as.sir | R Documentation |
Clean up existing SIR values, or interpret minimum inhibitory concentration (MIC) values and disk diffusion diameters according to EUCAST or CLSI. as.sir()
transforms the input to a new class sir
, which is an ordered factor.
Currently breakpoints are available:
For clinical microbiology from EUCAST 2011-2023 and CLSI 2011-2023;
For veterinary microbiology from EUCAST 2021-2023 and CLSI 2019-2023;
ECOFFs (Epidemiological cut-off values) from EUCAST 2020-2023 and CLSI 2022-2023.
All breakpoints used for interpretation are publicly available in the clinical_breakpoints data set.
as.sir(x, ...)
NA_sir_
is.sir(x)
is_sir_eligible(x, threshold = 0.05)
## S3 method for class 'mic'
as.sir(
x,
mo = NULL,
ab = deparse(substitute(x)),
guideline = getOption("AMR_guideline", "EUCAST"),
uti = NULL,
conserve_capped_values = FALSE,
add_intrinsic_resistance = FALSE,
reference_data = AMR::clinical_breakpoints,
include_screening = getOption("AMR_include_screening", FALSE),
include_PKPD = getOption("AMR_include_PKPD", TRUE),
breakpoint_type = getOption("AMR_breakpoint_type", "human"),
host = NULL,
...
)
## S3 method for class 'disk'
as.sir(
x,
mo = NULL,
ab = deparse(substitute(x)),
guideline = getOption("AMR_guideline", "EUCAST"),
uti = NULL,
add_intrinsic_resistance = FALSE,
reference_data = AMR::clinical_breakpoints,
include_screening = getOption("AMR_include_screening", FALSE),
include_PKPD = getOption("AMR_include_PKPD", TRUE),
breakpoint_type = getOption("AMR_breakpoint_type", "human"),
host = NULL,
...
)
## S3 method for class 'data.frame'
as.sir(
x,
...,
col_mo = NULL,
guideline = getOption("AMR_guideline", "EUCAST"),
uti = NULL,
conserve_capped_values = FALSE,
add_intrinsic_resistance = FALSE,
reference_data = AMR::clinical_breakpoints,
include_screening = getOption("AMR_include_screening", FALSE),
include_PKPD = getOption("AMR_include_PKPD", TRUE),
breakpoint_type = getOption("AMR_breakpoint_type", "human"),
host = NULL
)
sir_interpretation_history(clean = FALSE)
x |
vector of values (for class |
... |
for using on a data.frame: names of columns to apply |
threshold |
maximum fraction of invalid antimicrobial interpretations of |
mo |
any (vector of) text that can be coerced to valid microorganism codes with |
ab |
any (vector of) text that can be coerced to a valid antimicrobial drug code with |
guideline |
defaults to EUCAST 2023 (the latest implemented EUCAST guideline in the clinical_breakpoints data set), but can be set with the package option |
uti |
(Urinary Tract Infection) A vector with logicals ( |
conserve_capped_values |
a logical to indicate that MIC values starting with |
add_intrinsic_resistance |
(only useful when using a EUCAST guideline) a logical to indicate whether intrinsic antibiotic resistance must also be considered for applicable bug-drug combinations, meaning that e.g. ampicillin will always return "R" in Klebsiella species. Determination is based on the intrinsic_resistant data set, that itself is based on 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3 (2021). |
reference_data |
a data.frame to be used for interpretation, which defaults to the clinical_breakpoints data set. Changing this argument allows for using own interpretation guidelines. This argument must contain a data set that is equal in structure to the clinical_breakpoints data set (same column names and column types). Please note that the |
include_screening |
a logical to indicate that clinical breakpoints for screening are allowed - the default is |
include_PKPD |
a logical to indicate that PK/PD clinical breakpoints must be applied as a last resort - the default is |
breakpoint_type |
the type of breakpoints to use, either "ECOFF", "animal", or "human". ECOFF stands for Epidemiological Cut-Off values. The default is |
host |
a vector (or column name) with characters to indicate the host. Only useful for veterinary breakpoints, as it requires |
col_mo |
column name of the names or codes of the microorganisms (see |
clean |
a logical to indicate whether previously stored results should be forgotten after returning the 'logbook' with results |
Note: The clinical breakpoints in this package were validated through, and imported from, WHONET. The public use of this AMR
package has been endorsed by both CLSI and EUCAST. See clinical_breakpoints for more information.
