How to conduct AMR analysis
@@ -203,18 +245,19 @@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 RMarkdown. However, the methodology remains unchanged. This page was generated on 20 February 2019.
-Introduction
+Introduction
For this tutorial, we will create fake demonstration data to work with.
You can skip to Cleaning the data if you already have your own data ready. If you start your analysis, try to make the structure of your data generally look like this:
-| date | patient_id | mo | amox | cipr | -
|---|---|---|---|---|
| 2019-02-20 | @@ -240,81 +283,75 @@
-Needed R packages
+Needed R packages
As with many uses in R, we need some additional packages for AMR analysis. Our package works closely together with the tidyverse packages dplyr and ggplot2 by Dr Hadley Wickham. The tidyverse tremendously improves the way we conduct data science - it allows for a very natural way of writing syntaxes and creating beautiful plots in R.
Our AMR package depends on these packages and even extends their use and functions.
-Creation of data
+Creation of data
We will create some fake example data to use for analysis. For antimicrobial resistance analysis, we need at least: a patient ID, name or code of a microorganism, a date and antimicrobial results (an antibiogram). It could also include a specimen type (e.g. to filter on blood or urine), the ward type (e.g. to filter on ICUs).
With additional columns (like a hospital name, the patients gender of even [well-defined] clinical properties) you can do a comparative analysis, as this tutorial will demonstrate too.
-Patients
+Patients
To start with patients, we need a unique list of patients.
- +The LETTERS object is available in R - it’s a vector with 26 characters: A to Z. The patients object we just created is now a vector of length 260, with values (patient IDs) varying from A1 to Z10. Now we we also set the gender of our patients, by putting the ID and the gender in a table:
The first 135 patient IDs are now male, the other 125 are female.
-Dates
+Dates
Let’s pretend that our data consists of blood cultures isolates from 1 January 2010 until 1 January 2018.
- +This dates object now contains all days in our date range.
-Microorganisms
+Microorganisms
For this tutorial, we will uses four different microorganisms: Escherichia coli, Staphylococcus aureus, Streptococcus pneumoniae, and Klebsiella pneumoniae:
- +-Other variables
+Other variables
For completeness, we can also add the hospital where the patients was admitted and we need to define valid antibmicrobial results for our randomisation:
- +-Put everything together
-Using the sample() function, we can randomly select items from all objects we defined earlier. To let our fake data reflect reality a bit, we will also approximately define the probabilities of bacteria and the antibiotic results with the prob parameter.
Put everything together
+Using the sample() function, we can randomly select items from all objects we defined earlier. To let our fake data reflect reality a bit, we will also approximately define the probabilities of bacteria and the antibiotic results with the prob parameter.
sample_size <- 20000
-data <- data.frame(date = sample(dates, size = sample_size, replace = TRUE),
- patient_id = sample(patients, size = sample_size, replace = TRUE),
- hospital = sample(hospitals, size = sample_size, replace = TRUE,
- prob = c(0.30, 0.35, 0.15, 0.20)),
- bacteria = sample(bacteria, size = sample_size, replace = TRUE,
- prob = c(0.50, 0.25, 0.15, 0.10)),
- amox = sample(ab_interpretations, size = sample_size, replace = TRUE,
- prob = c(0.60, 0.05, 0.35)),
- amcl = sample(ab_interpretations, size = sample_size, replace = TRUE,
- prob = c(0.75, 0.10, 0.15)),
- cipr = sample(ab_interpretations, size = sample_size, replace = TRUE,
- prob = c(0.80, 0.00, 0.20)),
- gent = sample(ab_interpretations, size = sample_size, replace = TRUE,
- prob = c(0.92, 0.00, 0.08))
+data <- data.frame(date = sample(dates, size = sample_size, replace = TRUE),
+ patient_id = sample(patients, size = sample_size, replace = TRUE),
+ hospital = sample(hospitals, size = sample_size, replace = TRUE,
+ prob = c(0.30, 0.35, 0.15, 0.20)),
+ bacteria = sample(bacteria, size = sample_size, replace = TRUE,
+ prob = c(0.50, 0.25, 0.15, 0.10)),
+ amox = sample(ab_interpretations, size = sample_size, replace = TRUE,
+ prob = c(0.60, 0.05, 0.35)),
+ amcl = sample(ab_interpretations, size = sample_size, replace = TRUE,
+ prob = c(0.75, 0.10, 0.15)),
+ cipr = sample(ab_interpretations, size = sample_size, replace = TRUE,
+ prob = c(0.80, 0.00, 0.20)),
+ gent = sample(ab_interpretations, size = sample_size, replace = TRUE,
+ prob = c(0.92, 0.00, 0.08))
)Using the left_join() function from the dplyr package, we can ‘map’ the gender to the patient ID using the patients_table object we created earlier:
The resulting data set contains 5,000 blood culture isolates. With the head() function we can preview the first 6 values of this data set:
