From 702da574c0ceb54b45a0994747197b87d4314ede Mon Sep 17 00:00:00 2001
From: "Matthijs S. Berends" Now, let’s start the cleaning and the analysis! So, we can draw at least two conclusions immediately. From a data scientist perspective, the data looks clean: only values The data is already quite clean, but we still need to transform some variables. The So only 28.3% is suitable for resistance analysis! We can now filter on it with the Instead of 2, now 9 isolates are flagged. In total, 75.1% of all isolates are marked ‘first weighted’ - 46.8% 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 2, now 9 isolates are flagged. In total, 75.3% of all isolates are marked ‘first weighted’ - 47% 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 15,014 isolates for analysis. So we end up with 15,064 isolates for analysis. We can remove unneeded columns: Or can be used like the Frequency table of Frequency table of Columns: 2 Shortest: 16
-
2014-06-27
-V8
-Hospital B
-Klebsiella pneumoniae
-S
+2014-12-18
+S8
+Hospital D
+Escherichia coli
S
+R
S
S
F
-
2013-02-12
-L4
-Hospital A
-Streptococcus pneumoniae
-I
+2010-05-29
+Z4
+Hospital C
+Escherichia coli
S
S
S
-M
+S
+F
-
-2013-11-26
-T2
-Hospital A
-Staphylococcus aureus
-R
-R
-S
-S
-F
-
-
-2013-12-27
-J10
-Hospital A
+2015-11-20
+G6
+Hospital C
Escherichia coli
S
S
@@ -377,28 +366,39 @@
S
M
-
2013-09-22
-I7
-Hospital C
-Staphylococcus aureus
-S
-S
-S
-S
-M
-
-
+2014-01-26
-Q6
-Hospital A
-Klebsiella pneumoniae
-R
+2015-11-07
+Q10
+Hospital D
+Escherichia coli
+S
S
S
S
F
+
+2013-11-26
+P8
+Hospital B
+Streptococcus pneumoniae
+S
+R
+S
+S
+F
+
+
2014-07-30
+L3
+Hospital B
+Escherichia coli
+R
+S
+S
+S
+M
+M
and F
. From a researcher 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 %>%
@@ -449,14 +449,14 @@
# Pasteurella multocida (no new changes)
# Staphylococcus (no new changes)
# Streptococcus groups A, B, C, G (no new changes)
-# Streptococcus pneumoniae (1509 new changes)
+# Streptococcus pneumoniae (1421 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 (1276 new changes)
+# Table 01: Intrinsic resistance in Enterobacteriaceae (1367 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 (2758 new changes)
+# Table 04: Intrinsic resistance in Gram-positive bacteria (2632 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)
@@ -464,24 +464,24 @@
# Table 13: Interpretive rules for quinolones (no new changes)
#
# Other rules
-# Non-EUCAST: amoxicillin/clav acid = S where ampicillin = S (2254 new changes)
-# Non-EUCAST: ampicillin = R where amoxicillin/clav acid = R (138 new changes)
+# Non-EUCAST: amoxicillin/clav acid = S where ampicillin = S (2233 new changes)
+# Non-EUCAST: ampicillin = R where amoxicillin/clav acid = R (113 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 6,564 out of 20,000 rows, making a total of 7,935 edits
