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Interpret minimum inhibitory concentration (MIC) values and disk diffusion diameters according to EUCAST or CLSI, or clean up existing SIR values. This transforms the input to a new class sir, which is an ordered factor with levels S < I < R.

All breakpoints used for interpretation are publicly available in the clinical_breakpoints data set.

Usage

as.sir(x, ...)

NA_sir_

is.sir(x)

is_sir_eligible(x, threshold = 0.05)

# S3 method for mic
as.sir(
  x,
  mo = NULL,
  ab = deparse(substitute(x)),
  guideline = getOption("AMR_guideline", "EUCAST"),
  uti = NULL,
  conserve_capped_values = FALSE,
  add_intrinsic_resistance = FALSE,
  reference_data = AMR::clinical_breakpoints,
  include_screening = getOption("AMR_include_screening", FALSE),
  include_PKPD = getOption("AMR_include_PKPD", TRUE),
  ...
)

# S3 method for disk
as.sir(
  x,
  mo = NULL,
  ab = deparse(substitute(x)),
  guideline = getOption("AMR_guideline", "EUCAST"),
  uti = NULL,
  add_intrinsic_resistance = FALSE,
  reference_data = AMR::clinical_breakpoints,
  include_screening = getOption("AMR_include_screening", FALSE),
  include_PKPD = getOption("AMR_include_PKPD", TRUE),
  ...
)

# S3 method for data.frame
as.sir(
  x,
  ...,
  col_mo = NULL,
  guideline = getOption("AMR_guideline", "EUCAST"),
  uti = NULL,
  conserve_capped_values = FALSE,
  add_intrinsic_resistance = FALSE,
  reference_data = AMR::clinical_breakpoints,
  include_screening = getOption("AMR_include_screening", FALSE),
  include_PKPD = getOption("AMR_include_PKPD", TRUE)
)

sir_interpretation_history(clean = FALSE)

Format

An object of class sir (inherits from ordered, factor) of length 1.

Source

For interpretations of minimum inhibitory concentration (MIC) values and disk diffusion diameters:

Arguments

x

vector of values (for class mic: MIC values in mg/L, for class disk: a disk diffusion radius in millimetres)

...

for using on a data.frame: names of columns to apply as.sir() on (supports tidy selection such as column1:column4). Otherwise: arguments passed on to methods.

threshold

maximum fraction of invalid antimicrobial interpretations of x, see Examples

mo

any (vector of) text that can be coerced to valid microorganism codes with as.mo(), can be left empty to determine it automatically

ab

any (vector of) text that can be coerced to a valid antimicrobial drug code with as.ab()

guideline

defaults to EUCAST 2022 (the latest implemented EUCAST guideline in the clinical_breakpoints data set), but can be set with the package option AMR_guideline. Currently supports EUCAST (2013-2022) and CLSI (2013-2022), see Details.

uti

(Urinary Tract Infection) A vector with logicals (TRUE or FALSE) to specify whether a UTI specific interpretation from the guideline should be chosen. For using as.sir() on a data.frame, this can also be a column containing logicals or when left blank, the data set will be searched for a column 'specimen', and rows within this column containing 'urin' (such as 'urine', 'urina') will be regarded isolates from a UTI. See Examples.

conserve_capped_values

a logical to indicate that MIC values starting with ">" (but not ">=") must always return "R" , and that MIC values starting with "<" (but not "<=") must always return "S"

add_intrinsic_resistance

(only useful when using a EUCAST guideline) a logical to indicate whether intrinsic antibiotic resistance must also be considered for applicable bug-drug combinations, meaning that e.g. ampicillin will always return "R" in Klebsiella species. Determination is based on the intrinsic_resistant data set, that itself is based on 'EUCAST Expert Rules' and 'EUCAST Intrinsic Resistance and Unusual Phenotypes' v3.3 (2021).

reference_data

a data.frame to be used for interpretation, which defaults to the clinical_breakpoints data set. Changing this argument allows for using own interpretation guidelines. This argument must contain a data set that is equal in structure to the clinical_breakpoints data set (same column names and column types). Please note that the guideline argument will be ignored when reference_data is manually set.

include_screening

a logical to indicate that clinical breakpoints for screening are allowed - the default is FALSE. Can also be set with the package option AMR_include_screening.

