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 20 May 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 23 May 2019.
So, we can draw at least two conclusions immediately. From a data scientist perspective, the data looks clean: only values M and F. From a researcher 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 %>%
@@ -442,14 +449,14 @@
# Pasteurella multocida (no new changes)
# Staphylococcus (no new changes)
# Streptococcus groups A, B, C, G (no new changes)
-# Streptococcus pneumoniae (1443 new changes)
+# Streptococcus pneumoniae (1542 new changes)
# Viridans group streptococci (no new changes)
#
# EUCAST Expert Rules, Intrinsic Resistance and Exceptional Phenotypes (v3.1, 2016)
-# Table 01: Intrinsic resistance in Enterobacteriaceae (1303 new changes)
+# Table 01: Intrinsic resistance in Enterobacteriaceae (1286 new changes)
# Table 02: Intrinsic resistance in non-fermentative Gram-negative bacteria (no new changes)
# Table 03: Intrinsic resistance in other Gram-negative bacteria (no new changes)
-# Table 04: Intrinsic resistance in Gram-positive bacteria (2746 new changes)
+# Table 04: Intrinsic resistance in Gram-positive bacteria (2788 new changes)
# Table 08: Interpretive rules for B-lactam agents and Gram-positive cocci (no new changes)
# Table 09: Interpretive rules for B-lactam agents and Gram-negative rods (no new changes)
# Table 11: Interpretive rules for macrolides, lincosamides, and streptogramins (no new changes)
@@ -457,24 +464,24 @@
# Table 13: Interpretive rules for quinolones (no new changes)
#
# Other rules
-# Non-EUCAST: amoxicillin/clav acid = S where ampicillin = S (2313 new changes)
-# Non-EUCAST: ampicillin = R where amoxicillin/clav acid = R (115 new changes)
+# Non-EUCAST: amoxicillin/clav acid = S where ampicillin = S (2231 new changes)
+# Non-EUCAST: ampicillin = R where amoxicillin/clav acid = R (126 new changes)
# Non-EUCAST: piperacillin = R where piperacillin/tazobactam = R (no new changes)
# Non-EUCAST: piperacillin/tazobactam = S where piperacillin = S (no new changes)
# Non-EUCAST: trimethoprim = R where trimethoprim/sulfa = R (no new changes)
# Non-EUCAST: trimethoprim/sulfa = S where trimethoprim = S (no new changes)
#
# --------------------------------------------------------------------------
-# EUCAST rules affected 6,579 out of 20,000 rows, making a total of 7,920 edits
+# EUCAST rules affected 6,562 out of 20,000 rows, making a total of 7,973 edits
# => added 0 test results
#
-# => changed 7,920 test results
-# - 123 test results changed from S to I
-# - 4,680 test results changed from S to R
-# - 1,131 test results changed from I to S
-# - 306 test results changed from I to R
-# - 1,657 test results changed from R to S
-# - 23 test results changed from R to I
+# => changed 7,973 test results
+# - 114 test results changed from S to I
+# - 4,779 test results changed from S to R
