antibiotics
data set and updated to the latest version (LOINC v2.76)as.mic("1.28e-2")
)AMR_locale
is setclinical_breakpoints
data set for usage in as.sir()
. EUCAST 2023 (v13.0) is now the new default guideline for all MIC and disks diffusion interpretationsdosage
data setclinical_breakpoints
data set now also contains epidemiological cut-off (ECOFF) values and CLSI animal breakpoints. These two new breakpoint types can be used for MIC/disk interpretation using as.sir(..., breakpoint_type = "ECOFF")
oras.sir(..., breakpoint_type = "animal")
, which is an important new addition for veterinary microbiology.microorganisms.groups
and are used in clinical breakpoint interpretation. For example, CLSI 2023 contains breakpoints for the RGM group (Rapidly Growing Mycobacterium, containing over 80 species) which is now supported by our package.microorganisms
data setmo_oxygen_tolerance()
to retrieve the valuesmo_is_anaerobic()
to determine which genera/species are obligate anaerobic bacteriamo_info()
.xpt
) to our download page to use in SAS softwareas.mo()
by giving more weight to fungimo_rank()
now returns NA
for 'unknown' microorganisms (B_ANAER
, B_ANAER-NEG
, B_ANAER-POS
, B_GRAMN
, B_GRAMP
, F_FUNGUS
, F_YEAST
, and UNKNOWN
)as.sir()
sir_interpretation_history()
clinical_breakpoints
as.mo()
that led to coercion of NA
values when using custom microorganism codesicu_exclude
in first_isolates()
as.mo()
algorithm:keep_synonyms
argument when using MO codes as inputminimum_matching_score
argumentmicroorganisms.codes
reference_df
in as.mo()
as.sir()
as.sir()
This is a new major release of the AMR package, with great new additions but also some breaking changes for current users. These are all listed below.
as.sir()
instead of as.rsi()
) - all old functions still work for nowantibiogram()
(for generating traditional/combined/syndromic/WISCA antibiograms), sir_confidence_interval()
and mean_amr_distance()
, and add_custom_microorganisms()
to add custom microorganisms to this packagemicroorganisms
data set) updated to 2022 and now based on LPSN and GBIFantivirals
data set), with many new functionsFor this milestone version, we replaced all mentions of RSI with SIR, to comply with what is actually being commonly used in the field of clinical microbiology when it comes to this tri-form regarding AMR.
While existing functions such as as.rsi()
, rsi_df()
and ggplot_rsi()
still work, their replacements as.sir()
, sir_df()
, ggplot_sir()
are now the current functions for AMR data analysis. A warning will be thrown once a session to remind users about this. The data set rsi_translation
is now called clinical_breakpoints
to better reflect its content.
The 'RSI functions' will be removed in a future version, but not before late 2023 / early 2024.
With the new antibiogram()
function, users can now generate traditional, combined, syndromic, and even weighted-incidence syndromic combination antibiograms (WISCA). With this, we follow the logic in the previously described work of Klinker et al. (2021, DOI 10.1177/20499361211011373) and Barbieri et al. (2021, DOI 10.1186/s13756-021-00939-2).
The help page for antibiogram()
extensively elaborates on use cases, and antibiogram()
also supports printing in R Markdown and Quarto, with support for 20 languages.
Furthermore, different plotting methods were implemented to allow for graphical visualisations as well.
The clinical breakpoints and intrinsic resistance of EUCAST 2022 and CLSI 2022 have been added for as.sir()
. EUCAST 2022 (v12.0) is now the new default guideline for all MIC and disks diffusion interpretations, and for eucast_rules()
to apply EUCAST Expert Rules. The default guideline (EUCAST) can now be changed with the new AMR_guideline
option, such as: options(AMR_guideline = "CLSI 2020")
.
With the new arguments include_PKPD
(default: TRUE
) and include_screening
(default: FALSE
), users can now specify whether breakpoints for screening and from the PK/PD table should be included when interpreting MICs and disks diffusion values. These options can be set globally, which can be read in our new manual.
Interpretation guidelines older than 10 years were removed, the oldest now included guidelines of EUCAST and CLSI are from 2013.
We added support for the following ten languages: Chinese (simplified), Czech, Finnish, Greek, Japanese, Norwegian (bokmål), Polish, Romanian, Turkish and Ukrainian. All antibiotic names are now available in these languages, and the AMR package will automatically determine a supported language based on the user's system language.
We are very grateful for the valuable input by our colleagues from other countries. The AMR
package is now available in 20 languages in total, and according to download stats used in almost all countries in the world!
For analysis in outbreak management, we updated the get_episode()
and is_new_episode()
functions: they now contain an argument case_free_days
. This argument can be used to quantify the duration of case-free days (the inter-epidemic interval), after which a new episode will start.
This is common requirement in outbreak management, e.g. when determining the number of norovirus outbreaks in a hospital. The case-free period could then be 14 or 28 days, so that new norovirus cases after that time will be considered a different (or new) episode.
The microorganisms
data set no longer relies on the Catalogue of Life, but on the List of Prokaryotic names with Standing in Nomenclature (LPSN) and is supplemented with the 'backbone taxonomy' from the Global Biodiversity Information Facility (GBIF). The structure of this data set has changed to include separate LPSN and GBIF identifiers. Almost all previous MO codes were retained. It contains over 1,400 taxonomic names from 2022.
We previously relied on our own experience to categorise species into pathogenic groups, but we were very happy to encounter the very recent work of Bartlett et al. (2022, DOI 10.1099/mic.0.001269) who extensively studied medical-scientific literature to categorise all bacterial species into groups. See mo_matching_score()
on how their work was incorporated into the prevalence
column of the microorganisms
data set. Using their results, the as.mo()
and all mo_*()
functions are now much better capable of converting user input to valid taxonomic records.
