Introduction
@@ -195,21 +196,21 @@As with many uses in R, we need some additional packages for AMR analysis. The most important one is dplyr, which tremendously improves the way we work with data - it allows for a very natural way of writing syntaxes in R. Another important dependency is ggplot2. This package can be used to create beautiful plots in R.
Our AMR package depends on these packages and even extends their use and functions.
library(dplyr) # the data science package
-library(AMR) # this package, to simplify and automate AMR analysis
-library(ggplot2) # for appealing plotslibrary(dplyr) # the data science package
+library(AMR) # this package, to simplify and automate AMR analysis
+library(ggplot2) # for appealing plots
@@ -236,51 +237,51 @@
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:
patients_table <- data.frame(patient_id = patients,
- gender = c(rep("M", 135),
- rep("F", 125)))The first 135 patient IDs are now male, the other 125 are female.
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
For this tutorial, we will uses four different microorganisms: Escherichia coli, Staphylococcus aureus, Streptococcus pneumoniae, and Klebsiella pneumoniae:
-bacteria <- c("Escherichia coli", "Staphylococcus aureus",
- "Streptococcus pneumoniae", "Klebsiella pneumoniae")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.
data <- data.frame(date = sample(dates, 5000, replace = TRUE),
- patient_id = sample(patients, 5000, replace = TRUE),
- hospital = sample(hospitals, 5000, replace = TRUE, prob = c(0.30, 0.35, 0.15, 0.20)),
- bacteria = sample(bacteria, 5000, replace = TRUE, prob = c(0.50, 0.25, 0.15, 0.10)),
- amox = sample(ab_interpretations, 5000, replace = TRUE, prob = c(0.60, 0.05, 0.35)),
- amcl = sample(ab_interpretations, 5000, replace = TRUE, prob = c(0.75, 0.10, 0.15)),
- cipr = sample(ab_interpretations, 5000, replace = TRUE, prob = c(0.80, 0.00, 0.20)),
- gent = sample(ab_interpretations, 5000, 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:
data <- data %>% left_join(patients_table)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.
data <- data.frame(date = sample(dates, 5000, replace = TRUE),
+ patient_id = sample(patients, 5000, replace = TRUE),
+ hospital = sample(hospitals, 5000, replace = TRUE, prob = c(0.30, 0.35, 0.15, 0.20)),
+ bacteria = sample(bacteria, 5000, replace = TRUE, prob = c(0.50, 0.25, 0.15, 0.10)),
+ amox = sample(ab_interpretations, 5000, replace = TRUE, prob = c(0.60, 0.05, 0.35)),
+ amcl = sample(ab_interpretations, 5000, replace = TRUE, prob = c(0.75, 0.10, 0.15)),
+ cipr = sample(ab_interpretations, 5000, replace = TRUE, prob = c(0.80, 0.00, 0.20)),
+ gent = sample(ab_interpretations, 5000, 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:
head(data)| date | @@ -295,71 +296,71 @@ ab_interpretations <- -2014-02-20 | -D5 | -Hospital A | -Escherichia coli | -R | -S | -S | -S | -M | -|
|---|---|---|---|---|---|---|---|---|---|---|
| 2010-06-17 | -N4 | -Hospital A | -Escherichia coli | -S | -S | -R | -S | -M | -||
| 2017-08-02 | -A6 | -Hospital B | -Staphylococcus aureus | -R | -I | -R | -S | -M | -||
| 2012-11-25 | -K7 | -Hospital A | -Escherichia coli | -I | -S | -S | -S | -M | -||
| 2012-06-24 | -G9 | -Hospital B | -Escherichia coli | -R | -S | -S | -R | -M | -||
| 2011-05-20 | -T7 | +2011-02-27 | +M6 | Hospital C | Escherichia coli | S | S | +R | +S | +M | +
| 2012-07-12 | +C2 | +Hospital B | +Streptococcus pneumoniae | +S | +R | +S | +S | +M | +||
| 2016-09-13 | +O7 | +Hospital A | +Escherichia coli | +R | +R | S | S | F | ||
| 2016-12-05 | +E4 | +Hospital A | +Escherichia coli | +I | +S | +S | +S | +M | +||
| 2017-10-05 | +M1 | +Hospital A | +Escherichia coli | +R | +I | +R | +S | +M | +||
| 2012-02-22 | +H9 | +Hospital C | +Streptococcus pneumoniae | +S | +S | +R | +S | +M | +
Now, let’s start the cleaning and the analysis!