+# - 1,118 test results changed from I to S
+# - 330 test results changed from I to R
+# - 1,606 test results changed from R to S
+# - 26 test results changed from R to I
# --------------------------------------------------------------------------
#
# Use verbose = TRUE to get a data.frame with all specified edits instead.So only 28.4% is suitable for resistance analysis! We can now filter on it with the filter() function, also from the dplyr package:
So only 28.5% 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:
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(.)) %>%
@@ -650,7 +657,7 @@
# 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 14,968 first weighted isolates (74.8% of total)| isolate | @@ -667,8 +674,8 @@||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | -2010-03-19 | -N8 | +2010-04-13 | +S1 | B_ESCHR_COL | S | S | @@ -679,58 +686,58 @@|||||||
| 2 | -2010-05-06 | -N8 | +2010-06-02 | +S1 | B_ESCHR_COL | -R | +S | S | S | S | FALSE | -TRUE | +FALSE | |
| 3 | -2010-05-20 | -N8 | +2010-07-21 | +S1 | B_ESCHR_COL | +I | +S | R | S | -S | -S | -FALSE | -FALSE | -|
| 4 | -2010-07-20 | -N8 | -B_ESCHR_COL | -S | -S | -S | -S | FALSE | TRUE | |||||
| 5 | -2010-09-04 | -N8 | +||||||||||||
| 4 | +2010-09-19 | +S1 | B_ESCHR_COL | +R | S | -S | -S | +R | S | FALSE | FALSE | |||
| 5 | +2010-09-19 | +S1 | +B_ESCHR_COL | +S | +S | +S | +R | +FALSE | +TRUE | +|||||
| 6 | -2010-09-13 | -N8 | +2010-10-14 | +S1 | B_ESCHR_COL | -R | +S | S | S | S | @@ -739,20 +746,20 @@||||
| 7 | -2011-04-01 | -N8 | +2010-10-28 | +S1 | B_ESCHR_COL | S | S | S | S | -TRUE | -TRUE | +FALSE | +FALSE | |
| 8 | -2011-10-15 | -N8 | +2010-12-09 | +S1 | B_ESCHR_COL | R | S | @@ -763,8 +770,8 @@|||||||
| 9 | -2011-11-14 | -N8 | +2010-12-15 | +S1 | B_ESCHR_COL | S | S | @@ -775,23 +782,23 @@|||||||
| 10 | -2012-02-11 | -N8 | +2011-01-07 | +S1 | B_ESCHR_COL | -S | -S | +R | +I | S | S | FALSE | -FALSE | +TRUE |
Instead of 2, now 7 isolates are flagged. In total, 74.8% of all isolates are marked ‘first weighted’ - 46.5% 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 7 isolates are flagged. In total, 75.6% 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.
As with filter_first_isolate(), there’s a shortcut for this new algorithm too:
So we end up with 14,968 isolates for analysis.
+So we end up with 15,125 isolates for analysis.
We can remove unneeded columns:
@@ -799,6 +806,7 @@| date | patient_id | hospital | @@ -815,12 +823,13 @@|||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2013-08-26 | -Q7 | -Hospital A | +1 | +2012-10-26 | +T6 | +Hospital D | B_STPHY_AUR | -S | -S | +R | +R | S | S | F | @@ -830,9 +839,58 @@TRUE |
| 2012-10-04 | +2 | +2011-10-13 | +Y4 | +Hospital B | +B_STRPT_PNE | +R | +R | +S | +R | +F | +Gram positive | +Streptococcus | +pneumoniae | +TRUE | +|
| 3 | +2010-02-09 | O2 | -Hospital A | +Hospital B | +B_STRPT_PNE | +R | +R | +S | +R | +F | +Gram positive | +Streptococcus | +pneumoniae | +TRUE | +|