The as.sir()
function can work in four ways:
For cleaning raw / untransformed data. The data will be cleaned to only contain values S, I and R and will try its best to determine this with some intelligence. For example, mixed values with SIR interpretations and MIC values such as "<0.25; S"
will be coerced to "S"
. Combined interpretations for multiple test methods (as seen in laboratory records) such as "S; S"
will be coerced to "S"
, but a value like "S; I"
will return NA
with a warning that the input is unclear.
For interpreting minimum inhibitory concentration (MIC) values according to EUCAST or CLSI. You must clean your MIC values first using as.mic()
, that also gives your columns the new data class mic
. Also, be sure to have a column with microorganism names or codes. It will be found automatically, but can be set manually using the mo
argument.
Using dplyr
, SIR interpretation can be done very easily with either:
your_data %>% mutate_if(is.mic, as.sir) your_data %>% mutate(across(where(is.mic), as.sir)) # for veterinary breakpoints, also set `host`: your_data %>% mutate_if(is.mic, as.sir, host = "column_with_animal_hosts", guideline = "CLSI")
Operators like "<=" will be stripped before interpretation. When using conserve_capped_values = TRUE
, an MIC value of e.g. ">2" will always return "R", even if the breakpoint according to the chosen guideline is ">=4". This is to prevent that capped values from raw laboratory data would not be treated conservatively. The default behaviour (conserve_capped_values = FALSE
) considers ">2" to be lower than ">=4" and might in this case return "S" or "I".
For interpreting disk diffusion diameters according to EUCAST or CLSI. You must clean your disk zones first using as.disk()
, that also gives your columns the new data class disk
. Also, be sure to have a column with microorganism names or codes. It will be found automatically, but can be set manually using the mo
argument.
Using dplyr
, SIR interpretation can be done very easily with either:
your_data %>% mutate_if(is.disk, as.sir) your_data %>% mutate(across(where(is.disk), as.sir)) # for veterinary breakpoints, also set `host`: your_data %>% mutate_if(is.disk, as.sir, host = "column_with_animal_hosts", guideline = "CLSI")
For interpreting a complete data set, with automatic determination of MIC values, disk diffusion diameters, microorganism names or codes, and antimicrobial test results. This is done very simply by running as.sir(your_data)
.
For points 2, 3 and 4: Use sir_interpretation_history()
to retrieve a data.frame (or tibble if the tibble
package is installed) with all results of the last as.sir()
call.
For interpreting MIC values as well as disk diffusion diameters, currently implemented guidelines are for clinical microbiology: EUCAST 2011-2023 and CLSI 2011-2023, and for veterinary microbiology: EUCAST 2021-2023 and CLSI 2019-2023.
Thus, the guideline
argument must be set to e.g., "EUCAST 2023"
or "CLSI 2023"
. By simply using "EUCAST"
(the default) or "CLSI"
as input, the latest included version of that guideline will automatically be selected. You can set your own data set using the reference_data
argument. The guideline
argument will then be ignored.
You can set the default guideline with the package option AMR_guideline
(e.g. in your .Rprofile
file), such as:
options(AMR_guideline = "CLSI") options(AMR_guideline = "CLSI 2018") options(AMR_guideline = "EUCAST 2020") # or to reset: options(AMR_guideline = NULL)
For veterinary guidelines, these might be the best options:
options(AMR_guideline = "CLSI") options(AMR_breakpoint_type = "animal")
After using as.sir()
, you can use the eucast_rules()
defined by EUCAST to (1) apply inferred susceptibility and resistance based on results of other antimicrobials and (2) apply intrinsic resistance based on taxonomic properties of a microorganism.
The repository of this package contains a machine-readable version of all guidelines. This is a CSV file consisting of 29 883 rows and 13 columns. This file is machine-readable, since it contains one row for every unique combination of the test method (MIC or disk diffusion), the antimicrobial drug and the microorganism. This allows for easy implementation of these rules in laboratory information systems (LIS). Note that it only contains interpretation guidelines for humans - interpretation guidelines from CLSI for animals were removed.
The function is.sir()
detects if the input contains class sir
. If the input is a data.frame, it iterates over all columns and returns a logical vector.