include_PKPD

a logical to indicate that PK/PD clinical breakpoints must be applied as a last resort - the default is TRUE. Can also be set with the package option AMR_include_PKPD.

col_mo

column name of the names or codes of the microorganisms (see as.mo()) - the default is the first column of class mo. Values will be coerced using as.mo().

clean

a logical to indicate whether previously stored results should be forgotten after returning the 'logbook' with results

Value

Ordered factor with new class sir

Details

How it Works

The as.sir() function works in four ways:

  1. For cleaning raw / untransformed data. The data will be cleaned to only contain values S, I and R and will try its best to determine this with some intelligence. For example, mixed values with SIR interpretations and MIC values such as "<0.25; S" will be coerced to "S". Combined interpretations for multiple test methods (as seen in laboratory records) such as "S; S" will be coerced to "S", but a value like "S; I" will return NA with a warning that the input is unclear.

  2. For interpreting minimum inhibitory concentration (MIC) values according to EUCAST or CLSI. You must clean your MIC values first using as.mic(), that also gives your columns the new data class mic. Also, be sure to have a column with microorganism names or codes. It will be found automatically, but can be set manually using the mo argument.

    • Using dplyr, SIR interpretation can be done very easily with either:

      your_data %>% mutate_if(is.mic, as.sir)
      your_data %>% mutate(across(where(is.mic), as.sir))

    • Operators like "<=" will be stripped before interpretation. When using conserve_capped_values = TRUE, an MIC value of e.g. ">2" will always return "R", even if the breakpoint according to the chosen guideline is ">=4". This is to prevent that capped values from raw laboratory data would not be treated conservatively. The default behaviour (conserve_capped_values = FALSE) considers ">2" to be lower than ">=4" and might in this case return "S" or "I".

  3. For interpreting disk diffusion diameters according to EUCAST or CLSI. You must clean your disk zones first using as.disk(), that also gives your columns the new data class disk. Also, be sure to have a column with microorganism names or codes. It will be found automatically, but can be set manually using the mo argument.

    • Using dplyr, SIR interpretation can be done very easily with either:

      your_data %>% mutate_if(is.disk, as.sir)
      your_data %>% mutate(across(where(is.disk), as.sir))

  4. For interpreting a complete data set, with automatic determination of MIC values, disk diffusion diameters, microorganism names or codes, and antimicrobial test results. This is done very simply by running as.sir(your_data).

For points 2, 3 and 4: Use sir_interpretation_history() to retrieve a data.frame (or tibble if the tibble package is installed) with all results of the last as.sir() call.

Supported Guidelines

For interpreting MIC values as well as disk diffusion diameters, currently implemented guidelines are EUCAST (2013-2022) and CLSI (2013-2022).

Thus, the guideline argument must be set to e.g., "EUCAST 2022" or "CLSI 2022". By simply using "EUCAST" (the default) or "CLSI" as input, the latest included version of that guideline will automatically be selected. You can set your own data set using the reference_data argument. The guideline argument will then be ignored.

You can set the default guideline with the package option AMR_guideline (e.g. in your .Rprofile file), such as:

  options(AMR_guideline = "CLSI")
  options(AMR_guideline = "CLSI 2018")
  options(AMR_guideline = "EUCAST 2020")
  # or to reset:
  options(AMR_guideline = NULL)

After Interpretation

After using as.sir(), you can use the eucast_rules() defined by EUCAST to (1) apply inferred susceptibility and resistance based on results of other antimicrobials and (2) apply intrinsic resistance based on taxonomic properties of a microorganism.

Machine-Readable Clinical Breakpoints

The repository of this package contains a machine-readable version of all guidelines. This is a CSV file consisting of 18 271 rows and 11 columns. This file is machine-readable, since it contains one row for every unique combination of the test method (MIC or disk diffusion), the antimicrobial drug and the microorganism. This allows for easy implementation of these rules in laboratory information systems (LIS). Note that it only contains interpretation guidelines for humans - interpretation guidelines from CLSI for animals were removed.

Other

The function is.sir() detects if the input contains class sir. If the input is a data.frame, it iterates over all columns and returns a logical vector.

