From 95c49fdf79ba4e947642ecf6937ed299ff90c0a0 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 28.5% is suitable for resistance analysis! We can now filter on it with the So only 28.3% 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 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 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 If a column exists with a name like ‘key(…)ab’ the Instead of 2, now 9 isolates are flagged. In total, 79.5% of all isolates are marked ‘first weighted’ - 51% 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 8 isolates are flagged. In total, 79.5% of all isolates are marked ‘first weighted’ - 51.3% 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 15,909 isolates for analysis. So we end up with 15,906 isolates for analysis. We can remove unneeded columns: Time for the analysis! Or can be used like the Frequency table of Frequency table of Columns: 2 Shortest: 16
-
-2011-10-07
-O2
+2011-09-11
+K7
Hospital C
-Streptococcus pneumoniae
-R
-S
-S
-S
-F
-
-
-2013-10-20
-C10
-Hospital A
-Escherichia coli
-S
-R
-S
-S
-M
-
-
-2014-08-25
-O4
-Hospital A
-Escherichia coli
-S
-S
-S
-S
-F
-
-
-2011-10-28
-E3
-Hospital B
-Streptococcus pneumoniae
-S
-S
-S
-S
-M
-
-
-2010-08-31
-H1
-Hospital A
-Klebsiella pneumoniae
-R
-S
-R
-S
-M
-
-
+2014-11-03
-U7
-Hospital A
Staphylococcus aureus
S
S
R
S
+M
+
+
+2017-06-12
+M3
+Hospital C
+Klebsiella pneumoniae
+R
+S
+S
+S
+M
+
+
+2015-03-15
+S9
+Hospital A
+Streptococcus pneumoniae
+S
+S
+S
+S
F
+
+2016-01-03
+I7
+Hospital C
+Klebsiella pneumoniae
+S
+S
+S
+S
+M
+
+
+2017-11-25
+R3
+Hospital D
+Staphylococcus aureus
+R
+S
+S
+S
+F
+
+
2012-04-03
+B9
+Hospital B
+Staphylococcus aureus
+S
+I
+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 %>%
@@ -443,10 +443,10 @@
#> Kingella kingae (no changes)
#>
#> EUCAST Expert Rules, Intrinsic Resistance and Exceptional Phenotypes (v3.1, 2016)
-#> Table 1: Intrinsic resistance in Enterobacteriaceae (1256 changes)
+#> Table 1: Intrinsic resistance in Enterobacteriaceae (1252 changes)
#> Table 2: Intrinsic resistance in non-fermentative Gram-negative bacteria (no changes)
#> Table 3: Intrinsic resistance in other Gram-negative bacteria (no changes)
-#> Table 4: Intrinsic resistance in Gram-positive bacteria (2821 changes)
+#> Table 4: Intrinsic resistance in Gram-positive bacteria (2741 changes)
#> Table 8: Interpretive rules for B-lactam agents and Gram-positive cocci (no changes)
#> Table 9: Interpretive rules for B-lactam agents and Gram-negative rods (no changes)
#> Table 10: Interpretive rules for B-lactam agents and other Gram-negative bacteria (no changes)
@@ -462,9 +462,9 @@
#> Non-EUCAST: piperacillin/tazobactam = S where piperacillin = S (no changes)
#> Non-EUCAST: trimethoprim/sulfa = S where trimethoprim = S (no changes)
#>
-#> => EUCAST rules affected 7,457 out of 20,000 rows
+#> => EUCAST rules affected 7,388 out of 20,000 rows
#> -> added 0 test results
-#> -> changed 4,077 test results (0 to S; 0 to I; 4,077 to R)
@@ -489,8 +489,8 @@
#> NOTE: Using column `bacteria` as input for `col_mo`.
#> NOTE: Using column `date` as input for `col_date`.
#> NOTE: Using column `patient_id` as input for `col_patient_id`.
-#> => Found 5,704 first isolates (28.5% of total)
filter()
function, also from the dplyr
package:filter()
function, also from the dplyr
package:filter_first_isolate()
function:
1
-2010-01-18
-I8
+2010-02-18
+U6
B_ESCHR_COL
-I
+S
S
S
S
@@ -527,41 +527,41 @@
2
-2010-03-30
-I8
+2010-04-16
+U6
B_ESCHR_COL
S
-I
+R
S
S
FALSE
3
-2010-10-05
-I8
+2010-05-10
+U6
B_ESCHR_COL
-R
-R
+S
+S
S
S
FALSE
4
-2011-01-26
-I8
+2010-07-20
+U6
B_ESCHR_COL
S
-R
-R
S
-TRUE
+S
+S
+FALSE
5
-2011-02-01
-I8
+2010-07-24
+U6
B_ESCHR_COL
S
S
@@ -571,62 +571,62 @@
6
-2011-03-08
-I8
+2010-12-11
+U6
