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01-introduction.Rmd
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# General Introduction {#introduction}
# General Introduction {#ch01-introduction}
## Microbial epidemiology
@ -86,12 +86,11 @@ Although surveillance programs allow for signalling significant differences and
On the local level, clinical microbiology laboratories should communicate AMR surveillance data to healthcare providers in an understandable manner. Since MDROs can migrate between healthcare institutions, countries and continents by migrating people, local healthcare providers should be aware of local, regional, national and international surveillance program implementations and their ensuing results on AMR. On the other hand, such surveillance program implementations should be well-designed, well-maintained, longitudinal, and involve an appropriate collaboration with local laboratories over time ^[27]^.
<tbl>Table 1. Uses of antibiotic resistance surveillance system data by hospitals, university researchers, pharmaceutical companies and governments.</tbl>
<p class="tbl-caption">Table 1. Uses of antibiotic resistance surveillance system data by hospitals, university researchers, pharmaceutical companies and governments. From Critchley *et al.*, 2004 ^[27]^.</p>
```{r tbl1-1}
insert_graphic("images/01-t01.svg")
```
From Critchley *et al.*, 2004 ^[27]^.
As an example, ISIS-AR (Infectious disease Surveillance Information System for Antibiotic Resistance) is a Dutch national surveillance program, for which a large number of the Dutch clinical microbiology laboratories provide anonymised data on AMR to the National Institute for Public Health and the Environment (Rijksinstituut voor Volksgezondheid en Milieu, RIVM) ^[40]^. In Germany, ARS (Antibiotic Resistance Surveillance) is a similar laboratory-based national surveillance program, that attempts to enable differential statements according to structural characteristics of health care and regions ^[41,42]^. Both these national surveillance programs provide data for EARS-Net and GLASS of the WHO ^[37,43]^.

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02-diagnostic-stewardship.Rmd
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# Diagnostic Stewardship: Sense or Nonsense?! {#diagnostic-stewardship}
# Diagnostic Stewardship: Sense or Nonsense?! {#ch02-diagnostic-stewardship}
Published in Dutch Journal of Clinical Microbiology, 2019 Sep 27, 26:3
(Nederlands Tijdschrift voor Medische Microbiologie; original work in Dutch)
@ -21,12 +21,10 @@ Diagnostic stewardship or diagnostic stewardship programme (DSP), a trending top
Although the term DSP was first mentioned in an indexed PubMed article in 2016, articles on antimicrobial stewardship (ASP) have been appearing for 15 years (Figure \@ref(fig:fig2-1)).
```{r fig2-1, fig.cap = "The increase of articles indexed in PubMed. Search strategies: 'antimicrobial stewardship'[Title/Abstract]; 'diagnostic stewardship'[Title/Abstract]; 'antimicrobial resistance'[Title/Abstract]. Source: https://www.ncbi.nlm.nih.gov/pubmed/ (assessed: 2018-05-31).", out.width = "80%"}
```{r fig2-1, fig.cap = "The increase of articles indexed in PubMed. Search strategies: 'antimicrobial stewardship'[Title/Abstract]; 'diagnostic stewardship'[Title/Abstract]; 'antimicrobial resistance'[Title/Abstract]. Source: https://www.ncbi.nlm.nih.gov/pubmed/ (assessed: 2018-05-31). * Extrapolation based on count from 2018-01-01 to 2018-05-31.", out.width = "80%"}
insert_graphic("images/02-01.jpg")
```
\* Extrapolation based on count from 2018-01-01 to 2018-05-31.
Nevertheless, the concept of DSP is neither intended to replace other stewardship concepts (in particular ASP) nor to be an alternative. DSP concerns decision making and goes beyond microbiological diagnostics alone. Kahneman *et al.* ^[2]^ said about decision making:
> We think, each of us, that we're much more rational than we are. And we think that we make our decisions because we have good reasons to make them. Even when it's the other way around. We believe in the reasons, because we've already made the decision. ^[2]^

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03-introducing-method.Rmd
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# Introducing a New, Free, and Independent Method for Standardised, Reproducible and Reliable Analyses of Antimicrobial Resistance Data {#introducing-method}
# Introducing a New, Free, and Independent Method for Standardised, Reproducible and Reliable Analyses of Antimicrobial Resistance Data {#ch03-introducing-new-method}
In preparation
<p>In preparation <note>(as of date of PhD defence: 25 August 2021)</note></p>
Berends MS ^1,2^, Luz CF ^2^, Sinha BNM ^2^, Glasner C ^2‡^, Friedrich AW ^2‡^

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04-amr-package.Rmd 100644
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# `AMR` - An `R` Package for Working with Antimicrobial Resistance Data {#ch04-amr-r-package}
<p>Accepted in Journal of Statistical Software (ahead of print) <note>(as of date of PhD defence: 25 August 2021)</note></p>
Berends MS ^1,2^\*, Luz CF ^2^\*, Friedrich AW ^2^, Sinha BNM ^2^, Albers CJ ^3^, Glasner C ^2^
1. Certe Medical Diagnostics and Advice Foundation, Groningen, the Netherlands
2. University of Groningen, University Medical Center Groningen, Department of Medical Microbiology and Infection Prevention, Groningen, the Netherlands
3. University of Groningen, Heymans Institute for Psychological Research, Groningen, the Netherlands
\* These authors contributed equally
## Abstract {-}
Antimicrobial resistance is an increasing threat to global health. Evidence for this trend is generated in microbiological laboratories through testing microorganisms for resistance against antimicrobial agents. International standards and guidelines are in place for this process as well as for reporting data on (inter-)national levels. However, there is a gap in the availability of standardised and reproducible tools for working with laboratory data to produce the required reports. It is known that extensive efforts in data cleaning and validation are required when working with data from laboratory information systems. Furthermore, the global spread and relevance of antimicrobial resistance demands to incorporate international reference data in the analysis process. In this paper, we introduce the `AMR` package for` R` that aims at closing this gap by providing tools to simplify antimicrobial resistance data cleaning and analysis, while incorporating international guidelines and scientifically reliable reference data. The `AMR` package enables standardised and reproducible antimicrobial resistance analyses, including the application of evidence-based rules, determination of first isolates, translation of various codes for microorganisms and antimicrobial agents, determination of (multi-drug) resistant microorganisms, and calculation of antimicrobial resistance, prevalence and future trends. The `AMR` package works independently of any laboratory information system and provides several functions to integrate into international workflows (e.g., WHONET software provided by the World Health Organization).
## Introduction
Antimicrobial resistance is a global health problem and of great concern for human medicine, veterinary medicine, and the environment alike. It is associated with significant burdens to both patients and health care systems. Current estimates show the immense dimensions we are already facing, such as claiming at least 50,000 lives due to antimicrobial resistance each year across Europe and the US alone ^[1]^. Although estimates for the burden through antimicrobial resistance and their predictions are disputed ^[2]^ the rising trend is undeniable ^[3]^, thus calling for worldwide efforts on tackling this problem.
Surveillance programs and reliable data are key for controlling and streamlining these efforts. Surveillance data of antimicrobial resistance at higher levels (national or international) usually comprise aggregated numbers. The basis of this information is generated and stored at local microbiological laboratories where isolated microorganisms are tested for their susceptibility to a whole range of antimicrobial agents. The efficacy of these agents against microorganisms is nowadays interpreted as follows ^[4]^:
* R (“resistant”) - there is a high likelihood of therapeutic failure;
* S (“susceptible, standard dosing regimen”) - there is a high likelihood of therapeutic success using a standard dosing regimen of an antimicrobial agent;
* I (“susceptible, increased exposure”) - there is a high likelihood of therapeutic success, but only when exposure to an antimicrobial agent is increased by adjusting the dosing regimen or its concentration at the site of infection.
Generally, antimicrobial resistance is defined as the proportion of resistant microorganisms (R) among all tested microorganisms of the same species (R + S + I). Today, the two major guideline institutes to define the international standards on antimicrobial resistance are the European Committee on Antimicrobial Susceptibility Testing (EUCAST) ^[5]^ and the Clinical and Laboratory Standards Institute (CLSI) ^[6]^. The guidelines from these two institutes are adopted by 94% of all countries reporting antimicrobial resistance to the WHO ^[7]^.
Although these standardised guidelines are in place on the laboratory level for the data generation process, stored data in laboratory information systems are often not yet suitable for data analysis. Laboratory information systems are often designed to fit billing purposes rather than epidemiological data analysis. Furthermore, (inter-)national surveillance is hindered by inadequate standardisation of epidemiological definitions, different types of samples and data collection, settings included, microbiological testing methods (including susceptibility testing), and data sharing policies ^[8]^. The necessity of accurate data analysis in the field of antimicrobial resistance has just recently been further underlined ^[9]^. Antimicrobial resistance analyses require a thorough understanding of microbiological tests and their results, the biological taxonomy of microorganisms, the clinical and epidemiological relevance of the results, their pharmaceutical implications, and (inter-)national standards and guidelines for working with and reporting antimicrobial resistance.
Here, we describe the `AMR` package for` R` ^[10]^, which has been developed to standardise clean and reproducible antimicrobial resistance data analyses using international standardised recommendations ^[5,6]^ while incorporating scientifically reliable reference data about valid laboratory outcome, antimicrobial agents, and the complete biological taxonomy of microorganisms. The `AMR` package provides solutions and support for these aspects while being independent of underlying laboratory information systems, thereby democratising the analysis process. Developed in` R` and available on the Comprehensive` R` Archive Network (CRAN) since February 22nd 2018 ^[11]^, the `AMR` package enables reproducible workflows as described in other fields, such as environmental science ^[12]^. The `AMR` package provides a new technical instrument to aid in curbing the global threat of antimicrobial resistance. Furthermore, local, and regional data in the laboratories can now become relevant in any setting for public health.
While no other packages` R` package with the purpose of dealing with antimicrobial resistance data are available on CRAN or Bioconductor, the `AMR` package may be integrated in workflows of related packages. For example, the` R` Epidemics Consortium (RECON) provides high-quality packages for data analysis in infectious disease outbreaks or epidemics (for example incidence and epicontacts) ^[13,14]^. In addition, on the laboratory side the antibioticR package provides approaches to work with disc diffusion zone diameter and minimum inhibitory concentration data from environment samples ^[15]^. We aim at providing a comprehensive and standardised toolbox for antimicrobial resistance data processing and analysis, with a focus on microbiological, clinical, and epidemiological purposes that was yet missing.
The following sections describe the functionality of the `AMR` package according to its core functionalities for transforming, enhancing, and analysing antimicrobial resistance data using scientifically reliable reference data.
## Antimicrobial resistance data
Microbiological tests can be performed on different specimens, such as blood or urine samples or nasal swabs. After arrival at the microbiological laboratory, the specimens are traditionally cultured on specific media, such as blood agar. If a microorganism can be isolated from these media, it is tested against several antimicrobial agents. Based on the minimal inhibitory concentration (MIC) of the respective agent and interpretation guidelines, such as guidelines by EUCAST ^[5]^ and CLSI ^[6]^, test results are reported as “resistant” (R), “susceptible” (S) or “susceptible, increased exposure” (I). A typical data structure is illustrated in Table 1 ^[5]^.
<p class="tbl-caption">Table 1. Example of an antimicrobial resistance report.</p>
```{r tbl4-1}
insert_graphic("images/04-t01.jpg")
```
<p class="tbl-caption">Table 2. Example of an antimicrobial resistance report.</p>
```{r tbl4-2}
insert_graphic("images/04-t02.jpg")
```
The `AMR` package aims at providing a standardised and automated way of cleaning, transforming, and enhancing these typical data structures (Table 1 and 2), independent of the underlying data source. Processed data would be similar to Table 3 that highlights several package functionalities in the sections below.
<p class="tbl-caption">Table 3. Enhanced antimicrobial resistance report example.</p>
```{r tbl4-3}
insert_graphic("images/04-t03.jpg")
```
## Antimicrobial resistance data transformation
### Working with taxonomically valid microorganism names
Coercing is a computational process of forcing output based on an input. For microorganism names, coercing user input to taxonomically valid microorganism names is crucial to ensure correct interpretation and to enable grouping based on taxonomic properties. To this end, the `AMR` package includes all microbial entries from The Catalogue of Life (http://www.catalogueoflife.org), the most comprehensive and authoritative global index of species currently available ^[16]^. It holds essential information on the names, relationships, and distributions of more than 1.9 million species. The integration of it into the `AMR` package is described in Appendix A.
The `as.mo()` function makes use of this underlying data to transform a vector of characters to a new class ``‘mo’` of taxonomically valid microorganism name. The resulting values are microbial IDs, which are human-readable for the trained eye and contain information about the taxonomic kingdom, genus, species, and subspecies (Figure 1).