The function is_sir_eligible() returns TRUE when a columns contains at most 5% invalid antimicrobial interpretations (not S and/or I and/or R), and FALSE otherwise. The threshold of 5% can be set with the threshold argument. If the input is a data.frame, it iterates over all columns and returns a logical vector.

NA_sir_ is a missing value of the new sir class, analogous to e.g. base R's NA_character_.

Interpretation of SIR

In 2019, the European Committee on Antimicrobial Susceptibility Testing (EUCAST) has decided to change the definitions of susceptibility testing categories S, I, and R as shown below (https://www.eucast.org/newsiandr/):

  • S - Susceptible, standard dosing regimen
    A microorganism is categorised as "Susceptible, standard dosing regimen", when there is a high likelihood of therapeutic success using a standard dosing regimen of the agent.

  • I - Susceptible, increased exposure
    A microorganism is categorised as "Susceptible, Increased exposure
    " when there is a high likelihood of therapeutic success because exposure to the agent is increased by adjusting the dosing regimen or by its concentration at the site of infection.

  • R = Resistant
    A microorganism is categorised as "Resistant" when there is a high likelihood of therapeutic failure even when there is increased exposure.

    • Exposure is a function of how the mode of administration, dose, dosing interval, infusion time, as well as distribution and excretion of the antimicrobial agent will influence the infecting organism at the site of infection.

This AMR package honours this insight. Use susceptibility() (equal to proportion_SI()) to determine antimicrobial susceptibility and count_susceptible() (equal to count_SI()) to count susceptible isolates.

Reference Data Publicly Available

All data sets in this AMR package (about microorganisms, antibiotics, SIR interpretation, EUCAST rules, etc.) are publicly and freely available for download in the following formats: R, MS Excel, Apache Feather, Apache Parquet, SPSS, SAS, and Stata. We also provide tab-separated plain text files that are machine-readable and suitable for input in any software program, such as laboratory information systems. Please visit our website for the download links. The actual files are of course available on our GitHub repository.

