From a925a5d2f8f0d14bdd02bc67bf4c444e87b7c810 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 16 May 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 17 May 2019. 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 28.6% 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 Instead of 1, now 10 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 1, now 6 isolates are flagged. In total, 75.6% of all isolates are marked ‘first weighted’ - 47.1% 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 15,027 isolates for analysis. So we end up with 15,123 isolates for analysis. We can remove unneeded columns: Or can be used like the Frequency table of Frequency table of Columns: 2 Shortest: 16How to conduct AMR analysis
Matthijs S. Berends
- 16 May 2019
+ 17 May 2019
AMR.Rmd
Introduction
@@ -217,21 +217,21 @@
-
2019-05-16
+2019-05-17
abcd
Escherichia coli
S
S
-
2019-05-16
+2019-05-17
abcd
Escherichia coli
S
R
-
2019-05-16
+2019-05-17
efgh
Escherichia coli
R
@@ -327,69 +327,69 @@
-
+2013-05-13
-X4
-Hospital D
-Staphylococcus aureus
+2014-11-13
+S2
+Hospital B
+Escherichia coli
+S
+S
+S
+S
+F
+
+
+2013-01-01
+V1
+Hospital C
+Streptococcus pneumoniae
+S
+S
+S
+S
+F
+
+
+2016-11-12
+G5
+Hospital A
+Klebsiella pneumoniae
+S
+S
+S
+S
+M
+
+
-2011-09-23
+U6
+Hospital A
+Klebsiella pneumoniae
+R
I
-S
-S
+R
S
F
-
2016-09-19
-G9
-Hospital A
-Escherichia coli
-R
-R
-S
-S
-M
-
-
2015-09-22
-L6
+2010-04-06
+C9
Hospital D
-Escherichia coli
-R
+Streptococcus pneumoniae
+S
S
S
S
M
-
-2017-09-13
-N3
-Hospital D
-Escherichia coli
-S
-S
-S
-S
-M
-
-
-2015-12-01
-Z3
-Hospital A
-Escherichia coli
-S
-R
-S
-S
-F
-
-
@@ -411,8 +411,8 @@
#>
#> Item Count Percent Cum. Count Cum. Percent
#> --- ----- ------- -------- ----------- -------------
-#> 1 M 10,410 52.0% 10,410 52.0%
-#> 2 F 9,590 48.0% 20,000 100.0%
+#> 1 M 10,325 51.6% 10,325 51.6%
+#> 2 F 9,675 48.4% 20,000 100.0%
2011-02-25
-F10
+2012-04-11
+D2
Hospital B
Staphylococcus aureus
+R
+S
S
S
-R
-R
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 %>%
@@ -442,14 +442,14 @@
#> Pasteurella multocida (no new changes)
#> Staphylococcus (no new changes)
#> Streptococcus groups A, B, C, G (no new changes)
-#> Streptococcus pneumoniae (1532 new changes)
+#> Streptococcus pneumoniae (1508 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 (1341 new changes)
+#> Table 01: Intrinsic resistance in Enterobacteriaceae (1335 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 (2834 new changes)
+#> Table 04: Intrinsic resistance in Gram-positive bacteria (2792 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)
@@ -457,24 +457,24 @@
#> Table 13: Interpretive rules for quinolones (no new changes)
#>
#> Other rules
-#> Non-EUCAST: amoxicillin/clav acid = S where ampicillin = S (2225 new changes)
