Determine antimicrobial resistance (AMR) of all bug-drug combinations in your data set where at least 30 (default) isolates are available per species. Use format() on the result to prettify it to a publishable/printable format, see Examples.
Usage
bug_drug_combinations(x, col_mo = NULL, FUN = mo_shortname, ...)
# S3 method for bug_drug_combinations
format(
x,
translate_ab = "name (ab, atc)",
language = get_AMR_locale(),
minimum = 30,
combine_SI = TRUE,
combine_IR = FALSE,
add_ab_group = TRUE,
remove_intrinsic_resistant = FALSE,
decimal.mark = getOption("OutDec"),
big.mark = ifelse(decimal.mark == ",", ".", ","),
...
)Source
M39 Analysis and Presentation of Cumulative Antimicrobial Susceptibility Test Data, 4th Edition, 2014, Clinical and Laboratory Standards Institute (CLSI). https://clsi.org/standards/products/microbiology/documents/m39/.
Arguments
- x
data with antibiotic columns, such as
amox,AMXandAMC- col_mo
column name of the IDs of the microorganisms (see
as.mo()), defaults to the first column of classmo. Values will be coerced usingas.mo().- FUN
the function to call on the
mocolumn to transform the microorganism codes, defaults tomo_shortname()- ...
arguments passed on to
FUN- translate_ab
a character of length 1 containing column names of the antibiotics data set
- language
language of the returned text, defaults to system language (see
get_AMR_locale()) and can also be set withgetOption("AMR_locale"). Uselanguage = NULLorlanguage = ""to prevent translation.- minimum
the minimum allowed number of available (tested) isolates. Any isolate count lower than
minimumwill returnNAwith a warning. The default number of30isolates is advised by the Clinical and Laboratory Standards Institute (CLSI) as best practice, see Source.- combine_SI
a logical to indicate whether all values of S and I must be merged into one, so the output only consists of S+I vs. R (susceptible vs. resistant). This used to be the argument
combine_IR, but this now follows the redefinition by EUCAST about the interpretation of I (increased exposure) in 2019, see section 'Interpretation of S, I and R' below. Default isTRUE.- combine_IR
a logical to indicate whether values R and I should be summed
- add_ab_group
a logical to indicate where the group of the antimicrobials must be included as a first column
- remove_intrinsic_resistant
logical to indicate that rows and columns with 100% resistance for all tested antimicrobials must be removed from the table
- decimal.mark
the character to be used to indicate the numeric decimal point.
- big.mark
character; if not empty used as mark between every
big.intervaldecimals before (hencebig) the decimal point.
Value
The function bug_drug_combinations() returns a data.frame with columns "mo", "ab", "S", "I", "R" and "total".
Details
The function format() calculates the resistance per bug-drug combination. Use combine_IR = FALSE (default) to test R vs. S+I and combine_IR = TRUE to test R+I vs. S.
Examples
# \donttest{
x <- bug_drug_combinations(example_isolates)
#> ℹ Using column 'mo' as input for `col_mo`.
head(x)
#> mo ab S I R total
#> 1 (unknown species) PEN 14 0 1 15
#> 2 (unknown species) OXA 0 0 1 1
#> 3 (unknown species) FLC 0 0 0 0
#> 4 (unknown species) AMX 15 0 1 16
#> 5 (unknown species) AMC 15 0 0 15
#> 6 (unknown species) AMP 15 0 1 16
#> Use 'format()' on this result to get a publishable/printable format.