Examples

example_isolates
#> # A tibble: 2,000 × 46
#>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX  
#>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir>
#>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA   
#>  2 2002-01-03 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA   
#>  3 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA   
#>  4 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA   
#>  5 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA   
#>  6 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA   
#>  7 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R     NA    S     R    
#>  8 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R     NA    S     R    
#>  9 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA   
#> 10 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR R     NA    S     NA   
#> # ℹ 1,990 more rows
#> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>,
#> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>,
#> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>,
#> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>,
#> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>,
#> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …
summary(example_isolates) # see all SIR results at a glance
#>       date              patient               age           gender         
#>  Min.   :2002-01-02   Length:2000        Min.   : 0.00   Length:2000       
#>  1st Qu.:2005-07-31   Class :character   1st Qu.:63.00   Class :character  
#>  Median :2009-07-31   Mode  :character   Median :74.00   Mode  :character  
#>  Mean   :2009-11-20                      Mean   :70.69                     
#>  3rd Qu.:2014-05-30                      3rd Qu.:82.00                     
#>  Max.   :2017-12-28                      Max.   :97.00                     
#>      ward                mo                  PEN                
#>  Length:2000        Class :mo             Class:sir             
#>  Class :character   <NA>  :0              %R   :73.7% (n=1201)  
#>  Mode  :character   Unique:90             %SI  :26.3% (n=428)   
#>                     #1    :B_ESCHR_COLI   - %S :25.6% (n=417)   
#>                     #2    :B_STPHY_CONS   - %I : 0.7% (n=11)    
#>                     #3    :B_STPHY_AURS                         
#>     OXA                   FLC                   AMX               
#>  Class:sir             Class:sir             Class:sir            
#>  %R   :31.2% (n=114)   %R   :29.5% (n=278)   %R   :59.6% (n=804)  
#>  %SI  :68.8% (n=251)   %SI  :70.5% (n=665)   %SI  :40.4% (n=546)  
#>  - %S :68.8% (n=251)   - %S :70.5% (n=665)   - %S :40.2% (n=543)  
#>  - %I : 0.0% (n=0)     - %I : 0.0% (n=0)     - %I : 0.2% (n=3)    
#>                                                                   
#>     AMC                    AMP                   TZP               
#>  Class:sir              Class:sir             Class:sir            
#>  %R   :23.7% (n=446)    %R   :59.6% (n=804)   %R   :12.6% (n=126)  
#>  %SI  :76.3% (n=1433)   %SI  :40.4% (n=546)   %SI  :87.4% (n=875)  
#>  - %S :71.4% (n=1342)   - %S :40.2% (n=543)   - %S :86.1% (n=862)  
#>  - %I : 4.8% (n=91)     - %I : 0.2% (n=3)     - %I : 1.3% (n=13)   
#>                                                                    
#>     CZO                   FEP                   CXM                
#>  Class:sir             Class:sir             Class:sir             
#>  %R   :44.6% (n=199)   %R   :14.2% (n=103)   %R   :26.3% (n=470)   
#>  %SI  :55.4% (n=247)   %SI  :85.8% (n=621)   %SI  :73.7% (n=1319)  
#>  - %S :54.9% (n=245)   - %S :85.6% (n=620)   - %S :72.5% (n=1297)  
#>  - %I : 0.4% (n=2)     - %I : 0.1% (n=1)     - %I : 1.2% (n=22)    
#>                                                                    
#>     FOX                   CTX                   CAZ                
#>  Class:sir             Class:sir             Class:sir             
#>  %R   :27.4% (n=224)   %R   :15.5% (n=146)   %R   :66.5% (n=1204)  
#>  %SI  :72.6% (n=594)   %SI  :84.5% (n=797)   %SI  :33.5% (n=607)   
#>  - %S :71.6% (n=586)   - %S :84.4% (n=796)   - %S :33.5% (n=607)   
#>  - %I : 1.0% (n=8)     - %I : 0.1% (n=1)     - %I : 0.0% (n=0)     
#>                                                                    
#>     CRO                   GEN                    TOB               
#>  Class:sir             Class:sir              Class:sir            
#>  %R   :15.5% (n=146)   %R   :24.6% (n=456)    %R   :34.4% (n=465)  
#>  %SI  :84.5% (n=797)   %SI  :75.4% (n=1399)   %SI  :65.6% (n=886)  
#>  - %S :84.4% (n=796)   - %S :74.0% (n=1372)   - %S :65.1% (n=879)  
#>  - %I : 0.1% (n=1)     - %I : 1.5% (n=27)     - %I : 0.5% (n=7)    
#>                                                                    
#>     AMK                   KAN                    TMP               
#>  Class:sir             Class:sir              Class:sir            
#>  %R   :63.7% (n=441)   %R   :100.0% (n=471)   %R   :38.1% (n=571)  
#>  %SI  :36.3% (n=251)   %SI  : 0.0% (n=0)      %SI  :61.9% (n=928)  
#>  - %S :36.3% (n=251)   - %S : 0.0% (n=0)      - %S :61.2% (n=918)  
#>  - %I : 0.0% (n=0)     - %I : 0.0% (n=0)      - %I : 0.7% (n=10)   
#>                                                                    
#>     SXT                    NIT                   FOS               
#>  Class:sir              Class:sir             Class:sir            
#>  %R   :20.5% (n=361)    %R   :17.1% (n=127)   %R   :42.2% (n=148)  
#>  %SI  :79.5% (n=1398)   %SI  :82.9% (n=616)   %SI  :57.8% (n=203)  
#>  - %S :79.1% (n=1392)   - %S :76.0% (n=565)   - %S :57.8% (n=203)  
#>  - %I : 0.3% (n=6)      - %I : 6.9% (n=51)    - %I : 0.0% (n=0)    
#>                                                                    
#>     LNZ                   CIP                    MFX               
#>  Class:sir             Class:sir              Class:sir            
#>  %R   :69.3% (n=709)   %R   :16.2% (n=228)    %R   :33.6% (n=71)   
#>  %SI  :30.7% (n=314)   %SI  :83.8% (n=1181)   %SI  :66.4% (n=140)  
#>  - %S :30.7% (n=314)   - %S :78.9% (n=1112)   - %S :64.5% (n=136)  
#>  - %I : 0.0% (n=0)     - %I : 4.9% (n=69)     - %I : 1.9% (n=4)    
#>                                                                    
#>     VAN                    TEC                   TCY               
#>  Class:sir              Class:sir             Class:sir            
#>  %R   :38.3% (n=712)    %R   :75.7% (n=739)   %R   :29.8% (n=357)  
#>  %SI  :61.7% (n=1149)   %SI  :24.3% (n=237)   %SI  :70.3% (n=843)  
#>  - %S :61.7% (n=1149)   - %S :24.3% (n=237)   - %S :68.3% (n=820)  
#>  - %I : 0.0% (n=0)      - %I : 0.0% (n=0)     - %I : 1.9% (n=23)   
#>                                                                    
#>     TGC                   DOX                   ERY                
#>  Class:sir             Class:sir             Class:sir             
#>  %R   :12.7% (n=101)   %R   :27.7% (n=315)   %R   :57.2% (n=1084)  
#>  %SI  :87.3% (n=697)   %SI  :72.3% (n=821)   %SI  :42.8% (n=810)   
#>  - %S :87.3% (n=697)   - %S :71.7% (n=814)   - %S :42.3% (n=801)   
#>  - %I : 0.0% (n=0)     - %I : 0.6% (n=7)     - %I : 0.5% (n=9)     
#>                                                                    
#>     CLI                   AZM                    IPM               
#>  Class:sir             Class:sir              Class:sir            
#>  %R   :61.2% (n=930)   %R   :57.2% (n=1084)   %R   : 6.2% (n=55)   
#>  %SI  :38.8% (n=590)   %SI  :42.8% (n=810)    %SI  :93.8% (n=834)  
#>  - %S :38.6% (n=586)   - %S :42.3% (n=801)    - %S :92.7% (n=824)  
#>  - %I : 0.3% (n=4)     - %I : 0.5% (n=9)      - %I : 1.1% (n=10)   
#>                                                                    
#>     MEM                   MTR                  CHL               
#>  Class:sir             Class:sir            Class:sir            
#>  %R   : 5.9% (n=49)    %R   :14.7% (n=5)    %R   :21.4% (n=33)   
#>  %SI  :94.1% (n=780)   %SI  :85.3% (n=29)   %SI  :78.6% (n=121)  
#>  - %S :94.1% (n=780)   - %S :85.3% (n=29)   - %S :78.6% (n=121)  
#>  - %I : 0.0% (n=0)     - %I : 0.0% (n=0)    - %I : 0.0% (n=0)    
#>                                                                  
#>     COL                    MUP                   RIF               
#>  Class:sir              Class:sir             Class:sir            
#>  %R   :81.2% (n=1331)   %R   : 5.9% (n=16)    %R   :69.6% (n=698)  
#>  %SI  :18.8% (n=309)    %SI  :94.1% (n=254)   %SI  :30.4% (n=305)  
#>  - %S :18.8% (n=309)    - %S :93.0% (n=251)   - %S :30.2% (n=303)  
#>  - %I : 0.0% (n=0)      - %I : 1.1% (n=3)     - %I : 0.2% (n=2)    
#>                                                                    