-#> Non-EUCAST: ampicillin = R where amoxicillin/clav acid = R (109 new changes)
+#> Non-EUCAST: amoxicillin/clav acid = S where ampicillin = S (2206 new changes)
+#> Non-EUCAST: ampicillin = R where amoxicillin/clav acid = R (141 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 6,633 out of 20,000 rows, making a total of 8,041 edits
+#> EUCAST rules affected 6,587 out of 20,000 rows, making a total of 7,982 edits
#> => added 0 test results
#>
-#> => changed 8,041 test results
-#> - 118 test results changed from S to I
-#> - 4,906 test results changed from S to R
-#> - 1,094 test results changed from I to S
-#> - 326 test results changed from I to R
-#> - 1,577 test results changed from R to S
-#> - 20 test results changed from R to I
+#> => changed 7,982 test results
+#> - 102 test results changed from S to I
+#> - 4,844 test results changed from S to R
+#> - 1,047 test results changed from I to S
+#> - 345 test results changed from I to R
+#> - 1,615 test results changed from R to S
+#> - 29 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:filter()
function, also from the dplyr
package:filter_first_isolate()
function:
1
-2010-01-31
-O3
+2010-02-03
+Q6
B_ESCHR_COL
S
S
-R
+S
S
TRUE
2
-2010-04-24
-O3
+2010-03-18
+Q6
B_ESCHR_COL
S
S
@@ -551,10 +551,10 @@
3
-2010-04-26
-O3
+2010-04-04
+Q6
B_ESCHR_COL
-R
+S
S
S
S
@@ -562,8 +562,8 @@
4
-2010-05-07
-O3
+2010-04-12
+Q6
B_ESCHR_COL
S
S
@@ -573,10 +573,10 @@
5
-2010-07-07
-O3
+2010-05-08
+Q6
B_ESCHR_COL
-R
+S
S
S
S
@@ -584,10 +584,10 @@
6
-2010-07-28
-O3
+2010-08-03
+Q6
B_ESCHR_COL
-S
+R
S
S
S
@@ -595,30 +595,30 @@
7
-2010-08-30
-O3
+2010-08-04
+Q6
B_ESCHR_COL
-R
-R
+S
+S
S
S
FALSE
8
-2010-09-08
-O3
+2010-11-02
+Q6
B_ESCHR_COL
R
-S
R
S
+S
FALSE
9
-2010-10-11
-O3
+2010-11-09
+Q6
B_ESCHR_COL
R
S
@@ -628,11 +628,11 @@
10
-2010-11-11
-O3
+2010-12-18
+Q6
B_ESCHR_COL
-S
-S
+R
+R
S
S
FALSE
@@ -650,7 +650,7 @@
#> 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 15,027 first weighted isolates (75.1% of total)
+#> => Found 15,123 first weighted isolates (75.6% of total)
-
isolate
@@ -667,70 +667,70 @@
1
-2010-01-31
-O3
+2010-02-03
+Q6
B_ESCHR_COL
S
S
-R
+S
S
TRUE
TRUE
2
-2010-04-24
-O3
+2010-03-18
+Q6
B_ESCHR_COL
S
S
S
S
FALSE
-TRUE
+FALSE
3
-2010-04-26
-O3
+2010-04-04
+Q6
B_ESCHR_COL
-R
+S
S
S
S
FALSE
-TRUE
+FALSE
4
-2010-05-07
-O3
+2010-04-12
+Q6
B_ESCHR_COL
S
S
S
S
FALSE
-TRUE
+FALSE
5
-2010-07-07
-O3
+2010-05-08
+Q6
B_ESCHR_COL
-R
+S
S
S
S
FALSE
-TRUE
+FALSE
6
-2010-07-28
-O3
+2010-08-03
+Q6
B_ESCHR_COL
-S
+R
S
S
S
@@ -739,11 +739,11 @@
7
-2010-08-30
-O3
+2010-08-04
+Q6
B_ESCHR_COL
-R
-R
+S
+S
S
S
FALSE
@@ -751,20 +751,20 @@
8
-2010-09-08
-O3
+2010-11-02
+Q6
B_ESCHR_COL
R
-S
R
S
+S
FALSE
TRUE
9
-2010-10-11
-O3
+2010-11-09
+Q6
B_ESCHR_COL
R
S
@@ -775,11 +775,11 @@
10