| 6 | +2015-09-19 | +P5 | +Hospital D | +B_STRPT_PNE | +S | +S | +S | +R | +F | +Gram positive | +Streptococcus | +pneumoniae | +TRUE | +||
| 7 | +2011-05-13 | +X5 | +Hospital B | B_ESCHR_COL | R | S | @@ -844,60 +902,16 @@coli | TRUE | |||||||
| 2012-10-23 | -C4 | +||||||||||||||
| 8 | +2015-03-07 | +Z7 | Hospital C | B_ESCHR_COL | S | S | -S | -S | -M | -Gram negative | -Escherichia | -coli | -TRUE | -||
| 2011-08-17 | -R4 | -Hospital B | -B_ESCHR_COL | R | S | -R | -S | -F | -Gram negative | -Escherichia | -coli | -TRUE | -|||
| 2016-01-18 | -L2 | -Hospital B | -B_ESCHR_COL | -S | -S | -S | -S | -M | -Gram negative | -Escherichia | -coli | -TRUE | -|||
| 2011-09-06 | -O1 | -Hospital A | -B_ESCHR_COL | -R | -S | -S | -S | F | Gram negative | Escherichia | @@ -921,9 +935,9 @@|||||
| 1 | Escherichia coli | -7,394 | -49.4% | -7,394 | -49.4% | +7,486 | +49.5% | +7,486 | +49.5% | ||||||
| 2 | Staphylococcus aureus | -3,716 | -24.8% | -11,110 | +3,731 | +24.7% | +11,217 | 74.2% | |||||||
| 3 | Streptococcus pneumoniae | -2,291 | -15.3% | -13,401 | -89.5% | +2,360 | +15.6% | +13,577 | +89.8% | ||||||
| 4 | Klebsiella pneumoniae | -1,567 | -10.5% | -14,968 | +1,548 | +10.2% | +15,125 | 100.0% | |||||||
| Hospital A | -0.4708540 | +0.4556355 | |||||||||||||
| Hospital B | -0.4617585 | +0.4661553 | |||||||||||||
| Hospital C | -0.4622030 | +0.4759657 | |||||||||||||
| Hospital D | -0.4691689 | +0.4577347 |
EUCAST.RmdG_test.RmdMDR.RmdWith the function mdro(), you can determine multi-drug resistant organisms (MDRO). It currently support these guidelines:
As an example, I will make a data set to determine multi-drug resistant TB:
+# a helper function to get a random vector with values S, I and R
+# with the probabilities 50%-10%-40%
+sample_rsi <- function() {
+ sample(c("S", "I", "R"),
+ size = 5000,
+ prob = c(0.5, 0.1, 0.4),
+ replace = TRUE)
+}
+
+my_TB_data <- data.frame(rifampicin = sample_rsi(),
+ isoniazid = sample_rsi(),
+ gatifloxacin = sample_rsi(),
+ ethambutol = sample_rsi(),
+ pyrazinamide = sample_rsi(),
+ moxifloxacin = sample_rsi(),
+ kanamycin = sample_rsi())Because all column names are automatically verified for valid drug names or codes, this would have worked exactly the same:
+my_TB_data <- data.frame(RIF = sample_rsi(),
+ INH = sample_rsi(),
+ GAT = sample_rsi(),
+ ETH = sample_rsi(),
+ PZA = sample_rsi(),
+ MFX = sample_rsi(),
+ KAN = sample_rsi())The data set looks like this now:
+head(my_TB_data)
+# rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin
+# 1 R R R R R S
+# 2 S R R S R S
+# 3 R S S I S S
+# 4 R S S R S S
+# 5 R R S I I R
+# 6 R S S S S S
+# kanamycin
+# 1 S
+# 2 R
+# 3 S
+# 4 R
+# 5 S
+# 6 IWe can now add the interpretation of MDR-TB to our data set:
+my_TB_data$mdr <- mdr_tb(my_TB_data)
+# NOTE: No column found as input for `col_mo`, assuming all records contain Mycobacterium tuberculosis.
