How to determine multi-drug resistance (MDR)

With the function mdro(), you can determine which micro-organisms are multi-drug resistant organisms (MDRO).

Type of input

The mdro() function takes a data set as input, such as a regular data.frame. It tries to automatically determine the right columns for info about your isolates, like the name of the species and all columns with results of antimicrobial agents. See the help page for more info about how to set the right settings for your data with the command ?mdro.

For WHONET data (and most other data), all settings are automatically set correctly.


The function support multiple guidelines. You can select a guideline with the guideline parameter. Currently supported guidelines are (case-insensitive):

Please suggest your own (country-specific) guidelines by letting us know:

Custom Guidelines

You can also use your own custom guideline. Custom guidelines can be set with the custom_mdro_guideline() function. This is of great importance if you have custom rules to determine MDROs in your hospital, e.g., rules that are dependent on ward, state of contact isolation or other variables in your data.

If you are familiar with case_when() of the dplyr package, you will recognise the input method to set your own rules. Rules must be set using what considers to be the ‘formula notation’:

custom <- custom_mdro_guideline(CIP == "R" & age > 60 ~ "Elderly Type A",
                                ERY == "R" & age > 60 ~ "Elderly Type B")

If a row/an isolate matches the first rule, the value after the first ~ (in this case ‘Elderly Type A’) will be set as MDRO value. Otherwise, the second rule will be tried and so on. The number of rules is unlimited.

You can print the rules set in the console for an overview. Colours will help reading it if your console supports colours.

# A set of custom MDRO rules:
#   1. If CIP is "R" and age is higher than 60 then: Elderly Type A
#   2. If ERY is "R" and age is higher than 60 then: Elderly Type B
#   3. Otherwise: Negative
# Unmatched rows will return NA.
# Results will be of class <factor>, with ordered levels: Negative < Elderly Type A < Elderly Type B

The outcome of the function can be used for the guideline argument in the [mdro()] function:

x <- mdro(example_isolates, guideline = custom)
# x
#       Negative Elderly Type A Elderly Type B 
#           1070            198            732

The rules set (the custom object in this case) could be exported to a shared file location using saveRDS() if you collaborate with multiple users. The custom rules set could then be imported using readRDS().


The mdro() function always returns an ordered factor. For example, the output of the default guideline by Magiorakos et al. returns a factor with levels ‘Negative’, ‘MDR’, ‘XDR’ or ‘PDR’ in that order.

The next example uses the example_isolates data set. This is a data set included with this package and contains 2,000 microbial isolates with their full antibiograms. It reflects reality and can be used to practice AMR data analysis. If we test the MDR/XDR/PDR guideline on this data set, we get:

library(dplyr)   # to support pipes: %>%
library(cleaner) # to create frequency tables
example_isolates %>% 
  mdro() %>% 
  freq() # show frequency table of the result
# Warning: NA introduced for isolates where the available percentage of antimicrobial
# classes was below 50% (set with `pct_required_classes`)

Frequency table

Class: factor > ordered (numeric)
Length: 2,000
Levels: 4: Negative < Multi-drug-resistant (MDR) < Extensively drug-resistant …
Available: 1,745 (87.25%, NA: 255 = 12.75%)
Unique: 2

Item Count Percent Cum. Count Cum. Percent
1 Negative 1617 92.66% 1617 92.66%
2 Multi-drug-resistant (MDR) 128 7.34% 1745 100.00%

For another example, I will create a data set to determine multi-drug resistant TB:

# random_rsi() is a helper function to generate
# a random vector with values S, I and R
my_TB_data <- data.frame(rifampicin = random_rsi(5000),
                         isoniazid = random_rsi(5000),
                         gatifloxacin = random_rsi(5000),
                         ethambutol = random_rsi(5000),
                         pyrazinamide = random_rsi(5000),
                         moxifloxacin = random_rsi(5000),
                         kanamycin = random_rsi(5000))

Because all column names are automatically verified for valid drug names or codes, this would have worked exactly the same:

my_TB_data <- data.frame(RIF = random_rsi(5000),
                         INH = random_rsi(5000),
                         GAT = random_rsi(5000),
                         ETH = random_rsi(5000),
                         PZA = random_rsi(5000),
                         MFX = random_rsi(5000),
                         KAN = random_rsi(5000))

The data set now looks like this:

#   rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin
# 1          I         S            I          R            I            R
# 2          R         S            S          I            I            R
# 3          S         R            I          I            S            I
# 4          R         I            I          I            I            S
# 5          S         R            S          R            R            S
# 6          S         R            I          R            R            R
#   kanamycin
# 1         I
# 2         I
# 3         R
# 4         S
# 5         S
# 6         I

We can now add the interpretation of MDR-TB to our data set. You can use:

mdro(my_TB_data, guideline = "TB")

or its shortcut mdr_tb():

my_TB_data$mdr <- mdr_tb(my_TB_data)
# ℹ No column found as input for `col_mo`, assuming all rows contain
#   Mycobacterium tuberculosis.

Create a frequency table of the results:


Frequency table

Class: factor > ordered (numeric)
Length: 5,000
Levels: 5: Negative < Mono-resistant < Poly-resistant < Multi-drug-resistant <…
Available: 5,000 (100.0%, NA: 0 = 0.0%)
Unique: 5

Item Count Percent Cum. Count Cum. Percent
1 Mono-resistant 3170 63.40% 3170 63.40%
2 Negative 1016 20.32% 4186 83.72%
3 Multi-drug-resistant 465 9.30% 4651 93.02%
4 Poly-resistant 255 5.10% 4906 98.12%
5 Extensively drug-resistant 94 1.88% 5000 100.00%