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 21 December 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 22 December 2019.
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 %>%
@@ -437,8 +437,8 @@
# 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 (2,972 values changed)
-# Non-EUCAST: set ampicillin = R where amoxicillin/clav acid = R (139 values changed)
+# Non-EUCAST: set amoxicillin/clav acid = S where ampicillin = S (3,027 values changed)
+# Non-EUCAST: set ampicillin = R where amoxicillin/clav acid = R (129 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)
@@ -463,14 +463,14 @@
# Pasteurella multocida (no changes)
# Staphylococcus (no changes)
# Streptococcus groups A, B, C, G (no changes)
-# Streptococcus pneumoniae (959 values changed)
+# Streptococcus pneumoniae (1,039 values changed)
# Viridans group streptococci (no changes)
#
# EUCAST Expert Rules, Intrinsic Resistance and Exceptional Phenotypes (v3.1, 2016)
-# Table 01: Intrinsic resistance in Enterobacteriaceae (1,333 values changed)
+# Table 01: Intrinsic resistance in Enterobacteriaceae (1,305 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 (2,723 values changed)
+# Table 04: Intrinsic resistance in Gram-positive bacteria (2,835 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)
@@ -478,15 +478,15 @@
# Table 13: Interpretive rules for quinolones (no changes)
#
# -------------------------------------------------------------------------------
-# EUCAST rules affected 6,551 out of 20,000 rows, making a total of 8,126 edits
+# EUCAST rules affected 6,667 out of 20,000 rows, making a total of 8,335 edits
# => added 0 test results
#
-# => changed 8,126 test results
-# - 115 test results changed from S to I
-# - 4,709 test results changed from S to R
-# - 1,179 test results changed from I to S
-# - 330 test results changed from I to R
-# - 1,793 test results changed from R to S
+# => changed 8,335 test results
+# - 121 test results changed from S to I
+# - 4,855 test results changed from S to R
+# - 1,244 test results changed from I to S
+# - 332 test results changed from I to R
+# - 1,783 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 28.5% is suitable for resistance analysis! We can now filter on it with the filter() function, also from the dplyr package:
So only 28.6% 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:
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 Y10, 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 C4, sorted on date:
| isolate | @@ -541,21 +541,21 @@|||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | -2010-06-10 | -Y10 | +2010-02-20 | +C4 | B_ESCHR_COLI | S | S | -R | +S | S | TRUE |
| 2 | -2010-09-27 | -Y10 | +2010-03-07 | +C4 | B_ESCHR_COLI | -S | +R | S | S | S | @@ -563,8 +563,8 @@|
| 3 | -2010-11-27 | -Y10 | +2010-06-08 | +C4 | B_ESCHR_COLI | S | S | @@ -574,63 +574,19 @@||||
| 4 | -2010-12-06 | -Y10 | +2010-10-24 | +C4 | B_ESCHR_COLI | S | S | -R | +S | S | FALSE |
| 5 | -2011-02-18 | -Y10 | -B_ESCHR_COLI | -R | -S | -S | -S | -FALSE | -|||
| 6 | -2011-04-05 | -Y10 | -B_ESCHR_COLI | -R | -R | -S | -R | -FALSE | -|||
| 7 | -2011-05-29 | -Y10 | -B_ESCHR_COLI | -R | -S | -S | -R | -FALSE | -|||
| 8 | -2011-06-04 | -Y10 | -B_ESCHR_COLI | -S | -S | -S | -S | -FALSE | -|||
| 9 | -2011-06-14 | -Y10 | +2011-04-27 | +C4 | B_ESCHR_COLI | S | S | @@ -639,9 +595,20 @@TRUE | |||
| 10 | -2011-11-02 | -Y10 | +6 | +2011-05-13 | +C4 | +B_ESCHR_COLI | +R | +S | +S | +S | +FALSE | +
| 7 | +2011-12-08 | +C4 | B_ESCHR_COLI | S | S | @@ -649,9 +616,42 @@S | FALSE | ||||
| 8 | +2012-05-22 | +C4 | +B_ESCHR_COLI | +I | +S | +S | +S | +TRUE | +|||