The function is_sir_eligible()
returns TRUE
when a columns contains at most 5% invalid antimicrobial interpretations (not S and/or I and/or R), and FALSE
otherwise. The threshold of 5% can be set with the threshold
argument. If the input is a data.frame, it iterates over all columns and returns a logical vector.
NA_sir_
is a missing value of the new sir
class, analogous to e.g. base R's NA_character_
.
Ordered factor with new class sir
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 (https://www.eucast.org/newsiandr):
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 insight. Use susceptibility()
(equal to proportion_SI()
) to determine antimicrobial susceptibility and count_susceptible()
(equal to count_SI()
) to count susceptible isolates.
All data sets in this AMR
package (about microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) are publicly and freely available for download in the following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, and Stata. We also provide tab-separated plain text files that are machine-readable and suitable for input in any software program, such as laboratory information systems. Please visit our website for the download links. The actual files are of course available on our GitHub repository.
For interpretations of minimum inhibitory concentration (MIC) values and disk diffusion diameters:
CLSI M39: Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 2011-2023, Clinical and Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m39/.
CLSI M100: Performance Standard for Antimicrobial Susceptibility Testing, 2011-2023, Clinical and Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m100/.
CLSI VET01: Performance Standards for Antimicrobial Disk and Dilution Susceptibility Tests for Bacteria Isolated From Animals, 2019-2023, Clinical and Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/veterinary-medicine/documents/vet01//.
EUCAST Breakpoint tables for interpretation of MICs and zone diameters, 2011-2023, European Committee on Antimicrobial Susceptibility Testing (EUCAST). https://www.eucast.org/clinical_breakpoints.
as.mic()
, as.disk()
, as.mo()
example_isolates
summary(example_isolates) # see all SIR results at a glance
# For INTERPRETING disk diffusion and MIC values -----------------------
# a whole data set, even with combined MIC values and disk zones
df <- data.frame(
microorganism = "Escherichia coli",
AMP = as.mic(8),
CIP = as.mic(0.256),
GEN = as.disk(18),
TOB = as.disk(16),
ERY = "R"
)
as.sir(df)
# return a 'logbook' about the results:
sir_interpretation_history()
# for single values
as.sir(
x = as.mic(2),
mo = as.mo("S. pneumoniae"),
ab = "AMP",
guideline = "EUCAST"
)
as.sir(
x = as.disk(18),
mo = "Strep pneu", # `mo` will be coerced with as.mo()
ab = "ampicillin", # and `ab` with as.ab()
guideline = "EUCAST"
)
# the dplyr way
if (require("dplyr")) {
df %>% mutate_if(is.mic, as.sir)
df %>% mutate_if(function(x) is.mic(x) | is.disk(x), as.sir)
df %>% mutate(across(where(is.mic), as.sir))
df %>% mutate_at(vars(AMP:TOB), as.sir)
df %>% mutate(across(AMP:TOB, as.sir))
df %>%
mutate_at(vars(AMP:TOB), as.sir, mo = .$microorganism)
# to include information about urinary tract infections (UTI)
data.frame(
mo = "E. coli",
NIT = c("<= 2", 32),
from_the_bladder = c(TRUE, FALSE)
) %>%
as.sir(uti = "from_the_bladder")
data.frame(
mo = "E. coli",
NIT = c("<= 2", 32),
specimen = c("urine", "blood")
) %>%
as.sir() # automatically determines urine isolates
df %>%
mutate_at(vars(AMP:TOB), as.sir, mo = "E. coli", uti = TRUE)
}
# For CLEANING existing SIR values ------------------------------------
as.sir(c("S", "I", "R", "A", "B", "C"))
as.sir("<= 0.002; S") # will return "S"
sir_data <- as.sir(c(rep("S", 474), rep("I", 36), rep("R", 370)))
is.sir(sir_data)
plot(sir_data) # for percentages
barplot(sir_data) # for frequencies
# the dplyr way
if (require("dplyr")) {
example_isolates %>%
mutate_at(vars(PEN:RIF), as.sir)
# same:
example_isolates %>%
as.sir(PEN:RIF)
# fastest way to transform all columns with already valid AMR results to class `sir`:
example_isolates %>%
mutate_if(is_sir_eligible, as.sir)
# since dplyr 1.0.0, this can also be:
# example_isolates %>%
# mutate(across(where(is_sir_eligible), as.sir))
}
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