# For INTERPRETING disk diffusion and MIC values -----------------------

# a whole data set, even with combined MIC values and disk zones
df <- data.frame(
  microorganism = "Escherichia coli",
  AMP = as.mic(8),
  CIP = as.mic(0.256),
  GEN = as.disk(18),
  TOB = as.disk(16),
  ERY = "R"
)
as.sir(df)
#> => Interpreting MIC values of column 'AMP' (ampicillin) according to EUCAST
#>    2022...
#>  OK.
#> => Interpreting MIC values of column 'CIP' (ciprofloxacin) according to
#>    EUCAST 2022...
#>  OK.
#> => Interpreting disk diffusion zones of column 'GEN' (gentamicin) according
#>    to EUCAST 2022...
#>  Note:
#>    • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in
#>      Escherichia coli - assuming non-UTI. Use argument uti to set which
#>      isolates are from urine. See ?as.sir.
#> => Interpreting disk diffusion zones of column 'TOB' (tobramycin) according
#>    to EUCAST 2022...
#>  Note:
#>    • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in
#>      Escherichia coli - assuming non-UTI. Use argument uti to set which
#>      isolates are from urine. See ?as.sir.
#> => Assigning class 'sir' to already clean column 'ERY' (erythromycin)...
#>  OK.
#>      microorganism AMP CIP GEN TOB ERY
#> 1 Escherichia coli   S   I   S   S   R