| 9 | +2012-06-10 | +C4 | +B_ESCHR_COLI | +S | +S | +S | +S | +FALSE | +|||
| 10 | +2012-08-14 | +C4 | +B_ESCHR_COLI | +R | +S | +S | +S | +FALSE | +
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 3 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(.)) %>%
@@ -662,7 +662,7 @@
# 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 15,020 first weighted isolates (75.1% of total)| isolate | @@ -679,22 +679,22 @@|||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | -2010-06-10 | -Y10 | +2010-02-20 | +C4 | B_ESCHR_COLI | S | S | -R | +S | S | TRUE | TRUE | |
| 2 | -2010-09-27 | -Y10 | +2010-03-07 | +C4 | B_ESCHR_COLI | -S | +R | S | S | S | @@ -703,8 +703,8 @@|||
| 3 | -2010-11-27 | -Y10 | +2010-06-08 | +C4 | B_ESCHR_COLI | S | S | @@ -715,82 +715,82 @@||||||
| 4 | -2010-12-06 | -Y10 | +2010-10-24 | +C4 | B_ESCHR_COLI | S | S | -R | -S | -FALSE | -FALSE | -||
| 5 | -2011-02-18 | -Y10 | -B_ESCHR_COLI | -R | -S | S | S | FALSE | TRUE | ||||
| 6 | -2011-04-05 | -Y10 | +|||||||||||
| 5 | +2011-04-27 | +C4 | B_ESCHR_COLI | -R | -R | +S | S | R | +S | +TRUE | +TRUE | +||
| 6 | +2011-05-13 | +C4 | +B_ESCHR_COLI | +R | +S | +S | +S | FALSE | TRUE | ||||
| 7 | -2011-05-29 | -Y10 | +2011-12-08 | +C4 | B_ESCHR_COLI | -R | S | S | -R | +S | +S | FALSE | TRUE |
| 8 | -2011-06-04 | -Y10 | +2012-05-22 | +C4 | +B_ESCHR_COLI | +I | +S | +S | +S | +TRUE | +TRUE | +||
| 9 | +2012-06-10 | +C4 | B_ESCHR_COLI | S | S | S | S | FALSE | -TRUE | -||||
| 9 | -2011-06-14 | -Y10 | -B_ESCHR_COLI | -S | -S | -R | -S | -TRUE | -TRUE | +FALSE | |||
| 10 | -2011-11-02 | -Y10 | +2012-08-14 | +C4 | B_ESCHR_COLI | -S | +R | S | S | S | @@ -799,11 +799,11 @@
Instead of 2, now 9 isolates are flagged. In total, 75.1% of all isolates are marked ‘first weighted’ - 46.6% 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 3, now 9 isolates are flagged. In total, 74.7% of all isolates are marked ‘first weighted’ - 46.2% 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 15,020 isolates for analysis.
+So we end up with 14,948 isolates for analysis.
We can remove unneeded columns:
@@ -811,7 +811,6 @@| date | patient_id | hospital | @@ -828,101 +827,95 @@||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2 | -2017-05-17 | -R1 | +2017-03-14 | +J6 | Hospital B | -B_KLBSL_PNMN | +B_STPHY_AURS | +S | +S | R | S | -S | -S | -F | -Gram-negative | -Klebsiella | -pneumoniae | +M | +Gram-positive | +Staphylococcus | +aureus | TRUE |
| 9 | -2016-05-07 | -N8 | +2015-04-15 | +B9 | +Hospital D | +B_STPHY_AURS | +R | +S | +S | +S | +M | +Gram-positive | +Staphylococcus | +aureus | +TRUE | +|||||||
| 2017-01-04 | +Q9 | Hospital A | B_ESCHR_COLI | -R | S | S | R | +S | F | Gram-negative | Escherichia | coli | TRUE | |||||||||
| 11 | -2015-11-13 | -U7 | -Hospital B | -B_STPHY_AURS | -S | -S | -S | -S | -F | -Gram-positive | -Staphylococcus | -aureus | -TRUE | -|||||||||
| 14 | -2011-08-27 | -C10 | -Hospital C | -B_STPHY_AURS | -S | -S | -R | -R | -M | -Gram-positive | -Staphylococcus | -aureus | -TRUE | -|||||||||
| 15 | -2013-10-17 | -R10 | -Hospital B | -B_STPHY_AURS | -R | -S | -S | -S | -F | -Gram-positive | -Staphylococcus | -aureus | -TRUE | -|||||||||
| 16 | -2011-10-01 | -I6 | +2015-05-23 | +X2 | Hospital A | B_STRPT_PNMN | -S | -S | R | R | -M | +R | +R | +F | Gram-positive | Streptococcus | pneumoniae | TRUE | ||||
| 2017-05-20 | +X5 | +Hospital C | +B_STPHY_AURS | +R | +R | +S | +S | +F | +Gram-positive | +Staphylococcus | +aureus | +TRUE | +||||||||||
| 2016-09-27 | +N7 | +Hospital A | +B_ESCHR_COLI | +R | +S | +S | +S | +F | +Gram-negative | +Escherichia | +coli | +TRUE | +
Time for the analysis!