@@ -369,7 +370,7 @@ ab_interpretations <- Cleaning the dataUse the frequency table function freq() to look specifically for unique values in any variable. For example, for the gender variable:
data %>% freq(gender) # this would be the same: freq(data$gender)# Frequency table of `gender` # Class: factor (numeric) # Levels: F, M @@ -378,67 +379,67 @@ ab_interpretations <- mutate()function of thedplyrpackage 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. Themutate_at()will run theas.rsi()function on defined variables:The data is already quite clean, but we still need to transform some variables. The
+ +bacteriacolumn 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. Themutate()function of thedplyrpackage 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. Themutate_at()will run theas.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 forcingampicillin = R whenamoxicillin/clavulanic acid = R.Because the amoxicillin (column
-amox) and amoxicillin/clavulanic acid (columnamcl) in our data were generated randomly, some rows will undoubtedly contain amox = S and amcl = R, which is technically impossible. Theeucast_rules()fixes this:+data <- eucast_rules(data, col_mo = "bacteria") -# -# Rules by the European Committee on Antimicrobial Susceptibility Testing (EUCAST) -# -# EUCAST Clinical Breakpoints (v8.1, 2018) -# Enterobacteriales (Order) (no changes) -# Staphylococcus (no changes) -# Enterococcus (no changes) -# Streptococcus groups A, B, C, G (no changes) -# Streptococcus pneumoniae (no changes) -# Viridans group streptococci (no changes) -# Haemophilus influenzae (no changes) -# Moraxella catarrhalis (no changes) -# Anaerobic Gram positives (no changes) -# Anaerobic Gram negatives (no changes) -# Pasteurella multocida (no changes) -# Campylobacter jejuni and C. coli (no changes) -# Aerococcus sanguinicola and A. urinae (no changes) -# Kingella kingae (no changes) -# -# EUCAST Expert Rules, Intrinsic Resistance and Exceptional Phenotypes (v3.1, 2016) -# Table 1: Intrinsic resistance in Enterobacteriaceae (332 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 (658 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) -# Table 11: Interpretive rules for macrolides, lincosamides, and streptogramins (no changes) -# Table 12: Interpretive rules for aminoglycosides (no changes) -# Table 13: Interpretive rules for quinolones (no changes) -# -# Other rules -# Non-EUCAST: ampicillin = R where amoxicillin/clav acid = R (no changes) -# Non-EUCAST: piperacillin = R where piperacillin/tazobactam = R (no changes) -# Non-EUCAST: trimethoprim = R where trimethoprim/sulfa = R (no changes) -# Non-EUCAST: amoxicillin/clav acid = S where ampicillin = S (no changes) -# Non-EUCAST: piperacillin/tazobactam = S where piperacillin = S (no changes) -# Non-EUCAST: trimethoprim/sulfa = S where trimethoprim = S (no changes) -# -# => EUCAST rules affected 1,828 out of 5,000 rows -> changed 990 test results.data <- eucast_rules(data, col_mo = "bacteria") +# +# Rules by the European Committee on Antimicrobial Susceptibility Testing (EUCAST) +# +# EUCAST Clinical Breakpoints (v8.1, 2018) +# Enterobacteriales (Order) (no changes) +# Staphylococcus (no changes) +# Enterococcus (no changes) +# Streptococcus groups A, B, C, G (no changes) +# Streptococcus pneumoniae (no changes) +# Viridans group streptococci (no changes) +# Haemophilus influenzae (no changes) +# Moraxella catarrhalis (no changes) +# Anaerobic Gram positives (no changes) +# Anaerobic Gram negatives (no changes) +# Pasteurella multocida (no changes) +# Campylobacter jejuni and C. coli (no changes) +# Aerococcus sanguinicola and A. urinae (no changes) +# Kingella kingae (no changes) +# +# EUCAST Expert Rules, Intrinsic Resistance and Exceptional Phenotypes (v3.1, 2016) +# Table 1: Intrinsic resistance in Enterobacteriaceae (349 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 (694 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) +# Table 11: Interpretive rules for macrolides, lincosamides, and streptogramins (no changes) +# Table 12: Interpretive rules for aminoglycosides (no changes) +# Table 13: Interpretive rules for quinolones (no changes) +# +# Other rules +# Non-EUCAST: ampicillin = R where amoxicillin/clav acid = R (no changes) +# Non-EUCAST: piperacillin = R where piperacillin/tazobactam = R (no changes) +# Non-EUCAST: trimethoprim = R where trimethoprim/sulfa = R (no changes) +# Non-EUCAST: amoxicillin/clav acid = S where ampicillin = S (no changes) +# Non-EUCAST: piperacillin/tazobactam = S where piperacillin = S (no changes) +# Non-EUCAST: trimethoprim/sulfa = S where trimethoprim = S (no changes) +# +# => EUCAST rules affected 1,854 out of 5,000 rows -> changed 1,043 test results.