# return a 'logbook' about the results:
sir_interpretation_history()
#> # A tibble: 50 × 12
#>    datetime            index ab_input ab_guideline mo_input     mo_guideline    
#>    <dttm>              <int> <chr>    <ab>         <chr>        <mo>            
#>  1 2023-05-17 20:17:18     1 TOB      TOB          Escherichia… B_[ORD]_ENTRBCTR
#>  2 2023-05-17 20:17:18     1 GEN      GEN          Escherichia… B_[ORD]_ENTRBCTR
#>  3 2023-05-17 20:17:17     1 CIP      CIP          Escherichia… B_[ORD]_ENTRBCTR
#>  4 2023-05-17 20:17:17     1 AMP      AMP          Escherichia… B_[ORD]_ENTRBCTR
#>  5 2023-05-17 20:17:11     1 CIP      CIP          B_ESCHR_COLI B_[ORD]_ENTRBCTR
#>  6 2023-05-17 20:17:11     2 CIP      CIP          B_ESCHR_COLI B_[ORD]_ENTRBCTR
#>  7 2023-05-17 20:17:11     3 CIP      CIP          B_ESCHR_COLI B_[ORD]_ENTRBCTR
#>  8 2023-05-17 20:17:11     4 CIP      CIP          B_ESCHR_COLI B_[ORD]_ENTRBCTR
#>  9 2023-05-17 20:17:11     5 CIP      CIP          B_ESCHR_COLI B_[ORD]_ENTRBCTR
#> 10 2023-05-17 20:17:11     6 CIP      CIP          B_ESCHR_COLI B_[ORD]_ENTRBCTR
#> # ℹ 40 more rows
#> # ℹ 6 more variables: guideline <chr>, ref_table <chr>, method <chr>,
#> #   input <dbl>, outcome <sir>, breakpoint_S_R <chr>

# for single values
as.sir(
  x = as.mic(2),
  mo = as.mo("S. pneumoniae"),
  ab = "AMP",
  guideline = "EUCAST"
)
#> => Interpreting MIC values of 'AMP' (ampicillin) according to EUCAST
#>    2022...
#>  Note:
#>    • Multiple breakpoints available for ampicillin (AMP) in Streptococcus
#>      pneumoniae - assuming body site 'Non-meningitis'.
#> Class 'sir'
#> [1] R

as.sir(
  x = as.disk(18),
  mo = "Strep pneu", # `mo` will be coerced with as.mo()
  ab = "ampicillin", # and `ab` with as.ab()
  guideline = "EUCAST"
)
#> => Interpreting disk diffusion zones of 'ampicillin' (AMP) according to
#>    EUCAST 2022...
#>  OK.
#> Class 'sir'
#> [1] R

# \donttest{
# the dplyr way
if (require("dplyr")) {
  df %>% mutate_if(is.mic, as.sir)
  df %>% mutate_if(function(x) is.mic(x) | is.disk(x), as.sir)
  df %>% mutate(across(where(is.mic), as.sir))
  df %>% mutate_at(vars(AMP:TOB), as.sir)
  df %>% mutate(across(AMP:TOB, as.sir))

  df %>%
    mutate_at(vars(AMP:TOB), as.sir, mo = .$microorganism)

  # to include information about urinary tract infections (UTI)
  data.frame(
    mo = "E. coli",
    NIT = c("<= 2", 32),
    from_the_bladder = c(TRUE, FALSE)
  ) %>%
    as.sir(uti = "from_the_bladder")

  data.frame(
    mo = "E. coli",
    NIT = c("<= 2", 32),
    specimen = c("urine", "blood")
  ) %>%
    as.sir() # automatically determines urine isolates