B_ESCHR_COL
-R
S
R
S
+R
FALSE
7
-2011-04-11
-I8
+2010-12-14
+U6
B_ESCHR_COL
S
-R
S
+R
S
FALSE
8
-2011-04-23
-I8
+2010-12-31
+U6
B_ESCHR_COL
S
S
-R
+S
S
FALSE
9
-2011-06-21
-I8
+2011-02-02
+U6
B_ESCHR_COL
+S
+R
R
-S
-S
S
FALSE
-10
-2011-07-06
-I8
+2011-02-16
+U6
B_ESCHR_COL
+R
S
-S
-S
+R
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(.)) %>%
@@ -637,7 +637,7 @@
#> 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 15,909 first weighted isolates (79.5% of total)
-
isolate
@@ -654,10 +654,10 @@
1
-2010-01-18
-I8
+2010-02-18
+U6
B_ESCHR_COL
-I
+S
S
S
S
@@ -666,23 +666,23 @@
2
-2010-03-30
-I8
+2010-04-16
+U6
B_ESCHR_COL
S
-I
+R
S
S
FALSE
-FALSE
+TRUE
3
-2010-10-05
-I8
+2010-05-10
+U6
B_ESCHR_COL
-R
-R
+S
+S
S
S
FALSE
@@ -690,95 +690,95 @@
-4
-2011-01-26
-I8
-B_ESCHR_COL
-S
-R
-R
-S
-TRUE
-TRUE
-
-
+5
-2011-02-01
-I8
+2010-07-20
+U6
B_ESCHR_COL
S
S
S
S
FALSE
-TRUE
+FALSE
+
+
5
+2010-07-24
+U6
+B_ESCHR_COL
+S
+S
+S
+S
+FALSE
+FALSE
6
-2011-03-08
-I8
+2010-12-11
+U6
B_ESCHR_COL
-R
S
R
S
+R
FALSE
TRUE
7
-2011-04-11
-I8
+2010-12-14
+U6
B_ESCHR_COL
S
-R
S
+R
S
FALSE
TRUE
8
-2011-04-23
-I8
+2010-12-31
+U6
B_ESCHR_COL
S
S
-R
+S
S
FALSE
TRUE
9
-2011-06-21
-I8
+2011-02-02
+U6
B_ESCHR_COL
+S
+R
R
-S
-S
S
FALSE
TRUE
10
-2011-07-06
-I8
+2011-02-16
+U6
B_ESCHR_COL
+R
S
-S
-S
+R
S
FALSE
TRUE
filter_first_isolate()
, there’s a shortcut for this new algorithm too:
+
date
patient_id
hospital
@@ -802,73 +803,30 @@
-
2011-10-07
-O2
+1
+2011-09-11
+K7
Hospital C
-B_STRPT_PNE
-R
+B_STPHY_AUR
S
S
R
-F
+S
+M
Gram positive
-Streptococcus
-pneumoniae
+Staphylococcus
+aureus
TRUE
-
-2013-10-20
-C10
-Hospital A
-B_ESCHR_COL
-S
-R
-S
-S
-M
-Gram negative
-Escherichia
-coli
-TRUE
-
-
-2014-08-25
-O4
-Hospital A
-B_ESCHR_COL
-S
-S
-S
-S
-F
-Gram negative
-Escherichia
-coli
-TRUE
-
-
-2011-10-28
-E3
-Hospital B
-B_STRPT_PNE
-S
-S
-S
-R
-M
-Gram positive
-Streptococcus
-pneumoniae
-TRUE
-
-
-2010-08-31
-H1
-Hospital A
+2
+2017-06-12
+M3
+Hospital C
B_KLBSL_PNE
R
S
-R
+S
S
M
Gram negative
@@ -876,21 +834,70 @@
pneumoniae
TRUE
- 2014-11-03
-U7
+
+
+3
+2015-03-15
+S9
Hospital A
-B_STPHY_AUR
+B_STRPT_PNE
+S
S
S
R
-S
F
Gram positive
+Streptococcus
+pneumoniae
+TRUE
+
+
+4
+2016-01-03
+I7
+Hospital C
+B_KLBSL_PNE
+R
+S
+S
+S
+M
+Gram negative
+Klebsiella
+pneumoniae
+TRUE
+
+
+6
+2012-04-03
+B9
+Hospital B
+B_STPHY_AUR
+S
+I
+S
+S
+M
+Gram positive
Staphylococcus
aureus
TRUE
+
8
+2012-12-23
+D1
+Hospital C
+B_ESCHR_COL
+R
+S
+R
+S
+M
+Gram negative
+Escherichia
+coli
+TRUE
+dplyr
way, which is easier readable:genus
and species
from a data.frame
(15,909 x 13)genus
and species
from a data.frame
(15,906 x 13)
-Length: 15,909 (of which NA: 0 = 0.00%)
+Length: 15,906 (of which NA: 0 = 0.00%)
Unique: 4
Longest: 24
The functions portion_R
, portion_RI
, portion_I
, portion_IS
and portion_S
can be used to determine the portion of a specific antimicrobial outcome. They 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) %>%
@@ -977,19 +984,19 @@ Longest: 24
Hospital A
-0.4717819
+0.4828164
Hospital B
-0.4802632
+0.4792111
Hospital C
-0.4870184
+0.4738384
Hospital D
-0.4804169
+0.4661534
@@ -1007,23 +1014,23 @@ Longest: 24
Hospital A
-0.4717819
-4731
+0.4828164
+4772
Hospital B
-0.4802632
-5624
+0.4792111
+5628
Hospital C
-0.4870184
-2388
+0.4738384
+2389
Hospital D
-0.4804169
-3166
+0.4661534
+3117
@@ -1043,27 +1050,27 @@ Longest: 24
Escherichia
-0.7256603
-0.8983029
-0.9729488
+0.7335086
+0.9034923
+0.9759670
Klebsiella
-0.7338403
-0.8935361
-0.9689480
+0.7303889
+0.8998022
+0.9709954
Staphylococcus
-0.7309645
-0.9233503
-0.9794416
+0.7303772
+0.9161570
+0.9775739
Streptococcus
-0.7372749
+0.7302100
0.0000000
-0.7372749
+0.7302100
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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