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 30 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 11 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 %>%
@@ -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 (2,960 values changed)
-# Non-EUCAST: set ampicillin = R where amoxicillin/clav acid = R (144 values changed)
+# Non-EUCAST: set amoxicillin/clav acid = S where ampicillin = S (2,997 values changed)
+# Non-EUCAST: set ampicillin = R where amoxicillin/clav acid = R (165 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)
@@ -448,14 +448,14 @@
# Pasteurella multocida (no changes)
# Staphylococcus (no changes)
# Streptococcus groups A, B, C, G (no changes)
-# Streptococcus pneumoniae (997 values changed)
+# Streptococcus pneumoniae (1,063 values changed)
# Viridans group streptococci (no changes)
#
# EUCAST Expert Rules, Intrinsic Resistance and Exceptional Phenotypes (v3.1, 2016)
-# Table 01: Intrinsic resistance in Enterobacteriaceae (1,331 values changed)
+# Table 01: Intrinsic resistance in Enterobacteriaceae (1,242 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 (2,705 values changed)
+# Table 04: Intrinsic resistance in Gram-positive bacteria (2,782 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)
@@ -463,15 +463,15 @@
# Table 13: Interpretive rules for quinolones (no changes)
#
# -------------------------------------------------------------------------------
-# EUCAST rules affected 6,481 out of 20,000 rows, making a total of 8,137 edits
+# EUCAST rules affected 6,564 out of 20,000 rows, making a total of 8,249 edits
# => added 0 test results
#
-# => changed 8,137 test results
-# - 115 test results changed from S to I
-# - 4,732 test results changed from S to R
-# - 1,228 test results changed from I to S
-# - 330 test results changed from I to R
-# - 1,732 test results changed from R to S
+# => changed 8,249 test results
+# - 102 test results changed from S to I
+# - 4,787 test results changed from S to R
+# - 1,224 test results changed from I to S
+# - 363 test results changed from I to R
+# - 1,773 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:
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 E9, 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 D2, sorted on date:
| isolate | @@ -526,52 +526,52 @@||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | -2010-07-16 | -E9 | +2010-02-23 | +D2 | B_ESCHR_COLI | S | S | -S | +R | S | TRUE | |
| 2 | -2010-07-22 | -E9 | +2010-03-20 | +D2 | B_ESCHR_COLI | -S | -S | R | +R | +S | S | FALSE |
| 3 | -2010-08-26 | -E9 | +2010-04-13 | +D2 | B_ESCHR_COLI | -R | S | -R | +S | +S | S | FALSE |
| 4 | -2010-10-17 | -E9 | +2010-07-22 | +D2 | B_ESCHR_COLI | S | S | -S | +R | S | FALSE | |
| 5 | -2010-10-25 | -E9 | +2010-07-22 | +D2 | B_ESCHR_COLI | S | S | @@ -581,10 +581,10 @@|||||
| 6 | -2011-02-27 | -E9 | +2010-08-04 | +D2 | B_ESCHR_COLI | -S | +R | S | S | S | @@ -592,8 +592,8 @@||
| 7 | -2011-03-15 | -E9 | +2010-08-19 | +D2 | B_ESCHR_COLI | S | S | @@ -603,40 +603,40 @@|||||
| 8 | -2011-03-19 | -E9 | +2010-09-02 | +D2 | B_ESCHR_COLI | -R | -R | +I | S | +R | S | FALSE |
| 9 | -2011-06-20 | -E9 | +2010-09-04 | +D2 | B_ESCHR_COLI | S | S | -S | +R | S | FALSE | |
| 10 | -2011-12-03 | -E9 | +2010-09-18 | +D2 | B_ESCHR_COLI | +R | S | S | S | -S | -TRUE | +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 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 15,044 first weighted isolates (75.2% of total)| isolate | @@ -664,131 +664,131 @@|||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | -2010-07-16 | -E9 | +2010-02-23 | +D2 | B_ESCHR_COLI | S | S | -S | +R | S | TRUE | TRUE | |
