From db5dfff0ca6dbd1f9afffed234f99608bea999d8 Mon Sep 17 00:00:00 2001
From: "Matthijs S. Berends" 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 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 The data is already quite clean, but we still need to transform some variables. The So only 28.5% is suitable for resistance analysis! We can now filter on it with the So only 28.6% is suitable for resistance analysis! We can now filter on it with the For future use, the above two syntaxes can be shortened with the 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: 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 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 If a column exists with a name like ‘key(…)ab’ the 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 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: Time for the analysis! Frequency table Class: character Shortest: 16How to conduct AMR analysis
Matthijs S. Berends
- 21 December 2019
+ 22 December 2019
AMR.Rmd
Introduction
@@ -227,21 +227,21 @@
-
2019-12-21
+2019-12-22
abcd
Escherichia coli
S
S
-
2019-12-21
+2019-12-22
abcd
Escherichia coli
S
R
-
2019-12-21
+2019-12-22
efgh
Escherichia coli
R
@@ -336,70 +336,70 @@
-
2011-10-17
-V10
-Hospital D
+2017-03-14
+J6
+Hospital B
Staphylococcus aureus
S
+I
+R
S
-S
-S
-F
+M
-
-2017-05-17
-R1
-Hospital B
-Klebsiella pneumoniae
-S
-S
-S
-S
-F
-
-
-2015-07-05
-J5
-Hospital B
-Escherichia coli
-S
+2015-04-15
+B9
+Hospital D
+Staphylococcus aureus
+R
S
S
S
M
-
2012-01-01
-R10
-Hospital C
-Escherichia coli
-S
-S
-S
-S
-F
-
-
+2014-07-18
-T6
+2017-01-04
+Q9
Hospital A
Escherichia coli
S
S
+R
+S
+F
+
+
+2015-05-23
+X2
+Hospital A
+Streptococcus pneumoniae
+R
+S
+R
+S
+F
+
+
2017-05-20
+X5
+Hospital C
+Staphylococcus aureus
+R
+R
S
S
F
-
@@ -421,8 +421,8 @@
#
# Item Count Percent Cum. Count Cum. Percent
# --- ----- ------- -------- ----------- -------------
-# 1 M 10,402 52.01% 10,402 52.01%
-# 2 F 9,598 47.99% 20,000 100.00%
+# 1 M 10,464 52.32% 10,464 52.32%
+# 2 F 9,536 47.68% 20,000 100.00%
2016-07-17
-K1
-Hospital C
+2016-09-27
+N7
+Hospital A
Escherichia coli
-I
-I
+R
S
S
-M
+S
+F
M
and F
. From a researchers perspective: there are slightly more men. Nothing we didn’t already know.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.
filter()
function, also from the dplyr
package:filter()
function, also from the dplyr
package:filter_first_isolate()
function:
First weighted isolates
-
-
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
+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.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.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 @@
filter_first_isolate()
, there’s a shortcut for this new algorithm too:
-
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
+
-Length: 15,020 (of which NA: 0 = 0%)
+Length: 14,948 (of which NA: 0 = 0%)
Unique: 4
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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