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))data <- data %>%
+ mutate(gramstain = mo_gramstain(bacteria),
+ genus = mo_genus(bacteria),
+ species = mo_species(bacteria))First isolates
@@ -449,18 +450,18 @@ ab_interpretations <- M39-A4 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition. CLSI, 2014. Chapter 6.4This 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(.))
-# 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 2,950 first isolates (59.0% of total)So only 59% is suitable for resistance analysis! We can now filter on is with the filter() function, also from the dplyr package:
data_1st <- data %>%
- filter(first == TRUE)data <- data %>%
+ 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 2,929 first isolates (58.6% of total)So only 58.6% is suitable for resistance analysis! We can now filter on is 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:
data_1st <- data %>%
- filter_first_isolate()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 show 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(.))
-# NOTE: Using column `bacteria` as input for `col_mo`.
-# amox amcl cipr gent
-# "amox" "amcl" "cipr" "gent"
-# [1] "amox" "amcl" "cipr"
-# amox amcl cipr gent
-# "amox" "amcl" "cipr" "gent"
-# 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 4,430 first weighted isolates (88.6% of total)data <- data %>%
+ mutate(keyab = key_antibiotics(.)) %>%
+ mutate(first_weighted = first_isolate(.))
+# NOTE: Using column `bacteria` as input for `col_mo`.
+# amox amcl cipr gent
+# "amox" "amcl" "cipr" "gent"
+# [1] "amox" "amcl" "cipr"
+# amox amcl cipr gent
+# "amox" "amcl" "cipr" "gent"
+# 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 4,435 first weighted isolates (88.7% of total)Instead of 3, now 8 isolates are flagged. In total, 88.6% of all isolates are marked ‘first weighted’ - 147.6% 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 8 isolates are flagged. In total, 88.7% of all isolates are marked ‘first weighted’ - 147.3% 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 filter_first_isolate(), there’s a shortcut for this new algorithm too:
data_1st <- data %>%
- filter_first_weighted_isolate()So we end up with 4,430 isolates for analysis.
+ +So we end up with 4,435 isolates for analysis.
We can remove unneeded columns:
- +Now our data looks like:
-head(data_1st)| date | patient_id | hospital | @@ -772,87 +774,45 @@ ab_interpretations <- -2014-02-20 | -D5 | -Hospital A | -B_ESCHR_COL | -R | -S | -S | -S | -M | -Gram negative | -Escherichia | -coli | -TRUE | -|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2010-06-17 | -N4 | -Hospital A | -B_ESCHR_COL | -S | -S | -R | -S | -M | -Gram negative | -Escherichia | -coli | -TRUE | -||||
| 2017-08-02 | -A6 | -Hospital B | -B_STPHY_AUR | -R | -I | -R | -S | -M | -Gram positive | -Staphylococcus | -aureus | -TRUE | -||||
| 2012-11-25 | -K7 | -Hospital A | -B_ESCHR_COL | -I | -S | -S | -S | -M | -Gram negative | -Escherichia | -coli | -TRUE | -||||
| 2012-06-24 | -G9 | -Hospital B | -B_ESCHR_COL | -R | -S | -S | -R | -M | -Gram negative | -Escherichia | -coli | -TRUE | -||||
| 2011-05-20 | -T7 | +1 | +2011-02-27 | +M6 | Hospital C | B_ESCHR_COL | S | S | +R | +S | +M | +Gram negative | +Escherichia | +coli | +TRUE | +|
| 2 | +2012-07-12 | +C2 | +Hospital B | +B_STRPTC_PNE | +S | +R | +S | +R | +M | +Gram positive | +Streptococcus | +pneumoniae | +TRUE | +|||
| 3 | +2016-09-13 | +O7 | +Hospital A | +B_ESCHR_COL | +R | +R | S | S | F | @@ -861,6 +821,54 @@ ab_interpretations <- coliTRUE | ||||||
| 5 | +2017-10-05 | +M1 | +Hospital A | +B_ESCHR_COL | +R | +I | +R | +S | +M | +Gram negative | +Escherichia | +coli | +TRUE | +|||
| 6 | +2012-02-22 | +H9 | +Hospital C | +B_STRPTC_PNE | +S | +S | +R | +R | +M | +Gram positive | +Streptococcus | +pneumoniae | +TRUE | +|||
| 7 | +2011-01-13 | +S6 | +Hospital D | +B_STPHY_AUR | +I | +S | +S | +S | +F | +Gram positive | +Staphylococcus | +aureus | +TRUE | +
Time for the analysis!