| date | patient_id | hospital | @@ -324,84 +361,84 @@cipr | gent | gender | -|||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2012-08-24 | -V3 | +2013-02-06 | +O10 | Hospital B | -Escherichia coli | -S | +Streptococcus pneumoniae | S | +I | S | S | F |
| 2013-04-28 | -S3 | +2010-01-25 | +W1 | Hospital B | -Staphylococcus aureus | -R | +Klebsiella pneumoniae | S | +I | S | S | F |
| 2013-05-13 | -F2 | -Hospital D | -Staphylococcus aureus | -S | -S | -R | -S | -M | -||||
| 2017-04-04 | -I9 | +2011-02-16 | +N10 | Hospital C | Escherichia coli | -R | +S | +S | +S | +S | +F | +|
| 2010-04-01 | +J4 | +Hospital D | +Escherichia coli | +S | S | S | S | M | ||||
| 2015-12-23 | -G9 | +2015-02-27 | +O2 | Hospital B | -Staphylococcus aureus | +Escherichia coli | S | -S | -S | -S | -M | -|
| 2011-12-19 | -T2 | -Hospital B | -Staphylococcus aureus | R | S | S | -S | F | ||||
| 2013-03-19 | +E3 | +Hospital B | +Escherichia coli | +S | +I | +S | +S | +M | +
Now, let’s start the cleaning and the analysis!
-Cleaning the data
-Use the frequency table function freq() to look specifically for unique values in any variable. For example, for the gender variable:
Cleaning the data
+Use the frequency table function freq() to look specifically for unique values in any variable. For example, for the gender variable:
#> Frequency table of `gender` from a data.frame (20,000 x 9)
#>
#> Class: factor (numeric)
@@ -411,18 +448,18 @@
#>
#> Item Count Percent Cum. Count Cum. Percent
#> --- ----- ------- -------- ----------- -------------
-#> 1 M 10,471 52.4% 10,471 52.4%
-#> 2 F 9,529 47.6% 20,000 100.0%So, we can draw at least two conclusions immediately. From a data scientist perspective, the data looks clean: only values M and F. From a researcher 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:
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:
We also want to transform the antibiotics, because in real life data we don’t know if they are really clean. The as.rsi() function ensures reliability and reproducibility in these kind of variables. The mutate_at() will run the as.rsi() function on defined variables:
We also want to transform the antibiotics, because in real life data we don’t know if they are really clean. The as.rsi() function ensures reliability and reproducibility in these kind of variables. The mutate_at() will run the as.rsi() function on defined variables:
Finally, we will apply EUCAST rules on our antimicrobial results. In Europe, most medical microbiological laboratories already apply these rules. Our package features their latest insights on intrinsic resistance and exceptional phenotypes. Moreover, the eucast_rules() function can also apply additional rules, like forcing
Because the amoxicillin (column amox) and amoxicillin/clavulanic acid (column amcl) in our data were generated randomly, some rows will undoubtedly contain amox = S and amcl = R, which is technically impossible. The eucast_rules() fixes this:
Finally, we will apply EUCAST rules on our antimicrobial results. In Europe, most medical microbiological laboratories already apply these rules. Our package features their latest insights on intrinsic resistance and exceptional phenotypes. Moreover, the eucast_rules() function can also apply additional rules, like forcing
Because the amoxicillin (column amox) and amoxicillin/clavulanic acid (column amcl) in our data were generated randomly, some rows will undoubtedly contain amox = S and amcl = R, which is technically impossible. The eucast_rules() fixes this:
data <- eucast_rules(data, col_mo = "bacteria")
#>
#> Rules by the European Committee on Antimicrobial Susceptibility Testing (EUCAST)
#>
@@ -443,10 +480,10 @@
#> Kingella kingae (no changes)
#>
#> EUCAST Expert Rules, Intrinsic Resistance and Exceptional Phenotypes (v3.1, 2016)
-#> Table 1: Intrinsic resistance in Enterobacteriaceae (1288 changes)
+#> Table 1: Intrinsic resistance in Enterobacteriaceae (1260 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 (2757 changes)
+#> Table 4: Intrinsic resistance in Gram-positive bacteria (2753 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)
@@ -462,47 +499,45 @@
#> Non-EUCAST: piperacillin/tazobactam = S where piperacillin = S (no changes)
#> Non-EUCAST: trimethoprim/sulfa = S where trimethoprim = S (no changes)
#>
-#> => EUCAST rules affected 7,243 out of 20,000 rows
+#> => EUCAST rules affected 7,399 out of 20,000 rows
#> -> added 0 test results
-#> -> changed 4,045 test results (0 to S; 0 to I; 4,045 to R)-Adding new variables
+Adding new variables
Now that we have the microbial ID, we can add some taxonomic properties:
data <- data %>%
- mutate(gramstain = mo_gramstain(bacteria),
- genus = mo_genus(bacteria),
- species = mo_species(bacteria))-First isolates
+First isolates
We also need to know which isolates we can actually use for analysis.
