key_antibiotics.RdThese 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)
| x | table with antibiotics coloms, like |
|---|---|
| col_mo | column name of the unique IDs of the microorganisms (see |
| universal_1, universal_2, universal_3, universal_4, universal_5, universal_6 | column names of broad-spectrum antibiotics, case-insensitive. At default, the columns containing these antibiotics will be guessed with |
| GramPos_1, GramPos_2, GramPos_3, GramPos_4, GramPos_5, GramPos_6 | column names of antibiotics for Gram-positives, case-insensitive. At default, the columns containing these antibiotics will be guessed with |
| GramNeg_1, GramNeg_2, GramNeg_3, GramNeg_4, GramNeg_5, GramNeg_6 | column names of antibiotics for Gram-negatives, case-insensitive. At default, the columns containing these antibiotics will be guessed with |
| warnings | give warning about missing antibiotic columns, they will anyway be ignored |
| ... | other parameters passed on to function |
| y, z | characters to compare |
| type | type to determine weighed isolates; can be |
| ignore_I | logical to determine whether antibiotic interpretations with |
| points_threshold | points until the comparison of key antibiotics will lead to inclusion of an isolate when |
| info | print progress |
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 would not.
At default, the antibiotics that are used for Gram-positive bacteria are:
amoxicillin, amoxicillin/clavulanic acid, cefuroxime, piperacillin/tazobactam, ciprofloxacin, trimethoprim/sulfamethoxazole (until here is universal), 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 (until here is universal), 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.
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. Read more about this in the key_antibiotics function.
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, which default to 2, an isolate will be (re)selected as a first weighted isolate.
On our website https://msberends.gitlab.io/AMR you can find a tutorial about how to conduct AMR analysis, the complete documentation of all functions (which reads a lot easier than here in R) and an example analysis using WHONET data.
# NOT RUN { # `example_isolates` is a dataset available in the AMR package. # See ?example_isolates. library(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) # 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 # }