```{r fig4-1, fig.cap = "The structure of a typical microbial ID as used in the `AMR` package. An ID consists of two to four elements, separated by an underscore. The first element is the abbreviation of the taxonomic kingdom. The remaining elements consist of abbreviations of the lowest taxonomic levels of every microorganism: genus, species (if available) and subspecies (if available). Abbreviations used for the microbial IDs of microorganism names were created using the base` R` function `abbreviate()`.", out.width = "66%"}
insert_graphic("images/04-01.jpg")
```
The `as.mo()` function compares the user input with taxonomically valid microorganism names, rates the matching with a score and returns results based on the highest score. This matching score ($m$), ranging from $0$ to $1$, is calculated using the following equation:
$$m_{(x,n)} = \frac{l_{n} - 0.5 \cdot \min\{ l_n, \operatorname{lev}(x,n) \} }{l_{n} \cdot p_{n} \cdot k_{n}}$$
where:
* $x$ is the user input;
* $n$ is a taxonomic name (genus, species, and subspecies);
* $l_n$ is the length of $n$;
* lev is the Levenshtein distance function ^[17]^, which counts any insertion, deletion and substitution as $1$ that is needed to change $x$ into $n$;
* $p_n$ is the human pathogenic prevalence group of $n$, as described below;
* $k_n$ is the taxonomic kingdom of $n$, set as Bacteria = 1, Fungi = 2, Protozoa = 3, Archaea = 4, others = 5.
The grouping into human pathogenic prevalence ($p$) is based on experience from several microbiological laboratories in the Netherlands in conjunction with international reports on pathogen prevalence ^[7,18,19]^. **Group 1** (most prevalent microorganisms) consists of all microorganisms where the taxonomic class is Gammaproteobacteria or where the taxonomic genus is *Enterococcus*, *Staphylococcus* or *Streptococcus*. This group consequently contains all common Gram-negative bacteria, such as *Pseudomonas* and *Legionella* and all species within the order Enterobacterales. **Group 2** consists of all microorganisms where the taxonomic phylum is Proteobacteria, Firmicutes, Actinobacteria or Sarcomastigophora, or where the taxonomic genus is *Absidia*, *Acremonium*, *Actinotignum*, *Alternaria*, *Anaerosalibacter*, *Apophysomyces*, *Arachnia*, *Aspergillus*, *Aureobacterium*, *Aureobasidium*, *Bacteroides*, *Basidiobolus*, *Beauveria*, *Blastocystis*, *Branhamella*, *Calymmatobacterium*, *Candida*, *Capnocytophaga*, *Catabacter*, *Chaetomium*, *Chryseobacterium*, *Chryseomonas*, *Chrysonilia*, *Cladophialophora*, *Cladosporium*, *Conidiobolus*, *Cryptococcus*, *Curvularia*, *Exophiala*, *Exserohilum*, *Flavobacterium*, *Fonsecaea*, *Fusarium*, *Fusobacterium*, *Hendersonula*, *Hypomyces*, *Koserella*, *Lelliottia*, *Leptosphaeria*, *Leptotrichia*, *Malassezia*, *Malbranchea*, *Mortierella*, *Mucor*, *Mycocentrospora*, *Mycoplasma*, *Nectria*, *Ochroconis*, *Oidiodendron*, *Phoma*, *Piedraia*, *Pithomyces*, *Pityrosporum*, *Prevotella*, *Pseudallescheria*, *Rhizomucor*, *Rhizopus*, *Rhodotorula*, *Scolecobasidium*, *Scopulariopsis*, *Scytalidium*, *Sporobolomyces*, *Stachybotrys*, *Stomatococcus*, *Treponema*, *Trichoderma*, *Trichophyton*, *Trichosporon*, *Tritirachium* or *Ureaplasma*. **Group 3** consists of all other microorganisms.
This will lead to the effect that e.g., `"E. coli"` will return the microbial ID of *Escherichia coli* ($m = 0.688$, a highly prevalent microorganism found in humans) and not *Entamoeba coli* ($m = 0.079$, a less prevalent microorganism in humans), although the latter would alphabetically come first. The matching score function is for users available as `mo_matching_score()`.
If any coercion rules are applied, a warning is printed to the console and scores can be reviewed by calling `mo_uncertainties()`, that prints all other matches with their matching scores. Users can furthermore control the coercion rules by setting the allow_uncertain argument in the `as.mo()` function. The following values or levels can be used:
* `0`: no additional rules are applied;
* `1`: allow previously accepted (but now invalid) taxonomic names and minor spelling errors;
* `2`: allow all of 1, strip values between brackets, inverse the words of the input, strip off text elements from the end keeping at least two elements;
* `3`: allow all of level 1 and 2, strip off text elements from the end, allow any part of a taxonomic name;
* `TRUE` (default): equivalent to `2`;
* `FALSE`: equivalent to `0`.
To support organisation specific microbial IDs, users can specify a custom reference `‘data.frame’`, by using `as.mo(..., reference_df = ...)`. This process can also be automated by users with the `set_mo_source()` function.
#### Properties of microorganisms
The package contains functions to return a specific (taxonomic) property of a microorganism from the `microorganisms` data set (see Appendix A). Functions that start with `mo_*` can be used to retrieve the most recently defined taxonomic properties of any microorganism quickly and conveniently. These functions rely on the `as.mo()` function internally: `mo_name()`, `mo_fullname()`, `mo_shortname()`, `mo_subspecies()`, `mo_species()`, `mo_genus()`, `mo_family()`, `mo_order()`, `mo_class()`, `mo_phylum()`, `mo_kingdom()`, `mo_type()`, `mo_gramstain()`, `mo_ref()`, `mo_authors()`, `mo_year()`, `mo_rank()`, `mo_taxonomy()`, `mo_synonyms()`, `mo_info()` and `mo_url()`. Determination of the Gram stain, by using `mo_gramstain()`, is based on the taxonomic subkingdom and phylum. According to Cavalier-Smith ^[20]^, who defined the subkingdoms Negibacteria and Posibacteria, only the following phyla are Posibacteria: Actinobacteria, Chloroflexi, Firmicutes and Tenericutes. Bacteria from these phyla are considered Gram-positive - all other bacteria are considered Gram-negative. Gram stains are only relevant for species within the kingdom of Bacteria. For species outside this kingdom, `mo_gramstain()` will return `NA`.
### Working with antimicrobial names or codes
The `AMR` package includes the `antibiotics` data set, which comprises common laboratory information system codes, official names, ATC (Anatomical Therapeutic Chemical) codes, defined daily doses (DDD) and more than 5,000 trade names of 456 antimicrobial agents (see Appendix A). The ATC code system and the reference list for DDDs have been developed and made available by the World Health Organization Collaborating Centre for Drug Statistics Methodology (WHOCC) to standardise pharmaceutical classifications ^[21]^. All agents in the `antibiotics` data set have a unique antimicrobial ID, which is based on abbreviations used by the European Antimicrobial Resistance Surveillance Network (EARS-Net), the largest publicly funded system for antimicrobial resistance surveillance in Europe ^[22]^. Furthermore, the `AMR` package includes the antivirals data seta containing antiviral agents, which is also described in Appendix A.
#### Properties of antimicrobial agents
It is a common task in microbiological data analyses (and other clinical or epidemiological fields) to work with different antimicrobial agents. The `AMR` package provides several functions to translate inputs such as ATC codes, abbreviations, or names in any direction. Using `as.ab()`, any input will be transformed to an antimicrobial ID of class `‘ab’`. Helper functions are available to get specific properties of antimicrobial IDs, such as `ab_name()` for getting the official name, `ab_atc()` for the ATC code, or `ab_cid()` for the CID (Compound ID) used by PubChem ^[23]^. Trade names can be also used as input. For example, the input values "Amoxil", "dispermox", "amox" and "J01CA04" all return the ID of amoxicillin (AMX):
```r
as.ab("Amoxicillin")
#> Class <ab>
#> [1] AMX
as.ab(c("Amoxil", "dispermox", "amox", "J01CA04"))
#> Class <ab>
#> [1] AMX AMX AMX AMX
ab_name("Amoxil")
#> [1] "Amoxicillin"
ab_atc("amox")
#> [1] "J01CA04"
ab_name("J01CA04")
#> [1] "Amoxicillin"
```
If more than one antimicrobial agent is found in the input string, a warning with the additional findings is printed to the console.
#### Filtering data based on classes of antimicrobial agents
The application of the ATC classification system also enables grouping of antimicrobial agents for data analyses. Data sets with microbial isolates can be filtered on isolates with specific results for tested antimicrobial agents in a specific antimicrobial class. For example, using `filter_carbapenems(result = "R")` returns data of all isolates with tested resistance to any of the 14 available antimicrobial agents in in the group of carbapenems according to the `antibiotics` data set.
### Working with antimicrobial susceptibility test results
Minimal inhibitory concentrations (MIC) are susceptibility test results measured by microbiological laboratory equipment to determine at which minimum antimicrobial drug concentration 99.9% of a microorganism is inhibited in growth. These concentrations are often capped at a minimum and maximum, for example ≤0.02 µg/ml and ≥32 µg/ml, respectively. The `‘mic’` class, an ordered `‘factor’` containing valid MIC values, keeps these operators while still ordering all possible outcomes correctly so that e.g., "<=0.02" will be considered lower than "0.04".
Another susceptibility testing method is the use of drug diffusion disks, which are small tablets containing a specified concentration of an antimicrobial agent. These disks are applied onto a solid growth medium or a specific agar plate. After 24 hours of incubation, the diameter of the growth inhibition around a disk can be measured in millimetres with a ruler. The `‘disk’` class can be used to clean these kinds of measurements, since they should always be valid numeric values between 6 and 50. The supported minima and maxima of valid values for both classes, `‘mic’` and `‘disk’`, are displayed in Table 4.
<p class="tbl-caption">Table 4. Antimicrobial susceptibility test classes.</p>
```{r tbl4-4}
insert_graphic("images/04-t04.jpg")
```
The higher the MIC or the smaller the growth inhibition diameter, the more active substance of an antimicrobial agent is needed to inhibit cell growth, i.e. the higher the antimicrobial resistance against the tested antimicrobial agent. At high MICs and small diameters, guidelines interpret the microorganism as “resistant” (R) to the tested antimicrobial agent. At low MICs and wide diameters, guidelines interpret the microorganism as “susceptible” (S) to the tested antimicrobial agent. In between, the microorganism is classified as “susceptible, increased exposure” (I). For these three interpretations the `‘rsi’` class has been developed. When using `as.rsi()` on MIC values (of class `‘mic’`) or disk diffusion diameters (of class `‘disk’`), the values will be interpreted according to the guidelines from the CLSI or EUCAST (all guidelines between 2011 and 2020 are included in the `AMR` package) ^[24,25]^. Guidelines can be changed by setting the guidelines argument.
```r
# Low MIC value
as.rsi(as.mic(2), "E. coli", "ampicillin", guideline = "EUCAST 2020")
#> Class <rsi>
#> [1] S
# High MIC value
as.rsi(as.mic(32), "E. coli", "ampicillin", guideline = "EUCAST 2020")
#> Class <rsi>
#> [1] R
```
When using the `as.rsi()` function on existing antimicrobial interpretations, it tries to coerce the input to the values “R”, “S” or “I”. These values can in turn be used to calculate the proportion of antimicrobial resistance.
### Interpretative rules by EUCAST
Next to supplying guidelines to interpret raw MIC values, EUCAST has developed a set of expert rules to assist clinical microbiologists in the interpretation and reporting of antimicrobial susceptibility tests ^[5]^. The rules comprise assistance on intrinsic resistance, exceptional phenotypes, and interpretive rules. The `AMR` package covers intrinsic resistant and interpretive rules for data transformation and standardisation purposes. The first prevents false susceptibility reporting by providing a list of organisms with known intrinsic resistance to specific antimicrobial agents (e.g., cephalosporin resistance of all enterococci). Interpretative rules apply inference from established resistance mechanisms ^[26-29]^. Both groups of rules are based on classic IF THEN statements (e.g., IF Enterococcus spp. resistant to ampicillin THEN also report as resistant to imipenem). Some rules provide assistance for further actions when certain resistance has been detected, i.e., performing additional testing of the isolated microorganism. The `AMR` package function `eucast_rules()` can apply all EUCAST rules that do not rely on additional clinical information, such as additional information on patients’ diagnoses. Table 2 and 3 highlight the transformation for the reporting of AMX = S in patient_id = 000003 to the correct report according to EUCAST rules of AMX = R. Of note, however, EUCAST rules overwrite original data to correct for the difference in how antimicrobial agents affect the tested microorganism in vitro (in the laboratory) and in vivo (in the human body). This requires users to closely collaborate with the data source provider to ensure correct versioning, backward compatibility, reproducibility, and taking into account specific local regulation for resistance reporting. Typical scenarios where changes to the original data points apply include in vitro test results indicating susceptibility when resistance in vivo is known. The changes are based on scientific consensus to ensure reliable high-quality reporting of antimicrobial susceptibility results. All changes to the data are printed to the console and can also be reviewed in detail by setting the argument `eucast_rules(..., verbose = TRUE)`.