format(x, translate_ab = "name (atc)")
#> Group Drug
#> 1 Aminoglycosides Amikacin (J01GB06)
#> 2 Gentamicin (J01GB03)
#> 3 Kanamycin (J01GB04)
#> 4 Tobramycin (J01GB01)
#> 5 Amphenicols Chloramphenicol (J01BA01)
#> 6 Antimycobacterials Rifampicin (J04AB02)
#> 7 Beta-lactams/penicillins Amoxicillin (J01CA04)
#> 8 Amoxicillin/clavulanic acid (J01CR02)
#> 9 Ampicillin (J01CA01)
#> 10 Benzylpenicillin (J01CE01)
#> 11 Flucloxacillin (J01CF05)
#> 12 Oxacillin (J01CF04)
#> 13 Piperacillin/tazobactam (J01CR05)
#> 14 Carbapenems Imipenem (J01DH51)
#> 15 Meropenem (J01DH02)
#> 16 Cephalosporins (1st gen.) Cefazolin (J01DB04)
#> 17 Cephalosporins (2nd gen.) Cefoxitin (J01DC01)
#> 18 Cefuroxime (J01DC02)
#> 19 Cephalosporins (3rd gen.) Cefotaxime (J01DD01)
#> 20 Ceftazidime (J01DD02)
#> 21 Ceftriaxone (J01DD04)
#> 22 Cephalosporins (4th gen.) Cefepime (J01DE01)
#> 23 Glycopeptides Teicoplanin (J01XA02)
#> 24 Vancomycin (J01XA01)
#> 25 Macrolides/lincosamides Azithromycin (J01FA10)
#> 26 Clindamycin (J01FF01)
#> 27 Erythromycin (J01FA01)
#> 28 Other antibacterials Fosfomycin (J01XX01)
#> 29 Mupirocin (D06AX09)
#> 30 Nitrofurantoin (J01XE01)
#> 31 Oxazolidinones Linezolid (J01XX08)
#> 32 Polymyxins Colistin (J01XB01)
#> 33 Quinolones Ciprofloxacin (J01MA02)
#> 34 Moxifloxacin (J01MA14)
#> 35 Tetracyclines Doxycycline (J01AA02)
#> 36 Tetracycline (J01AA07)
#> 37 Tigecycline (J01AA12)
#> 38 Trimethoprims Trimethoprim (J01EA01)
#> 39 Trimethoprim/sulfamethoxazole (J01EE01)
#> CoNS E. coli E. faecalis K. pneumoniae
#> 1 100.0% (43/43) 0.0% (0/171) 100.0% (39/39)
#> 2 13.6% (42/309) 2.0% (9/460) 100.0% (39/39) 10.3% (6/58)
#> 3 100.0% (43/43) 100.0% (39/39)
#> 4 78.2% (43/55) 2.6% (12/462) 100.0% (39/39) 10.3% (6/58)
#> 5
#> 6 100.0% (467/467) 100.0% (58/58)
#> 7 93.0% (132/142) 50.0% (196/392) 100.0% (58/58)
#> 8 42.6% (132/310) 13.1% (61/467) 10.3% (6/58)
#> 9 93.0% (132/142) 50.0% (196/392) 100.0% (58/58)
#> 10 77.6% (228/294) 100.0% (467/467) 100.0% (58/58)
#> 11 42.8% (134/313)
#> 12 54.8% (34/62)
#> 13 69.7% (23/33) 5.5% (23/416) 11.3% (6/53)
#> 14 47.9% (23/48) 0.0% (0/422) 0.0% (0/38) 0.0% (0/51)
#> 15 47.9% (23/48) 0.0% (0/418) 0.0% (0/53)
#> 16 47.9% (23/48) 2.4% (2/82) 100.0% (39/39)
#> 17 47.9% (23/48) 6.9% (26/377) 100.0% (39/39) 2.2% (1/46)
#> 18 42.6% (133/312) 5.4% (25/465) 100.0% (39/39) 10.3% (6/58)
#> 19 47.9% (23/48) 2.4% (11/459) 100.0% (39/39) 5.2% (3/58)
