How to determine multi-drug resistance (MDR)

With the function mdro(), you can determine multi-drug resistant organisms (MDRO). It currently support these guidelines:

“Intrinsic Resistance and Exceptional Phenotypes Tables”, by EUCAST (European Committee on Antimicrobial Susceptibility Testing)
“Companion handbook to the WHO guidelines for the programmatic management of drug-resistant tuberculosis”, by WHO (World Health Organization)
“WIP-Richtlijn Bijzonder Resistente Micro-organismen (BRMO)”, by RIVM (Rijksinstituut voor de Volksgezondheid, the Netherlands National Institute for Public Health and the Environment)

As an example, I will make a data set to determine multi-drug resistant TB:

# a helper function to get a random vector with values S, I and R
# with the probabilities 50%-10%-40%
sample_rsi <- function() {
  sample(c("S", "I", "R"),
         size = 5000,
         prob = c(0.5, 0.1, 0.4),
         replace = TRUE)
}

my_TB_data <- data.frame(rifampicin = sample_rsi(),
                         isoniazid = sample_rsi(),
                         gatifloxacin = sample_rsi(),
                         ethambutol = sample_rsi(),
                         pyrazinamide = sample_rsi(),
                         moxifloxacin = sample_rsi(),
                         kanamycin = sample_rsi())

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 = sample_rsi(),
                         INH = sample_rsi(),
                         GAT = sample_rsi(),
                         ETH = sample_rsi(),
                         PZA = sample_rsi(),
                         MFX = sample_rsi(),
                         KAN = sample_rsi())

The data set looks like this now:

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

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

my_TB_data$mdr <- mdr_tb(my_TB_data)
# NOTE: No column found as input for `col_mo`, assuming all records contain Mycobacterium tuberculosis.
# Determining multidrug-resistant organisms (MDRO), according to:
# Guideline: Companion handbook to the WHO guidelines for the programmatic management of drug-resistant tuberculosis
# Version:   WHO/HTM/TB/2014.11
# Author:    WHO (World Health Organization)
# Source:    https://www.who.int/tb/publications/pmdt_companionhandbook/en/
# Warning: Reliability might be improved if these antimicrobial results would
# be available too: CAP (capreomycin), RIB (rifabutin), RFP (rifapentine)

And review the result with a frequency table:

freq(my_TB_data$mdr)

Frequency table of mdr from my_TB_data (5,000 x 8)

Class: factor > ordered (numeric)
Length: 5,000 (of which NA: 0 = 0.00%)
Levels: 5: Negative < Mono-resistance < Poly-resistance < Multidrug resistance…
Unique: 5

	Item	Count	Percent	Cum. Count	Cum. Percent
1	Mono-resistance	3,214	64.3%	3,214	64.3%
2	Negative	685	13.7%	3,899	78.0%
3	Multidrug resistance	576	11.5%	4,475	89.5%
4	Poly-resistance	294	5.9%	4,769	95.4%
5	Extensive drug resistance	231	4.6%	5,000	100.0%

Matthijs S. Berends

28 May 2019