With the function mdro(), you can determine which
micro-organisms are multi-drug resistant organisms (MDRO).
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, such as 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 mdro() function support multiple guidelines. You can
select a guideline with the guideline parameter. Currently
supported guidelines are (case-insensitive):
guideline = "CMI2012" (default)
Magiorakos AP, Srinivasan A et al. “Multidrug-resistant, extensively drug-resistant and pandrug-resistant bacteria: an international expert proposal for interim standard definitions for acquired resistance.” Clinical Microbiology and Infection (2012) (link)
guideline = "EUCAST3.2" (or simply
guideline = "EUCAST")
The European international guideline - EUCAST Expert Rules Version 3.2 “Intrinsic Resistance and Unusual Phenotypes” (link)
guideline = "EUCAST3.1"
The European international guideline - EUCAST Expert Rules Version 3.1 “Intrinsic Resistance and Exceptional Phenotypes Tables” (link)
guideline = "TB"
The international guideline for multi-drug resistant tuberculosis - World Health Organization “Companion handbook to the WHO guidelines for the programmatic management of drug-resistant tuberculosis” (link)
guideline = "MRGN"
The German national guideline - Mueller et al. (2015) Antimicrobial Resistance and Infection Control 4:7. DOI: 10.1186/s13756-015-0047-6
guideline = "BRMO"
The Dutch national guideline - Rijksinstituut voor Volksgezondheid en Milieu “WIP-richtlijn BRMO (Bijzonder Resistente Micro-Organismen) (ZKH)” (link)
Please suggest your own (country-specific) guidelines by letting us know: https://github.com/msberends/AMR/issues/new.
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 R 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
maximum 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.
custom
# 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 BThe outcome of the function can be used for the
guideline argument in the mdro() function:
x <- mdro(example_isolates, guideline = custom)
# Determining MDROs based on custom rules, resulting in factor levels:
# Negative < Elderly Type A < Elderly Type B.
# - Custom MDRO rule 1: `CIP == "R" & age > 60` (198 rows matched)
# - Custom MDRO rule 2: `ERY == "R" & age > 60` (732 rows matched)
table(x)
# x
# Negative Elderly Type A Elderly Type B
# 1070 198 732The 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 predefined guidelines. 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 full
antibiograms of 2,000 microbial isolates. It reflects reality and can be
used to practise AMR data analysis. If we test the MDR/XDR/PDR guideline
on this data set, we get:
example_isolates %>%
mdro() %>%
freq() # show frequency table of the result
# ℹ Using column 'mo' as input for `col_mo`.
# Auto-guessing columns suitable for analysis... OK.
# ℹ Reliability would be improved if these antimicrobial results would be
# available too: ampicillin/sulbactam (SAM), aztreonam (ATM), cefotetan
# (CTT), ceftaroline (CPT), daptomycin (DAP), doripenem (DOR), ertapenem
# (ETP), fusidic acid (FUS), gentamicin-high (GEH), levofloxacin (LVX),
# minocycline (MNO), netilmicin (NET), polymyxin B (PLB),
# quinupristin/dalfopristin (QDA), streptomycin-high (STH), telavancin (TLV)
# and ticarcillin/clavulanic acid (TCC)
# Table 1 - Staphylococcus aureus... OK.
# Table 2 - Enterococcus spp.... OK.
# Table 3 - Enterobacteriaceae... OK.
# Table 4 - Pseudomonas aeruginosa... OK.
# Table 5 - Acinetobacter spp.... OK.
# Warning: in `mdro()`: NA introduced for isolates where the available percentage of
# antimicrobial classes was below 50% (set with `pct_required_classes`)Only results with ‘R’ are considered as resistance. Use
combine_SI = FALSE to also consider ‘I’ as resistance.
Determining multidrug-resistant organisms (MDRO), according to: Guideline: Multidrug-resistant, extensively drug-resistant and pandrug-resistant bacteria: an international expert proposal for interim standard definitions for acquired resistance. Author(s): Magiorakos AP, Srinivasan A, Carey RB, …, Vatopoulos A, Weber JT, Monnet DL Source: Clinical Microbiology and Infection 18:3, 2012; doi: 10.1111/j.1469-0691.2011.03570.x
(16 isolates had no test results)
Frequency table
Class: factor > ordered (numeric)
Length: 2,000
Levels: 4: Negative < Multi-drug-resistant (MDR) < Extensively
drug-resistant …
Available: 1,729 (86.45%, NA: 271 = 13.55%)
Unique: 2
| Item | Count | Percent | Cum. Count | Cum. Percent | |
|---|---|---|---|---|---|
| 1 | Negative | 1601 | 92.6% | 1601 | 92.6% |
| 2 | Multi-drug-resistant (MDR) | 128 | 7.4% | 1729 | 100.0% |
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 way:
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:
head(my_TB_data)
# rifampicin isoniazid gatifloxacin ethambutol pyrazinamide moxifloxacin
# 1 R I R R I S
# 2 I I S S R S
# 3 I S R R S R
# 4 R I S R R I
# 5 R I I I R I
# 6 R I R S R I
# kanamycin
# 1 R
# 2 R
# 3 S
# 4 I
# 5 I
# 6 RWe 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$mdr <- mdr_tb(my_TB_data)
# ℹ No column found as input for `col_mo`, assuming all rows contain
# Mycobacterium tuberculosis.
# Auto-guessing columns suitable for analysis... OK.
# ℹ Reliability would be improved if these antimicrobial results would be
# available too: capreomycin (CAP), rifabutin (RIB) and rifapentine (RFP)
#
# Only results with 'R' are considered as resistance. Use `combine_SI = FALSE` to also consider 'I' as resistance.
#
# Determining multidrug-resistant organisms (MDRO), according to:
# Guideline: Companion handbook to the WHO guidelines for the programmatic
# management of drug-resistant tuberculosis
# Author(s): WHO (World Health Organization)
# Version: WHO/HTM/TB/2014.11, 2014
# Source: https://www.who.int/publications/i/item/9789241548809Create a frequency table of the results:
freq(my_TB_data$mdr)Frequency table
Class: factor > ordered (numeric)
Length: 5,000
Levels: 5: Negative < Mono-resistant < Poly-resistant <
Multi-drug-resistant <…
Available: 5,000 (100%, NA: 0 = 0%)
Unique: 5
| Item | Count | Percent | Cum. Count | Cum. Percent | |
|---|---|---|---|---|---|
| 1 | Mono-resistant | 3218 | 64.36% | 3218 | 64.36% |
| 2 | Negative | 979 | 19.58% | 4197 | 83.94% |
| 3 | Multi-drug-resistant | 458 | 9.16% | 4655 | 93.10% |
| 4 | Poly-resistant | 237 | 4.74% | 4892 | 97.84% |
| 5 | Extensively drug-resistant | 108 | 2.16% | 5000 | 100.00% |