#> 20 100.0% (313/313) 2.4% (11/460) 100.0% (39/39) 5.2% (3/58)
#> 21 47.9% (23/48) 2.4% (11/459) 100.0% (39/39) 5.2% (3/58)
#> 22 47.9% (23/48) 2.8% (9/317) 100.0% (39/39) 5.3% (2/38)
#> 23 100.0% (467/467) 100.0% (58/58)
#> 24 0.3% (1/304) 100.0% (467/467) 0.0% (0/39) 100.0% (58/58)
#> 25 44.1% (138/313) 100.0% (467/467) 100.0% (39/39) 100.0% (58/58)
#> 26 33.5% (59/176) 100.0% (467/467) 100.0% (39/39) 100.0% (58/58)
#> 27 44.1% (138/313) 100.0% (467/467) 100.0% (39/39) 100.0% (58/58)
#> 28 0.0% (0/61)
#> 29
#> 30 2.8% (13/458) 19.0% (11/58)
#> 31 100.0% (467/467) 100.0% (58/58)
#> 32 100.0% (313/313) 0.0% (0/240) 100.0% (39/39) 5.9% (2/34)
#> 33 27.4% (69/252) 12.5% (57/456) 3.6% (2/55)
#> 34 100.0% (57/57)
#> 35 22.2% (67/302)
#> 36 22.5% (59/262)
#> 37 0.0% (0/195) 0.0% (0/68)
#> 38 41.4% (126/304) 39.1% (155/396) 100.0% (39/39) 18.4% (9/49)
#> 39 12.2% (30/246) 31.6% (147/465) 100.0% (39/39) 10.3% (6/58)
#> P. aeruginosa P. mirabilis S. aureus S. epidermidis
#> 1 100.0% (44/44)
#> 2 0.0% (0/30) 5.9% (2/34) 0.9% (2/233) 21.5% (35/163)
#> 3 100.0% (30/30) 100.0% (44/44)
#> 4 0.0% (0/30) 5.9% (2/34) 2.3% (2/86) 49.4% (44/89)
#> 5 100.0% (30/30) 0.0% (0/46) 3.1% (1/32)
#> 6 100.0% (30/30) 100.0% (36/36) 0.0% (0/108) 2.7% (2/73)
#> 7 100.0% (30/30) 93.9% (123/131) 98.9% (90/91)
#> 8 100.0% (30/30) 2.8% (1/36) 0.4% (1/235) 54.5% (90/165)
#> 9 100.0% (30/30) 93.9% (123/131) 98.9% (90/91)
#> 10 100.0% (30/30) 100.0% (36/36) 80.9% (123/152) 89.4% (93/104)
#> 11 0.4% (1/235) 55.7% (97/174)
#> 12 0.0% (0/99) 50.0% (37/74)
#> 13
#> 14 6.3% (2/32)
#> 15
#> 16 100.0% (30/30)
#> 17 100.0% (30/30)
#> 18 100.0% (30/30) 0.0% (0/36) 0.4% (1/235) 56.1% (97/173)
#> 19 100.0% (30/30) 0.0% (0/36)
#> 20 3.3% (1/30) 0.0% (0/36) 100.0% (235/235) 100.0% (174/174)
#> 21 100.0% (30/30) 0.0% (0/36)
#> 22
#> 23 100.0% (30/30) 100.0% (36/36) 0.0% (0/80) 64.1% (25/39)
#> 24 100.0% (30/30) 100.0% (36/36) 0.0% (0/232) 0.0% (0/171)
#> 25 100.0% (30/30) 100.0% (36/36) 8.9% (21/235) 53.8% (93/173)
#> 26 100.0% (30/30) 100.0% (36/36) 6.0% (9/151) 37.5% (42/112)
#> 27 100.0% (30/30) 100.0% (36/36) 8.9% (21/235) 53.8% (93/173)
#> 28 0.0% (0/80) 21.9% (14/64)
#> 29 0.0% (0/84) 8.7% (6/69)
#> 30 100.0% (36/36)
#> 31 100.0% (30/30) 100.0% (36/36) 0.0% (0/82) 0.0% (0/68)
#> 32 100.0% (36/36) 100.0% (235/235) 100.0% (174/174)
