portion.RdThese functions can be used to calculate the (co-)resistance of microbial isolates (i.e. percentage of S, SI, I, IR or R). All functions support quasiquotation with pipes, can be used in dplyrs summarise and support grouped variables, see Examples.
portion_R and portion_IR can be used to calculate resistance, portion_S and portion_SI can be used to calculate susceptibility.
portion_R(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE) portion_IR(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE) portion_I(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE) portion_SI(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE) portion_S(..., minimum = 30, as_percent = FALSE, only_all_tested = FALSE) portion_df(data, translate_ab = "name", language = get_locale(), minimum = 30, as_percent = FALSE, combine_SI = TRUE, combine_IR = FALSE) rsi_df(data, translate_ab = "name", language = get_locale(), minimum = 30, as_percent = FALSE, combine_SI = TRUE, combine_IR = FALSE)
| ... | one or more vectors (or columns) with antibiotic interpretations. They will be transformed internally with |
|---|---|
| minimum | the minimum allowed number of available (tested) isolates. Any isolate count lower than |
| as_percent | a logical to indicate whether the output must be returned as a hundred fold with % sign (a character) using |
| only_all_tested | (for combination therapies, i.e. using more than one variable for |
| data | a |
| translate_ab | a column name of the |
| language | language of the returned text, defaults to system language (see |
| combine_SI | a logical to indicate whether all values of S and I must be merged into one, so the output only consists of S+I vs. R (susceptible vs. resistant). This used to be the parameter |
| combine_IR | a logical to indicate whether all values of I and R must be merged into one, so the output only consists of S vs. I+R (susceptible vs. non-susceptible). This is outdated, see parameter |
M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition, 2014, Clinical and Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m39/.
Wickham H. Tidy Data. The Journal of Statistical Software, vol. 59, 2014. http://vita.had.co.nz/papers/tidy-data.html
Double or, when as_percent = TRUE, a character.
Remember that you should filter your table to let it contain only first isolates! This is needed to exclude duplicates and to reduce selection bias. Use first_isolate to determine them in your data set.
These functions are not meant to count isolates, but to calculate the portion of resistance/susceptibility. Use the count functions to count isolates. The function portion_SI() is essentially equal to count_SI() / count_all(). Low counts can infuence the outcome - the portion functions may camouflage this, since they only return the portion (albeit being dependent on the minimum parameter).
The function portion_df takes any variable from data that has an "rsi" class (created with as.rsi) and calculates the portions R, I and S. The resulting tidy data (see Source) data.frame will have three rows (S/I/R) and a column for each group and each variable with class "rsi".
The function rsi_df works exactly like portion_df, but adds the number of isolates.
When using more than one variable for ... (= combination therapy)), use only_all_tested to only count isolates that are tested for all antibiotics/variables that you test them for. See this example for two antibiotics, Antibiotic A and Antibiotic B, about how portion_SI works to calculate the %SI:
--------------------------------------------------------------------
only_all_tested = FALSE only_all_tested = TRUE
----------------------- -----------------------
Drug A Drug B include as include as include as include as
numerator denominator numerator denominator
-------- -------- ---------- ----------- ---------- -----------
S or I S or I X X X X
R S or I X X X X
<NA> S or I X X - -
S or I R X X X X
R R - X - X
<NA> R - - - -
S or I <NA> X X - -
R <NA> - - - -
<NA> <NA> - - - -
--------------------------------------------------------------------
Please note that, in combination therapies, for only_all_tested = TRUE applies that:
count_S() + count_I() + count_R() == count_all() portion_S() + portion_I() + portion_R() == 1
and that, in combination therapies, for only_all_tested = FALSE applies that:
count_S() + count_I() + count_R() >= count_all() portion_S() + portion_I() + portion_R() >= 1
Using only_all_tested has no impact when only using one antibiotic as input.
In 2019, EUCAST has decided to change the definitions of susceptibility testing categories S, I and R as shown below (http://www.eucast.org/newsiandr/). Results of several consultations on the new definitions are available on the EUCAST website under "Consultations".
