and excretion of the antimicrobial agent will influence the infecting organism at the site of infection.
+S = Susceptible
+A microorganism is categorised as Susceptible, standard dosing regimen, when there is a high likelihood of therapeutic success using a standard dosing regimen of the agent.
+I = Increased exposure, but still susceptible
+A microorganism is categorised as Susceptible, Increased exposure when there is a high likelihood of therapeutic success because exposure to the agent is increased by adjusting the dosing regimen or by its concentration at the site of infection.- Arguments
- Value
- Details -
- Interpretation of S, I and R +
- Interpretation of R and S/I
- Read more on our website!
- See also
- Examples diff --git a/docs/reference/atc_online.html b/docs/reference/atc_online.html index c6c63352..ea8d32bb 100644 --- a/docs/reference/atc_online.html +++ b/docs/reference/atc_online.html @@ -86,7 +86,7 @@ This function requires an internet connection." /> diff --git a/docs/reference/availability.html b/docs/reference/availability.html index fc0c2082..4ef6ef04 100644 --- a/docs/reference/availability.html +++ b/docs/reference/availability.html @@ -85,7 +85,7 @@ diff --git a/docs/reference/bug_drug_combinations.html b/docs/reference/bug_drug_combinations.html index cb62482f..a59b53de 100644 --- a/docs/reference/bug_drug_combinations.html +++ b/docs/reference/bug_drug_combinations.html @@ -85,7 +85,7 @@ diff --git a/docs/reference/catalogue_of_life.html b/docs/reference/catalogue_of_life.html index 2d1a97ca..e0aaebcf 100644 --- a/docs/reference/catalogue_of_life.html +++ b/docs/reference/catalogue_of_life.html @@ -85,7 +85,7 @@ diff --git a/docs/reference/catalogue_of_life_version.html b/docs/reference/catalogue_of_life_version.html index 82c8f543..5c7debe5 100644 --- a/docs/reference/catalogue_of_life_version.html +++ b/docs/reference/catalogue_of_life_version.html @@ -85,7 +85,7 @@ diff --git a/docs/reference/count.html b/docs/reference/count.html index 22307f5a..f6917b09 100644 --- a/docs/reference/count.html +++ b/docs/reference/count.html @@ -86,7 +86,7 @@ count_resistant() should be used to count resistant isolates, count_susceptible( @@ -278,7 +278,7 @@ count_resistant() should be used to count resistant isolates, count_susceptible(
S - Susceptible, standard dosing regimen: A microorganism is categorised as "Susceptible, standard dosing regimen", when there is a high likelihood of therapeutic success using a standard dosing regimen of the agent.
-I - Susceptible, increased exposure: A microorganism is categorised as "Susceptible, Increased exposure" when there is a high likelihood of therapeutic success because exposure to the agent is increased by adjusting the dosing regimen or by its concentration at the site of infection.
-R - Resistant: A microorganism is categorised as "Resistant" when there is a high likelihood of therapeutic failure even when there is increased exposure.
+R = Resistant
+A microorganism is categorised as Resistant when there is a high likelihood of therapeutic failure even when there is increased exposure. Exposure is a function of how the mode of administration, dose, dosing interval, infusion time, as well as distribution and excretion of the antimicrobial agent will influence the infecting organism at the site of infection.
+S = Susceptible
+A microorganism is categorised as Susceptible, standard dosing regimen, when there is a high likelihood of therapeutic success using a standard dosing regimen of the agent.
+I = Increased exposure, but still susceptible
+A microorganism is categorised as Susceptible, Increased exposure when there is a high likelihood of therapeutic success because exposure to the agent is increased by adjusting the dosing regimen or by its concentration at the site of infection.- Arguments
- Value
- Details -
- Interpretation of S, I and R +
- Interpretation of R and S/I
- Combination therapy
- Read more on our website!