EUCAST rules are subject to regular updates which are implemented into the `AMR` package by the `AMR` maintenance team shortly after publication. The `eucast_rules()` function supports versioning of the rules. The arguments version_breakpoints and version_expertrules can be set to current or previous versions. By default, the `eucast_rules()` function uses the latest implemented version.
### Working with defined daily doses (DDD)
DDDs are essential for standardising antimicrobial consumption analysis, for inter-institutional or international comparison. The official DDDs are published by the WHOCC ^[36]^. Updates to the official publication are monitored by the `AMR` maintenance team and implemented in the `antibiotics` data set included in the `AMR` package. Other metrics exist such as the recommended daily dose (RDD) or the prescribed daily dose (PDD). However, DDDs are the only metric that is independent of a patient’s disease and therapeutic choices and thus suitable for the `AMR` package.
Functions from the `atc_online_*()` family take any text as input that can be coerced with `as.ab()` (i.e., to class `‘ab’`). Next, the functions access the WHOCC online registry ^[30]^ (internet connection required) and download the property defined in the arguments (e.g., administration = "O" or administration = "P" for oral or parenteral administration and property = "ddd" or property = "groups" to get DDD or the group of the selected antimicrobial defined by its ATC code).
```r
atc_online_ddd("amoxicillin", administration = "O")
#> [1] 1.5
atc_online_groups("amoxicillin")
#> [1] "ANTIINFECTIVES FOR SYSTEMIC USE"
#> [2] "ANTIBACTERIALS FOR SYSTEMIC USE"
#> [3] "BETA-LACTAM ANTIBACTERIALS, PENICILLINS"
#> [4] "Penicillins with extended spectrum"
```
## Enhancing antimicrobial resistance data
### Determining first isolates
Determining antimicrobial resistance or susceptibility can be done for a single agent (mono- therapy) or multiple agents (combination therapy). The calculation of antimicrobial resistance statistics is dependent on two prerequisites: the data should only comprise the first isolates and a minimum required number of 30 isolates should be met for every stratum in further analysis ^[6]^.
An isolate is a microorganism strain cultivated on specified growth media in a laboratory, so its phenotype can be determined. First isolates are isolates of any species found first in a patient per episode, regardless of the body site or the type of specimen (such as blood or urine) ^[6]^. The selection on first isolates (using function `first_isolate()`) is important to prevent selection bias, as it would lead to overestimated or underestimated resistance to an antimicrobial agent. For example, if a patient is admitted with a multi-drug resistant microorganism and that microorganism is found in five different blood cultures the following week, it would overestimate resistance if all isolates were to be included in the analysis.
The episode in days can be set with the argument episode_days, which defaults to 365 as suggested by the CLSI guideline ^[6]^.
### Determining multi-drug resistant organisms (MDRO)
Definitions of multi-drug resistant organisms (MDRO) are regulated by national and international expert groups and differ between nations. The `AMR` package provides the functionality to quickly identify MDROs in a data set using the `mdro()` function. Guidelines can be set with the argument guideline. At default, it applies the guideline as proposed by Magiorakos *et al.* ^[31]^. Their work describes the definitions for bacteria being `‘MDR’` (multi-drug-resistant), `‘XDR’` (extensively drug-resistant) or `‘PDR’` (pan-drug-resistant). These definitions are widely adopted ^[32]^ and known in the field of medical microbiology.
Other guidelines currently supported are the international EUCAST guideline (guideline = "EUCAST" ^[33]^), the international WHO guideline on the management of drug-resistant tuberculosis (guideline = "TB" ^[34]^), and the national guidelines of The Netherlands (guideline = "NL" ^[35]^), and Germany (guideline = "DE" ^[36]^).
Some guidelines require a minimum availability of tested antimicrobial agents per isolate. This is needed to prevent false-negatives, since no reliable determination can be performed on only a few test results. This required minimum defaults to 50%, but can be set by the user with the pct_minimum_classes. Isolates that do not meet this requirement will be skipped for determination and will return `NA` (not applicable), with an informative warning printed to the console.
The rules are applied per row of the data. The `mdro()` function automatically identifies the variables containing the microorganism codes and antimicrobial agents based on the `guess_ab_col()` function. Following the guideline set by the user, it analyses the specific antimicrobial resistance of a microorganism and flags that microorganism accordingly. The outcome is demonstrated in Table 5, where the first row is an MDRO according to the Dutch guidelines ^[35]^.
<p class="tbl-caption">Table 5. Example of a multi-drug resistant organism (MDRO) in a data set identified by applying Dutch guidelines.</p>
```{r tbl4-5}
insert_graphic("images/04-t05.jpg")
```
#### Multi-drug resistant tuberculosis
Tuberculosis is a major threat to global health caused by Mycobacterium tuberculosis (MTB) and is one of the top ten causes of death worldwide ^[37]^. Exceptional antimicrobial resistance in MTB is therefore of special interest. To this end, the international WHO guideline for the classification of drug resistance in MTB ^[34]^ is included in the `AMR` package. The `mdr_tb()` function is a convenient wrapper around `mdro(..., guideline = "TB")`, which returns an other ordered `‘factor’` than other `mdro()` functions. The output will contain the `‘factor’` levels `‘Negative’` < `‘Mono-resistant’` < `‘Poly-resistant’` < `‘Multi-drug-resistant’` < `‘Extensively drug-resistant’`, following the WHO guideline.
## Analysing antimicrobial resistance data
### Calculation of antimicrobial resistance
The `AMR` package contains several functions for fast and simple resistance calculations of bacterial or fungal isolates. A minimum number of available isolates is needed for the reliability of the outcome. The CLSI guideline suggests a minimum of 30 available first isolates irrespective of the type of statistical analysis ^[6]^. Therefore, this number is used as the default setting for any function in the package that calculates antimicrobial resistance or susceptibility, which can be changed with the minimum argument in all applicable functions.
#### Counts
The `AMR` package relies on the concept of tidy data ^[38]^, although not strictly following its rules (one row per test rather than one row per observation). Function names to calculate the number of available isolates follow these general resistance interpretation standards with `count_S()`, `count_I()`, and `count_R()` respectively. Combinations of antimicrobial resistance interpretations can be counted with `count_SI()` and `count_IR()`. All these functions take a vector of interpretations of the class `‘rsi’` (as discussed above) or are internally transformed with `as.rsi()`. The returned value is the sum of the respective interpretation in the selected test column. All `count_*()` functions support quasi-quotation with pipes, grouped variables, and can be used with `dplyr::summarise()` ^[39]^.
#### Proportions
Calculation of antimicrobial resistance is carried out by counting the number of first resistant isolates (interpretation of “R”) and dividing it by the number of all first isolates, see Equation 2. This is implemented in the `proportion_R()` function. To calculate antimicrobial susceptibility, the number of susceptible first isolates (interpretation of “S” and “I”) has to be counted and divided by the number of all first isolates, which is implemented in the `proportion_SI()` function. For convenience, the `resistance()` function is an alias of the `proportion_R()` function, and the `susceptibility()` function is an alias of the `proportion_SI()` function.
The functions `proportion_R()`, `proportion_IR()`, `proportion_I()`, `proportion_SI()`, and `proportion_S()` follow the same logic as the `count_*()` functions and all return a vector of class `‘double’` with a value between 0 and 1. The argument `minimum` defines the minimal allowed number of available (tested) isolates (default: `minimum` = 30). Any number below the set minimum will return `NA` with a warning.
For calculating the proportion ($P$) of antimicrobial resistance or susceptibility to one antimicrobial agent, the following equation is used:
$$P_{(x, o)} = \frac{\sum_{i=1}^k [x_i \in o]}{\sum_{i=1}^k [x_i \in \{R,S,I\}]}$$
where $P$ is the proportion of outcome $o$ (that is either “R”, “S”, “I”, or a combination of two of them), where $x$ is a character vector of length $k$ only consisting of values “R”, “S”, or “I” and $[x_i \in o]$ is the indicator function, returning $1$ if the indicator function is true and $0$ otherwise. The denominator must include the collection $\{R,S,I\}$ so that ’`wrong’` elements in $x$ (i.e., elements not being “R”, “S”, or “I”) will not be counted. Thus, the theoretical antimicrobial susceptibility of the vector $x = \{S,S,I,R,R\}$ is:
$$P_{(x, o = \{S, I\})} = \frac{3}{5} = 0.6$$
For the proportion of empiric susceptibility ($s$) for more than one antimicrobial agent, the calculation can be carried out in two ways (Table 6).
<p class="tbl-caption">Table 6. Example calculation for determining empiric susceptibility (%SI) for more than one antimicrobial agent.</p>
```{r tbl4-6}
insert_graphic("images/04-t06.jpg")
```
The first method is to count the total number of first isolates where at least one agent was tested as “S” or “I” and divide it by the number of first isolates tested where any of the agents was tested (Equation 4). This method will be used when setting `only_all_tested = FALSE` in the `susceptibility()` function:
$$s_{(x, y)} = \frac{\sum_{i=1}^k [x_i \in \{S,I\} \lor y_i \in \{S,I\}]}{\sum_{i=1}^k [x_i \in \{R,S,I\} \lor y_i \in \{R,S,I\}]}$$
where $x$ is a character vector only consisting of values “R”, “S”, or “I” (i.e., ’`agent A’`) and $y$ is another character vector only consisting of values “R”, “S”, or “I” (i.e., ’`agent B’`).
The second method is to count the total number of first isolates where at least one agent was tested as “S” or “I” and where all agents were tested divided by the number of first isolates tested where all of the agents were tested (Equation 5). This method will be used when setting `only_all_tested = TRUE` in the `susceptibility()` function:
$$s'_{(x, y)} = \frac{\sum_{i=1}^k [(x_i \in \{S,I\} \lor y_i \in \{S,I\}) \, \land x_i \in \{R,S,I\} \land y_i \in \{R,S,I\}]}{\sum_{i=1}^k [x_i \in \{R,S,I\} \land y_i \in \{R,S,I\}]}$$
Based on Equation 2, the overall resistance and susceptibility of antimicrobial agents like gentamicin (GEN) and amoxicillin (AMX) can be calculated using the following syntax. The `example_isolates` data set is an example data set included in the `AMR` package, see Appendix A. The `n_rsi()` function is analogous to the `n()` function of the `dplyr` package. It counts the number of available isolates, but only includes observations with valid antimicrobial results (i.e., “R”, “S”, or “I”):
```r
library("dplyr")
example_isolates %>%
summarise(r_gen = proportion_R(GEN),
r_amx = proportion_R(AMX),
n_gen = n_rsi(GEN),
n_amx = n_rsi(AMX),
n_total = n())
#> r_gen r_amx n_gen n_amx n_total
#> [1] 0.2458221 0.5955556 1855 1350 2000
```
This output reads: the resistance to gentamicin of all isolates in the `example_isolates` data set is $P{(x = GEN, o = \{R\})} = 24.6\%$, based on $1855$ out of $2000$ available isolates. This means that the susceptibility is $P{(x = GEN, o = \{S,I\})} = 75.4\%$. The susceptibility to amoxicillin is $P{(x = AMX, o = \{S,I\})} = 40.4\%$ based on $1350$ isolates.
To calculate the effect of combination therapy, i.e., treating patients with multiple agents at the same time, all `proportion_*()` functions can handle multiple variables as arguments as defined in Equation 4 and 5. For example, to calculate the empiric susceptibility of a combination therapy comprising gentamicin (GEN) and amoxicillin (AMX):
```r
example_isolates %>%
summarise(si_gen_amx = proportion_SI(GEN, AMX),
n_gen_amx = n_rsi(GEN, AMX),
n_total = n())
#> si_gen_amx n_gen_amx n_total
#> [1] 0.931843 1921 2000
```
This leads to the conclusion that combining gentamicin with amoxicillin would cover $s{(x = GEN, y = AMX)} = 93.2\%$ based on $1921$ out of $2000$ available isolates, which is $17.8\%$ more than when treating with gentamicin alone ($P{(x = GEN, o = \{S,I\})} = 75.4\%$). With these functions, exact calculations can be done to evaluate the empiric success of treating infections with one or more antimicrobial agents.