@@ -942,7 +935,7 @@Frequency table
Class: character
-Length: 15,020 (of which NA: 0 = 0%)
+Length: 14,948 (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) %>%
@@ -1010,19 +1003,19 @@ Longest: 24
Hospital A
-0.4687360
+0.4639385
Hospital B
-0.4664905
+0.4647623
Hospital C
-0.4576649
+0.4596631
Hospital D
-0.4561813
+0.4755767
@@ -1040,23 +1033,23 @@ Longest: 24
Hospital A
-0.4687360
-4478
+0.4639385
+4423
Hospital B
-0.4664905
-5297
+0.4647623
+5321
Hospital C
-0.4576649
-2244
+0.4596631
+2256
Hospital D
-0.4561813
-3001
+0.4755767
+2948
@@ -1076,27 +1069,27 @@ Longest: 24
Escherichia
-0.9259160
-0.8915485
-0.9933137
+0.9298056
+0.8983531
+0.9950054
Klebsiella
-0.9177994
-0.8990291
-0.9967638
+0.9313131
+0.8942761
+0.9932660
Staphylococcus
-0.9244107
-0.9260363
-0.9943105
+0.9212385
+0.9166214
+0.9934818
Streptococcus
-0.6183868
+0.6080910
0.0000000
-0.6183868
+0.6080910
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diff --git a/docs/articles/benchmarks.html b/docs/articles/benchmarks.html
index 74b073bd..bf4f0537 100644
--- a/docs/articles/benchmarks.html
+++ b/docs/articles/benchmarks.html
@@ -41,7 +41,7 @@
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 15.5 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 always very fast and only take some thousands of seconds to coerce - they are the most probable input from most data sets.
+# 1574.00 10 +# 1563.00 10 +# 36.46 10 +# 44.17 10 +# 30.28 10 +That takes 6.2 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 Methanosarcina semesiae) are always very fast and only take some thousands of seconds to coerce - they 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):
The highest outliers are the first times. All next determinations were done in only thousands of seconds, because the as.mo() function learns from its own output to speed up determinations for next times.
So transforming 500,000 values (!!) of 50 unique values only takes 0.59 seconds (586 ms). You only lose time on your unique input values.
+# mo_name(x) 548 581 605 593 611 735 100 +So transforming 500,000 values (!!) of 50 unique values only takes 0.59 seconds (593 ms). You only lose time on your unique input values.
So going from mo_name("Staphylococcus aureus") to "Staphylococcus aureus" takes 0.0008 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"),
@@ -341,14 +326,14 @@
print(run_it, unit = "ms", signif = 3)
# Unit: milliseconds
# expr min lq mean median uq max neval
-# A 0.429 0.450 0.458 0.453 0.469 0.493 10
-# B 0.484 0.499 0.519 0.506 0.518 0.646 10
-# C 0.674 0.721 0.777 0.796 0.816 0.853 10
-# D 0.485 0.490 0.507 0.499 0.518 0.554 10
-# E 0.439 0.453 0.462 0.460 0.476 0.482 10
-# F 0.425 0.446 0.454 0.455 0.462 0.477 10
-# G 0.447 0.450 0.464 0.457 0.471 0.508 10
-# H 0.449 0.457 0.477 0.459 0.464 0.641 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.
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+@Article{,
+ title = {AMR - An R Package for Working with Antimicrobial Resistance Data},
+ author = {M S Berends and C F Luz and A W Friedrich and B N M Sinha and C J Albers and C Glasner},
+ journal = {bioRxiv},
+ publisher = {Cold Spring Harbor Laboratory},
+ year = {2019},
+ doi = {10.1101/810622},
+ url = {https://doi.org/10.1101/810622},
+}
+
- Used in almost 80 countries
- Since its first public release in early 2018, this package has been downloaded over 25,000 times from 79 countries (as of December 2019, CRAN logs). Click the map to enlarge.
as.disk() limited to a maximum of 50 millimetersclean to cleaner, as this package was renamed accordingly upon CRAN requestalso_single_tested w
as.mic())lintr packagealso_single_tested w
as.mo(..., allow_uncertain = 3)if using different lengths of pattern and x in %like%, it will now return the call
as.mo(..., allow_uncertain = 3)Percentages will now will rounded more logically (e.g. in freq function)
crayon, to support formatted text in the consoletidyr is now mandatory (went to Import field) since portion_df and count_df rely on itas.mo(..., allow_uncertain = 3)
as.mo(..., allow_uncertain = 3)Other small fixes
as.mo(..., allow_uncertain = 3)Functions as.rsi and as.mic now add the package name and version as attributesREADME.md with more examplesas.mo(..., allow_uncertain = 3)
Contents
- Used in almost 80 countries
- Since its first public release in early 2018, this package has been downloaded over 25,000 times from 79 countries (as of December 2019, CRAN logs). Click the map to enlarge.