@@ -871,12 +879,12 @@ ab_interpretations <- Analysing the dataYou might want to start by getting an idea of how the data is distributed. It’s an important start, because it also decides how you will continue your analysis. ## Dispersion of species To just get an idea how the species are distributed, create a frequency table with our freq() function. We created the genus and species column earlier based on the microbial ID. With paste(), we can concatenate them together.
The freq() function can be used like the base R language was intended:
Or can be used like the dplyr way, which is easier readable:
data_1st %>% freq(genus, species)Frequency table of genus and species
Columns: 2
-Length: 4,430 (of which NA: 0 = 0.00%)
+Length: 4,435 (of which NA: 0 = 0.00%)
Unique: 4
Shortest: 16
Longest: 24
Resistance percentages
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:
data_1st %>% portion_IR(amox)
-# [1] 0.46614Or can be used in conjuction with group_by() and summarise(), both from the dplyr package:
data_1st %>%
- group_by(hospital) %>%
- summarise(amoxicillin = portion_IR(amox))Or can be used in conjuction with group_by() and summarise(), both from the dplyr package:
| hospital | @@ -942,27 +950,27 @@ Longest: 24||
|---|---|---|
| Hospital A | -0.4605873 | +0.4504164 |
| Hospital B | -0.4624277 | +0.4782034 |
| Hospital C | -0.4853801 | +0.4670571 |
| Hospital D | -0.4659218 | +0.4471101 |
Of course it would be very convenient to know the number of isolates responsible for the percentages. For that purpose the n_rsi() can be used, which works exactly like n_distinct() from the dplyr package. It counts all isolates available for every group (i.e. values S, I or R):
data_1st %>%
- group_by(hospital) %>%
- summarise(amoxicillin = portion_IR(amox),
- available = n_rsi(amox))Of course it would be very convenient to know the number of isolates responsible for the percentages. For that purpose the n_rsi() can be used, which works exactly like n_distinct() from the dplyr package. It counts all isolates available for every group (i.e. values S, I or R):
data_1st %>%
+ group_by(hospital) %>%
+ summarise(amoxicillin = portion_IR(amox),
+ available = n_rsi(amox))| hospital | @@ -972,32 +980,32 @@ Longest: 24||||
|---|---|---|---|---|
| Hospital A | -0.4605873 | -1294 | +0.4504164 | +1321 |
| Hospital B | -0.4624277 | -1557 | +0.4782034 | +1514 |
| Hospital C | -0.4853801 | -684 | +0.4670571 | +683 |
| Hospital D | -0.4659218 | -895 | +0.4471101 | +917 |
These functions can also be used to get the portion of multiple antibiotics, to calculate co-resistance very easily:
-data_1st %>%
- group_by(genus) %>%
- summarise(amoxicillin = portion_S(amcl),
- gentamicin = portion_S(gent),
- "amox + gent" = portion_S(amcl, gent))data_1st %>%
+ group_by(genus) %>%
+ summarise(amoxicillin = portion_S(amcl),
+ gentamicin = portion_S(gent),
+ "amox + gent" = portion_S(amcl, gent))| genus | @@ -1008,94 +1016,94 @@ Longest: 24||||||
|---|---|---|---|---|---|---|
| Escherichia | -0.7307344 | -0.9120580 | -0.9782412 | +0.7162960 | +0.9051294 | +0.9786655 |
| Klebsiella | -0.7521552 | -0.9181034 | -0.9806034 | +0.7494692 | +0.9129512 | +0.9766454 |
| Staphylococcus | -0.7419945 | -0.9268070 | -0.9798719 | +0.7341418 | +0.9244403 | +0.9776119 |
| Streptococcus | -0.7256372 | +0.7576197 | 0.0000000 | -0.7256372 | +0.7576197 |
To make a transition to the next part, let’s see how this difference could be plotted:
-data_1st %>%
- group_by(genus) %>%
- summarise("1. Amoxicillin" = portion_S(amcl),
- "2. Gentamicin" = portion_S(gent),
- "3. Amox + gent" = portion_S(amcl, gent)) %>%
- tidyr::gather("Antibiotic", "S", -genus) %>%
- ggplot(aes(x = genus,
- y = S,
- fill = Antibiotic)) +
- geom_col(position = "dodge2")data_1st %>%
+ group_by(genus) %>%
+ summarise("1. Amoxicillin" = portion_S(amcl),
+ "2. Gentamicin" = portion_S(gent),
+ "3. Amox + gent" = portion_S(amcl, gent)) %>%
+ tidyr::gather("Antibiotic", "S", -genus) %>%
+ ggplot(aes(x = genus,
+ y = S,
+ fill = Antibiotic)) +
+ geom_col(position = "dodge2")