## Design decisions
The `AMR` package follows the rationale of tidyverse packages as authored by Wickham *et al.* ^[40]^. Most functions take a `‘data.frame’` or `‘tibble’` as input, support piping (%>%) operations, can work with quasi-quotations, and can be integrated into `dplyr` workflows, such as `mutate()` to create new variables and `group_by()` to group by variables. Although the `AMR` package integrates well into tidyverse workflows, it can also be used with base `R `only. To this extent, the `AMR` package was developed to be independent of any other `R `package to ensure and maintain sustainability.
The `AMR` package supports multiple languages. Currently supported languages are English, Dutch, French, German, Italian, Portuguese, and Spanish. The system language will be used if the language is supported but can be overwritten with `options(AMR_locale = ...)`. Multi-language support affects language-dependent output of functions such as `mo_name()`, `mo_gramstain()`, `mo_type()`, and `ab_name()`.
The `AMR` package uses S3 classes, object oriented (OO) systems available in R. They allow different types of output based on the user input. The `AMR` package introduces 5 S3 classes (`‘mo’`, `‘ab’`, `‘rsi’`, `‘mic’`, and `‘disk’`) to increase the convenience when working with antimicrobial susceptibility data.
## Reproducible example
We consider the problem of working with antimicrobial resistance data from three different hospitals between 2011-01-01 and 2020-01-01. After loading the `AMR` package and additional tidyverse packages to allow transformation and plotting, we load the `example_isolates`_unclean example data from the `AMR` package into the global environment and assign it a new name.
```r
library("dplyr")
library("tidyr")
library("AMR")
options(AMR_locale = "en")
data <- example_isolates_unclean
glimpse(data)
#> Rows: 3,000
#> Columns: 8
#> $ patient_id <chr> "J3", "R7", "P3", "P10", "B7", "W3", "J8", "M3",…
#> $ hospital <chr> "A", "A", "A", "A", "A", "A", "A", "A", "A", "A"…
#> $ date <date> 2012-11-21, 2018-04-03, 2014-09-19, 2015-12-10,…
#> $ bacteria <chr> "E. coli", "K. pneumoniae", "E. coli", "E. coli"…
#> $ AMX <chr> "R", "R", "R", "S", "S", "R", "R", "R", "S", "S"…
#> $ AMC <chr> "I", "I", "S", "I", "S", "S", "S", "S", "S", "S"…
#> $ CIP <chr> "S", "S", "S", "S", "S", "R", "S", "S", "S", "S"…
#> $ GEN <chr> "S", "S", "S", "S", "S", "S", "S", "S", "S", "S"…
unique(data$hospital)
#> [1] "A" "B" "C"
unique(data$bacteria)
#> [1] "E. coli" "K. pneumoniae"
#> [3] "S. aureus" "S. pneumoniae"
#> [5] "klepne" "strpne"
#> [7] "esccol" "staaur"
#> [9] "Escherichia coli" "Staphylococcus aureus"
#> [11] "Streptococcus pneumoniae" "Klebsiella pneumoniae"
data %>%
count(bacteria)
#> bacteria n
#> 1 E. coli 494
#> 2 esccol 508
#> 3 Escherichia coli 516
#> 4 K. pneumoniae 108
#> 5 Klebsiella pneumoniae 102
#> 6 klepne 116
#> 7 S. aureus 247
#> 8 S. pneumoniae 151
#> 9 staaur 240
#> 10 Staphylococcus aureus 243
#> 11 Streptococcus pneumoniae 139
#> 12 strpne 136
```
The data contains 3,000 observations of 8 variables from 3 hospitals. The “bacteria” variable comprises 12 unique elements. However, they appear to encode the same information in different formats (’`E. coli’`, ’`K. pneumoniae’`, ’`S. aureus’`, ’`S. pneumoniae’`, ’`klepne’`, ’`strpne’`, ’`esccol’`, ’`staaur’`, ’`Escherichia coli’`, ’`Staphylococcus aureus’`, ’`Streptococcus pneumoniae’`, ’`Klebsiella pneumoniae’`). We can use the `as.mo()` function to standardise the bacterial codes and add a variable with the official scientific name. The correct transformation of the bacterial codes can be reviewed by calling the `mo_uncertainties()` function.
```r
data <- data %>%
mutate(bacteria = as.mo(bacteria),
bacteria_name = mo_name(bacteria))
mo_uncertainties()
#> "E. coli" -> Escherichia coli (B_ESCHR_COLI, matching score =
#> 0.688)
#> Also matched: Entamoeba coli (0.079)
#> "K. pneumoniae" -> Klebsiella pneumoniae (B_KLBSL_PNMN, matching
#> score = 0.786)
#> Also matched: Klebsiella pneumoniae ozaenae
#> (0.707), Klebsiella pneumoniae rhinoscleromatis
#> (0.658)
#>
#> "S. aureus" -> Staphylococcus aureus (B_STPHY_AURS, matching score
#> = 0.690)
#> Also matched: Staphylococcus aureus anaerobius
#> (0.625), Streptomyces aureus (0.355), Stentor aureus
#> (0.052)
data %>%
count(bacteria, bacteria_name)
#> bacteria bacteria_name n
#> 1 B_ESCHR_COLI Escherichia coli 1518
#> 2 B_KLBSL_PNMN Klebsiella pneumoniae 326
#> 3 B_STPHY_AURS Staphylococcus aureus 730
#> 4 B_STRPT_PNMN Streptococcus pneumoniae 426
```
In a next step, we can further enrich the data with additional microbial taxonomic data based on the “bacteria” variable, such as Gram-stain and microorganism family.
```r
data <- data %>%
mutate(gram_stain = mo_gramstain(bacteria),
family = mo_family(bacteria))
data %>%
count(gram_stain)
#> gram_stain n
#> 1 Gram-negative 1844
#> 2 Gram-positive 1156
data %>%
count(family)
#> family n
#> 1 Enterobacteriaceae 1844
#> 2 Staphylococcaceae 730
#> 3 Streptococcaceae 426
```
The variables “AMX”, “AMC”, “CIP”, and “GEN” contain antimicrobial susceptibility test results. The abbreviations stand for the tested antimicrobial agent. The official names and additional information about the antimicrobial agents can be checked with the `ab_info()` function from the `AMR` package.
```r
ab_info("AMX")
#> $ab
#> [1] "AMX"
#>
#> $atc
#> [1] "J01CA04"
#>
#> $cid
#> [1] 33613
#>
#> $name
#> [1] "Amoxicillin"
#>
#> $group
#> [1] "Beta-lactams/penicillins"
#>
#> $atc_group1
#> [1] "Beta-lactam antibacterials, penicillins"
#>
#> $atc_group2
#> [1] "Penicillins with extended spectrum"
#>
#> $tradenames
#> [1] "actimoxi" "amoclen" "amolin"
#> [4] "amopen" "amopenixin" "amoxibiotic"
#> [7] "amoxicaps" "amoxicilina" "amoxicillin"
#> [10] "amoxicilline" "amoxicillinum" "amoxiden"
#> [13] "amoxil" "amoxivet" "amoxy"
#> [16] "amoxycillin" "anemolin" "aspenil"
#> [19] "biomox" "bristamox" "cemoxin"
#> [22] "clamoxyl" "delacillin" "dispermox"
#> [25] "efpenix" "flemoxin" "hiconcil"
#> [28] "histocillin" "hydroxyampicillin" "ibiamox"
#> [31] "imacillin" "lamoxy" "metafarma capsules"
#> [34] "metifarma capsules" "moxacin" "moxatag"
#> [37] "ospamox" "pamoxicillin" "piramox"
#> [40] "robamox" "sawamox pm" "tolodina"
#> [43] "unicillin" "utimox" "vetramox"
#>
#> $ddd
#> $ddd$oral
#> $ddd$oral$amount
#> [1] 1.5
#>
#> $ddd$oral$units
#> [1] "g"
#>
#>
#> $ddd$iv
#> $ddd$iv$amount
#> [1] 3
#>
#> $ddd$iv$units
#> [1] "g"
```
In a data set containing antimicrobial names or codes (e.g., antimicrobial prescription data), the `as.ab()` function can be used to transform all values to valid antimicrobial codes. Extra columns with the official name and the defined daily dose (DDD) for intravenous administration could be added using `ab_name()` and `ab_ddd()`.
```r
antimicrobial_example <- data.frame(agents = c("AMX",
"Ceftriaxon",
"Cipro"))
antimicrobial_example %>%
mutate(agents = as.ab(agents),
agent_names = ab_name(agents),
ddd_iv = ab_ddd(agents, administration = "iv"))
#> agents agent_names ddd_iv
#> 1 AMX Amoxicillin 3.0
#> 2 CRO Ceftriaxone 2.0
#> 3 CIP Ciprofloxacin 0.8
```
Coming back to the cleaning of the data, the columns for the antimicrobial susceptibility test results (“AMX”, “AMC”, “CIP”, “GEN”) need to be checked to contain only standard values (“R”, “S”, “I”).
```r
data %>%
select(AMX:GEN) %>%
pivot_longer(everything(),
names_to = "antimicrobials",
values_to = "interpretation") %>%
count(interpretation)
#> # A tibble: 4 x 2
#> interpretation n
#> <chr> <int>
#> 1 < 0.5 S 143
#> 2 I 1105
#> 3 R 4607
#> 4 S 6145
```
The values contain some mixed values. The `as.rsi()` function can be used to clean these values and to assign a new class (`‘rsi’`) for further use of AMR functions.
```r
data <- data %>%
mutate_at(vars(AMX:GEN), as.rsi)
data %>%
select(AMX:GEN) %>%
pivot_longer(everything(),
names_to = "antimicrobials",
values_to = "interpretation") %>%
count(interpretation)
#> # A tibble: 3 x 2
#> interpretation n
#> <rsi> <int>
#> 1 S 6288
#> 2 I 1105
#> 3 R 4607
```
After this transformation, the `eucast_rules()` function can be applied to apply the latest resistance reporting guidelines.
```r
data <- data %>%
eucast_rules()
```
The output to the console lists the changes made to data:
```r
#> The rules affected 508 out of 3,000 rows, making a total of 657 edits
#> => added 0 test results
#>
#> => changed 657 test results
#> - 11 test results changed from "S" to "I"
#> - 473 test results changed from "S" to "R"
#> - 85 test results changed from "I" to "R"
#> - 19 test results changed from "I" to "S"
#> - 33 test results changed from "R" to "I"
#> - 36 test results changed from "R" to "S"
```
The data is now clean and ready for further analysis, for example, the identification of multi-drug resistant microorganisms. In this example, we use the Dutch guideline to determine multi-drug resistance ^[35]^.
```r
data <- data %>%
mutate(mdro = mdro(., guideline = "nl"))
data %>%
count(bacteria_name, mdro)
#> bacteria_name mdro n
#> 1 Escherichia coli Negative 1123
#> 2 Escherichia coli Positive 395
#> 3 Klebsiella pneumoniae Negative 237
#> 4 Klebsiella pneumoniae Positive 89
#> 5 Staphylococcus aureus Negative 730
#> 6 Streptococcus pneumoniae Negative 426
```
According to the Dutch guideline, 484 (395 + 89) multi-drug resistant microorganisms were found in 3,000 tested isolates. No multi-drug resistance was found in Staphylococcus aureus and Streptococcus pneumoniae.
As described in Section 4.4.1, the identification of first isolates is essential for the reporting of resistance patterns. Using the `filter_first_isolate()` function and `proportion_df()` in combination with `group_by()`, we get a complete resistance analysis per hospital, bacteria, first isolate, and tested antimicrobial agent in one call:
```r
resistance_proportion <- data %>%
filter_first_isolate() %>%
group_by(hospital) %>%
proportion_df()
head(resistance_proportion)
#> hospital antibiotic interpretation value
#> 1 A Amoxicillin SI 0.5773050
#> 2 A Amoxicillin R 0.4226950
#> 3 A Amoxicillin/clavulanic acid SI 0.8085106
#> 4 A Amoxicillin/clavulanic acid R 0.1914894
#> 5 A Ciprofloxacin SI 0.8042553
#> 6 A Ciprofloxacin R 0.1957447
```
From the console we get the information how many first isolates were identified and used in the filter.
From here on, the data is ready for further analysis with functions for plotting (e.g., the `ggplot2` package ^[41]^), AMR extension functions for base `R `(e.g., `summary()`, `plot()`), or `AMR` helper functions for plotting and basic modelling (e.g., `ggplot_rsi()`, `geom_rsi()`, `resistance_predict()`).
## Discussion
For the first time, a free and open-source software solution is available to cover all aspects of working with antimicrobial resistance data. The `AMR` package provides functionalities that enable standardised and reproducible workflows from raw laboratory data to publishable results, for research and clinical workflows alike. In the field of clinical microbiology and infectious diseases, research and clinical workflows are closely linked. For example, a performed research study on the prevalence of antimicrobial-resistant bacteria can have direct implications on the choice of antimicrobial agents for the treatment of patients. The `AMR` package was developed to be used in any research or clinical setting where the data analysis on microorganisms, antimicrobial resistance, antimicrobial agents is required.