- See also diff --git a/docs/reference/eucast_rules.html b/docs/reference/eucast_rules.html index f4a2dbff..856e6bd4 100644 --- a/docs/reference/eucast_rules.html +++ b/docs/reference/eucast_rules.html @@ -86,7 +86,7 @@ To improve the interpretation of the antibiogram before EUCAST rules are applied diff --git a/docs/reference/example_isolates.html b/docs/reference/example_isolates.html index 5db02055..d46311a3 100644 --- a/docs/reference/example_isolates.html +++ b/docs/reference/example_isolates.html @@ -85,7 +85,7 @@ @@ -252,7 +252,7 @@
gender
gender of the patientpatient_id
ID of the patientmo
ID of microorganism created withas.mo(), see also microorganisms
-PEN:RIF
40 different antibiotics with classrsi(seeas.rsi()); these column names occur in antibiotics data set and can be translated withab_name()
+PEN:RIF
40 different antibiotics with classrsi(seeas.rsi()); these column names occur in antibiotics data set and can be translated withab_name()S - Susceptible, standard dosing regimen: A microorganism is categorised as "Susceptible, standard dosing regimen", when there is a high likelihood of therapeutic success using a standard dosing regimen of the agent.
-I - Susceptible, increased exposure: A microorganism is categorised as "Susceptible, Increased exposure" when there is a high likelihood of therapeutic success because exposure to the agent is increased by adjusting the dosing regimen or by its concentration at the site of infection.
-R - Resistant: A microorganism is categorised as "Resistant" when there is a high likelihood of therapeutic failure even when there is increased exposure.
+R = Resistant
+A microorganism is categorised as Resistant when there is a high likelihood of therapeutic failure even when there is increased exposure. Exposure is a function of how the mode of administration, dose, dosing interval, infusion time, as well as distribution and excretion of the antimicrobial agent will influence the infecting organism at the site of infection.
+S = Susceptible
+A microorganism is categorised as Susceptible, standard dosing regimen, when there is a high likelihood of therapeutic success using a standard dosing regimen of the agent.
+I = Increased exposure, but still susceptible
+A microorganism is categorised as Susceptible, Increased exposure when there is a high likelihood of therapeutic success because exposure to the agent is increased by adjusting the dosing regimen or by its concentration at the site of infection.- Value
- Details
- Antibiotics -
- Interpretation of S, I and R +
- Interpretation of R and S/I
- Read more on our website!
- Examples
S - Susceptible, standard dosing regimen: A microorganism is categorised as "Susceptible, standard dosing regimen", when there is a high likelihood of therapeutic success using a standard dosing regimen of the agent.
-I - Susceptible, increased exposure: A microorganism is categorised as "Susceptible, Increased exposure" when there is a high likelihood of therapeutic success because exposure to the agent is increased by adjusting the dosing regimen or by its concentration at the site of infection.
-R - Resistant: A microorganism is categorised as "Resistant" when there is a high likelihood of therapeutic failure even when there is increased exposure.
+R = Resistant
+A microorganism is categorised as Resistant when there is a high likelihood of therapeutic failure even when there is increased exposure. Exposure is a function of how the mode of administration, dose, dosing interval, infusion time, as well as distribution and excretion of the antimicrobial agent will influence the infecting organism at the site of infection.
+S = Susceptible
+A microorganism is categorised as Susceptible, standard dosing regimen, when there is a high likelihood of therapeutic success using a standard dosing regimen of the agent.
+I = Increased exposure, but still susceptible
+A microorganism is categorised as Susceptible, Increased exposure when there is a high likelihood of therapeutic success because exposure to the agent is increased by adjusting the dosing regimen or by its concentration at the site of infection.- Value
- Details
- Combination therapy -
- Interpretation of S, I and R +
- Interpretation of R and S/I
- Read more on our website!
