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(tbl, col_mo = NULL, universal_1 = guess_ab(tbl,
  "amox"), universal_2 = guess_ab(tbl, "amcl"),
  universal_3 = guess_ab(tbl, "cfur"), universal_4 = guess_ab(tbl,
  "pita"), universal_5 = guess_ab(tbl, "cipr"),
  universal_6 = guess_ab(tbl, "trsu"), GramPos_1 = guess_ab(tbl,
  "vanc"), GramPos_2 = guess_ab(tbl, "teic"), GramPos_3 = guess_ab(tbl,
  "tetr"), GramPos_4 = guess_ab(tbl, "eryt"), GramPos_5 = guess_ab(tbl,
  "oxac"), GramPos_6 = guess_ab(tbl, "rifa"), GramNeg_1 = guess_ab(tbl,
  "gent"), GramNeg_2 = guess_ab(tbl, "tobr"), GramNeg_3 = guess_ab(tbl,
  "coli"), GramNeg_4 = guess_ab(tbl, "cfot"), GramNeg_5 = guess_ab(tbl,
  "cfta"), GramNeg_6 = guess_ab(tbl, "mero"), warnings = TRUE, ...)

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

Arguments

tbl

table with antibiotics coloms, like amox and amcl.
col_mo

column name of the unique IDs of the microorganisms (see 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
GramPos_1, GramPos_2, GramPos_3, GramPos_4, GramPos_5, GramPos_6

column names of antibiotics for Gram positives, case-insensitive
GramNeg_1, GramNeg_2, GramNeg_3, GramNeg_4, GramNeg_5, GramNeg_6

column names of antibiotics for Gram negatives, case-insensitive
warnings

give warning about missing antibiotic columns, they will anyway be ignored
...

other parameters passed on to function
x, y

characters 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 ("."). The first_isolate function only uses this function on the same microbial species from the same patient. Using this, an MRSA will be included after a susceptible S. aureus (MSSA) found within the same episode (see episode parameter of first_isolate). Without key antibiotic comparison it wouldn't.

At default, the antibiotics that are used for Gram positive bacteria are (colum names):
"amox", "amcl", "cfur", "pita", "cipr", "trsu" (until here is universal), "vanc", "teic", "tetr", "eryt", "oxac", "rifa".

At default, the antibiotics that are used for Gram negative bacteria are (colum names):
"amox", "amcl", "cfur", "pita", "cipr", "trsu" (until here is universal), "gent", "tobr", "coli", "cfot", "cfta", "mero".

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

Key antibiotics

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

1. 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.

2. 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, an isolate will be (re)selected as a first weighted isolate.

Read more on our website!


On our website https://msberends.gitlab.io/AMR you can find a omprehensive tutorial about how to conduct AMR analysis and find the complete documentation of all functions, which reads a lot easier than in R.

See also

first_isolate

Examples

# NOT RUN {
# septic_patients is a dataset available in the AMR package
?septic_patients
library(dplyr)
# set key antibiotics to a new variable
my_patients <- septic_patients %>%
  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)


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

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
# }