#> 33 0.0% (0/30) 5.6% (2/36) 10.5% (20/191) 36.0% (49/136)
#> 34 2.2% (1/46) 0.0% (0/31)
#> 35 100.0% (30/30) 100.0% (36/36) 3.0% (7/231) 32.4% (55/170)
#> 36 100.0% (30/30) 100.0% (36/36) 3.2% (7/217) 32.9% (55/167)
#> 37 100.0% (30/30) 100.0% (36/36) 0.0% (0/209) 0.0% (0/109)
#> 38 100.0% (30/30) 36.4% (12/33) 8.4% (13/155) 58.5% (69/118)
#> 39 100.0% (30/30) 25.0% (9/36) 3.9% (9/231) 17.5% (24/137)
#> S. hominis S. pneumoniae
#> 1 100.0% (117/117)
#> 2 7.5% (6/80) 100.0% (117/117)
#> 3 100.0% (117/117)
#> 4 14.5% (9/62) 100.0% (117/117)
#> 5 6.5% (2/31)
#> 6 0.0% (0/61)
#> 7 0.0% (0/112)
#> 8 35.0% (28/80) 0.0% (0/112)
#> 9 0.0% (0/112)
#> 10 0.0% (0/117)
#> 11 34.6% (28/81)
#> 12 39.7% (23/58)
#> 13 0.0% (0/112)
#> 14
#> 15
#> 16
#> 17
#> 18 33.3% (27/81) 0.0% (0/61)
#> 19
#> 20 100.0% (81/81) 100.0% (117/117)
#> 21
#> 22
#> 23 6.8% (4/59)
#> 24 0.0% (0/81) 0.0% (0/94)
#> 25 40.7% (33/81) 7.8% (9/116)
#> 26 29.4% (20/68) 6.5% (4/62)
#> 27 40.7% (33/81) 7.8% (9/116)
#> 28 100.0% (59/59)
#> 29 1.6% (1/61)
#> 30
#> 31 0.0% (0/60)
#> 32 100.0% (81/81) 100.0% (117/117)
#> 33 20.0% (14/70)
#> 34 0.0% (0/32)
#> 35 55.0% (44/80) 4.5% (5/112)
#> 36 54.3% (44/81) 4.7% (5/106)
#> 37 0.0% (0/36)
#> 38 57.9% (22/38) 17.9% (17/95)
#> 39 24.7% (20/81) 7.3% (8/109)
# Use FUN to change to transformation of microorganism codes
bug_drug_combinations(example_isolates,
FUN = mo_gramstain)
#> ℹ Using column 'mo' as input for `col_mo`.
#> mo ab S I R total
#> 1 Gram-negative PEN 8 0 717 725
#> 2 Gram-negative OXA 6 0 0 6
#> 3 Gram-negative FLC 6 0 0 6
#> 4 Gram-negative AMX 226 0 405 631
#> 5 Gram-negative AMC 463 89 174 726
#> 6 Gram-negative AMP 226 0 405 631
#> 7 Gram-negative TZP 554 11 76 641
#> 8 Gram-negative CZO 94 2 110 206
#> 9 Gram-negative FEP 470 1 14 485
#> 10 Gram-negative CXM 539 22 142 703
#> 11 Gram-negative FOX 435 8 135 578
#> 12 Gram-negative CTX 578 1 57 636
#> 13 Gram-negative CAZ 607 0 27 634
#> 14 Gram-negative CRO 578 1 57 636
#> 15 Gram-negative GEN 651 8 25 684
#> 16 Gram-negative TOB 651 7 28 686
#> 17 Gram-negative AMK 251 0 5 256
#> 18 Gram-negative KAN 0 0 35 35
#> 19 Gram-negative TMP 364 1 223 588
#> 20 Gram-negative SXT 506 0 197 703
#> 21 Gram-negative NIT 491 51 122 664
#> 22 Gram-negative FOS 71 0 7 78
#> 23 Gram-negative LNZ 0 0 707 707
#> 24 Gram-negative CIP 610 11 63 684
#> 25 Gram-negative MFX 0 0 63 63