+# Determining multidrug-resistant organisms (MDRO), according to:
+# Guideline: Companion handbook to the WHO guidelines for the programmatic management of drug-resistant tuberculosis
+# Version: WHO/HTM/TB/2014.11
+# Author: WHO (World Health Organization)
+# Source: https://www.who.int/tb/publications/pmdt_companionhandbook/en/
+# Warning: Reliability might be improved if these antimicrobial results
+# would be available too: `CAP` (capreomycin), `RIB` (rifabutin), `RFP`
+# (rifapentine)And review the result with a frequency table:
+freq(my_TB_data$mdr)
+#
+#
+# **Frequency table of `mdr` from `my_TB_data` (5,000 x 8)**
+#
+# Class: `factor` > `ordered` (`numeric`)
+# Length: 5,000 (of which NA: 0 = 0.00%)
+# Levels: 5: `Negative` < `Mono-resistance` < `Poly-resistance` < `Multidrug res...`
+# Unique: 5
+#
+#
+# | |Item | Count| Percent| Cum. Count| Cum. Percent|
+# |:--|:-------------------------|-----:|-------:|----------:|------------:|
+# |1 |Mono-resistance | 3,297| 65.9%| 3,297| 65.9%|
+# |2 |Negative | 627| 12.5%| 3,924| 78.5%|
+# |3 |Multidrug resistance | 612| 12.2%| 4,536| 90.7%|
+# |4 |Poly-resistance | 263| 5.3%| 4,799| 96.0%|
+# |5 |Extensive drug resistance | 201| 4.0%| 5,000| 100.0%|SPSS.RmdWHONET.Rmdab_property.Rmdbenchmarks.RmdIn 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 Thermus islandicus (B_THERMS_ISL), a bug probably never found before in humans:
T.islandicus <- microbenchmark(as.mo("theisl"),
@@ -236,12 +243,12 @@
print(T.islandicus, unit = "ms", signif = 2)
# Unit: milliseconds
# expr min lq mean median uq max neval
-# as.mo("theisl") 470 480 510 520 530 530 10
-# as.mo("THEISL") 470 470 500 490 520 530 10
-# as.mo("T. islandicus") 74 75 85 76 81 120 10
-# as.mo("T. islandicus") 74 75 92 76 120 140 10
-# as.mo("Thermus islandicus") 73 73 74 74 74 75 10That takes 8.2 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 Thermus islandicus) are almost fast - these are the most probable input from most data sets.
+# as.mo("theisl") 470 470 500 500 520 520 10 +# as.mo("THEISL") 470 470 510 520 520 530 10 +# as.mo("T. islandicus") 73 74 79 74 74 120 10 +# as.mo("T. islandicus") 73 74 84 74 75 130 10 +# as.mo("Thermus islandicus") 74 74 94 75 120 140 10 +That takes 8.1 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 Thermus islandicus) 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 Thermus islandicus (which is very uncommon):
par(mar = c(5, 16, 4, 2)) # set more space for left margin text (16)
@@ -287,8 +294,8 @@
print(run_it, unit = "ms", signif = 3)
# Unit: milliseconds
# expr min lq mean median uq max neval
-# mo_fullname(x) 677 770 812 790 884 988 10So transforming 500,000 values (!!) of 50 unique values only takes 0.79 seconds (789 ms). You only lose time on your unique input values.
+# mo_fullname(x) 633 675 747 722 763 933 10 +So transforming 500,000 values (!!) of 50 unique values only takes 0.72 seconds (721 ms). You only lose time on your unique input values.
So going from mo_fullname("Staphylococcus aureus") to "Staphylococcus aureus" takes 0.0017 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"),
@@ -317,14 +324,14 @@
print(run_it, unit = "ms", signif = 3)
# Unit: milliseconds
# expr min lq mean median uq max neval
-# A 0.442 0.477 0.583 0.572 0.673 0.806 10
-# B 0.442 0.502 0.626 0.611 0.761 0.826 10
-# C 1.360 1.620 1.820 1.810 2.060 2.150 10
-# D 0.449 0.514 0.622 0.639 0.725 0.798 10
-# E 0.401 0.440 0.530 0.508 0.568 0.799 10
-# F 0.353 0.391 0.502 0.494 0.635 0.695 10
-# G 0.451 0.559 0.595 0.609 0.646 0.747 10
-# H 0.191 0.278 0.395 0.376 0.528 0.653 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-5-1.png b/docs/articles/benchmarks_files/figure-html/unnamed-chunk-5-1.png index f1fd99d1..924101ae 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/freq.html b/docs/articles/freq.html index 5694c455..6fc26e20 100644 --- a/docs/articles/freq.html +++ b/docs/articles/freq.html @@ -40,7 +40,7 @@ @@ -76,6 +76,13 @@ Predict antimicrobial resistancefreq.RmdA frequency table is actaually a regular data.frame, with the exception that it contains an additional class.
A frequency table is actually a regular data.frame, with the exception that it contains an additional class.