- See also
- Examples diff --git a/docs/reference/read.4D.html b/docs/reference/read.4D.html index 60c43fa1..8b562917 100644 --- a/docs/reference/read.4D.html +++ b/docs/reference/read.4D.html @@ -85,7 +85,7 @@ diff --git a/docs/reference/reexports.html b/docs/reference/reexports.html index f3a5db5e..d8a57d81 100644 --- a/docs/reference/reexports.html +++ b/docs/reference/reexports.html @@ -90,7 +90,7 @@ below to see their documentation. diff --git a/docs/reference/resistance_predict.html b/docs/reference/resistance_predict.html index 161340a5..c8bc4bb1 100644 --- a/docs/reference/resistance_predict.html +++ b/docs/reference/resistance_predict.html @@ -85,7 +85,7 @@ @@ -359,17 +359,19 @@
"lin"or"linear": a linear regression modelS - Susceptible, standard dosing regimen: A microorganism is categorised as "Susceptible, standard dosing regimen", when there is a high likelihood of therapeutic success using a standard dosing regimen of the agent.
-I - Susceptible, increased exposure: A microorganism is categorised as "Susceptible, Increased exposure" when there is a high likelihood of therapeutic success because exposure to the agent is increased by adjusting the dosing regimen or by its concentration at the site of infection.
-R - Resistant: A microorganism is categorised as "Resistant" when there is a high likelihood of therapeutic failure even when there is increased exposure.
+R = Resistant
+A microorganism is categorised as Resistant when there is a high likelihood of therapeutic failure even when there is increased exposure. Exposure is a function of how the mode of administration, dose, dosing interval, infusion time, as well as distribution and excretion of the antimicrobial agent will influence the infecting organism at the site of infection.
+S = Susceptible
+A microorganism is categorised as Susceptible, standard dosing regimen, when there is a high likelihood of therapeutic success using a standard dosing regimen of the agent.
+I = Increased exposure, but still susceptible
+A microorganism is categorised as Susceptible, Increased exposure when there is a high likelihood of therapeutic success because exposure to the agent is increased by adjusting the dosing regimen or by its concentration at the site of infection.- Arguments
- Value
- Details -
- Interpretation of S, I and R +
- Interpretation of R and S/I
- Read more on our website!
- See also
- Examples diff --git a/docs/reference/rsi_translation.html b/docs/reference/rsi_translation.html index 4d5776f8..acec260d 100644 --- a/docs/reference/rsi_translation.html +++ b/docs/reference/rsi_translation.html @@ -85,7 +85,7 @@ diff --git a/docs/reference/skewness.html b/docs/reference/skewness.html index fec2d660..830449c8 100644 --- a/docs/reference/skewness.html +++ b/docs/reference/skewness.html @@ -86,7 +86,7 @@ When negative: the left tail is longer; the mass of the distribution is concentr diff --git a/docs/reference/translate.html b/docs/reference/translate.html index 1bed50af..e8341521 100644 --- a/docs/reference/translate.html +++ b/docs/reference/translate.html @@ -85,7 +85,7 @@ diff --git a/git_commit.sh b/git_commit.sh deleted file mode 100755 index e71cf26e..00000000 --- a/git_commit.sh +++ /dev/null @@ -1,38 +0,0 @@ -####################################################################### -# To push new commits to the premaster branch, run: # -# bash git_premaster.sh "commit message" # -# # -# After successful CRAN checks, merge it to the master branch with: # -# bash git_merge.sh # -####################################################################### - -if [ -z "$1" ]; then - echo "FATAL - no commit message" - exit 1 -fi - -echo "••••••••••••••••••••••••••••••••••••••••••••" -echo "• Updating package date and version number •" -echo "••••••••••••••••••••••••••••••••••••••••••••" -sed -i -- "s/^Date: .*/Date: $(date '+%Y-%m-%d')/" DESCRIPTION -# get latest tags -git pull --tags --quiet -# get version number: latest tag + .90 + number of commits (like 0.6.1.9033) -newversion=`git describe --tags | sed 's/-/.90/' | sed 's/-.*//' | sed 's/v//'` -sed -i -- "s/^Version: .