Both, researchers and clinicians rely on the data from electronic laboratory information systems (LIS) where laboratory test results are processed, stored, and archived. Although some commercial solutions exist to conduct medical microbiological data analysis, these solutions are not comprehensive enough to apply antimicrobial resistance analysis for any clinical or research setting. Costs of these tools are a further constraint in resource-limited settings. Moreover, researchers and clinicians that require data from multiple LIS sources to be used in multi-center studies experience major barriers which cannot be solved by available commercial solutions.
Firstly, simple codes for microorganisms show substantial differences between different LIS and presumably correct taxonomic names are often misspelled or outdated. We analysed the taxonomic names of bacteria used in reports from seven different public health institutions that perform microbiological diagnostics in the Netherlands and compared them with an official scientific up-to-date source for microbial taxonomy, the Catalogue of Life ^[16]^. These institutions cover microbiological diagnostics for hospitals and primary care for 15% of the total Dutch population. All institutions reported outdated taxonomic names with a maximum lag ranging between 34 and 41 years. Given that antimicrobial resistance guidelines are strongly based on the microbial taxonomy (some rules only apply to a specific genus, other rules apply to a specific family), it is crucial that this information is correct and timely updated. All institutions admitted that there was no standard operating procedure to maintain their taxonomic reference data. Implementing and maintaining the taxonomic data for these and other institutions has been challenging, since no common machine-readable, reliable and up-to-date resource for the microbial taxonomy was publicly available. For reliable reference data about antimicrobial agents, this also holds true. The `AMR` package provides machine-readable reference data files for the complete microbial taxonomy and for more than 500 antimicrobial agents. Using functions starting with `mo_*` and `ab_*`, names of microorganisms and antimicrobial agents can be translated between different LIS codes or other forms of text codes for microorganisms and consequently allows to merge data sets from different sites with little effort.
Secondly, antimicrobial resistance interpretation guidelines ^[5,6]^ and taxonomic definitions of microorganisms are under constant change and are continually published in dedicated peer-reviewed journals. This is further complicated by differences between local, regional, and national guidelines. Yet, comparability and reproducibility across setting and time are key in research and clinics. The `AMR` package functions `eucast_rules()` (to apply guidelines to data), `mdro()` (to check for multi-drug resistance according to guidelines), or `first_isolate()` (to determine first isolates according to guidelines) address the needs to standardise comparability, and empower data analysts beyond the capabilities of their local LIS. The `AMR` package can be used as an extra layer of data validation when retrieving raw data from a LIS. Overall, the functionality of the `AMR` package has the potential to improve data validity in clinical settings, to ease multi-center study workflows, and to foster research reporting practices. The inherent global nature of antimicrobial resistances requires researchers, clinicians, and policy makers to reach beyond the borders of their local laboratory. The `AMR` package can build the bridge to link these sources and further encourages open science principles through its open-source approach.
The `AMR` package also has limitations. It does not introduce novel statistical tests or models, nor does it add additional analytical approaches for AMR research. The calculation of the proportion of susceptibility for more than one antimicrobial agent simultaneously (see Section 4.5.1) seems simple but is subject to unclear reporting in clinical practice ^[42,43]^. The lack of clearly defined algorithms can lead to the effect that co-resistance rates for more than one antimicrobial agent are dropped altogether ^[44]^. The inclusion of isolates that are tested for some agents (only_all_tested = FALSE) or only isolates tested for all agents (only_all_tested = TRUE) can have an imminent clinical impact on patient care, if one combination of antimicrobial agents is preferred over another. Therefore, the `AMR` package provides different algorithms to standardise this crucial calculation. Unfortunately, unambiguous methodology for determining the right algorithm is lacking in scientific literature. An analysis on the algorithms used in the `AMR` package and their clinical impact is in preparation.
Reliable information about antimicrobial resistance is vital for clinical decision-making in infectious diseases, since the outcome of local antimicrobial resistance analyses support medical professionals/clinicians in the treatment choices for their patients. Moreover, when this information can be reliably stratified by, for example, year, hospital, and type of patients, new information can lead to new insights for choosing the best antimicrobial therapy for patients suffering from infections. The `AMR` package enables this by providing all required analysis tools and can therefore empower decision-making in infectious management. The `AMR` package is already being applied to this end in six hospitals in the Netherlands. The choice of empirical antimicrobial treatment (meaning; choosing the initial therapy at a time of not knowing the infection-causing pathogen) for septic non-post-surgical patients has been altered in at least one Dutch hospital, by analysing antimicrobial resistance data with the `AMR` package. The clinical effect of this adjustment is being studied at the moment. To improve the quality of such analyses, planned future developments comprise the implementations of an imputation algorithm specifically for antimicrobial agents, and method guidance for applying prediction modelling in a health care setting based on patient-specific properties.
Since the first package release, users from different public and private settings have been suggesting additional functionalities, in particular, the incorporation of country- or time- specific guidelines (e.g., Magiorakos *et al.* ^[31]^). This community-centred development will be continued and maintained by researchers at the University Medical Center Groningen and data scientists at Certe Medical Diagnostics and Advice, both non-profit public health organisations located in Groningen, the Netherlands. Moreover, a group of contributors from five different Dutch health care institutions has been formed at the Dutch Association for Medical Microbiology (Nederlandse Vereniging voor Medische Microbiologie - NVMM) that also peer-review major changes to the package, including the implementation of guideline updates. This way, updates required for scientific developments as well as maintaining consistent reproducibility are ensured. Updates to databases and guidelines included in the `AMR` package are incorporated on a regular and automated basis, while preserving version control. Any function making use of guidelines (e.g., `eucast_rules()`) refers to the latest implemented version of the guideline by default.
The aim of the `AMR` package is to provide a comprehensive toolbox of solutions for antimicrobial resistance data processing and analysis on an institution- and country-independent scale for clinical practice and research that are required according to international standards, but were not available to date.
## Computational Details {-}
The results in this paper were obtained using `R `4.0.2 in RStudio 1.3.1093 ^[45]^ with the `AMR` package 1.5.0, running under macOS Catalina 10.15.
`R `itself and all packages used are available from the Comprehensive `R `Archive Network (CRAN) at https://CRAN.R-project.org/. All development versions of the `AMR` package are available at https://github.com/msberends/AMR/.
## Acknowledgements {-}
The authors Matthijs S. Berends and Christian F. Luz contributed equally to this publication.
For their contributions to the development of the `AMR` package, we would like to thank (in alphabetical order) Judith M. Fonville, Erwin E.A. Hassing, Eric H.L.C.M. Hazenberg, Gwen Knight, Annick Lenglet, Bart C. Meijer, Sofia Ny, Rogier P. Schade, Dennis Souverein, and Anthony Underwood.
The development of the `AMR` package was partly supported by the INTERREG V A (202085) funded project EurHealth-1Health (http://www.eurhealth1health.eu), part of a Dutch-German cross-border network supported by the European Commission, the Dutch Ministry of Health, Welfare and Sport, the Ministry of Economy, Innovation, Digitalisation and Energy of the German Federal State of North Rhine-Westphalia and the Ministry for National and European Affairs and Regional Development of Lower Saxony.
Furthermore, the `AMR` package was developed as part of a project funded by the European Commission Horizon 2020 Framework Marie Skłodowska-Curie Actions (grant agreement number: 713660-PRONKJEWAIL-H2020-MSCA-COFUND-2015).
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
## References {-}
1. O’Neill J. Review on antimicrobial resistance: tackling a crisis for the health and wealth of nations. London: Wellcome Trust; 2014.
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3. Centers for Disease Control and Prevention. AR threats report: antibiotic resistance threats in the United States, 2019. Atlanta: Centers for Disease Control and Prevention; 2019.
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## Appendix A: Included Data Sets {-}
* `microorganisms`
A `‘data.frame’` containing 67,151 (sub)species with 16 columns comprising their complete microbial taxonomy according to the Catalogue of Life ^[16]^. Included microorganisms and their complete taxonomic tree of all included (sub)species from kingdom to subspecies with year of scientific publication and responsible author(s):
* All 55,415 (sub)species from the kingdoms of Archaea, Bacteria, Chromista and Protozoa;
* All 9,582 (sub)species from these orders of the kingdom of Fungi: Eurotiales, Onygenales, Pneumocystales, Saccharomycetales, Schizosaccharomycetales and Tremellales;
* All 2,153 (sub)species from 47 other relevant genera from the kingdom of Animalia (like Strongyloides and Taenia);
* All 12,708 previously accepted names of included (sub)species that have been taxonomically renamed.
The kingdom of Fungi is a very large taxon with almost 300,000 different (sub)species, of which most are not microbial (but rather macroscopic, such as mushrooms). Therefore, not all fungi fit the scope of the `AMR` package. By only including the aforementioned taxonomic orders, the most relevant fungi are covered (such as all species of Aspergillus, Candida, Cryptococcus, Histoplasma, Pneumocystis, Saccharomyces and Trichophyton).
* `antibiotics`
A `‘data.frame’` containing 456 antibiotic agents with 14 columns. All entries in this data set have three different identifiers: a human readable EARS-Net code (as used by ECDC ^[19]^ and WHONET ^[46]^ and primarily used by this package), an ATC code (as used by the WHO ^[21]^), and a CID code (Compound ID, as used by PubChem ^[23]^). The data set contains more than 5,000 official brand names from many different countries, as found in PubChem. Other properties in this data set are derived from one or more of these codes, such as official names of pharmacological and chemical subgroups, and defined daily doses (DDD).
* `antivirals`
A `‘data.frame’` containing 102 antiviral agents with 9 columns. Like the `antibiotics` data set, it contains ATC codes (as used by the WHO ^[21]^), and a CID code (Compound ID, as used by PubChem ^[23]^), as well as the official name and defined daily dose (DDD) for each antiviral agent.
* `example_isolates`
A `‘data.frame’` containing test results of 2,000 microbial isolates. The data set reflects real patient data and can be used to practice AMR analysis. It is structured in the typical format of laboratory information systems with one row per isolate and one column per tested antimicrobial agent (i.e., an antibiogram).
* `example_isolates_unclean`
A `‘data.frame’` containing test results of 3,000 microbial isolates that require cleaning up before they can be used for analysis. This data set can be used to practice AMR analysis and is featured in section 4.7.
* `WHONET`
A ``‘data.frame’` containing 500 observations and 53 columns, with the exact same structure as an export file from WHONET 2019 software ^[46]^. Such files can be used with the `AMR` package, as this example data set demonstrates. The antibiotic test results are from the `example_isolates` data set. All patient names are created using online surname generators and are only in place for practice purposes.

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# Rapid Analysis of Diagnostic and Antimicrobial Patterns in R (RadaR): Interactive Open-Source Software App for Infection Management and Antimicrobial Stewardship {#ch05-radar}
Published in Journal of Medical Internet Research, 2019 (21);6, e12843
Luz CF 1, Berends MS 1,2, Dik JWH 1, Lokate ML 1, Pulcini C 3,4, Glasner C 1, Sinha BNM 1
1. University of Groningen, University Medical Center Groningen, Department of Medical Microbiology and Infection Prevention, Groningen, The Netherlands
2. Certe Medical Diagnostics and Advice Foundation, Groningen, The Netherlands
3. Université de Lorraine, APEMAC, Nancy, France
4. Université de Lorraine, CHRU-Nancy, Infectious Diseases Department, Nancy, France
## Abstract {-}
Analysing process and outcome measures for all patients diagnosed with an infection in a hospital, including those suspected of having an infection, requires not only processing of large datasets but also accounting for numerous patient parameters and guidelines. Substantial technical expertise is required to conduct such rapid, reproducible, and adaptable analyses; however, such analyses can yield valuable insights for infection management and antimicrobial stewardship (AMS) teams. The aim of this study was to present the design, development, and testing of RadaR (Rapid analysis of diagnostic and antimicrobial patterns in R), a software app for infection management, and to ascertain whether RadaR can facilitate user-friendly, intuitive, and interactive analyses of large datasets in the absence of prior in-depth software or programming knowledge. RadaR was built in the open-source programming language R, using Shiny, an additional package to implement web-app frameworks in R. It was developed in the context of a 1339-bed academic tertiary referral hospital to handle data of more than 180,000 admissions. RadaR enabled visualisation of analytical graphs and statistical summaries in a rapid and interactive manner. It allowed users to filter patient groups by 17 different criteria and investigate antimicrobial use, microbiological diagnostic use and results including antimicrobial resistance, and outcome in length of stay. Furthermore, with RadaR, results can be stratified and grouped to compare defined patient groups on the basis of individual patient features. AMS teams can use RadaR to identify areas within their institutions that might benefit from increased support and targeted interventions. It can be used for the assessment of diagnostic and therapeutic procedures and for visualizing and communicating analyses. RadaR demonstrated the feasibility of developing software tools for use in infection management and for AMS teams in an open-source approach, thus making it free to use and adaptable to different settings.