[1] “frequency_tbl” “data.frame”
@@ -729,7 +736,7 @@ Median: 31 July 2009 (47.39%)na.rm
With the na.rm parameter (defaults to TRUE, but they will always be shown into the header), you can include NA values in the frequency table:
With the na.rm parameter you can remove NA values from the frequency table (defaults to TRUE, but the number of NA values will always be shown into the header):
Frequency table of AMX from a data.frame (2,000 x 49)
Frequency table of AMX from a data.frame (2,000 x 49)
Class: factor > ordered > rsi (numeric)
+Length: 2,000 (of which NA: 771 = 38.55%)
+Levels: 3: S < I < R
+Unique: 4
Drug: Amoxicillin (AMX, J01CA04)
+Group: Beta-lactams/penicillins
+%SI: 44.43%
| + | Item | +Count | +Percent | +Cum. Count | +Cum. Percent | +
|---|---|---|---|---|---|
| 1 | +(NA) | +771 | +38.6% | +771 | +38.6% | +
| 2 | +R | +683 | +34.2% | +1,454 | +72.7% | +
| 3 | +S | +543 | +27.2% | +1,997 | +99.8% | +
| 4 | +I | +3 | +0.2% | +2,000 | +100.0% | +
row.names
The default frequency tables shows row indices. To remove them, use row.names = FALSE:
A frequency table shows row indices. To remove them, use row.names = FALSE:
Frequency table of hospital_id from a data.frame (2,000 x 49)
Class: factor (numeric)
Length: 2,000 (of which NA: 0 = 0.00%)
@@ -842,8 +903,8 @@ Unique: 4
markdown
The markdown parameter is TRUE at default in non-interactive sessions, like in reports created with R Markdown. This will always print all rows, unless nmax is set.
Frequency table of hospital_id from a data.frame (2,000 x 49)
Class: factor (numeric)
Length: 2,000 (of which NA: 0 = 0.00%)
diff --git a/docs/articles/index.html b/docs/articles/index.html
index 1ce938da..d552f329 100644
--- a/docs/articles/index.html
+++ b/docs/articles/index.html
@@ -78,7 +78,7 @@
mo_property.Rmdresistance_predict.RmdWe created this package for both academic research and routine analysis at the Faculty of Medical Sciences of the University of Groningen, the Netherlands, and the Medical Microbiology & Infection Prevention (MMBI) department of the University Medical Center Groningen (UMCG). This R package is actively maintained and is free software; you can freely use and distribute it for both personal and commercial (but not patent) purposes under the terms of the GNU General Public License version 2.0 (GPL-2), as published by the Free Software Foundation. Read the full license here.
This package can be used for:
This package is ready-to-use for a professional environment by specialists in the following fields:
Medical Microbiology
diff --git a/docs/news/index.html b/docs/news/index.html index ca6d8444..744bdd2d 100644 --- a/docs/news/index.html +++ b/docs/news/index.html @@ -78,7 +78,7 @@ @@ -114,6 +114,13 @@ Predict antimicrobial resistanceNote: latest development version
as.rsi() on an MIC value (created with as.mic()), a disk diffusion value (created with the new as.disk()) or on a complete date set containing columns with MIC or disk diffusion values.mo_name() as alias of mo_fullname()
mdr_tb()) and added a new vignette about MDRcid contains the CID code (Compound ID), used by PubChemAMX for amoxicillinatc_certe, ab_umcg and atc_trivial_nl have been removedatc_* functions are superceded by ab_* functionsportion_df() and count_df() this means that their new parameter combine_SI is TRUE at default.guess_mo(), guess_atc(), EUCAST_rules(), interpretive_reading(), rsi()
freq()):
+./inst/eucast/eucast.tsv
as.mo(..., allow_uncertain = 3)
Contents
A data.frame with 455 observations and 13 variables:
A data.frame with 454 observations and 13 variables:
abAntibiotic ID as used in this package (like AMC), using the official EARS-Net (European Antimicrobial Resistance Surveillance Network) codes where available
atcATC code (Anatomical Therapeutic Chemical) as defined by the WHOCC, like J01CR02
cidCompound ID as found in PubChem
In 2019, EUCAST has decided to change the definitions of susceptibility testing categories S, I and R as shown below. Results of several consultations on the new definitions are available on the EUCAST website under "Consultations".
