Calculate resistance of isolates

These functions can be used to calculate the (co-)resistance of microbial isolates (i.e. percentage S, SI, I, IR or R). All functions support quasiquotation with pipes, can be used in dplyrs summarise and support grouped variables, see Examples.

portion_R and portion_IR can be used to calculate resistance, portion_S and portion_SI can be used to calculate susceptibility.

portion_R(..., minimum = 30, as_percent = FALSE,
  also_single_tested = FALSE)

portion_IR(..., minimum = 30, as_percent = FALSE,
  also_single_tested = FALSE)

portion_I(..., minimum = 30, as_percent = FALSE,
  also_single_tested = FALSE)

portion_SI(..., minimum = 30, as_percent = FALSE,
  also_single_tested = FALSE)

portion_S(..., minimum = 30, as_percent = FALSE,
  also_single_tested = FALSE)

portion_df(data, translate_ab = getOption("get_antibiotic_names",
  "official"), minimum = 30, as_percent = FALSE, combine_IR = FALSE)

Arguments

...	one or more vectors (or columns) with antibiotic interpretations. They will be transformed internally with as.rsi if needed. Use multiple columns to calculate (the lack of) co-resistance: the probability where one of two drugs have a resistant or susceptible result. See Examples.
minimum	the minimal amount of available isolates. Any number 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.
as_percent	a logical to indicate whether the output must be returned as a hundred fold with % sign (a character). A value of 0.123456 will then be returned as "12.3%".
also_single_tested	a logical to indicate whether (in combination therapies) also observations should be included where not all antibiotics were tested, but at least one of the tested antibiotics contains a target interpretation (e.g. S in case of portion_S and R in case of portion_R). This would lead to selection bias in almost all cases.
data	a data.frame containing columns with class rsi (see as.rsi)
translate_ab	a column name of the antibiotics data set to translate the antibiotic abbreviations to, using abname. This can be set with getOption("get_antibiotic_names").
combine_IR	a logical to indicate whether all values of I and R must be merged into one, so the output only consists of S vs. IR (susceptible vs. non-susceptible)

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/.

Wickham H. Tidy Data. The Journal of Statistical Software, vol. 59, 2014. http://vita.had.co.nz/papers/tidy-data.html

Value

Double or, when as_percent = TRUE, a character.

Details

Remember that you should filter your table to let it contain only first isolates! Use first_isolate to determine them in your data set.

These functions are not meant to count isolates, but to calculate the portion of resistance/susceptibility. Use the count functions to count isolates. Low counts can infuence the outcome - these portion functions may camouflage this, since they only return the portion albeit being dependent on the minimum parameter.

portion_df takes any variable from data that has an "rsi" class (created with as.rsi) and calculates the portions R, I and S. The resulting tidy data (see Source) data.frame will have three rows (S/I/R) and a column for each variable with class "rsi".

The old rsi function is still available for backwards compatibility but is deprecated.

To calculate the probability (p) of susceptibility of one antibiotic, we use this formula:

To calculate the probability (p) of susceptibility of more antibiotics (i.e. combination therapy), we need to check whether one of them has a susceptible result (as numerator) and count all cases where all antibiotics were tested (as denominator).

For two antibiotics:

For three antibiotics:

And so on.

See also

count_* to count resistant and susceptible isolates.

Examples

# septic_patients is a data set available in the AMR package. It is true, genuine data.
?septic_patients
#> Rendering development documentation for 'septic_patients'

# Calculate resistance
portion_R(septic_patients$amox)
#> [1] 0.5827645
portion_IR(septic_patients$amox)
#> [1] 0.5853242