| 2 | -2010-07-22 | -E9 | +2010-03-20 | +D2 | B_ESCHR_COLI | -S | -S | R | +R | +S | S | FALSE | TRUE |
| 3 | -2010-08-26 | -E9 | +2010-04-13 | +D2 | B_ESCHR_COLI | -R | S | -R | +S | +S | S | FALSE | TRUE |
| 4 | -2010-10-17 | -E9 | +2010-07-22 | +D2 | B_ESCHR_COLI | S | S | -S | +R | S | FALSE | TRUE | |
| 5 | -2010-10-25 | -E9 | +2010-07-22 | +D2 | B_ESCHR_COLI | S | S | S | S | FALSE | -FALSE | +TRUE | |
| 6 | -2011-02-27 | -E9 | +2010-08-04 | +D2 | B_ESCHR_COLI | -S | +R | S | S | S | FALSE | -FALSE | +TRUE |
| 7 | -2011-03-15 | -E9 | +2010-08-19 | +D2 | B_ESCHR_COLI | S | S | S | S | FALSE | -FALSE | +TRUE | |
| 8 | -2011-03-19 | -E9 | +2010-09-02 | +D2 | B_ESCHR_COLI | -R | -R | +I | S | +R | S | FALSE | TRUE |
| 9 | -2011-06-20 | -E9 | +2010-09-04 | +D2 | B_ESCHR_COLI | S | S | +R | +S | +FALSE | +FALSE | +||
| 10 | +2010-09-18 | +D2 | +B_ESCHR_COLI | +R | +S | S | S | FALSE | TRUE | ||||
| 10 | -2011-12-03 | -E9 | -B_ESCHR_COLI | -S | -S | -S | -S | -TRUE | -TRUE | -
Instead of 2, now 7 isolates are flagged. In total, 75.2% of all isolates are marked ‘first weighted’ - 46.9% 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, 75.1% of all isolates are marked ‘first weighted’ - 46.9% 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 15,044 isolates for analysis.
+So we end up with 15,026 isolates for analysis.
We can remove unneeded columns:
@@ -796,7 +796,6 @@| date | patient_id | hospital | @@ -813,62 +812,58 @@|||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 3 | -2016-12-08 | -P9 | -Hospital B | -B_ESCHR_COLI | -S | -S | -R | -S | -F | -Gram-negative | -Escherichia | -coli | -TRUE | -||||||||||
| 4 | -2014-11-30 | -V1 | -Hospital B | -B_KLBSL_PNMN | -R | -S | -S | -R | -F | -Gram-negative | -Klebsiella | -pneumoniae | -TRUE | -||||||||||
| 5 | -2017-09-12 | -T5 | +2017-05-19 | +J2 | Hospital D | -B_STPHY_AURS | -S | -S | -S | +B_ESCHR_COLI | R | -F | -Gram-positive | -Staphylococcus | -aureus | +R | +S | +S | +M | +Gram-negative | +Escherichia | +coli | TRUE |
| 6 | -2010-01-31 | -W3 | +2016-07-09 | +K4 | Hospital A | +B_STRPT_PNMN | +S | +S | +R | +R | +M | +Gram-positive | +Streptococcus | +pneumoniae | +TRUE | +||||||||
| 2014-05-10 | +R6 | +Hospital C | B_ESCHR_COLI | R | S | +S | +S | +F | +Gram-negative | +Escherichia | +coli | +TRUE | +|||||||||||
| 2011-06-29 | +T4 | +Hospital B | +B_ESCHR_COLI | R | +R | +S | S | F | Gram-negative | @@ -877,35 +872,33 @@TRUE | |||||||||||||
| 7 | -2015-09-28 | -K4 | -Hospital B | -B_KLBSL_PNMN | +2012-02-11 | +O10 | +Hospital A | +B_STRPT_PNMN | +I | +I | R | -S | R | -S | -M | -Gram-negative | -Klebsiella | +F | +Gram-positive | +Streptococcus | pneumoniae | TRUE | |
| 8 | -2011-08-30 | -B6 | -Hospital A | -B_STPHY_AURS | -S | -S | +2017-04-09 | +D3 | +Hospital D | +B_ESCHR_COLI | +R | +R | S | S | M | -Gram-positive | -Staphylococcus | -aureus | +Gram-negative | +Escherichia | +coli | TRUE | |
| 1 | Escherichia coli | -7,493 | -49.81% | -7,493 | -49.81% | +7,425 | +49.41% | +7,425 | +49.41% | ||||||||||||||
| 2 | Staphylococcus aureus | -3,718 | -24.71% | -11,211 | -74.52% | +3,806 | +25.33% | +11,231 | +74.74% | ||||||||||||||
| 3 | Streptococcus pneumoniae | -2,293 | -15.24% | -13,504 | -89.76% | +2,311 | +15.38% | +13,542 | +90.12% | ||||||||||||||
| 4 | Klebsiella pneumoniae | -1,540 | -10.24% | -15,044 | +1,484 | +9.88% | +15,026 | 100.00% | |||||||||||||||
| Hospital A | -0.4644833 | +0.4553786 | |||||||||||||||||||||
