antibiogram: Generate Antibiogram: Traditional, Combined, Syndromic, or...

In AMR: Antimicrobial Resistance Data Analysis

Generate Antibiogram: Traditional, Combined, Syndromic, or Weighted-Incidence Syndromic Combination (WISCA)

Description

Generate an antibiogram, and communicate the results in plots or tables. These functions follow the logic of Klinker et al. and Barbieri et al. (see Source), and allow reporting in e.g. R Markdown and Quarto as well.

Usage

antibiogram(
  x,
  antibiotics = where(is.sir),
  mo_transform = "shortname",
  ab_transform = NULL,
  syndromic_group = NULL,
  add_total_n = TRUE,
  only_all_tested = FALSE,
  digits = 0,
  col_mo = NULL,
  language = get_AMR_locale(),
  minimum = 30,
  combine_SI = TRUE,
  sep = " + ",
  info = interactive()
)

## S3 method for class 'antibiogram'
plot(x, ...)

## S3 method for class 'antibiogram'
autoplot(object, ...)

## S3 method for class 'antibiogram'
knit_print(
  x,
  italicise = TRUE,
  na = getOption("knitr.kable.NA", default = ""),
  ...
)

Arguments

x	a data.frame containing at least a column with microorganisms and columns with antibiotic results (class 'sir', see as.sir())
antibiotics	vector of any antibiotic name or code (will be evaluated with as.ab(), column name of x, or (any combinations of) antibiotic selectors such as aminoglycosides() or carbapenems(). For combination antibiograms, this can also be set to values separated with "+", such as "TZP+TOB" or "cipro + genta", given that columns resembling such antibiotics exist in x. See Examples.
mo_transform	a character to transform microorganism input - must be "name", "shortname", "gramstain", or one of the column names of the microorganisms data set: "mo", "fullname", "status", "kingdom", "phylum", "class", "order", "family", "genus", "species", "subspecies", "rank", "ref", "oxygen_tolerance", "source", "lpsn", "lpsn_parent", "lpsn_renamed_to", "gbif", "gbif_parent", "gbif_renamed_to", "prevalence", or "snomed". Can also be NULL to not transform the input.
ab_transform	a character to transform antibiotic input - must be one of the column names of the antibiotics data set: "ab", "cid", "name", "group", "atc", "atc_group1", "atc_group2", "abbreviations", "synonyms", "oral_ddd", "oral_units", "iv_ddd", "iv_units", or "loinc". Can also be NULL to not transform the input.
syndromic_group	a column name of x, or values calculated to split rows of x, e.g. by using ifelse() or case_when(). See Examples.
add_total_n	a logical to indicate whether total available numbers per pathogen should be added to the table (default is TRUE). This will add the lowest and highest number of available isolate per antibiotic (e.g, if for E. coli 200 isolates are available for ciprofloxacin and 150 for amoxicillin, the returned number will be "150-200").
only_all_tested	(for combination antibiograms): a logical to indicate that isolates must be tested for all antibiotics, see Details
digits	number of digits to use for rounding
col_mo	column name of the names or codes of the microorganisms (see as.mo()) - the default is the first column of class mo. Values will be coerced using as.mo().
language	language to translate text, which defaults to the system language (see get_AMR_locale())
minimum	the minimum allowed number of available (tested) isolates. Any isolate count lower than minimum will return NA with a warning. The default number of 30 isolates is advised by the Clinical and Laboratory Standards Institute (CLSI) as best practice, see Source.
combine_SI	a logical to indicate whether all susceptibility should be determined by results of either S or I, instead of only S (default is TRUE)
sep	a separating character for antibiotic columns in combination antibiograms
info	a logical to indicate info should be printed - the default is TRUE only in interactive mode
...	when used in R Markdown or Quarto: arguments passed on to knitr::kable() (otherwise, has no use)
object	an antibiogram() object
italicise	a logical to indicate whether the microorganism names in the knitr table should be made italic, using italicise_taxonomy().
na	character to use for showing NA values

Details

This function returns a table with values between 0 and 100 for susceptibility, not resistance.

Remember that you should filter your data 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 with one of the four available algorithms.

All types of antibiograms as listed below can be plotted (using ggplot2::autoplot() or base R plot()/barplot()). The antibiogram object can also be used directly in R Markdown / Quarto (i.e., knitr) for reports. In this case, knitr::kable() will be applied automatically and microorganism names will even be printed in italics at default (see argument italicise). You can also use functions from specific 'table reporting' packages to transform the output of antibiogram() to your needs, e.g. with flextable::as_flextable() or gt::gt().