*/Version: ${newversion}/" DESCRIPTION -echo "First 3 lines of DESCRIPTION:" -head -3 DESCRIPTION -echo -echo "•••••••••••••••••••••••" -echo "• Documenting package •" -echo "•••••••••••••••••••••••" -Rscript -e "devtools::document()" -echo -echo "••••••••••••••" -echo "• Committing •" -echo "••••••••••••••" -git add . -git commit -a -m "$1" --quiet - -echo -echo "Done." diff --git a/git_merge.sh b/git_merge.sh index 027f15bc..e9fe463e 100755 --- a/git_merge.sh +++ b/git_merge.sh @@ -1,9 +1,13 @@ ####################################################################### # To push new commits to the premaster branch, run: # # bash git_premaster.sh "commit message" # +# This creates auto version numbering in DESCRIPTION and NEWS.md. # # # # After successful CRAN checks, merge it to the master branch with: # # bash git_merge.sh # +# # +# To prerelease a new version number, run: # +# bash git_premaster.sh "v0.x.x" FALSE "0.x.x" # ####################################################################### # stash current changes diff --git a/git_premaster.sh b/git_premaster.sh index 190194a5..ad0e2d5f 100755 --- a/git_premaster.sh +++ b/git_premaster.sh @@ -20,24 +20,31 @@ echo "•••••••••••••••••••••••• echo "• Updating package date and version number •" echo "••••••••••••••••••••••••••••••••••••••••••••" sed -i -- "s/^Date: .*/Date: $(date '+%Y-%m-%d')/" DESCRIPTION -# get latest tags -git pull --tags --quiet -current_tag=`git describe --tags --abbrev=0 | sed 's/v//'` -current_commit=`git describe --tags | sed 's/.*-\(.*\)-.*/\1/'` -# combine tag (e.g. 0.1.0) and commit number (like 40) increased by 9000 to indicate beta version -new_version="$current_tag.$((current_commit + 9000))" # results in 0.1.0.9040 -if [ -z "$new_version" ]; then - new_version="$current_tag.9000" - echo - echo "** COULD NOT CREATE NEW VERSION NUMBER! **" - echo "Are there some unpushed changes in a new tag? Then mind NEWS.md. Assuming sequence number 9000." - echo +if [ -z "$3" ]; then + # no version number set, so get latest tags to create it + git pull --tags --quiet + current_tag=`git describe --tags --abbrev=0 | sed 's/v//'` + current_commit=`git describe --tags | sed 's/.*-\(.*\)-.*/\1/'` + # combine tag (e.g. 0.1.0) and commit number (like 40) increased by 9000 to indicate beta version + new_version="$current_tag.$((current_commit + 9000))" # results in 0.1.0.9040 + if [ -z "$new_version" ]; then + new_version="$current_tag.9000" + echo + echo "** COULD NOT CREATE NEW VERSION NUMBER! **" + echo "Are there some unpushed changes in a new tag? Then mind NEWS.md. Assuming sequence number 9000." + echo + fi + # add date to 2nd line of NEWS.md when no version number was set + sed -i -- "2s/.*/## \
In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories R and S/I as shown below (http://www.eucast.org/newsiandr/). Results of several consultations on the new definitions are available on the EUCAST website under "Consultations".
-
+
Exposure is a function of how the mode of administration, dose, dosing interval, infusion time, as well as distribution and excretion of the antimicrobial agent will influence the infecting organism at the site of infection.
This AMR package honours this new insight. Use susceptibility() (equal to proportion_SI()) to determine antimicrobial susceptibility and count_susceptible() (equal to count_SI()) to count susceptible isolates.
Read more on our website!
@@ -357,7 +359,7 @@a data.frame containing columns with class rsi (see as.rsi())
a data.frame containing columns with class rsi (see as.rsi())
These functions are meant to count isolates. Use the resistance()/susceptibility() functions to calculate microbial resistance/susceptibility.
The function count_resistant() is equal to the function count_R(). The function count_susceptible() is equal to the function count_SI().
The function n_rsi() is an alias of count_all(). They can be used to count all available isolates, i.e. where all input antibiotics have an available result (S, I or R). Their use is equal to n_distinct(). Their function is equal to count_susceptible(...) + count_resistant(...).
The function count_df() takes any variable from data that has an rsi class (created with as.rsi()) and counts the number of S's, I's and R's. The function rsi_df() works exactly like count_df(), but adds the percentage of S, I and R.