+# EUCAST rules affected 6,458 out of 20,000 rows, making a total of 7,766 edits
# => added 0 test results
#
-# => changed 7,935 test results
-# - 109 test results changed from S to I
-# - 4,768 test results changed from S to R
-# - 1,081 test results changed from I to S
-# - 345 test results changed from I to R
-# - 1,615 test results changed from R to S
-# - 17 test results changed from R to I
+# => changed 7,766 test results
+# - 106 test results changed from S to I
+# - 4,695 test results changed from S to R
+# - 1,068 test results changed from I to S
+# - 323 test results changed from I to R
+# - 1,556 test results changed from R to S
+# - 18 test results changed from R to I
# --------------------------------------------------------------------------
#
# Use verbose = TRUE to get a data.frame with all specified edits instead.
filter()
function, also from the dplyr
package:
1
-2010-01-07
-K9
+2010-01-18
+T2
B_ESCHR_COL
S
S
@@ -547,43 +547,43 @@
2
-2010-02-14
-K9
+2010-01-26
+T2
B_ESCHR_COL
R
S
-S
+R
S
FALSE
3
-2010-02-18
-K9
+2010-02-26
+T2
B_ESCHR_COL
-R
+I
+S
S
-R
S
FALSE
4
-2010-06-01
-K9
+2010-03-13
+T2
B_ESCHR_COL
S
S
-R
+S
S
FALSE
5
-2010-08-14
-K9
+2010-08-18
+T2
B_ESCHR_COL
-S
+R
S
S
S
@@ -591,8 +591,8 @@
6
-2010-11-08
-K9
+2010-12-10
+T2
B_ESCHR_COL
S
S
@@ -602,21 +602,21 @@
7
-2010-11-16
-K9
+2011-05-02
+T2
B_ESCHR_COL
S
S
+R
S
-S
-FALSE
+TRUE
8
-2010-12-11
-K9
+2011-05-02
+T2
B_ESCHR_COL
-S
+R
S
S
S
@@ -624,22 +624,22 @@
9
-2011-02-27
-K9
+2011-05-15
+T2
B_ESCHR_COL
-R
S
S
S
-TRUE
+S
+FALSE
10
-2011-03-06
-K9
+2011-05-15
+T2
B_ESCHR_COL
R
-R
+S
S
S
FALSE
@@ -657,7 +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 15,014 first weighted isolates (75.1% of total)
+# => Found 15,064 first weighted isolates (75.3% of total)
-
isolate
@@ -674,8 +674,8 @@
1
-2010-01-07
-K9
+2010-01-18
+T2
B_ESCHR_COL
S
S
@@ -686,46 +686,46 @@
2
-2010-02-14
-K9
+2010-01-26
+T2
B_ESCHR_COL
R
S
-S
+R
S
FALSE
TRUE
3
-2010-02-18
-K9
+2010-02-26
+T2
B_ESCHR_COL
-R
+I
+S
S
-R
S
FALSE
TRUE
4
-2010-06-01
-K9
+2010-03-13
+T2
B_ESCHR_COL
S
S
-R
+S
S
FALSE
-TRUE
+FALSE
5
-2010-08-14
-K9
+2010-08-18
+T2
B_ESCHR_COL
-S
+R
S
S
S
@@ -734,8 +734,8 @@
6
-2010-11-08
-K9
+2010-12-10
+T2
B_ESCHR_COL
S
S
@@ -746,47 +746,47 @@
7
-2010-11-16
-K9
+2011-05-02
+T2
B_ESCHR_COL
S
S
+R
S
-S
-FALSE
+TRUE
TRUE
-8
-2010-12-11
-K9
-B_ESCHR_COL
-S
-S
-S
-S
-FALSE
-FALSE
-
-
+9
-2011-02-27
-K9
+2011-05-02
+T2
B_ESCHR_COL
R
S
S
S
+FALSE
TRUE
+
+
9
+2011-05-15
+T2
+B_ESCHR_COL
+S
+S
+S
+S
+FALSE
TRUE
10
-2011-03-06
-K9
+2011-05-15
+T2
B_ESCHR_COL
R
-R
+S
S
S
FALSE
@@ -794,11 +794,11 @@
filter_first_isolate()
, there’s a shortcut for this new algorithm too:
-
+1
-2014-06-27
-V8
-Hospital B
-B_KLBSL_PNE
-R
+3
+2015-11-20
+G6
+Hospital C
+B_ESCHR_COL