Antibiogram Types

There are four antibiogram types, as proposed by Klinker et al. (2021, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1177/20499361211011373")}), and they are all supported by antibiogram():

1. Traditional Antibiogram

   Case example: Susceptibility of Pseudomonas aeruginosa to piperacillin/tazobactam (TZP)

   Code example:

   antibiogram(your_data,
               antibiotics = "TZP")
   

2. Combination Antibiogram

   Case example: Additional susceptibility of Pseudomonas aeruginosa to TZP + tobramycin versus TZP alone

   Code example:

   antibiogram(your_data,
               antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"))
   

3. Syndromic Antibiogram

   Case example: Susceptibility of Pseudomonas aeruginosa to TZP among respiratory specimens (obtained among ICU patients only)

   Code example:

   antibiogram(your_data,
               antibiotics = penicillins(),
               syndromic_group = "ward")
   

4. Weighted-Incidence Syndromic Combination Antibiogram (WISCA)

   Case example: Susceptibility of Pseudomonas aeruginosa to TZP among respiratory specimens (obtained among ICU patients only) for male patients age >=65 years with heart failure

   Code example:

   library(dplyr)
   your_data %>%
     filter(ward == "ICU" & specimen_type == "Respiratory") %>%
     antibiogram(antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
                 syndromic_group = ifelse(.$age >= 65 &
                                            .$gender == "Male" &
                                            .$condition == "Heart Disease",
                                          "Study Group", "Control Group"))
   

Note that for combination antibiograms, it is important to realise that susceptibility can be calculated in two ways, which can be set with the only_all_tested argument (default is FALSE). See this example for two antibiotics, Drug A and Drug B, about how antibiogram() 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>        -            -            -            -
--------------------------------------------------------------------

Source

• Klinker KP et al. (2021). Antimicrobial stewardship and antibiograms: importance of moving beyond traditional antibiograms. Therapeutic Advances in Infectious Disease, May 5;8:20499361211011373; \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1177/20499361211011373")}

• Barbieri E et al. (2021). Development of a Weighted-Incidence Syndromic Combination Antibiogram (WISCA) to guide the choice of the empiric antibiotic treatment for urinary tract infection in paediatric patients: a Bayesian approach Antimicrobial Resistance & Infection Control May 1;10(1):74; \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1186/s13756-021-00939-2")}

• M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 5th Edition, 2022, Clinical and Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m39/.

Examples

# example_isolates is a data set available in the AMR package.
# run ?example_isolates for more info.
example_isolates


# Traditional antibiogram ----------------------------------------------

antibiogram(example_isolates,
  antibiotics = c(aminoglycosides(), carbapenems())
)

antibiogram(example_isolates,
  antibiotics = aminoglycosides(),
  ab_transform = "atc",
  mo_transform = "gramstain"
)

antibiogram(example_isolates,
  antibiotics = carbapenems(),
  ab_transform = "name",
  mo_transform = "name"
)


# Combined antibiogram -------------------------------------------------

# combined antibiotics yield higher empiric coverage
antibiogram(example_isolates,
  antibiotics = c("TZP", "TZP+TOB", "TZP+GEN"),
  mo_transform = "gramstain"
)

# names of antibiotics do not need to resemble columns exactly:
antibiogram(example_isolates,
  antibiotics = c("Cipro", "cipro + genta"),
  mo_transform = "gramstain",
  ab_transform = "name",
  sep = " & "
)


# Syndromic antibiogram ------------------------------------------------

# the data set could contain a filter for e.g. respiratory specimens
antibiogram(example_isolates,
  antibiotics = c(aminoglycosides(), carbapenems()),
  syndromic_group = "ward"
)

# now define a data set with only E. coli
ex1 <- example_isolates[which(mo_genus() == "Escherichia"), ]

# with a custom language, though this will be determined automatically
# (i.e., this table will be in Spanish on Spanish systems)
antibiogram(ex1,
  antibiotics = aminoglycosides(),
  ab_transform = "name",
  syndromic_group = ifelse(ex1$ward == "ICU",
    "UCI", "No UCI"
  ),
  language = "es"
)


# Weighted-incidence syndromic combination antibiogram (WISCA) ---------

# the data set could contain a filter for e.g. respiratory specimens/ICU
antibiogram(example_isolates,
  antibiotics = c("AMC", "AMC+CIP", "TZP", "TZP+TOB"),
  mo_transform = "gramstain",
  minimum = 10, # this should be >=30, but now just as example
  syndromic_group = ifelse(example_isolates$age >= 65 &
    example_isolates$gender == "M",
  "WISCA Group 1", "WISCA Group 2"
  )
)


# Print the output for R Markdown / Quarto -----------------------------

ureido <- antibiogram(example_isolates,
  antibiotics = ureidopenicillins(),
  ab_transform = "name"
)

# in an Rmd file, you would just need to return `ureido` in a chunk,
# but to be explicit here:
if (requireNamespace("knitr")) {
  cat(knitr::knit_print(ureido))
}


# Generate plots with ggplot2 or base R --------------------------------

ab1 <- antibiogram(example_isolates,
  antibiotics = c("AMC", "CIP", "TZP", "TZP+TOB"),
  mo_transform = "gramstain"
)
ab2 <- antibiogram(example_isolates,
  antibiotics = c("AMC", "CIP", "TZP", "TZP+TOB"),
  mo_transform = "gramstain",
  syndromic_group = "ward"
)

if (requireNamespace("ggplot2")) {
  ggplot2::autoplot(ab1)
}
if (requireNamespace("ggplot2")) {
  ggplot2::autoplot(ab2)
}

plot(ab1)
plot(ab2)

AMR documentation built on Oct. 22, 2023, 1:08 a.m.