Interpretation of S, I and R
+The function count_df() takes any variable from data that has an rsi class (created with as.rsi()) and counts the number of S's, I's and R's. The function rsi_df() works exactly like count_df(), but adds the percentage of S, I and R.
Interpretation of R and S/I
-In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories S, I and R as shown below (http://www.eucast.org/newsiandr/). Results of several consultations on the new definitions are available on the EUCAST website under "Consultations".
-
-
In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories R and S/I as shown below (http://www.eucast.org/newsiandr/). Results of several consultations on the new definitions are available on the EUCAST website under "Consultations".
-
+
Exposure is a function of how the mode of administration, dose, dosing interval, infusion time, as well as distribution and excretion of the antimicrobial agent will influence the infecting organism at the site of infection.
This AMR package honours this new insight. Use susceptibility() (equal to proportion_SI()) to determine antimicrobial susceptibility and count_susceptible() (equal to count_SI()) to count susceptible isolates.
Combination therapy
@@ -423,7 +425,7 @@ count_resistant() should be used to count resistant isolates, count_susceptible(Read more on our website!
diff --git a/docs/reference/extended-functions.html b/docs/reference/extended-functions.html index ec19c987..b205d4ac 100644 --- a/docs/reference/extended-functions.html +++ b/docs/reference/extended-functions.html @@ -85,7 +85,7 @@ diff --git a/docs/reference/filter_ab_class.html b/docs/reference/filter_ab_class.html index 2fbb21bd..c2a10af2 100644 --- a/docs/reference/filter_ab_class.html +++ b/docs/reference/filter_ab_class.html @@ -85,7 +85,7 @@ diff --git a/docs/reference/first_isolate.html b/docs/reference/first_isolate.html index e0f4a60f..847c85e6 100644 --- a/docs/reference/first_isolate.html +++ b/docs/reference/first_isolate.html @@ -85,7 +85,7 @@ diff --git a/docs/reference/g.test.html b/docs/reference/g.test.html index 662a0ebb..061a4e3a 100644 --- a/docs/reference/g.test.html +++ b/docs/reference/g.test.html @@ -85,7 +85,7 @@ diff --git a/docs/reference/ggplot_rsi.html b/docs/reference/ggplot_rsi.html index 60b7ff6c..020c193b 100644 --- a/docs/reference/ggplot_rsi.html +++ b/docs/reference/ggplot_rsi.html @@ -85,7 +85,7 @@ @@ -300,7 +300,7 @@a data.frame with column(s) of class rsi (see as.rsi())
a data.frame with column(s) of class rsi (see as.rsi())
At default, the names of antibiotics will be shown on the plots using ab_name(). This can be set with the translate_ab parameter. See count_df().
The functions
-geom_rsi() will take any variable from the data that has an rsi class (created with as.rsi()) using rsi_df() and will plot bars with the percentage R, I and S. The default behaviour is to have the bars stacked and to have the different antibiotics on the x axis.
geom_rsi() will take any variable from the data that has an rsi class (created with as.rsi()) using rsi_df() and will plot bars with the percentage R, I and S. The default behaviour is to have the bars stacked and to have the different antibiotics on the x axis.
facet_rsi() creates 2d plots (at default based on S/I/R) using ggplot2::facet_wrap().
scale_y_percent() transforms the y axis to a 0 to 100% range using ggplot2::scale_continuous().
scale_rsi_colours() sets colours to the bars: pastel blue for S, pastel turquoise for I and pastel red for R, using ggplot2::scale_brewer().
a character vector where matches are sought, or an
- object which can be coerced by as.character to a character
- vector. Long vectors are supported.
a character vector where matches are sought, or an object which can be coerced by as.character() to a character vector.
character string containing a regular expression
- (or character string for fixed = TRUE) to be matched
- in the given character vector. Coerced by
- as.character to a character string if possible. If a
- character vector of length 2 or more is supplied, the first element
- is used with a warning. Missing values are allowed except for
- regexpr and gregexpr.
a character string containing a regular expression (or character string for fixed = TRUE) to be matched in the given character vector. Coerced by as.character() to a character string if possible. If a character vector of length 2 or more is supplied, the first element is used with a warning.
if FALSE, the pattern matching is case
- sensitive and if TRUE, case is ignored during matching.
if FALSE, the pattern matching is case sensitive and if TRUE, case is ignored during matching.