The new function add_custom_microorganisms()
allows users to add custom microorganisms to the AMR
package.
We also made the following changes regarding the included taxonomy or microorganisms functions:
mo_current()
to get the currently valid taxonomic name of a microorganismsubspecies
column of the microorganisms
data set and "enterica" is in the species
column, but the full name does not contain the species name (enterica).as.mo()
(and thus all mo_*()
functions) while still following our original set-up as described in our recently published JSS paper (DOI 10.18637/jss.v104.i03).keep_synonyms
allows to not correct for updated taxonomy, in favour of the now deleted argument allow_uncertain
mo_reset_session()
function.mo_matching_score()
) now counts deletions and substitutions as 2 instead of 1, which impacts the outcome of as.mo()
and any mo_*()
functionmicroorganisms.old
data set was removed, and all previously accepted names are now included in the microorganisms
data set. A new column status
contains "accepted"
for currently accepted names and "synonym"
for taxonomic synonyms; currently invalid names. All previously accepted names now have a microorganisms ID and - if available - an LPSN, GBIF and SNOMED CT identifier.The new function add_custom_antimicrobials()
allows users to add custom antimicrobial codes and names to the AMR
package.
The antibiotics
data set was greatly updated:
as.ab()
or any of the ab_*()
functions.B_ANAER
to the microorganisms
data set and adding the breakpoints of anaerobics to the clinical_breakpoints
data set, which is used by as.sir()
for interpretion of MIC and disk diffusion valuesAlso, we added support for using antibiotic selectors in scoped dplyr
verbs (with or without using vars()
), such as in: ... %>% summarise_at(aminoglycosides(), resistance)
, please see resistance()
for examples.
We now added extensive support for antiviral agents! For the first time, the AMR
package has extensive support for antiviral drugs and to work with their names, codes and other data in any way.
antivirals
data set has been extended with 18 new drugs (also from the new J05AJ ATC group) and now also contains antiviral identifiers and LOINC codesav
(antivirals) has been added, which is functionally similar to ab
for antibioticsas.av()
, av_name()
, av_atc()
, av_synonyms()
, av_from_text()
have all been added as siblings to their ab_*()
equivalentssir_confidence_interval()
to add confidence intervals in AMR calculation. This is now also included in sir_df()
and proportion_df()
.mean_amr_distance()
to calculate the mean AMR distance. The mean AMR distance is a normalised numeric value to compare AMR test results and can help to identify similar isolates, without comparing antibiograms by hand.sir_interpretation_history()
to view the history of previous runs of as.sir()
(previously as.rsi()
). This returns a 'logbook' with the selected guideline, reference table and specific interpretation of each row in a data set on which as.sir()
was run.get_episode()
(and its wrapper is_new_episode()
):NA
valuesinteger
instead of numeric
since they are always whole numberscombine_IR
has been removed from this package (affecting functions count_df()
, proportion_df()
, and sir_df()
and some plotting functions), since it was replaced with combine_SI
three years agounits
in ab_ddd(..., units = "...")
had been deprecated for some time and is now not supported anymore. Use ab_ddd_units()
instead.data.frame
-enhancing R packages, more specifically: data.table::data.table
, janitor::tabyl
, tibble::tibble
, and tsibble::tsibble
. AMR package functions that have a data set as output (such as sir_df()
and bug_drug_combinations()
), will now return the same data type as the input.tibble
, instead of base R data.frame
s. Older R versions are still supported, even if they do not support tibble
s.as.sir()
:NA
values (e.g. as.sir(as.disk(NA), ...)
)as.integer()
method for MIC values, since MIC are not integer values and running table()
on MIC values consequently failed for not being able to retrieve the level position (as that's how normally as.integer()
on factor
s work)mo_gramstain()
), since the taxonomic phyla Actinobacteria, Chloroflexi, Firmicutes, and Tenericutes have been renamed to respectively Actinomycetota, Chloroflexota, Bacillota, and Mycoplasmatota in 2021droplevels()
on MIC will now return a common factor
at default and will lose the mic
class. Use droplevels(..., as.mic = TRUE)
to keep the mic
class.ab_from_text()
set_mo_source()
, which now also allows the source file to contain valid taxonomic names instead of only valid microorganism ID of this packagemdro()
when using similar column names with the Magiorakos guidelinerandom_*()
function (such as random_mic()
) is now possible by directly calling the package without loading it first: AMR::random_mic(10)
vctrs
package, used internally by the tidyverse. This allows to change values of class mic
, disk
, sir
, mo
and ab
in tibbles, and to use antibiotic selectors for selecting/filtering, e.g. df[carbapenems() == "R", ]
info = FALSE
in mdro()
as.sir()
on amoxicillin, the rules for ampicillin will be used if amoxicillin rules are not availableab_atc()
on non-existing ATC codesmo_snomed()
now returns class character
, not numeric
anymore (to make long SNOMED codes readable)as.ab()
on NA
valuesas.sir()
mo_shortname()
in case of higher taxonomic ranks (order, class, phylum)as.sir()
, as.mic()
, or as.disk()
will now show the column name in the warning for invalid resultsg.test()
with zeroes in a 2x2 tablemo_synonyns()
now contains the scientific reference as namesstyler
package&&
and ||
) where possible to comply with the upcoming R 4.3This changelog only contains changes from AMR v2.0 (January 2023) and later. For prior versions, please see our archive.
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