#> 26 Gram-negative VAN 0 0 707 707
#> 27 Gram-negative TEC 0 0 707 707
#> 28 Gram-negative TCY 15 1 110 126
#> 29 Gram-negative TGC 87 0 101 188
#> 30 Gram-negative DOX 10 0 108 118
#> 31 Gram-negative ERY 1 2 696 699
#> 32 Gram-negative CLI 18 1 709 728
#> 33 Gram-negative AZM 1 2 696 699
#> 34 Gram-negative IPM 616 10 5 631
#> 35 Gram-negative MEM 624 0 2 626
#> 36 Gram-negative MTR 21 0 2 23
#> 37 Gram-negative CHL 1 0 30 31
#> 38 Gram-negative COL 309 0 78 387
#> 39 Gram-negative MUP 0 0 0 0
#> 40 Gram-negative RIF 1 0 695 696
#> 41 Gram-positive PEN 395 11 483 889
#> 42 Gram-positive OXA 245 0 113 358
#> 43 Gram-positive FLC 659 0 278 937
#> 44 Gram-positive AMX 302 3 398 703
#> 45 Gram-positive AMC 864 2 272 1138
#> 46 Gram-positive AMP 302 3 398 703
#> 47 Gram-positive TZP 294 2 49 345
#> 48 Gram-positive CZO 151 0 89 240
#> 49 Gram-positive FEP 150 0 89 239
#> 50 Gram-positive CXM 756 0 328 1084
#> 51 Gram-positive FOX 151 0 89 240
#> 52 Gram-positive CTX 218 0 89 307
#> 53 Gram-positive CAZ 0 0 1177 1177
#> 54 Gram-positive CRO 218 0 89 307
#> 55 Gram-positive GEN 721 19 430 1170
#> 56 Gram-positive TOB 228 0 437 665
#> 57 Gram-positive AMK 0 0 436 436
#> 58 Gram-positive KAN 0 0 436 436
#> 59 Gram-positive TMP 553 9 343 905
#> 60 Gram-positive SXT 883 6 163 1052
#> 61 Gram-positive NIT 73 0 5 78
#> 62 Gram-positive FOS 132 0 141 273
#> 63 Gram-positive LNZ 312 0 2 314
#> 64 Gram-positive CIP 502 58 164 724
#> 65 Gram-positive MFX 136 4 8 148
#> 66 Gram-positive VAN 1141 0 5 1146
#> 67 Gram-positive TEC 237 0 32 269
#> 68 Gram-positive TCY 800 22 245 1067
#> 69 Gram-positive TGC 610 0 0 610
#> 70 Gram-positive DOX 802 7 206 1015
#> 71 Gram-positive ERY 797 7 385 1189
#> 72 Gram-positive CLI 556 3 220 779
#> 73 Gram-positive AZM 797 7 385 1189
#> 74 Gram-positive IPM 207 0 50 257
#> 75 Gram-positive MEM 156 0 47 203
#> 76 Gram-positive MTR 1 0 2 3
#> 77 Gram-positive CHL 120 0 3 123
#> 78 Gram-positive COL 0 0 1237 1237
#> 79 Gram-positive MUP 251 3 16 270
#> 80 Gram-positive RIF 301 2 3 306
#> Use 'format()' on this result to get a publishable/printable format.
bug_drug_combinations(example_isolates,
FUN = function(x) ifelse(x == as.mo("E. coli"),
"E. coli",
"Others"))
#> ℹ Using column 'mo' as input for `col_mo`.
#> ℹ Function `as.mo()` is uncertain about "E. coli" (assuming Escherichia
#> coli). Run `mo_uncertainties()` to review this.