## Introduction
### Background
With antimicrobial resistance (AMR) on the rise, efforts are being made worldwide to focus on the preservation of antimicrobials as a precious non-renewable resource. Infection management in the form of antimicrobial stewardship (AMS) programs has emerged as an effective solution to address this global health problem in hospitals. AMS programs are defined as “a coherent set of actions which promote using antimicrobials responsibly” [1]. Stewardship interventions and activities focus on individual patients (personalised medicine and consulting) as well as patient groups or clinical syndromes (guidelines, protocols, information technology infrastructure, and clinical decision support systems) while prioritizing improvement in quality of care and patient safety for any intervention. The appropriate use of antimicrobials based on accurate and timely diagnostics is integral for the successful management of infections. In doing so, the diagnostics contribute to efforts in minimizing AMR by optimizing the use of antimicrobials.
AMS setups in hospitals are often heterogeneous, but audit and feedback to assess the goals are essential parts of most programs, and they are included in international guidelines and reviews [2-7]. Important data for AMS programs include, for example, days of therapy (DOT), daily defined doses (DDD), admission dates, length of stay (LOS), and adherence to local or national diagnostic, therapeutic, or infection management guidelines [1]. Clinical outcomes, quality of care, or consumption of hospital resources can be measured, for example, using mortality data or surrogate parameters such as LOS. The collection of these data is facilitated by electronic health records (EHRs) and administrative local databases. Notably, administrative data have also been shown to be a reliable source for assessing clinical outcomes [8].
EHRs usually offer quick insights into useful infection management data on the individual patient level. However, easy access to analyse patient groups (e.g., stratified by departments or wards, specific antimicrobials, or diagnostic procedures used) is difficult to implement in daily practice. It is even more challenging to rapidly analyse larger patient populations (e.g., spread over multiple specialties) even though this information might be available. Nevertheless, this is vital for meaningful analysis, including possible confounders and pattern recognition across different populations. Moreover, when aggregated data are available, it is often not possible to trace individual patients, and analyses lack the ability to be further adjusted or stratified.
AMS teams are multidisciplinary, and they act beyond the borders of single specialties [9]. They are usually understaffed, with limited data analysis support [10,11]. Therefore, they need user-friendly and time-saving data analysis resources, without the need for profound technical expertise once the system is set up. Aggregating and linking data of antimicrobial use, guideline adherence, and clinical outcomes at the institutional level can build the basis for important insights for these teams. These could be used to identify areas within hospitals that might benefit most from supportive AMS interventions (e.g., subspecialties with lower guideline adherence or unusual patterns of antimicrobial use). Moreover, feedback from these data could help physicians better understand their patient population as a whole; in addition, hospital administration could allocate resources in a more targeted fashion.
Furthermore, aggregated data and simultaneous analysis of multiple areas (e.g., use of diagnostics and antimicrobials) present an extensive insight into large patient populations. This also enables the development of comprehensive and multidisciplinary approaches of infection management, combining diagnostic and therapeutic perspectives [1,9,12]. Unfortunately, these kinds of analyses still require substantial statistical knowledge and software skills, and it is time consuming when performed.
Technology, data science, and software app development can bring solutions to complex data handling problems such as those described above. Software app development for medical and epidemiological (research) questions has found many important answers during recent years. For example, software apps at hospital emergency departments (EDs) in the form of a dashboard have been shown to improve efficiency and quality of care for patients requiring emergency admission to hospital [13]. These software apps are used to communicate clearly defined clinical problems, such as mortality ratio, number of cardiac arrests, or readmission rate to the EDs. This has led to a decreased LOS and mortality at the EDs. Others used similar approaches to rapidly and interactively display geographical locations of tuberculosis cases without the need of technical expertise improving the understanding of transmission and detection [14]. Furthermore, data-driven fields such as genomics are front runners in developing new, innovative software apps to handle large datasets, in close collaboration with bioinformatics [15].
It is important to note that all of these abovementioned software apps have been created in an open-source approach. This means that the underlying source code can be easily shared, easily modified, and freely distributed through open repositories, such as GitHub [16], taking open-source software license obligations into account. This facilitates collaboration, quality control through code review, and easy adaptation to many different settings and information technology systems, and this supports the use of advanced data visualisation for users with minimal experience in programming and little or no budget for professional database engineers [15].
In the field of medical microbiology, different approaches have been described to interactively work with microbiological diagnostics data and EHRs: electronic antibiograms, centralised resistance analysis, EHR data mining, and clinical decision support systems for AMS are great examples for innovation in the field [17-19]. However, a full open-source approach for software apps working with combined antimicrobials use and diagnostic data of individual patients on the hospital level in the field of infection management is still lacking.
### Objectives
We followed principles of open knowledge [20] to address the need for an interactive, easy-to-use software app that allows users to investigate antimicrobial use, microbiological diagnostic use, and patient outcomes at an institutional (hospital) level. We developed an open-source, web-based software app – Rapid analysis of diagnostic and antimicrobial patterns in R (RadaR) that can be used for AMS and infection management. This free software app can be run on regular computers or implemented on local or web-based servers to be accessed through standard web browsers. The focus user group of this software app is health care professionals involved in AMS (e.g., infectious disease specialists, clinical microbiologists, and pharmacists). Although some technical expertise (basic R knowledge) is needed for installation and implementation, the use of RadaR follows usual web browser user experiences. RadaR enables rapid and reproducible data analysis without extensive previous analysis expertise in a graphically appealing way while being adaptable to different settings. RadaR’s analyses are based on datasets of individual patients. Therefore, aggregated results can also be stripped down, and additional patient features can be investigated. With this software app, we aim at supporting data-driven hospital insights and decision making for actors in the field of AMS in a free, transparent, and reproducible way.
## Methods
For the development of software in an open-source environment, we used the open-source programming language R in conjunction with RStudio version 1.1.463 (RStudio, Inc) [21], an open-source integrated desktop environment for R [22]. Both R and RStudio are free of charge, and they need to be installed for the development and implementation of RadaR. To build RadaR as a web-based software app, we used the Shiny package for R [23]. Shiny allows R users to build interactive web apps without extensive knowledge in web design and its programming languages. The web apps can be run and hosted on the web for free [24], as well as on local or cloud-based servers or on personal computers.
The functionality of R can be easily extended by installing additional packages. All packages used for the development of RadaR are listed in Table 1. RadaR is developed in an open-source environment and licensed under GNU General Public License v2.0 [25], giving options to change, modify, and adapt RadaR to both personal and commercial users’ needs while requiring the need to document code changes [25].
RadaR’s calculations and data aggregation are done reactively on the basis of the selection of the user. Single observations on the patient level build the basis for any calculation. RadaR uses common CSV files as input. A total of three different data sources are read in RadaR for admission, antimicrobial, and microbiological data, which are merged and transformed upon start. A patient number or study number is used as a unique identifier. All antimicrobial and microbiological data are checked to ascertain whether they fall in the interval of admission dates.
<p class="tbl-caption">Table 1. Required R packages for RadaR.</p>
```{r tbl5-1}
# insert_graphic("images/05-t01.jpg")
```
The input data should be structured in a dataset format, where each variable is one column and each observation is one row. This follows the concept of “tidy data,” as defined by Hadley Wickham [26]. Table 2 displays the set of variables underlying RadaR’s functionality. In our setting for the development of RadaR, these variables originated from three different data sources: administrative data from the hospital data warehouse, microbiological data from the laboratory information system, and antimicrobial prescription data from the computerised prescriber order entry system. The data preparation and cleaning process are very specific for each data source, dependent on local data standards, and difficult to generalise. Therefore, Table 2 represents the final variables and formats for the analysis and use with RadaR, referring to the “tidy data” concept above and to the R package collection tidyverse for the preparation process [26,27]. Additional variables are calculated and transformed using the packages lubridate and zoo for time points and intervals, and AMR for antimicrobial (group) names, microbial isolate names, first isolate identification, and resistance analysis [28-30]. Microbiological resistance is calculated per antimicrobial substance or as co-resistance if more than one substance is selected.
<p class="tbl-caption">Table 2. Input variables for RadaR.</p>
```{r tbl5-2}
# insert_graphic("images/05-t02.jpg")
```
The input data should be structured in a dataset format, where each variable is one column and each observation is one row. This follows the concept of “tidy data,” as defined by Hadley Wickham [26]. Table 2 displays the set of variables underlying RadaR’s functionality. In our setting for the development of RadaR, these variables originated from three different data sources: administrative data from the hospital data warehouse, microbiological data from the laboratory information system, and antimicrobial prescription data from the computerised prescriber order entry system. The data preparation and cleaning process are very specific for each data source, dependent on local data standards, and difficult to generalise. Therefore, Table 2 represents the final variables and formats for the analysis and use with RadaR, referring to the “tidy data” concept above and to the R package collection tidyverse for the preparation process [26,27]. Additional variables are calculated and transformed using the packages lubridate and zoo for time points and intervals, and AMR for antimicrobial (group) names, microbial isolate names, first isolate identification, and resistance analysis [28-30]. Microbiological resistance is calculated per antimicrobial substance or as co-resistance if more than one substance is selected.
RadaR can be used for graphical exploratory data analysis. Differences in LOS are displayed by a Kaplan-Meier curve in conjunction with a log-rank test, using the survminer package [32]. Time trends for number of admissions, antimicrobial consumption, and resistance counts per year, quarter, or month, are visualised in run charts using the qicharts2 package [33]. Nonrandom variation in these run charts is tested using Anhøj’s rules [34].
RadaR has been developed in macOS High Sierra (1.4 GHz, 4 GB RAM), and it was successfully tested in Windows 7 (3.2 GHz, 8 GB RAM) and Linux (Ubuntu 16.04.4 LTS, 3.4 GHz, 12 GB RAM). A running example version has been deployed to shinyapps.io, a publicly available web hosting service for R Shiny apps [35]. The entire source code of RadaR is freely accessible on GitHub [36]. We intend to integrate suggestions and feedback coming from its users and the R community. RadaR was developed using data of patients admitted to the University Medical Center Groningen, Groningen, the Netherlands. Data were collected retrospectively, and permission was granted by the ethical committee (METc 2014/530). RadaR can be used locally in protected environments or hosted on the web, provided appropriate measures have been taken to guarantee data protection, depending on national regulations.
## Results
### Overview
We have developed RadaR, a web-based software app providing an intuitive platform for rapid analysis of large datasets containing information about patients’ admission, antimicrobial use, and results of microbiological diagnostic tests. This software app can help users (i.e., AMS team members) find answers to questions, such as “What are the most commonly used antimicrobials at an institution/specialty/department and have they changed over time?”, “Were adequate microbiological diagnostics performed at the start of antimicrobial treatments?”, “What are the most frequent microorganisms found and their resistance patterns in different departments?”, and “Can we identify priority areas within a hospital where antimicrobial or microbiological diagnostic use has the largest room for improvement?”.
### Application Design
RadaR is designed in the form of a web browser–based dashboard that most users are familiar with from typical websites and web-based tools (see Figure 1). The basis of RadaR’s functionality is filtering datasets and producing analytical graphs according to selection criteria defined by the user. Any calculations and data aggregation are based on single observations of individual patients. To identify and analyse groups of patients, 17 different selection criteria can be found in the sidebar (Table 3). The output of RadaR is grouped into four panels (patient, antimicrobials, diagnostics, and outcome) that each comprise three to four output boxes displaying the results.
<p class="tbl-caption">Table 3. Selection criteria in sidebar.</p>
```{r tbl5-3}
# insert_graphic("images/05-t03.jpg")
```
All output is based on the selection criteria defined by the user in the sidebar. Each new selection and any change need to be confirmed by clicking the confirm selection button (see Figure 1). Users can navigate among the different analysis panels by clicking the respective button.
```{r fig5-1, fig.cap = "Application design."}
# insert_graphic("images/05-01.jpg")
```
Results are shown in bar charts, density plots, run charts, a bubble plot, and a Kaplan-Meier curve for LOS in hospital. Each panel further displays a table summarizing the respective data analyses. All output boxes and their content are described in Table 4. Most output boxes include modification options that can be identified by small gear icons (see Figure 1). These clickable icons allow for further specification of the generated plots and tables. Users can compare different groups (e.g., antimicrobial use by antimicrobial agent, resistance patterns per isolate, or LOS by specialty) or modify the plots (e.g., switch from count to proportion, change the chart type, or show or hide the legend). Plots and tables can be downloaded through download buttons as PNG files for plots and CSV, Excel, or PDF files for tables.