+S
+S
+S
+S
+M
+Gram negative
+Escherichia
+coli
+TRUE
+
+
-4
+2015-11-07
+Q10
+Hospital D
+B_ESCHR_COL
+S
S
S
S
F
Gram negative
-Klebsiella
-pneumoniae
+Escherichia
+coli
TRUE
- 2
-2013-02-12
-L4
-Hospital A
+
+
-5
+2013-11-26
+P8
+Hospital B
B_STRPT_PNE
-I
-I
+S
+S
S
R
-M
+F
Gram positive
Streptococcus
pneumoniae
TRUE
-
3
-2013-11-26
-T2
-Hospital A
-B_STPHY_AUR
-R
-R
-S
-S
-F
-Gram positive
-Staphylococcus
-aureus
-TRUE
-
-
5
-2013-09-22
-I7
-Hospital C
-B_STPHY_AUR
-S
+6
+2014-07-30
+L3
+Hospital B
+B_ESCHR_COL
+R
S
S
S
M
-Gram positive
-Staphylococcus
-aureus
+Gram negative
+Escherichia
+coli
TRUE
7
-2012-03-30
-L8
-Hospital B
-B_STPHY_AUR
-S
-S
+2016-04-08
+C7
+Hospital A
+B_ESCHR_COL
+R
S
+R
S
M
-Gram positive
-Staphylococcus
-aureus
+Gram negative
+Escherichia
+coli
TRUE
8
-2015-08-27
-L4
-Hospital A
+2010-02-28
+A8
+Hospital D
B_ESCHR_COL
-R
-R
+S
+S
S
S
M
@@ -935,9 +935,9 @@
dplyr
way, which is easier readable:genus
and species
from a data.frame
(15,014 x 13)genus
and species
from a data.frame
(15,064 x 13)
-Length: 15,014 (of which NA: 0 = 0.00%)
+Length: 15,064 (of which NA: 0 = 0.00%)
Unique: 4
Longest: 24
1
Escherichia coli
-7,496
-49.9%
-7,496
-49.9%
+7,474
+49.6%
+7,474
+49.6%
2
Staphylococcus aureus
-3,663
-24.4%
-11,159
-74.3%
+3,691
+24.5%
+11,165
+74.1%
3
Streptococcus pneumoniae
-2,303
-15.3%
-13,462
-89.7%
+2,238
+14.9%
+13,403
+89.0%
@@ -991,7 +991,7 @@ Longest: 244
Klebsiella pneumoniae
-1,552
-10.3%
-15,014
+1,661
+11.0%
+15,064
100.0%
The functions portion_S()
, portion_SI()
, portion_I()
, portion_IR()
and portion_R()
can be used to determine the portion of a specific antimicrobial outcome. As per the EUCAST guideline of 2019, we calculate resistance as the portion of R (portion_R()
) and susceptibility as the portion of S and I (portion_SI()
). 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) %>%
@@ -1004,19 +1004,19 @@ Longest: 24
Hospital A
-0.4625681
+0.4641785
Hospital B
-0.4799232
+0.4697084
Hospital C
-0.4727660
+0.4838129
Hospital D
-0.4660767
+0.4796271
@@ -1034,23 +1034,23 @@ Longest: 24
Hospital A
-0.4625681
-4408
+0.4641785
+4662
Hospital B
-0.4799232
-5205
+0.4697084
+5282
Hospital C
-0.4727660
-2350
+0.4838129
+2224
Hospital D
-0.4660767
-3051
+0.4796271
+2896
@@ -1070,27 +1070,27 @@ Longest: 24
Escherichia
-0.9252935
-0.8992796
-0.9945304
+0.9211935
+0.8898849
+0.9933101
Klebsiella
-0.8150773
-0.9059278
-0.9864691
+0.8169777
+0.8832029
+0.9843468
Staphylococcus
-0.9216489
-0.9211029
-0.9923560
+0.9184503
+0.9170956
+0.9937686
Streptococcus
-0.6026921
+0.6090259
0.0000000
-0.6026921
+0.6090259
diff --git a/docs/articles/AMR_files/figure-html/plot 1-1.png b/docs/articles/AMR_files/figure-html/plot 1-1.png
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