SSusceptible, 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.
ISusceptible, 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.
RResistant: A microorganism is categorised as "Resistant" when there is a high likelihood of therapeutic failure even when there is increased exposure.
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.
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.
Source: http://www.eucast.org/newsiandr/.
diff --git a/docs/reference/count.html b/docs/reference/count.html index 343beaed..37d638bc 100644 --- a/docs/reference/count.html +++ b/docs/reference/count.html @@ -81,7 +81,7 @@ count_R and count_IR can be used to count resistant isolates, count_S and count_a logical to indicate whether all values of S and I must be merged into one, so the output only consists of S+I vs. R (susceptible vs. resistant). This used to be the parameter combine_IR, but this now follows the redefinition by EUCAST about the interpretion of I (increased exposure) in 2019, see below. Default is now TRUE.
a logical to indicate whether all values of S and I must be merged into one, so the output only consists of S+I vs. R (susceptible vs. resistant). This used to be the parameter combine_IR, but this now follows the redefinition by EUCAST about the interpretion of I (increased exposure) in 2019, see section 'Interpretation of S, I and R' below. Default is TRUE.
In 2019, EUCAST has decided to change the definitions of susceptibility testing categories S, I and R as shown below. Results of several consultations on the new definitions are available on the EUCAST website under "Consultations".
SSusceptible, 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.
ISusceptible, 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.
RResistant: A microorganism is categorised as "Resistant" when there is a high likelihood of therapeutic failure even when there is increased exposure.
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.
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.
Source: http://www.eucast.org/newsiandr/.
diff --git a/docs/reference/eucast_rules.html b/docs/reference/eucast_rules.html index 71aeb914..b516af56 100644 --- a/docs/reference/eucast_rules.html +++ b/docs/reference/eucast_rules.html @@ -80,7 +80,7 @@ @@ -116,6 +116,13 @@ Predict antimicrobial resistance +The input of tbl_, possibly with edited values of antibiotics. Or, if verbose = TRUE, a data.frame with all original and new values of the affected bug-drug combinations.
The input of x, possibly with edited values of antibiotics. Or, if verbose = TRUE, a data.frame with all original and new values of the affected bug-drug combinations.
a logical to indicate whether all values of S and I must be merged into one, so the output only consists of S+I vs. R (susceptible vs. resistant). This used to be the parameter combine_IR, but this now follows the redefinition by EUCAST about the interpretion of I (increased exposure) in 2019, see below. Default is now TRUE.
a logical to indicate whether all values of S and I must be merged into one, so the output only consists of S+I vs. R (susceptible vs. resistant). This used to be the parameter combine_IR, but this now follows the redefinition by EUCAST about the interpretion of I (increased exposure) in 2019, see section 'Interpretation of S, I and R' below. Default is TRUE.
