Data Set for R/SI Interpretation

Source: R/data.R

Data set containing reference data to interpret MIC and disk diffusion to R/SI values, according to international guidelines. Currently implemented guidelines are EUCAST (2011-2022) and CLSI (2011-2022). Use as.rsi() to transform MICs or disks measurements to R/SI values.

rsi_translation

Format

A data.frame with 20,369 observations and 11 variables:

  • guideline
    Name of the guideline

  • method
    Either "DISK" or "MIC"

  • site
    Body site, e.g. "Oral" or "Respiratory"

  • mo
    Microbial ID, see as.mo()

  • rank_index
    Taxonomic rank index of mo from 1 (subspecies/infraspecies) to 5 (unknown microorganism)

  • ab
    Antibiotic ID, see as.ab()

  • ref_tbl
    Info about where the guideline rule can be found

  • disk_dose
    Dose of the used disk diffusion method

  • breakpoint_S
    Lowest MIC value or highest number of millimetres that leads to "S"

  • breakpoint_R
    Highest MIC value or lowest number of millimetres that leads to "R"

  • uti
    A logical value (TRUE/FALSE) to indicate whether the rule applies to a urinary tract infection (UTI)

Details

The repository of this AMR package contains a file comprising this exact data set: https://github.com/msberends/AMR/blob/main/data-raw/rsi_translation.txt. This file allows for machine reading EUCAST and CLSI guidelines, which is almost impossible with the Excel and PDF files distributed by EUCAST and CLSI. The file is updated automatically and the mo and ab columns have been transformed to contain the full official names instead of codes.

Reference Data Publicly Available

All reference data sets (about microorganisms, antibiotics, R/SI interpretation, EUCAST rules, etc.) in this AMR package are publicly and freely available. We continually export our data sets to formats for use in R, SPSS, SAS, Stata and Excel. We also supply flat files that are machine-readable and suitable for input in any software program, such as laboratory information systems. Please find all download links on our website, which is automatically updated with every code change.

Read more on Our Website!

On our website https://msberends.github.io/AMR/ you can find a comprehensive tutorial about how to conduct AMR data analysis, the complete documentation of all functions and an example analysis using WHONET data.