  df %>%
    mutate_at(vars(AMP:TOB), as.sir, mo = "E. coli", uti = TRUE)
}
#> => Interpreting MIC values of 'AMP' (ampicillin) based on column
#>    'microorganism' according to EUCAST 2022...
#>  OK.
#> => Interpreting MIC values of 'CIP' (ciprofloxacin) based on column
#>    'microorganism' according to EUCAST 2022...
#>  OK.
#> => Interpreting MIC values of 'AMP' (ampicillin) based on column
#>    'microorganism' according to EUCAST 2022...
#>  OK.
#> => Interpreting MIC values of 'CIP' (ciprofloxacin) based on column
#>    'microorganism' according to EUCAST 2022...
#>  OK.
#> => Interpreting disk diffusion zones of 'GEN' (gentamicin) based on column
#>    'microorganism' according to EUCAST 2022...
#>  Note:
#>    • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in
#>      Escherichia coli - assuming non-UTI. Use argument uti to set which
#>      isolates are from urine. See ?as.sir.
#> => Interpreting disk diffusion zones of 'TOB' (tobramycin) based on column
#>    'microorganism' according to EUCAST 2022...
#>  Note:
#>    • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in
#>      Escherichia coli - assuming non-UTI. Use argument uti to set which
#>      isolates are from urine. See ?as.sir.
#> => Interpreting MIC values of 'AMP' (ampicillin) based on column
#>    'microorganism' according to EUCAST 2022...
#>  OK.
#> => Interpreting MIC values of 'CIP' (ciprofloxacin) based on column
#>    'microorganism' according to EUCAST 2022...
#>  OK.
#> => Interpreting MIC values of 'AMP' (ampicillin) based on column
#>    'microorganism' according to EUCAST 2022...
#>  OK.
#> => Interpreting MIC values of 'CIP' (ciprofloxacin) based on column
#>    'microorganism' according to EUCAST 2022...
#>  OK.
#> => Interpreting disk diffusion zones of 'GEN' (gentamicin) based on column
#>    'microorganism' according to EUCAST 2022...
#>  Note:
#>    • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in
#>      Escherichia coli - assuming non-UTI. Use argument uti to set which
#>      isolates are from urine. See ?as.sir.
#> => Interpreting disk diffusion zones of 'TOB' (tobramycin) based on column
#>    'microorganism' according to EUCAST 2022...
#>  Note:
#>    • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in
#>      Escherichia coli - assuming non-UTI. Use argument uti to set which
#>      isolates are from urine. See ?as.sir.
#> => Interpreting MIC values of 'AMP' (ampicillin) based on column
#>    'microorganism' according to EUCAST 2022...
#>  OK.
#> => Interpreting MIC values of 'CIP' (ciprofloxacin) based on column
#>    'microorganism' according to EUCAST 2022...
#>  OK.
#> => Interpreting disk diffusion zones of 'GEN' (gentamicin) based on column
#>    'microorganism' according to EUCAST 2022...
#>  Note:
#>    • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in
#>      Escherichia coli - assuming non-UTI. Use argument uti to set which
#>      isolates are from urine. See ?as.sir.
#> => Interpreting disk diffusion zones of 'TOB' (tobramycin) based on column
#>    'microorganism' according to EUCAST 2022...
#>  Note:
#>    • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in
#>      Escherichia coli - assuming non-UTI. Use argument uti to set which
#>      isolates are from urine. See ?as.sir.
#> => Interpreting MIC values of 'AMP' (ampicillin) according to EUCAST
#>    2022...
#>  OK.
#> => Interpreting MIC values of 'CIP' (ciprofloxacin) according to EUCAST
#>    2022...
#>  OK.
#> => Interpreting disk diffusion zones of 'GEN' (gentamicin) according to
#>    EUCAST 2022...
#>  Note:
#>    • Breakpoints for UTI and non-UTI available for gentamicin (GEN) in
#>      Escherichia coli - assuming non-UTI. Use argument uti to set which
#>      isolates are from urine. See ?as.sir.
#> => Interpreting disk diffusion zones of 'TOB' (tobramycin) according to
#>    EUCAST 2022...
#>  Note:
#>    • Breakpoints for UTI and non-UTI available for tobramycin (TOB) in
#>      Escherichia coli - assuming non-UTI. Use argument uti to set which
#>      isolates are from urine. See ?as.sir.
#> => Interpreting MIC values of column 'NIT' (nitrofurantoin) according to
#>    EUCAST 2022...
#> Warning: in as.sir(): interpretation of nitrofurantoin (NIT) is only available for
#> (uncomplicated) urinary tract infections (UTI) for some microorganisms,
#> thus assuming uti = TRUE. See ?as.sir.
#>  * WARNING *
#> ℹ Assuming value "urine" in column 'specimen' reflects a urinary tract
#>   infection.
#>   Use as.sir(uti = FALSE) to prevent this.
#> => Interpreting MIC values of column 'NIT' (nitrofurantoin) according to
#>    EUCAST 2022...
#> Warning: in as.sir(): interpretation of nitrofurantoin (NIT) is only available for
#> (uncomplicated) urinary tract infections (UTI) for some microorganisms,
#> thus assuming uti = TRUE. See ?as.sir.
#>  * WARNING *
#> => Interpreting MIC values of 'AMP' (ampicillin) according to EUCAST
#>    2022...
#>  OK.
#> => Interpreting MIC values of 'CIP' (ciprofloxacin) according to EUCAST
#>    2022...
#>  OK.
#> => Interpreting disk diffusion zones of 'GEN' (gentamicin) according to
#>    EUCAST 2022...
#>  OK.
#> => Interpreting disk diffusion zones of 'TOB' (tobramycin) according to
#>    EUCAST 2022...
#>  OK.
#>      microorganism AMP CIP GEN TOB ERY
#> 1 Escherichia coli   S   I   S   S   R