To conduct an analysis of antimicrobial resistance, you must only include the first isolate of every patient per episode (Hindler et al., Clin Infect Dis. 2007). If you would not do this, you could easily get an overestimate or underestimate of the resistance of an antibiotic. Imagine that a patient was admitted with an MRSA and that it was found in 5 different blood cultures the following weeks (yes, some countries like the Netherlands have these blood drawing policies). The resistance percentage of oxacillin of all isolates would be overestimated, because you included this MRSA more than once. It would clearly be selection bias.
The Clinical and Laboratory Standards Institute (CLSI) appoints this as follows:
-(…) When preparing a cumulative antibiogram to guide clinical decisions about empirical antimicrobial therapy of initial infections, only the first isolate of a given species per patient, per analysis period (eg, one year) should be included, irrespective of body site, antimicrobial susceptibility profile, or other phenotypical characteristics (eg, biotype). The first isolate is easily identified, and cumulative antimicrobial susceptibility test data prepared using the first isolate are generally comparable to cumulative antimicrobial susceptibility test data calculated by other methods, providing duplicate isolates are excluded.
M39-A4 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition. CLSI, 2014. Chapter 6.4
This AMR package includes this methodology with the first_isolate() function. It adopts the episode of a year (can be changed by user) and it starts counting days after every selected isolate. This new variable can easily be added to our data:
This AMR package includes this methodology with the first_isolate() function. It adopts the episode of a year (can be changed by user) and it starts counting days after every selected isolate. This new variable can easily be added to our data:
data <- data %>%
- mutate(first = first_isolate(.))
+ mutate(first = first_isolate(.))
#> 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 5,692 first isolates (28.5% of total)So only 28.5% is suitable for resistance analysis! We can now filter on it with the filter() function, also from the dplyr package:
So only 28.2% is suitable for resistance analysis! We can now filter on it with the filter() function, also from the dplyr package:
For future use, the above two syntaxes can be shortened with the filter_first_isolate() function:
For future use, the above two syntaxes can be shortened with the filter_first_isolate() function:
-First weighted isolates
+First weighted isolates
We made a slight twist to the CLSI algorithm, to take into account the antimicrobial susceptibility profile. Imagine this data, sorted on date:
-| isolate | date | patient_id | @@ -512,33 +547,34 @@cipr | gent | first | -||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | -2010-01-31 | +2010-01-19 | C6 | B_ESCHR_COL | R | S | -S | +R | S | TRUE | |
| 2 | -2010-03-09 | +2010-03-29 | C6 | B_ESCHR_COL | S | -S | -S | +R | +R | S | FALSE |
| 3 | -2010-05-30 | +2010-05-27 | C6 | B_ESCHR_COL | S | @@ -549,7 +585,7 @@||||||
| 4 | -2010-06-22 | +2010-06-25 | C6 | B_ESCHR_COL | S | @@ -560,65 +596,65 @@||||||
| 5 | -2011-01-10 | +2010-06-28 | C6 | B_ESCHR_COL | S | -I | R | S | +S | FALSE | |
| 6 | -2011-02-19 | +2010-07-31 | C6 | B_ESCHR_COL | -I | S | S | S | -TRUE | +S | +FALSE |
| 7 | -2011-02-21 | +2010-08-07 | C6 | B_ESCHR_COL | R | -I | -R | +S | +S | S | FALSE |
| 8 | -2011-05-11 | +2010-11-13 | C6 | B_ESCHR_COL | -S | -S | +R | +I | S | S | FALSE |
| 9 | -2011-07-06 | +2011-04-02 | C6 | B_ESCHR_COL | +R | S | S | S | -S | -FALSE | +TRUE |
| 10 | -2011-08-11 | +2011-06-26 | C6 | B_ESCHR_COL | -S | +R | S | S | S | @@ -626,20 +662,21 @@
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.
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:
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.
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(.)) %>%
- mutate(first_weighted = first_isolate(.))
+ mutate(keyab = key_antibiotics(.)) %>%
+ mutate(first_weighted = first_isolate(.))
#> NOTE: Using column `bacteria` as input for `col_mo`.
#> 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`.