# Or susceptibility
portion_S(septic_patients$amox)
#> [1] 0.4146758
portion_SI(septic_patients$amox)
#> [1] 0.4172355

# Do the above with pipes:
library(dplyr)
septic_patients %>% portion_R(amox)
#> [1] 0.5827645
septic_patients %>% portion_IR(amox)
#> [1] 0.5853242
septic_patients %>% portion_S(amox)
#> [1] 0.4146758
septic_patients %>% portion_SI(amox)
#> [1] 0.4172355

septic_patients %>%
  group_by(hospital_id) %>%
  summarise(p = portion_S(cipr),
            n = n_rsi(cipr)) # n_rsi works like n_distinct in dplyr
#> # A tibble: 4 x 3
#>   hospital_id     p     n
#>   <fct>       <dbl> <int>
#> 1 A           0.860   207
#> 2 B           0.760   499
#> 3 C           0.688   202
#> 4 D           0.830   501

septic_patients %>%
  group_by(hospital_id) %>%
  summarise(R = portion_R(cipr, as_percent = TRUE),
            I = portion_I(cipr, as_percent = TRUE),
            S = portion_S(cipr, as_percent = TRUE),
            n = n_rsi(cipr), # works like n_distinct in dplyr
            total = n())     # NOT the amount of tested isolates!
#> # A tibble: 4 x 6
#>   hospital_id R     I     S         n total
#>   <fct>       <chr> <chr> <chr> <int> <int>
#> 1 A           12.6% 1.4%  86.0%   207   321
#> 2 B           17.0% 7.0%  76.0%   499   663
#> 3 C           20.3% 10.9% 68.8%   202   254
#> 4 D           15.2% 1.8%  83.0%   501   762

# Calculate co-resistance between amoxicillin/clav acid and gentamicin,
# so we can see that combination therapy does a lot more than mono therapy:
septic_patients %>% portion_S(amcl)       # S = 67.1%
#> [1] 0.7062363
septic_patients %>% count_all(amcl)       # n = 1576
#> [1] 1828

septic_patients %>% portion_S(gent)       # S = 74.0%
#> [1] 0.7396226
septic_patients %>% count_all(gent)       # n = 1855
#> [1] 1855

septic_patients %>% portion_S(amcl, gent) # S = 92.0%
#> [1] 0.9210074
septic_patients %>% count_all(amcl, gent) # n = 1517
#> [1] 1747


septic_patients %>%
  group_by(hospital_id) %>%
  summarise(cipro_p = portion_S(cipr, as_percent = TRUE),
            cipro_n = count_all(cipr),
            genta_p = portion_S(gent, as_percent = TRUE),
            genta_n = count_all(gent),
            combination_p = portion_S(cipr, gent, as_percent = TRUE),
            combination_n = count_all(cipr, gent))
#> # A tibble: 4 x 7
#>   hospital_id cipro_p cipro_n genta_p genta_n combination_p combination_n
#>   <fct>       <chr>     <int> <chr>     <int> <chr>                 <int>
#> 1 A           86.0%       207 76.0%       300 95.0%                   202
#> 2 B           76.0%       499 71.5%       606 91.4%                   488
#> 3 C           68.8%       202 75.8%       240 91.5%                   201
#> 4 D           83.0%       501 74.6%       709 95.0%                   499

# Get portions S/I/R immediately of all rsi columns
septic_patients %>%
  select(amox, cipr) %>%
  portion_df(translate = FALSE)
#>   Interpretation Antibiotic       Value
#> 1              S       amox 0.414675768
#> 2              I       amox 0.002559727
#> 3              R       amox 0.582764505
#> 4              S       cipr 0.789212207
#> 5              I       cipr 0.048970901
#> 6              R       cipr 0.161816891

# It also supports grouping variables
septic_patients %>%
  select(hospital_id, amox, cipr) %>%
  group_by(hospital_id) %>%
  portion_df(translate = FALSE)
#> # A tibble: 24 x 4
#>    Interpretation hospital_id Antibiotic   Value
#>    <ord>          <fct>       <chr>        <dbl>
#>  1 S              A           amox       0.42   
#>  2 S              B           amox       0.378  
#>  3 S              C           amox       0.373  
#>  4 S              D           amox       0.454  
#>  5 I              A           amox       0      
#>  6 I              B           amox       0.00268
#>  7 I              C           amox       0      
#>  8 I              D           amox       0.00407
#>  9 R              A           amox       0.580  
#> 10 R              B           amox       0.619  
#> # ... with 14 more rows


# NOT RUN {
# calculate current empiric combination therapy of Helicobacter gastritis:
my_table %>%
  filter(first_isolate == TRUE,
         genus == "Helicobacter") %>%
  summarise(p = portion_S(amox, metr),  # amoxicillin with metronidazole
            n = count_all(amox, metr))
# }