Key antibiotics for first weighted isolates

These function can be used to determine first isolates (see first_isolate()). Using key antibiotics to determine first isolates is more reliable than without key antibiotics. These selected isolates will then be called first weighted isolates.

key_antibiotics(
  x,
  col_mo = NULL,
  universal_1 = guess_ab_col(x, "amoxicillin"),
  universal_2 = guess_ab_col(x, "amoxicillin/clavulanic acid"),
  universal_3 = guess_ab_col(x, "cefuroxime"),
  universal_4 = guess_ab_col(x, "piperacillin/tazobactam"),
  universal_5 = guess_ab_col(x, "ciprofloxacin"),
  universal_6 = guess_ab_col(x, "trimethoprim/sulfamethoxazole"),
  GramPos_1 = guess_ab_col(x, "vancomycin"),
  GramPos_2 = guess_ab_col(x, "teicoplanin"),
  GramPos_3 = guess_ab_col(x, "tetracycline"),
  GramPos_4 = guess_ab_col(x, "erythromycin"),
  GramPos_5 = guess_ab_col(x, "oxacillin"),
  GramPos_6 = guess_ab_col(x, "rifampin"),
  GramNeg_1 = guess_ab_col(x, "gentamicin"),
  GramNeg_2 = guess_ab_col(x, "tobramycin"),
  GramNeg_3 = guess_ab_col(x, "colistin"),
  GramNeg_4 = guess_ab_col(x, "cefotaxime"),
  GramNeg_5 = guess_ab_col(x, "ceftazidime"),
  GramNeg_6 = guess_ab_col(x, "meropenem"),
  warnings = TRUE,
  ...
)

key_antibiotics_equal(
  y,
  z,
  type = c("keyantibiotics", "points"),
  ignore_I = TRUE,
  points_threshold = 2,
  info = FALSE
)

Arguments

x	a data.frame with antibiotics columns, like `AMX` or `amox`
col_mo	column name of the IDs of the microorganisms (see `as.mo()`), defaults to the first column of class `mo`. Values will be coerced using `as.mo()`.
universal_1, universal_2, universal_3, universal_4, universal_5, universal_6	column names of broad-spectrum antibiotics, case-insensitive. See details for which antibiotics will be used at default (which are guessed with `guess_ab_col()`).
GramPos_1, GramPos_2, GramPos_3, GramPos_4, GramPos_5, GramPos_6	column names of antibiotics for Gram-positives, case-insensitive. See details for which antibiotics will be used at default (which are guessed with `guess_ab_col()`).
GramNeg_1, GramNeg_2, GramNeg_3, GramNeg_4, GramNeg_5, GramNeg_6	column names of antibiotics for Gram-negatives, case-insensitive. See details for which antibiotics will be used at default (which are guessed with `guess_ab_col()`).
warnings	give a warning about missing antibiotic columns (they will be ignored)
...	other parameters passed on to functions
y, z	character vectors to compare
type	type to determine weighed isolates; can be `"keyantibiotics"` or `"points"`, see Details
ignore_I	logical to determine whether antibiotic interpretations with `"I"` will be ignored when `type = "keyantibiotics"`, see Details
points_threshold	points until the comparison of key antibiotics will lead to inclusion of an isolate when `type = "points"`, see Details
info	print progress

Details

The function key_antibiotics() returns a character vector with 12 antibiotic results for every isolate. These isolates can then be compared using key_antibiotics_equal(), to check if two isolates have generally the same antibiogram. Missing and invalid values are replaced with a dot (".") by key_antibiotics() and ignored by key_antibiotics_equal().

The first_isolate() function only uses this function on the same microbial species from the same patient. Using this, e.g. an MRSA will be included after a susceptible S. aureus (MSSA) is found within the same patient episode. Without key antibiotic comparison it would not. See first_isolate() for more info.

At default, the antibiotics that are used for Gram-positive bacteria are:

Amoxicillin
Amoxicillin/clavulanic acid
Cefuroxime
Piperacillin/tazobactam
Ciprofloxacin
Trimethoprim/sulfamethoxazole
Vancomycin
Teicoplanin
Tetracycline
Erythromycin
Oxacillin
Rifampin

At default the antibiotics that are used for Gram-negative bacteria are:

Amoxicillin
Amoxicillin/clavulanic acid
Cefuroxime
Piperacillin/tazobactam
Ciprofloxacin
Trimethoprim/sulfamethoxazole
Gentamicin
Tobramycin
Colistin
Cefotaxime
Ceftazidime
Meropenem

The function key_antibiotics_equal() checks the characters returned by key_antibiotics() for equality, and returns a logical vector.

Stable lifecycle

The lifecycle of this function is stable. In a stable function, major changes are unlikely. This means that the unlying code will generally evolve by adding new arguments; removing arguments or changing the meaning of existing arguments will be avoided.

If the unlying code needs breaking changes, they will occur gradually. For example, a parameter will be deprecated and first continue to work, but will emit an message informing you of the change. Next, typically after at least one newly released version on CRAN, the message will be transformed to an error.

Key antibiotics

There are two ways to determine whether isolates can be included as first weighted isolates which will give generally the same results:

Using type = "keyantibiotics" and parameter ignore_I

Any difference from S to R (or vice versa) will (re)select an isolate as a first weighted isolate. With ignore_I = FALSE, also differences from I to S|R (or vice versa) will lead to this. This is a reliable method and 30-35 times faster than method 2. Read more about this in the key_antibiotics() function.
Using type = "points" and parameter points_threshold

A difference from I to S|R (or vice versa) means 0.5 points, a difference from S to R (or vice versa) means 1 point. When the sum of points exceeds points_threshold, which default to 2, an isolate will be (re)selected as a first weighted isolate.

Examples

# `example_isolates` is a dataset available in the AMR package.
# See ?example_isolates.

# output of the `key_antibiotics` function could be like this:
strainA <- "SSSRR.S.R..S"
strainB <- "SSSIRSSSRSSS"

# can those strings can be compared with:
key_antibiotics_equal(strainA, strainB)
# TRUE, because I is ignored (as well as missing values)

key_antibiotics_equal(strainA, strainB, ignore_I = FALSE)
# FALSE, because I is not ignored and so the 4th value differs

# \donttest{
if (require("dplyr")) {
  # set key antibiotics to a new variable
  my_patients <- example_isolates %>%
    mutate(keyab = key_antibiotics(.)) %>%
    mutate(
      # now calculate first isolates
      first_regular = first_isolate(., col_keyantibiotics = FALSE),
      # and first WEIGHTED isolates
      first_weighted = first_isolate(., col_keyantibiotics = "keyab")
    )
 
  # Check the difference, in this data set it results in 7% more isolates:
  sum(my_patients$first_regular, na.rm = TRUE)
  sum(my_patients$first_weighted, na.rm = TRUE)
}
# }

Arguments

Details

Stable lifecycle

Key antibiotics

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See also

Examples