-2010-11-11
-O3
+2010-12-18
+Q6
B_ESCHR_COL
-S
-S
+R
+R
S
S
FALSE
@@ -787,11 +787,11 @@
filter_first_isolate()
, there’s a shortcut for this new algorithm too:
1
-2013-05-13
-X4
-Hospital D
-B_STPHY_AUR
-I
+2014-11-13
+S2
+Hospital B
+B_ESCHR_COL
+S
S
S
S
F
-Gram positive
-Staphylococcus
-aureus
+Gram negative
+Escherichia
+coli
TRUE
-
2
-2016-09-19
-G9
+3
+2016-11-12
+G5
Hospital A
-B_ESCHR_COL
-R
+B_KLBSL_PNE
R
S
S
+S
M
Gram negative
-Escherichia
-coli
+Klebsiella
+pneumoniae
TRUE
-
3
-2015-09-22
-L6
-Hospital D
-B_ESCHR_COL
+4
+2011-09-23
+U6
+Hospital A
+B_KLBSL_PNE
+R
+I
R
S
-S
-S
-M
+F
Gram negative
-Escherichia
-coli
+Klebsiella
+pneumoniae
TRUE
5
-2015-12-01
-Z3
-Hospital A
-B_ESCHR_COL
+2010-04-06
+C9
+Hospital D
+B_STRPT_PNE
S
S
S
-S
-F
-Gram negative
-Escherichia
-coli
+R
+M
+Gram positive
+Streptococcus
+pneumoniae
TRUE
6
-2011-02-25
-F10
+2012-04-11
+D2
Hospital B
B_STPHY_AUR
+R
+S
S
S
-R
-R
M
Gram positive
Staphylococcus
@@ -896,16 +896,16 @@
TRUE
- 7
-2010-08-13
-G10
+8
+2012-08-17
+Z4
Hospital C
B_ESCHR_COL
+R
S
+R
S
-S
-S
-M
+F
Gram negative
Escherichia
coli
@@ -928,9 +928,9 @@
dplyr
way, which is easier readable:genus
and species
from a data.frame
(15,027 x 13)genus
and species
from a data.frame
(15,123 x 13)
-Length: 15,027 (of which NA: 0 = 0.00%)
+Length: 15,123 (of which NA: 0 = 0.00%)
Unique: 4
Longest: 24
1
Escherichia coli
-7,318
-48.7%
-7,318
-48.7%
+7,466
+49.4%
+7,466
+49.4%
2
Staphylococcus aureus
-3,729
-24.8%
-11,047
+3,649
+24.1%
+11,115
73.5%
3
Streptococcus pneumoniae
-2,370
-15.8%
-13,417
-89.3%
+2,381
+15.7%
+13,496
+89.2%
@@ -984,7 +984,7 @@ Longest: 244
Klebsiella pneumoniae
-1,610
-10.7%
-15,027
+1,627
+10.8%
+15,123
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. 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) %>%
@@ -997,19 +997,19 @@ Longest: 24
Hospital A
-0.4993375
+0.5100641
Hospital B
-0.4931245
+0.4945632
Hospital C
-0.5068182
+0.4960457
Hospital D
-0.4949396
+0.5036765
@@ -1027,23 +1027,23 @@ Longest: 24
Hospital A
-0.4993375
-4528
+0.5100641
+4521
Hospital B
-0.4931245
-5236
+0.4945632
+5334
Hospital C
-0.5068182
-2200
+0.4960457
+2276
Hospital D
-0.4949396
-3063
+0.5036765
+2992
@@ -1063,27 +1063,27 @@ Longest: 24
Escherichia
-0.8800219
-0.8938234
-0.9885215
+0.8775784
+0.8984731
+0.9910260
Klebsiella
-0.7136646
-0.9012422
-0.9732919
+0.7221881
+0.9053473
+0.9778734
Staphylococcus
-0.8806651
-0.9115044
-0.9895414
+0.8766785
+0.9188819
+0.9887640
Streptococcus
-0.5751055
+0.5732885
0.0000000
-0.5751055
+0.5732885
diff --git a/docs/articles/AMR_files/figure-html/plot 1-1.png b/docs/articles/AMR_files/figure-html/plot 1-1.png
index 21c067cde84e4068446aafec89f2f665837c5224..2ee99121e0b559bb74f4c02000c227fefd98a657 100644
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