<p class="tbl-caption">Table 4. Output boxes for analysis results.</p>
```{r tbl5-4}
# insert_graphic("images/05-t04.jpg")
```
Finally, two datasets (antimicrobial/admission data and microbiological data) of the user-defined selection can be downloaded from the sidebar menu in a CSV-file format for further analysis (e.g., retrieving a list of patient numbers of the selected patient group).
### Development Process
RadaR has been developed in close contact with the AMS team and senior consulting specialists at the University Medical Center Groningen, Groningen, the Netherlands, to meet the needs and requirements of this user group. Subsequently, all members of the European Society of Clinical Microbiology and Infectious Diseases Study Group for Antimicrobial Stewardship (ESGAP) were asked to evaluate and test the software app through a running web-based example of RadaR and by filling out a web-based survey. The ESGAP comprises around 200 members from more than 30 countries worldwide. A total of 12 members from 9 different countries took part in the evaluation. This yielded important information on user experiences with the software app, which in turn led to further improvements that are reflected in the version we presented in this report. In a next phase, RadaR will be tested in different settings of ESGAP members and other interested partners using locally available data (e.g., an 837-bed tertiary care hospital in the Netherlands and a 750-bed tertiary care hospital in Greece).
### Workflow
RadaR was developed and tested with a dataset of all patients admitted to our institution, a 1339-bed academic tertiary referral hospital, within the years of 2009 to 2016, comprising over 180,000 admissions. For simulation purposes and web-based user testing, we have created a test dataset of 60,000 simulated patients. This sample dataset allows testing of RadaR’s functionality, but it does not produce meaningful results.
A typical example workflow with RadaR comprises 6 steps (with examples from the test dataset). They are listed below:
1. Define the selection: For example, patients receiving intravenous second- or third-generation cephalosporins as first treatment for at least two days, starting within the first two days of hospital admission from any specialty in all years in the dataset.
2. Patients’ panel: Identify the total number of patients and the subspecialties with the highest number of included patients (e.g., 537 patients selected in total, with 97 patients from internal medicine). Investigate patients’ gender and age distribution.
3. Antimicrobials panel: Identify the total use of the initial cefuroxime treatment in DDD and DOT per 100 bed days (e.g., 4.51 and 1.5, respectively). Stratify the results by subspecialty and identify the highest number of DDD and DOT per 100 bed days (e.g., highest use by DDD and DOT in internal medicine).
4. Diagnostics panel: Check if the selected microbiological diagnostic test (e.g., blood culture test) has been performed on the same day as the start of the treatment (defined in the sidebar). Investigate the proportion of tests performed over the years and investigate which subspecialty performs best compared with others (e.g., paediatrics). Check which microorganisms (as first isolates) were found in the selected diagnostic specimens (the most common isolate: Escherichia coli). Investigate the proportion of isolates resistant to cefuroxime (8.9%) and analyse the trend over time.
5. Outcome panel: Check for patterns of differences in LOS in the defined patient group by subspecialties or performed diagnostics (e.g., highest mean LOS of 7.8 days in Surgery).
6. Refine the selection: Investigate a subgroup of the original selection. For example, select only the top three subspecialties by number of patients and repeat step 2 to 5.
### Customisation
For setting up RadaR in a new environment after data preparation, users only need to perform the following four steps:
1. Downloading R and RStudio [21,22], which are free to use and open-source software
2. Download or copy and paste RadaR’s source code [36] into three files in RStudio – global.R, server.R, and ui.R
3. In global.R, manually edit the paths for the prepared datasets to be imported into RadaR
4. Run the app in RStudio with the calling the function runApp() in the console or by clicking the green run app button. This will download and install the required R packages needed for the app if they have not been installed previously, and this will create the final dataset for analysis. The RadaR interface will open in the RStudio viewer pane or in a new window of the standard browser of the user’s operating system.
RadaR’s appearance has been customised using a cascading style sheets (CSS) script [37] that is loaded into the app upon its start. This script needs to be saved into a subdirectory of the directory of the three main files (global.R, server.R, and ui.R) called www. We recommend RStudio’s project function to create a single project for RadaR and to store all information in this project directory. Users with experience in using CSS can fully alter RadaR’s design by changing the underlying CSS script.
## Discussion
### Principal Findings
We have developed a web-based software app for rapid analysis of diagnostic and antimicrobial patterns that can support AMS teams to tailor their interventions. It has been designed to enhance communication of relevant findings while being easy to use. This also applies to users without extensive prior software skills, as it follows usual web browser user experiences. Moreover, it has been developed using open-source software. It is therefore free to use and accessible for download. In our experience, this system can be adapted to new settings within one day, when the required data (Table 2) are available.
Commercial software for infection management is available (e.g., Epic Antimicrobial Stewardship Module, TREAT Steward). These offer extensive options for filtering, analysing, and visualizing EHRs with real-time connections to hospital data infrastructures and have been shown to be useful in clinical practice [38]. However, it is difficult to compare functionalities of these tools because of their non–open-source nature. This fact, along with the required budget to purchase the software, drastically limits their use. We are convinced that transparent software development can support the adoption of data-driven developments while enhancing optimal quality of care and patient safety, which is crucial in the light of new data-driven developments of using EHRs [39,40].
The global nature of infections further calls to develop software tools applicable in resource-limited settings [41]. Open-source approaches for data analysis, such as RadaR, have advantages over traditional methods, such as Excel or SPSS. Hughes et al described those in their report of a software app for RNA-sequencing data analysis [15]. They highlight aspects that were also fundamental for the development of RadaR. First, R allows transparent, reproducible, and sustainable data analysis through scripts that can easily be shared and changed. This can build the basis for collaboration, and this enforces the spirit of open science (also through the strong collaborative R community on the web). Second, R is open source and free to use; therefore, it also enables use in resource-limited settings. Finally, Shiny empowers users to interact with the data, making even very large datasets quickly interpretable.
Innovative approaches used in supporting infection management by leveraging EHRs are being investigated [17-19]. Reporting on AMR, antimicrobial use, and hospital infections (e.g., for quality assurance) is well established, but it is important to integrate these data sources in an approach that allows detailed filtering options on all input. Merely looking at antimicrobial use alone or comparing aggregated results (e.g., total amount of a specific antimicrobial substance per hospital correlated with the total count of a resistant isolate) will result in loss of information or even misleading interpretation. Detailed data and calculations on the basis of each individual patient are crucial to draw informed conclusions. Unfortunately, the abovementioned infection management approaches [17-19] either depend on additional commercial software for data visualisation or the source code is not openly available. We want to encourage others to turn toward available open-source software solutions, such as R, for an increased potential of collaboration and transparency. However, their strength is the connection to real-time data flows. This enables the prospective use and increases their usability for daily clinical practice. RadaR is currently still limited to retrospective data analysis because of a changing hospital data infrastructure in our setting. Technically, it is feasible to connect R-based software apps such as RadaR to real-time hospital data infrastructures running with clinical data standards [42]. For a start, access to static data extraction is often easier and faster to achieve. RadaR can be used to advocate the use of data visualisation tools and improved accessibility of hospital data sources. Until connection to real-time hospital data is established, RadaR can support users as a stand-alone option for retrospective data analysis in infection management. Next steps will involve testing in multiple settings and forming a user and research group to continue and expand the use of open-source technology and open science principles in infection management.
### Conclusions
RadaR demonstrates the feasibility of developing software tools for infection management and AMS teams in an open-source approach, making it free to use, share, or modify according to various needs in different settings. RadaR has the potential to be a highly useful tool for infection management and AMS in daily practice.
## Acknowledgements {-}
The authors would like to thank the ESGAP executive committee for supporting the evaluation of RadaR in the ESGAP study group and all its members for their valuable input, suggestions, and comments. Furthermore, the authors wish to thank Igor van der Weide, Jan Arends, and Prashant Nannan Panday for their great support in obtaining required data at our institution that built the basis for the development of RadaR. The authors also thank the online R community as well as the valuable comments, suggestions, and input from reviewers that they have received to improve RadaR.
RadaR was developed as part of a project funded by the European Commission Horizon 2020 Framework Marie Skłodowska-Curie Actions (grant agreement number: 713660-PRONKJEWAIL-H2020-MSCA-COFUND-2015).
## Conflicts of interests {-}
None declared.
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<li><a href="https://doi.org/10.33612/diss.177417131" target="blank"><img src="images/cover.jpg" style="width:90%; margin-left:5%;"></a></li>
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<li class="chapter" data-level="1" data-path="introduction.html"><a href="introduction.html"><i class="fa fa-check"></i><b>1</b> General Introduction</a>
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<li class="chapter" data-level="1.1" data-path="introduction.html"><a href="introduction.html#microbial-epidemiology"><i class="fa fa-check"></i><b>1.1</b> Microbial epidemiology</a></li>
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<li class="chapter" data-level="2.1.1" data-path="diagnostic-stewardship.html"><a href="diagnostic-stewardship.html#case-1"><i class="fa fa-check"></i><b>2.1.1</b> Case 1</a></li>
<li class="chapter" data-level="2.1.2" data-path="diagnostic-stewardship.html"><a href="diagnostic-stewardship.html#case-2"><i class="fa fa-check"></i><b>2.1.2</b> Case 2</a></li>
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<li class="chapter" data-level="2.2.2" data-path="diagnostic-stewardship.html"><a href="diagnostic-stewardship.html#dsp-in-the-microbiological-laboratory"><i class="fa fa-check"></i><b>2.2.2</b> DSP in the microbiological laboratory</a></li>
<li class="chapter" data-level="2.2.3" data-path="diagnostic-stewardship.html"><a href="diagnostic-stewardship.html#dsp-as-process-optimisation"><i class="fa fa-check"></i><b>2.2.3</b> DSP as process optimisation</a></li>
<li class="chapter" data-level="2.2.4" data-path="diagnostic-stewardship.html"><a href="diagnostic-stewardship.html#multidisciplinary-aspects-of-dsp-and-infection-management"><i class="fa fa-check"></i><b>2.2.4</b> Multidisciplinary aspects of DSP and infection management</a></li>
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<li class="chapter" data-level="2.3" data-path="diagnostic-stewardship.html"><a href="diagnostic-stewardship.html#conclusion"><i class="fa fa-check"></i><b>2.3</b> Conclusion</a></li>
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<li class="chapter" data-level="3" data-path="introducing-method.html"><a href="introducing-method.html"><i class="fa fa-check"></i><b>3</b> Introducing a New, Free, and Independent Method for Standardised, Reproducible and Reliable Analyses of Antimicrobial Resistance Data</a></li>
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<li class="chapter" data-level="3.1" data-path="abstract-1.html"><a href="abstract-1.html#background"><i class="fa fa-check"></i><b>3.1</b> Background</a></li>
<li class="chapter" data-level="3.2" data-path="abstract-1.html"><a href="abstract-1.html#standardising-amr-data-analysis"><i class="fa fa-check"></i><b>3.2</b> Standardising AMR data analysis</a></li>
<li class="chapter" data-level="3.3" data-path="abstract-1.html"><a href="abstract-1.html#comparison-with-existing-software-methods"><i class="fa fa-check"></i><b>3.3</b> Comparison with existing software methods</a></li>
<li class="chapter" data-level="3.4" data-path="abstract-1.html"><a href="abstract-1.html#user-feedback"><i class="fa fa-check"></i><b>3.4</b> User feedback</a></li>
<li class="chapter" data-level="3.5" data-path="abstract-1.html"><a href="abstract-1.html#conclusion-1"><i class="fa fa-check"></i><b>3.5</b> Conclusion</a></li>
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<i class="fa fa-circle-o-notch fa-spin"></i><a href="./">A New Instrument for Microbial Epidemiology</a>
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<div id="abstract-1" class="section level1 unnumbered">
<h1>Abstract</h1>
<p>As the burden of antimicrobial resistance (AMR) is continuously increasing, reliable and reproducible data and data analysis are of utmost importance. Conducting AMR data analysis is challenging since it requires (1) a thorough understanding of (clinical) epidemiology; (2) expertise in (clinical) microbiology and infectious diseases; (3) experience in microbiological data analysis; (4) availability of reference data, such as the biological taxonomy of microorganisms and defined daily doses (DDD) for antimicrobials; and (5) availability of (inter-)national guidelines and software methods to apply them. Furthermore, data stored in laboratory information systems lack the right structure, (inter-) national guidelines for interpreting raw laboratory test results cannot be easily applied, and scientifically reliable reference data about microorganisms and antimicrobial agents are not readily available. To fill this gap, we developed a free, independent, and open-source software solution to cover all those aspects of working with AMR data. The AMR package for R enables AMR data analysis for research and clinical workflows alike. Through an online survey package users reported more reproducibility of analysis results (83%), more reliable outcomes of AMR analyses (72%), and new or improved insight into AMR patterns (61%). The AMR package was also used to support clinical decision-making (44%) and for clinical research (28%). Our first insights into the usage and the usability of the AMR package confirm that this package is fulfilling its intended aim, as regional, national, and international organisations already use the package to support clinical decision-making in infection management. The flexible open-source design also enables rapid integration of updated guidelines (e.g., new EUCAST breakpoints) and setting-specific adaptations are encouraged. Together, the AMR package for R can thus empower any specialist in the field working with AMR data by providing a comprehensive toolbox of solutions for AMR data analyses.</p>