S - Susceptible, standard dosing regimen: A microorganism is categorised as "Susceptible, standard dosing regimen", when there is a high likelihood of therapeutic success using a standard dosing regimen of the agent.
I - Susceptible, increased exposure: A microorganism is categorised as "Susceptible, Increased exposure" when there is a high likelihood of therapeutic success because exposure to the agent is increased by adjusting the dosing regimen or by its concentration at the site of infection.
R - Resistant: A microorganism is categorised as "Resistant" when there is a high likelihood of therapeutic failure even when there is increased exposure.
Exposure is a function of how the mode of administration, dose, dosing interval, infusion time, as well as distribution and excretion of the antimicrobial agent will influence the infecting organism at the site of infection.
This AMR package honours this new insight. Use portion_SI to determine antimicrobial susceptibility and count_SI to count susceptible isolates.
On our website https://msberends.gitlab.io/AMR you can find a tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data.
count_* to count resistant and susceptible isolates.
# example_isolates is a data set available in the AMR package. ?example_isolates # Calculate resistance portion_R(example_isolates$AMX) portion_IR(example_isolates$AMX) # Or susceptibility portion_S(example_isolates$AMX) portion_SI(example_isolates$AMX) # Do the above with pipes: library(dplyr) example_isolates %>% portion_R(AMX) example_isolates %>% portion_IR(AMX) example_isolates %>% portion_S(AMX) example_isolates %>% portion_SI(AMX) example_isolates %>% group_by(hospital_id) %>% summarise(p = portion_SI(CIP), n = n_rsi(CIP)) # n_rsi works like n_distinct in dplyr example_isolates %>% group_by(hospital_id) %>% summarise(R = portion_R(CIP, as_percent = TRUE), I = portion_I(CIP, as_percent = TRUE), S = portion_S(CIP, as_percent = TRUE), n1 = count_all(CIP), # the actual total; sum of all three n2 = n_rsi(CIP), # same - analogous to n_distinct total = n()) # NOT the number of tested isolates! # Calculate co-resistance between amoxicillin/clav acid and gentamicin, # so we can see that combination therapy does a lot more than mono therapy: example_isolates %>% portion_SI(AMC) # %SI = 76.3% example_isolates %>% count_all(AMC) # n = 1879 example_isolates %>% portion_SI(GEN) # %SI = 75.4% example_isolates %>% count_all(GEN) # n = 1855 example_isolates %>% portion_SI(AMC, GEN) # %SI = 94.1% example_isolates %>% count_all(AMC, GEN) # n = 1939 # See Details on how `only_all_tested` works. Example: example_isolates %>% summarise(numerator = count_SI(AMC, GEN), denominator = count_all(AMC, GEN), portion = portion_SI(AMC, GEN)) # numerator denominator portion # 1764 1936 0.9408 example_isolates %>% summarise(numerator = count_SI(AMC, GEN, only_all_tested = TRUE), denominator = count_all(AMC, GEN, only_all_tested = TRUE), portion = portion_SI(AMC, GEN, only_all_tested = TRUE)) # numerator denominator portion # 1687 1798 0.9383 example_isolates %>% group_by(hospital_id) %>% summarise(cipro_p = portion_SI(CIP, as_percent = TRUE), cipro_n = count_all(CIP), genta_p = portion_SI(GEN, as_percent = TRUE), genta_n = count_all(GEN), combination_p = portion_SI(CIP, GEN, as_percent = TRUE), combination_n = count_all(CIP, GEN)) # Get portions S/I/R immediately of all rsi columns example_isolates %>% select(AMX, CIP) %>% portion_df(translate = FALSE) # It also supports grouping variables example_isolates %>% select(hospital_id, AMX, CIP) %>% group_by(hospital_id) %>% portion_df(translate = FALSE) if (FALSE) { # calculate current empiric combination therapy of Helicobacter gastritis: my_table %>% filter(first_isolate == TRUE, genus == "Helicobacter") %>% summarise(p = portion_S(AMX, MTR), # amoxicillin with metronidazole n = count_all(AMX, MTR)) }