#> 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 15,864 first weighted isolates (79.3% of total)| isolate | date | patient_id | @@ -650,23 +687,36 @@gent | first | first_weighted | -||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | -2010-01-31 | +2010-01-19 | C6 | B_ESCHR_COL | R | S | -S | +R | S | TRUE | TRUE | ||
| 2 | -2010-03-09 | +2010-03-29 | +C6 | +B_ESCHR_COL | +S | +R | +R | +S | +FALSE | +TRUE | +|||
| 3 | +2010-05-27 | C6 | B_ESCHR_COL | S | @@ -676,21 +726,9 @@FALSE | TRUE | |||||||
| 3 | -2010-05-30 | -C6 | -B_ESCHR_COL | -S | -S | -S | -S | -FALSE | -FALSE | -||||
| 4 | -2010-06-22 | +2010-06-25 | C6 | B_ESCHR_COL | S | @@ -702,70 +740,70 @@||||||||
| 5 | -2011-01-10 | +2010-06-28 | C6 | B_ESCHR_COL | S | -I | R | S | +S | FALSE | TRUE | ||
| 6 | -2011-02-19 | +2010-07-31 | C6 | B_ESCHR_COL | -I | S | S | S | -TRUE | +S | +FALSE | TRUE | |
| 7 | -2011-02-21 | +2010-08-07 | C6 | B_ESCHR_COL | R | -I | -R | +S | +S | S | FALSE | TRUE | |
| 8 | -2011-05-11 | +2010-11-13 | C6 | B_ESCHR_COL | -S | -S | +R | +I | S | S | FALSE | -TRUE | +FALSE |
| 9 | -2011-07-06 | +2011-04-02 | C6 | B_ESCHR_COL | +R | S | S | S | -S | -FALSE | -FALSE | +TRUE | +TRUE |
| 10 | -2011-08-11 | +2011-06-26 | C6 | B_ESCHR_COL | -S | +R | S | S | S | @@ -774,18 +812,19 @@
Instead of 2, now 6 isolates are flagged. In total, 79.3% of all isolates are marked ‘first weighted’ - 50.9% 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:
Instead of 2, now 7 isolates are flagged. In total, 79% of all isolates are marked ‘first weighted’ - 50.8% more than when using the CLSI guideline. In real life, this novel algorithm will yield 5-10% more isolates than the classic CLSI guideline.
+As with filter_first_isolate(), there’s a shortcut for this new algorithm too:
So we end up with 15,864 isolates for analysis.
+ filter_first_weighted_isolate() +So we end up with 15,809 isolates for analysis.
We can remove unneeded columns:
+ select(-c(first, keyab))Now our data looks like:
- -| date | patient_id | @@ -800,13 +839,30 @@genus | species | first_weighted | -||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | -2012-08-24 | -V3 | +2 | +2010-01-25 | +W1 | Hospital B | +B_KLBSL_PNE | +R | +I | +S | +S | +F | +Gram negative | +Klebsiella | +pneumoniae | +TRUE | +||||||
| 3 | +2011-02-16 | +N10 | +Hospital C | B_ESCHR_COL | S | S | @@ -818,84 +874,68 @@coli | TRUE | ||||||||||||||
| 2 | -2013-04-28 | -S3 | -Hospital B | -B_STPHY_AUR | -R | -S | -S | -S | -F | -Gram positive | -Staphylococcus | -aureus | -TRUE | -|||||||||
| 3 | -2013-05-13 | -F2 | +4 | +2010-04-01 | +J4 | Hospital D | -B_STPHY_AUR | +B_ESCHR_COL | +S | S | S | -R | S | M | -Gram positive | -Staphylococcus | -aureus | +Gram negative | +Escherichia | +coli | TRUE | |
| 5 | -2015-12-23 | -G9 | +2015-02-27 | +O2 | Hospital B | -B_STPHY_AUR | +B_ESCHR_COL | S | -S | -S | -S | -M | -Gram positive | -Staphylococcus | -aureus | -TRUE | -||||||
| 6 | -2011-12-19 | -T2 | -Hospital B | -B_STPHY_AUR | R | S | S | -S | F | +Gram negative | +Escherichia | +coli | +TRUE | +|||||||||
| 7 | +2011-08-19 | +D6 | +Hospital B | +B_STPHY_AUR | +S | +I | +S | +S | +M | Gram positive | Staphylococcus | aureus | TRUE | |||||||||
| 7 | -2011-11-26 | -J1 | +10 | +2017-01-12 | +P10 | Hospital A | -B_ESCHR_COL | +B_STPHY_AUR | +S | R | S | S | -R | -M | -Gram negative | -Escherichia | -coli | +F | +Gram positive | +Staphylococcus | +aureus | TRUE |