#> mo ab S I R total
#> 1 E. coli PEN 0 0 467 467
#> 2 E. coli OXA 0 0 0 0
#> 3 E. coli FLC 0 0 0 0
#> 4 E. coli AMX 196 0 196 392
#> 5 E. coli AMC 332 74 61 467
#> 6 E. coli AMP 196 0 196 392
#> 7 E. coli TZP 388 5 23 416
#> 8 E. coli CZO 79 1 2 82
#> 9 E. coli FEP 308 0 9 317
#> 10 E. coli CXM 425 15 25 465
#> 11 E. coli FOX 347 4 26 377
#> 12 E. coli CTX 448 0 11 459
#> 13 E. coli CAZ 449 0 11 460
#> 14 E. coli CRO 448 0 11 459
#> 15 E. coli GEN 451 0 9 460
#> 16 E. coli TOB 450 0 12 462
#> 17 E. coli AMK 171 0 0 171
#> 18 E. coli KAN 0 0 0 0
#> 19 E. coli TMP 241 0 155 396
#> 20 E. coli SXT 318 0 147 465
#> 21 E. coli NIT 429 16 13 458
#> 22 E. coli FOS 61 0 0 61
#> 23 E. coli LNZ 0 0 467 467
#> 24 E. coli CIP 398 1 57 456
#> 25 E. coli MFX 0 0 57 57
#> 26 E. coli VAN 0 0 467 467
#> 27 E. coli TEC 0 0 467 467
#> 28 E. coli TCY 1 0 2 3
#> 29 E. coli TGC 68 0 0 68
#> 30 E. coli DOX 0 0 0 0
#> 31 E. coli ERY 0 0 467 467
#> 32 E. coli CLI 0 0 467 467
#> 33 E. coli AZM 0 0 467 467
#> 34 E. coli IPM 422 0 0 422
#> 35 E. coli MEM 418 0 0 418
#> 36 E. coli MTR 2 0 0 2
#> 37 E. coli CHL 0 0 0 0
#> 38 E. coli COL 240 0 0 240
#> 39 E. coli MUP 0 0 0 0
#> 40 E. coli RIF 0 0 467 467
#> 41 Others PEN 417 11 734 1162
#> 42 Others OXA 251 0 114 365
#> 43 Others FLC 665 0 278 943
#> 44 Others AMX 347 3 608 958
#> 45 Others AMC 1010 17 385 1412
#> 46 Others AMP 347 3 608 958
#> 47 Others TZP 474 8 103 585
#> 48 Others CZO 166 1 197 364
#> 49 Others FEP 312 1 94 407
#> 50 Others CXM 872 7 445 1324
#> 51 Others FOX 239 4 198 441
#> 52 Others CTX 348 1 135 484
#> 53 Others CAZ 158 0 1193 1351
#> 54 Others CRO 348 1 135 484
#> 55 Others GEN 921 27 447 1395
#> 56 Others TOB 429 7 453 889
#> 57 Others AMK 80 0 441 521
#> 58 Others KAN 0 0 471 471
#> 59 Others TMP 677 10 416 1103
#> 60 Others SXT 1074 6 214 1294
#> 61 Others NIT 136 35 114 285
#> 62 Others FOS 142 0 148 290
#> 63 Others LNZ 314 0 242 556
#> 64 Others CIP 714 68 171 953
#> 65 Others MFX 136 4 14 154
#> 66 Others VAN 1149 0 245 1394
#> 67 Others TEC 237 0 272 509
#> 68 Others TCY 819 23 355 1197
#> 69 Others TGC 629 0 101 730
#> 70 Others DOX 814 7 315 1136
#> 71 Others ERY 801 9 617 1427
#> 72 Others CLI 586 4 463 1053
#> 73 Others AZM 801 9 617 1427
#> 74 Others IPM 402 10 55 467
#> 75 Others MEM 362 0 49 411
#> 76 Others MTR 27 0 5 32
#> 77 Others CHL 121 0 33 154
#> 78 Others COL 69 0 1331 1400
#> 79 Others MUP 251 3 16 270
#> 80 Others RIF 303 2 231 536
#> Use 'format()' on this result to get a publishable/printable format.
# }