<div id="background" class="section level2" number="3.1">
<h2><span class="header-section-number">3.1</span> Background</h2>
<p>As the burden of antimicrobial resistance (AMR) is continuously increasing, surveillance programs with reliable and reproducible data and data analysis methods are of utmost importance for controlling and streamlining efforts to curb AMR <sup>[1,2]</sup>. To guide these efforts and to support clinical decision-making and infection-control interventions, AMR data analysis has to be conducted in a clinically and epidemiologically sensible way <sup>[3]</sup>. Conducting AMR data analysis is challenging since it requires (1) a thorough understanding of (clinical) epidemiology; (2) expertise in (clinical) microbiology and infectious diseases; (3) experience in microbiological data analysis; (4) availability of reference data, such as the biological taxonomy of microorganisms and defined daily doses (DDD) for antimicrobials; and (5) availability of (inter-)national guidelines and software methods to apply them.</p>
<p>Moreover, AMR data analysis is often also hindered by three key aspects. Firstly, data stored in microbiological laboratory information systems (LIS) are typically not readily suitable for (epidemiological) data analyses. LIS were initially designed to fit result registration and billing purposes rather than AMR data analysis. Consequently, fundamental requirements for (epidemiological) data analyses are often lacking, such as isolate selection criteria, phenotypic determination of (multi-)drug resistance, and the ability to extract data for analysis in an automated, structured, fast, and reliable way. Moreover, data analyses that require data from multiple LIS sources (e.g., in multi-centre studies) face major barriers in data aggregation which, to the best of our knowledge, cannot be solved by currently available commercial software solutions. Besides, as applications of artificial intelligence are expected of being increasingly developed in the coming years, also in clinical microbiology, microbiological data technologies and structures need to become compatible for these future applications.</p>
<p>Secondly, AMR data analysis depends on (inter-)national standards and guidelines for the interpretation of raw laboratory measurements and the reporting of AMR results. In Europe, guidelines from the European Committee on Antimicrobial Susceptibility Testing (EUCAST) are the predominantly implemented set of rules in clinical microbiological laboratories <sup>[4,5]</sup>. LIS need to be well-maintained to be able to integrate continuous guideline updates. In our experience, this maintenance can often not be guaranteed and depends on the availability of local or external software support services. This is further hindered by the current distribution of manually formatted guidelines in Microsoft Excel and Portable Document Format (PDF) formats that are not often readily machine-readable. LIS maintainers, in collaboration with clinical staff, are therefore forced to manually implement updated guidelines which can be time-consuming and error-prone</p>
<p>Thirdly, reliable AMR data analysis depends on taxonomic reference data to interpret raw LIS data using AMR interpretation guidelines, such as EUCAST Expert Rules and EUCAST Clinical Breakpoints <sup>[5,6]</sup>. Unfortunately, typical LIS contain local, static taxonomic data. We found that these data are often poorly maintained. We collected the taxonomic names of bacteria used in clinical reports from seven different public health institutions in the Netherlands which cover microbiological diagnostics in hospitals and primary care for 15% of the total Dutch population. The taxonomic names were compared to publicly available and authoritative reference databases; the Catalogue of Life and the List of Prokaryotic names with Standing in Nomenclature (LPSN, previously known as the Deutsche Sammlung von Mikroorganismen und Zellkulturen, DSMZ) <sup>[7,8]</sup>. We found that all participating institutions reported taxonomic names in clinical reports that did not match current taxonomic standards according to reference databases. For example, <em>Enterobacter aerogenes</em> and <em>Enterobacter massiliensis</em> were renamed <em>Klebsiella aerogenes</em> and <em>Metakosakonia massiliensis</em> respectively in 2017 <sup>[9,10]</sup>. LIS that are not kept up to date are consequently not entirely compatible with recent interpretation guidelines. Given that AMR guidelines are strongly based on the microbial taxonomy (some rules only apply to a specific genus, other rules apply to a specific family) it is crucial that this information is correct and kept up to date. In the studied institutions, the lag between the reported taxonomic names and the taxonomic standard was up to 41 years as of March 2021.</p>
</div>
<div id="standardising-amr-data-analysis" class="section level2" number="3.2">
<h2><span class="header-section-number">3.2</span> Standardising AMR data analysis</h2>
<p>Previously, no dedicated software solution was available to address all aforementioned aspects. To fill this gap, we developed a free, independent, and open-source software solution to cover all those aspects of working with AMR data. The AMR package for R <sup>[11]</sup> provides functionalities that enable standardised and reproducible workflows from any raw LIS data to results ready to publish, for research and clinical workflows alike. The AMR package for R was developed with a team of contributors from 12 public health organisations in seven countries aiming to be used in any research or clinical setting where (epidemiological) data analysis of microorganisms, AMR, or antimicrobial agents is required. It is independent of any other software solution and was designed to work in any setting, including those with limited computational and financial resources.</p>
<p>With this AMR package, we aimed at providing: (1) tools to simplify AMR data cleaning, transformation, and analysis; (2) methods to easily incorporate (inter)national guidelines; and (3) scientifically reliable reference data, including the aforementioned aspects. The AMR package enables standardised and reproducible AMR data analysis with the application of evidence-based rules (e.g., EUCAST expert rules for intrinsic resistance), the selection of first isolates, the translation of various codes for microorganisms and antimicrobial agents, determination of (multi-)drug-resistant microorganisms, and the calculation of antimicrobial resistance rates, prevalence, and future trends. The AMR package supports all EUCAST MIC/disk diffusion interpretation guidelines from 2011 until 2021 and EUCAST Expert rules versions 3.1 (2016) and 3.2 (2020) <sup>[12,13]</sup> In addition, the AMR package supports all CLSI MIC/disk diffusion interpretation guidelines from 2011 until 2019 (non-veterinary only). For all mentioned guidelines, files readable for LIS are provided for easy implementation.</p>
<p>As of 30 April 2021, the AMR package for R has been downloaded from 162 countries since its first release in early 2018 (Figure 1), according to data from a popular public repository where users can download R packages. After 19 releases, the median number of downloads per release is 2,548 (range: 269-5,050).</p>
<div class="figure" style="text-align: center"><span id="fig:fig3-1"></span>
<img src="images/03-01.jpg" alt="Countries (grey, n = 162) with registered downloads of the AMR package for R between March 2018 and April 2021. Sources: cran.rstudio.org and cloud.r-project.org." width="100%" />
<p class="caption">
Figure 3.1: Countries (grey, n = 162) with registered downloads of the AMR package for R between March 2018 and April 2021. Sources: cran.rstudio.org and cloud.r-project.org.
</p>
</div>
<p>A technical validation of the AMR package has been accepted for publication <sup>[11]</sup>. Additionally, it has been clinically and epidemiologically validated in a tertiary care hospital and across seven clinical microbiology laboratories in the Netherlands [Berends <em>et al.</em>, unpublished, see chapter 6 and 7 of this thesis]. Moreover, the AMR package has already been used in several scientific publications that focused on different aspects in the field of AMR <sup>[14–17]</sup>.</p>
</div>
<div id="comparison-with-existing-software-methods" class="section level2" number="3.3">
<h2><span class="header-section-number">3.3</span> Comparison with existing software methods</h2>
<p>Popular statistical software such as SPSS, Stata and SAS, focus on a broad implementation of statistical functions but are proprietary software, disallowing users to freely use, modify, or share the software. This also prohibits extending the software by unaffiliated developers. Since R is free, open software and extendible, users and developers can contribute to the software, to which end the AMR package is a practical example.</p>
<p>Other free software alternatives for AMR data analysis exist, for example WHONET, a free microbiology laboratory database software supported by the WHO <sup>[18]</sup>. WHONET allows manual data entry from LIS reports and provides AMR interpretation using recent CLSI and EUCAST guidelines with a particular focus on AMR surveillance. Results from WHONET can also be shared to surveillance programs such as the European Antimicrobial Resistance Surveillance Network (EARS-Net) and the WHO Global Antimicrobial Resistance Surveillance System (GLASS). Yet, the latest release, WHONET 2020, does not provide tools for cleaning and transforming data and relies on outdated EUCAST guidelines. Furthermore, we found a lag between the included taxonomic database and the current taxonomic standard of up to 59 years (median 7 years). Another alternative of a free software program is Epi Info which is provided by the United States Centers for Disease Control and Prevention (CDC) and aims at public health practitioners and researchers <sup>[19]</sup>. While Epi Info provides statistical and epidemiological methods for analysing data, it does not offer tools nor reference data for working with AMR test results or antimicrobial drugs, thus, ruling out the option for dedicated AMR data analysis. With the AMR package for R, an open and dedicated software solution is available that covers all aspects of working with AMR data.</p>
</div>
<div id="user-feedback" class="section level2" number="3.4">
<h2><span class="header-section-number">3.4</span> User feedback</h2>
<p>In July 2020, we published a survey on the website created for this package (<a href="https://msberends.github.io/AMR" class="uri">https://msberends.github.io/AMR</a>) to seek voluntary feedback from package users about user backgrounds and usage of the AMR package. Until December 2020, 18 participants completed the survey. Participants have used the AMR package in Australia, Colombia, Egypt, France, Germany, Haiti, India, Mali, Mexico, the Netherlands, Nigeria, Philippines, Spain, Sweden, and the United Kingdom.</p>
<p>Participants were asked to rate their experience in the statistical programming language R and in using the AMR package on a scale from 1 (not experienced/useful) to 10 (very experienced/useful). The overall experience in R was reported with a median of 7 (range: 4-9)., whereas Ssuit ability for AMR analyses using the AMR package was rated with a median of 9 (range: 6-9). The participants rated the usefulness of the AMR package for their work with a median of 9 (range: 5-9). The convenience of the included software functions was rated with a median of 8 (range: 6-9) and the documentation of the AMR package was rated with a median of 8.5 (range: 7-10). Of all participants, 83% reported more reproducibility of analysis results and, 72% reported more reliable outcomes of AMR analyses (Figure 2). Notably, 61% reported new or improved insight into AMR for their institution or region. The AMR package was also used to support clinical decision-making (44%) and for clinical research (28%). Furthermore, 66% reported a faster and streamlined analysis workflow and 39% reported improved communicating analysis results. In 33%, participants started using R more often because of the capabilities that the AMR package provides.</p>
<div class="figure" style="text-align: center"><span id="fig:fig3-2"></span>
<img src="images/03-02.jpg" alt="The outcome of the survey amongst 18 participants. MIC: minimal inhibitory concentration, MDRO: multidrug-resistant organism, SNOMED: Systematised Nomenclature of Medicine." width="100%" />
<p class="caption">
Figure 3.2: The outcome of the survey amongst 18 participants. MIC: minimal inhibitory concentration, MDRO: multidrug-resistant organism, SNOMED: Systematised Nomenclature of Medicine.
</p>
</div>
<p>Aside from AMR data analysis, most participants (78%) used the AMR package as a reference for the taxonomy of microorganisms. It was also regularly used for interpreting raw MIC and disk diffusion values (56%) and applying EUCAST expert rules (67%). This is in line with the original aims of the AMR package development.</p>
</div>
<div id="conclusion-1" class="section level2" number="3.5">
<h2><span class="header-section-number">3.5</span> Conclusion</h2>
<p>AMR data analysis is dependent on (inter-)national guidelines and reliable (reference) data on the one hand but constrained by diverse and often inadequate data analysis tools and poor data quality on the other. We aimed to address these dependencies and constraints by introducing the AMR package for R for standardised and reproducible AMR data analyses. Our first insights into the usage and the usability of the AMR package confirm that this package is fulfilling its intended aim. Regional, national, and international organisations already use the AMR package to support clinical decision-making in infection management by gaining new or improved insights into resistance levels. We invite others to make use of our open-source approach and adapt it to their needs. The advantages of sharing open-source software such as the AMR package allow for a collaborative, transparent use and further development that can lead to more standardised analysis processes for AMR data. The flexible open-source design also enables rapid integration of updated guidelines (e.g., new EUCAST breakpoints), and setting-specific adaptations are encouraged. Together, the AMR package for R can thus empower any specialist in the field working with AMR data by providing a comprehensive toolbox of solutions for AMR data analysis.</p>