mdro() brmo() mrgn() eucast_exceptional_phenotypes()
mdro() brmo() mrgn() mdr_tb() eucast_exceptional_phenotypes()
Determine multidrug-resistant organisms (MDRO)
key_antibiotics(tbl, col_mo = NULL, universal_1 = guess_ab_col(tbl, - "AMX"), universal_2 = guess_ab_col(tbl, "AMC"), - universal_3 = guess_ab_col(tbl, "CXM"), - universal_4 = guess_ab_col(tbl, "TZP"), - universal_5 = guess_ab_col(tbl, "CIP"), - universal_6 = guess_ab_col(tbl, "SXT"), GramPos_1 = guess_ab_col(tbl, - "VAN"), GramPos_2 = guess_ab_col(tbl, "TEC"), - GramPos_3 = guess_ab_col(tbl, "TCY"), GramPos_4 = guess_ab_col(tbl, - "ERY"), GramPos_5 = guess_ab_col(tbl, "OXA"), - GramPos_6 = guess_ab_col(tbl, "RIF"), GramNeg_1 = guess_ab_col(tbl, - "GEN"), GramNeg_2 = guess_ab_col(tbl, "TOB"), - GramNeg_3 = guess_ab_col(tbl, "COL"), GramNeg_4 = guess_ab_col(tbl, - "CTX"), GramNeg_5 = guess_ab_col(tbl, "CAZ"), - GramNeg_6 = guess_ab_col(tbl, "MEM"), warnings = TRUE, ...) +key_antibiotics(x, col_mo = NULL, universal_1 = guess_ab_col(x, "AMX"), + universal_2 = guess_ab_col(x, "AMC"), universal_3 = guess_ab_col(x, + "CXM"), universal_4 = guess_ab_col(x, "TZP"), + universal_5 = guess_ab_col(x, "CIP"), universal_6 = guess_ab_col(x, + "SXT"), GramPos_1 = guess_ab_col(x, "VAN"), + GramPos_2 = guess_ab_col(x, "TEC"), GramPos_3 = guess_ab_col(x, + "TCY"), GramPos_4 = guess_ab_col(x, "ERY"), + GramPos_5 = guess_ab_col(x, "OXA"), GramPos_6 = guess_ab_col(x, + "RIF"), GramNeg_1 = guess_ab_col(x, "GEN"), + GramNeg_2 = guess_ab_col(x, "TOB"), GramNeg_3 = guess_ab_col(x, + "COL"), GramNeg_4 = guess_ab_col(x, "CTX"), + GramNeg_5 = guess_ab_col(x, "CAZ"), GramNeg_6 = guess_ab_col(x, + "MEM"), warnings = TRUE, ...) key_antibiotics_equal(x, y, type = c("keyantibiotics", "points"), ignore_I = TRUE, points_threshold = 2, info = FALSE)@@ -263,8 +269,8 @@
| tbl | -table with antibiotics coloms, like |
+ x | +table with antibiotics coloms, like |
|---|---|---|---|
| col_mo | @@ -290,10 +296,6 @@... | other parameters passed on to function |
|
| x, y | -characters to compare |
- ||
| type | type to determine weighed isolates; can be |
@@ -310,6 +312,10 @@
info | print progress |
| x, y | +characters to compare |
+
mdro(x, country = NULL, col_mo = NULL, info = TRUE, - verbose = FALSE, ...) +mdro(x, country = NULL, guideline = NULL, col_mo = NULL, + info = TRUE, verbose = FALSE, ...) brmo(..., country = "nl") mrgn(x, country = "de", ...) -eucast_exceptional_phenotypes(x, country = "EUCAST", ...)+mdr_tb(x, guideline = "TB", ...) + +eucast_exceptional_phenotypes(x, guideline = "EUCAST", ...)
| country | -country code to determine guidelines. EUCAST rules will be used when left empty, see Details. Should be or a code from the list of ISO 3166-1 alpha-2 country codes. Case-insensitive. Currently supported are |
+ country code to determine guidelines. Should be or a code from the list of ISO 3166-1 alpha-2 country codes. Case-insensitive. |
+
|---|---|---|
| guideline | +a specific guideline to mention. For some countries this will be determined automatically, see Details. EUCAST guidelines will be used when left empty, see Details. |
|
| col_mo | @@ -285,7 +298,15 @@||
| combine_SI | -a logical to indicate whether all values of S and I must be merged into one, so the output only consists of S+I vs. R (susceptible vs. resistant). This used to be the parameter |
+ a logical to indicate whether all values of S and I must be merged into one, so the output only consists of S+I vs. R (susceptible vs. resistant). This used to be the parameter |
| combine_IR | @@ -335,9 +342,9 @@ portion_R and portion_IR can be used to calculate resistance, portion_S and port