# For CLEANING existing SIR values ------------------------------------

as.sir(c("S", "I", "R", "A", "B", "C"))
#> Warning: in as.sir(): 3 results in column '24' truncated (50%) that were invalid
#> antimicrobial interpretations: "A", "B", and "C"
#> Class 'sir'
#> [1] S    I    R    <NA> <NA> <NA>
as.sir("<= 0.002; S") # will return "S"
#> Class 'sir'
#> [1] S
sir_data <- as.sir(c(rep("S", 474), rep("I", 36), rep("R", 370)))
is.sir(sir_data)
#> [1] TRUE
plot(sir_data) # for percentages

barplot(sir_data) # for frequencies


# the dplyr way
if (require("dplyr")) {
  example_isolates %>%
    mutate_at(vars(PEN:RIF), as.sir)
  # same:
  example_isolates %>%
    as.sir(PEN:RIF)

  # fastest way to transform all columns with already valid AMR results to class `sir`:
  example_isolates %>%
    mutate_if(is_sir_eligible, as.sir)

  # since dplyr 1.0.0, this can also be:
  # example_isolates %>%
  #   mutate(across(where(is_sir_eligible), as.sir))
}
#> # A tibble: 2,000 × 46
#>    date       patient   age gender ward     mo           PEN   OXA   FLC   AMX  
#>    <date>     <chr>   <dbl> <chr>  <chr>    <mo>         <sir> <sir> <sir> <sir>
#>  1 2002-01-02 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA   
#>  2 2002-01-03 A77334     65 F      Clinical B_ESCHR_COLI R     NA    NA    NA   
#>  3 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA   
#>  4 2002-01-07 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA   
#>  5 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA   
#>  6 2002-01-13 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA   
#>  7 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R     NA    S     R    
#>  8 2002-01-14 462729     78 M      Clinical B_STPHY_AURS R     NA    S     R    
#>  9 2002-01-16 067927     45 F      ICU      B_STPHY_EPDR R     NA    R     NA   
#> 10 2002-01-17 858515     79 F      ICU      B_STPHY_EPDR R     NA    S     NA   
#> # ℹ 1,990 more rows
#> # ℹ 36 more variables: AMC <sir>, AMP <sir>, TZP <sir>, CZO <sir>, FEP <sir>,
#> #   CXM <sir>, FOX <sir>, CTX <sir>, CAZ <sir>, CRO <sir>, GEN <sir>,
#> #   TOB <sir>, AMK <sir>, KAN <sir>, TMP <sir>, SXT <sir>, NIT <sir>,
#> #   FOS <sir>, LNZ <sir>, CIP <sir>, MFX <sir>, VAN <sir>, TEC <sir>,
#> #   TCY <sir>, TGC <sir>, DOX <sir>, ERY <sir>, CLI <sir>, AZM <sir>,
#> #   IPM <sir>, MEM <sir>, MTR <sir>, CHL <sir>, COL <sir>, MUP <sir>, …
# }