| Hospital B | -0.4626231 | +0.4653638 | |||||||||||||||||||||
| Hospital C | -0.4616060 | +0.4613043 | |||||||||||||||||||||
| Hospital D | -0.4776800 | +0.4729412 |
EUCAST.RmdMDR.RmdThe data set now looks like this:
head(my_TB_data)
# rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin
-# 1 S S I R R S
-# 2 S S R R I S
+# 1 R R R R S S
+# 2 S S S S R R
# 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
+# 4 S R I S S S
+# 5 R I S S S R
+# 6 R R R R R I
# kanamycin
# 1 S
-# 2 I
-# 3 I
-# 4 R
-# 5 R
-# 6 SWe 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
@@ -356,40 +356,40 @@ Unique: 5SPSS.RmdWHONET.Rmdbenchmarks.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 15.6 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.
+That takes 14.9 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 0.72 seconds (717 ms). You only lose time on your unique input values.
+# mo_name(x) 647 669 689 685 701 770 10 +So transforming 500,000 values (!!) of 50 unique values only takes 0.69 seconds (685 ms). You only lose time on your unique input values.
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:
So going from mo_name("Staphylococcus aureus") to "Staphylococcus aureus" takes 0.001 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"),
C = mo_name("Staphylococcus aureus"),
@@ -326,14 +326,14 @@
print(run_it, unit = "ms", signif = 3)
# Unit: milliseconds
# expr min lq mean median uq max neval
-# A 0.444 0.449 0.473 0.460 0.466 0.630 10
-# B 0.483 0.496 0.511 0.505 0.535 0.539 10
-# C 0.709 0.839 0.850 0.846 0.886 0.921 10
-# D 0.463 0.483 0.521 0.499 0.548 0.694 10
-# E 0.433 0.451 0.493 0.484 0.513 0.611 10
-# F 0.434 0.450 0.481 0.458 0.484 0.582 10
-# G 0.434 0.443 0.471 0.455 0.489 0.577 10
-# H 0.434 0.436 0.471 0.451 0.480 0.614 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 5702abb8..ff9cd6be 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 e0d2422f..75f2ee6a 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 6ee94219..f0182e1d 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 7b5c5307..647d3904 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 bf4224fb..bb494701 100644 --- a/docs/articles/resistance_predict.html +++ b/docs/articles/resistance_predict.html @@ -187,7 +187,7 @@resistance_predict.Rmd
Used in over 70 countries
- Since its first public release in early 2018, this package has been downloaded over 25,000 times from 75 countries (as of November 2019, CRAN logs). Click the map to enlarge.
This package is available on the official R network (CRAN), which has a peer-reviewed submission process. Install this package in R with:
+This package is available here on the official R network (CRAN), which has a peer-reviewed submission process. Install this package in R from CRAN by using the command:
It will be downloaded and installed automatically. For RStudio, click on the menu Tools > Install Packages… and then type in “AMR” and press Install.
Note: Not all functions on this website may be available in this latest release. To use all functions and data sets mentioned on this website, install the latest development version.
diff --git a/docs/news/index.html b/docs/news/index.html index f806f7ac..4e024002 100644 --- a/docs/news/index.html +++ b/docs/news/index.html @@ -84,7 +84,7 @@as.mo(..., allow_uncertain = 3)
Contents
Used in over 70 countries
- Since its first public release in early 2018, this package has been downloaded over 25,000 times from 75 countries (as of November 2019, CRAN logs). Click the map to enlarge.