Interpretation of S, I and R
+Interpretation of R and S/I
-In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories S, I and R as shown below (http://www.eucast.org/newsiandr/). Results of several consultations on the new definitions are available on the EUCAST website under "Consultations".
-
-
In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories R and S/I as shown below (http://www.eucast.org/newsiandr/). Results of several consultations on the new definitions are available on the EUCAST website under "Consultations".
-
+
Exposure is a function of how the mode of administration, dose, dosing interval, infusion time, as well as distribution and excretion of the antimicrobial agent will influence the infecting organism at the site of infection.
This AMR package honours this new insight. Use susceptibility() (equal to proportion_SI()) to determine antimicrobial susceptibility and count_susceptible() (equal to count_SI()) to count susceptible isolates.
Read more on our website!
@@ -455,7 +457,7 @@ The Dutch national guideline - Rijksinstituut voor Volksgezondheid en Milieu "WIa data.frame containing columns with class rsi (see as.rsi())
a data.frame containing columns with class rsi (see as.rsi())
The function resistance() is equal to the function proportion_R(). The function susceptibility() is equal to the function proportion_SI().
Remember that you should filter your table to let it contain only first isolates! This is needed to exclude duplicates and to reduce selection bias. Use first_isolate() to determine them in your data set.
These functions are not meant to count isolates, but to calculate the proportion of resistance/susceptibility. Use the AMR::count() functions to count isolates. The function susceptibility() is essentially equal to count_susceptible() / count_all(). Low counts can infuence the outcome - the proportion functions may camouflage this, since they only return the proportion (albeit being dependent on the minimum parameter).
The function proportion_df() takes any variable from data that has an rsi class (created with as.rsi()) and calculates the proportions R, I and S. The function rsi_df() works exactly like proportion_df(), but adds the number of isolates.
The function proportion_df() takes any variable from data that has an rsi class (created with as.rsi()) and calculates the proportions R, I and S. The function rsi_df() works exactly like proportion_df(), but adds the number of isolates.
Combination therapy
@@ -357,17 +357,19 @@ resistance() should be used to calculate resistance, susceptibility() should beUsing only_all_tested has no impact when only using one antibiotic as input.
Interpretation of S, I and R
+Interpretation of R and S/I
-In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories S, I and R as shown below (http://www.eucast.org/newsiandr/). Results of several consultations on the new definitions are available on the EUCAST website under "Consultations".
-
-
In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories R and S/I as shown below (http://www.eucast.org/newsiandr/). Results of several consultations on the new definitions are available on the EUCAST website under "Consultations".
-
+
Exposure is a function of how the mode of administration, dose, dosing interval, infusion time, as well as distribution and excretion of the antimicrobial agent will influence the infecting organism at the site of infection.
This AMR package honours this new insight. Use susceptibility() (equal to proportion_SI()) to determine antimicrobial susceptibility and count_susceptible() (equal to count_SI()) to count susceptible isolates.
Read more on our website!
@@ -468,7 +470,7 @@ resistance() should be used to calculate resistance, susceptibility() should beInterpretation of S, I and R
+Interpretation of R and S/I
-In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories S, I and R as shown below (http://www.eucast.org/newsiandr/). Results of several consultations on the new definitions are available on the EUCAST website under "Consultations".
-
-
In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories R and S/I as shown below (http://www.eucast.org/newsiandr/). Results of several consultations on the new definitions are available on the EUCAST website under "Consultations".
-
+
Exposure is a function of how the mode of administration, dose, dosing interval, infusion time, as well as distribution and excretion of the antimicrobial agent will influence the infecting organism at the site of infection.
This AMR package honours this new insight. Use susceptibility() (equal to proportion_SI()) to determine antimicrobial susceptibility and count_susceptible() (equal to count_SI()) to count susceptible isolates.