count.RdThese functions can be used to count resistant/susceptible microbial isolates. All functions support quasiquotation with pipes, can be used in dplyrs summarise and support grouped variables, see Examples.
count_resistant should be used to count resistant isolates, count_susceptible should be used to count susceptible isolates.
count_resistant(..., only_all_tested = FALSE) count_susceptible(..., only_all_tested = FALSE) count_R(..., only_all_tested = FALSE) count_IR(..., only_all_tested = FALSE) count_I(..., only_all_tested = FALSE) count_SI(..., only_all_tested = FALSE) count_S(..., only_all_tested = FALSE) count_all(..., only_all_tested = FALSE) n_rsi(..., only_all_tested = FALSE) count_df(data, translate_ab = "name", language = get_locale(), combine_SI = TRUE, combine_IR = FALSE)
| ... | one or more vectors (or columns) with antibiotic interpretations. They will be transformed internally with |
|---|---|
| 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 This is outdated, see parameter |
Integer
These functions are meant to count isolates. Use the resistance/susceptibility functions to calculate microbial resistance/susceptibility.
The function count_resistant is equal to the function count_R. The function count_susceptible is equal to the function count_SI.
The function n_rsi is an alias of count_all. They can be used to count all available isolates, i.e. where all input antibiotics have an available result (S, I or R). Their use is equal to n_distinct. Their function is equal to count_susceptible(...) + count_resistant(...).
The function count_df takes any variable from data that has an "rsi" class (created with as.rsi) and counts the number of S's, I's and R's. The function rsi_df works exactly like count_df, but adds the percentage of S, I and R.
In 2019, the European Committee on Antimicrobial Susceptibility Testing (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 susceptibility (equal to proportion_SI) to determine antimicrobial susceptibility and count_susceptible (equal to count_SI) to count susceptible 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 susceptibility 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()
proportion_S() + proportion_I() + proportion_R() = 1
and that, in combination therapies, for only_all_tested = FALSE applies that:
count_S() + count_I() + count_R() >= count_all()
proportion_S() + proportion_I() + proportion_R() >= 1
Using only_all_tested has no impact when only using one antibiotic as input.
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.
proportion_* to calculate microbial resistance and susceptibility.
# example_isolates is a data set available in the AMR package. ?example_isolates count_resistant(example_isolates$AMX) # counts "R" count_susceptible(example_isolates$AMX) # counts "S" and "I" count_all(example_isolates$AMX) # counts "S", "I" and "R" # be more specific count_S(example_isolates$AMX) count_SI(example_isolates$AMX) count_I(example_isolates$AMX) count_IR(example_isolates$AMX) count_R(example_isolates$AMX) # Count all available isolates count_all(example_isolates$AMX) n_rsi(example_isolates$AMX) # n_rsi() is an alias of count_all(). # Since it counts all available isolates, you can # calculate back to count e.g. susceptible isolates. # These results are the same: count_susceptible(example_isolates$AMX) susceptibility(example_isolates$AMX) * n_rsi(example_isolates$AMX) library(dplyr) example_isolates %>% group_by(hospital_id) %>% summarise(R = count_R(CIP), I = count_I(CIP), S = count_S(CIP), 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! # Count co-resistance between amoxicillin/clav acid and gentamicin, # so we can see that combination therapy does a lot more than mono therapy. # Please mind that `susceptibility()` calculates percentages right away instead. example_isolates %>% count_susceptible(AMC) # 1433 example_isolates %>% count_all(AMC) # 1879 example_isolates %>% count_susceptible(GEN) # 1399 example_isolates %>% count_all(GEN) # 1855 example_isolates %>% count_susceptible(AMC, GEN) # 1764 example_isolates %>% count_all(AMC, GEN) # 1936 # Get number of S+I vs. R immediately of selected columns example_isolates %>% select(AMX, CIP) %>% count_df(translate = FALSE) # It also supports grouping variables example_isolates %>% select(hospital_id, AMX, CIP) %>% group_by(hospital_id) %>% count_df(translate = FALSE)