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# A Prospective Multicentre MDRO Screening Study on ICUs in the Dutch-German Cross-Border Region (2017-2018): The Importance of Healthcare Structures {#ch10-multi-mdro-screening}
<p>Accepted in Eurosurveillance (ahead of print) <note>(as of date of PhD defence: 25 August 2021)</note></p>
Berends MS ^1,2^\*, Glasner C ^2^\*, Becker K ^3,4^, Esser J ^5^, Gieffers J ^6^, Jurke A ^7^, Kampinga G ^2^, Kampmeier S ^8^, Klont R ^9^, Köck R ^8,10^, Al Naemi N ^9^, Ott A ^1^, Ruijs G ^11^, Saris K ^12^, Tami A ^2^, Van Zeijl J ^13^, Von Müller L ^14^, Voss A ^12^, Waar K ^13^, Friedrich AW ^2^
1. Certe Medical Diagnostics and Advice Foundation, Groningen, The Netherlands
2. Department of Medical Microbiology and Infection Control, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
3. Institute of Medical Microbiology, University Hospital Münster, Münster, Germany
4. Friedrich Loeffler-Institute of Medical Microbiology, University Medicine Greifswald, Greifswald, Germany
5. Practice of Laboratory Medicine and University Osnabrück, Department of Dermatology, Environmental Medicine and Health Theory, Osnabrück, Germany
6. Institute for Microbiology, Hygiene and Laboratory Medicine, Klinikum Lippe, Detmold, Germany
7. North Rhine-Westphalian Centre for Health, Section Infectious Disease Epidemiology, Bochum, Germany
8. Institute of Hygiene, University Hospital Münster, Münster, Germany
9. Laboratory Microbiology Twente Achterhoek, Hengelo, The Netherlands
10. Institute of Hygiene, DRK Kliniken Berlin, Berlin, Germany
11. Laboratory for Medical Microbiology and Infectious Diseases, Isala, Zwolle, The Netherlands
12. Department of Medical Microbiology, Radboud University Medical Centre and Canisius-Wilhelmina Hospital, Nijmegen, The Netherlands
13. Izore, Centre for Infectious Diseases Friesland, Leeuwarden, The Netherlands
14. Institute for Laboratory Medicine, Microbiology and Hygiene, Christophorus-Kliniken GmbH, Coesfeld, Germany
\* These authors contributed equally
## Abstract {-}
Antimicrobial resistance poses a risk for healthcare, both in the community and hospitals. The spread of multi-drug resistant organisms (MDROs) occurs mostly on a local and regional level, following movement of patients, also across national borders. The aim of this observational study was to determine the prevalence of MDROs in a European cross-border region to understand differences and improve infection prevention based on real-time routine data and workflows. Between September 2017 and June 2018, 23 hospitals in the Dutch-German cross-border region (NL-BR and DE-BR) participated in the study. During eight consecutive weeks, patients were screened upon admission to intensive care units (ICUs) for nasal carriage of methicillin-resistant *Staphylococcus aureus* (MRSA) and rectal carriage of vancomycin-resistant *Enterococcus faecium*/*E. faecalis* (VRE), third-generation cephalosporin-resistant *Enterobacteriaceae* (3GCRE) and carbapenem-resistant *Enterobacteriaceae* (CRE). All samples were processed in the associated laboratories. A total of 3,365 patients were screened (NL-BR: 1,202, DE-BR: 2,163). The median screening compliance was 60.4% (NL-BR: 56.9%, DE-BR: 62.9%). The MDRO prevalence was higher in the DE-BR than in the NL-BR, namely 1.7% vs 0.6% for MRSA (*p* = 0.006), 2.7% vs 0.1% for VRE (*p* < 0.001) and 6.6% vs 3.6% for 3GCRE (*p* < 0.001), whereas the prevalence for CRE was comparable, with 0.2% in DE-BR ICUs vs 0.0% in NL-BR ICUs. This first prospective multi-centre screening study in a European cross-border region, shows high heterogenicity in MDRO carriage prevalence on NL-BR and DE-BR ICUs. This indicates that the prevalence is influenced by the different healthcare structures.
## Introduction
Antimicrobial resistance (AMR) is a growing public health threat worldwide. Like global pandemics, multi-drug resistant bacteria pose one of the largest health risks to humans both in the community and within healthcare facilities ^[1,2]^. Specifically, hospitals are exposed to this risk and are challenged at multiple levels, e.g., the individual patient, the healthcare team, the organization and the political and economic environment. In hospitals, patients colonised and/or infected with multi-drug resistant organisms (MDROs) lead to higher costs, have prolonged hospital stays, have higher risks for complications, and an increased morbidity and mortality ^[3,4]^. To decrease these risks, the World Health Organization (WHO) urgently advised to change the way antibiotics are prescribed, and in addition highlighted that behavioural changes, resulting from the implementation of infection prevention measures, are indispensable to successfully combat AMR ^[5,6]^. According to WHO analyses, one key pitfall is that international AMR surveillance is neither coordinated nor harmonised and that there are still information gaps, especially with respect to twelve MDROs, which have been categorised as urgently requiring new antibiotics and improved combat strategies ^[6,7]^. These MDROs include amongst others: methicillin-resistant *Staphylococcus aureus* (MRSA), carbapenem-resistant *Enterobacteriaceae* (CRE), extended spectrum beta-lactamase (ESBL)-producing *Enterobacteriaceae* and vancomycin-resistant *Enterococcus faecium* (VRE) ^[7]^.
The prevalence of such MDROs varies not only between countries, but also between different regions (henceforth called healthcare regions), within one country or comprising a cross-border region, such as the Dutch-German cross-border region ^[8,9]^. Hospital transfer of patients within or between healthcare regions (i.e., from a local or regional hospital to a university medical centre or vice versa) can be a substantial driver of AMR ^[9]^. Thus, prevalence estimates of MDROs at regional level may better reflect the actual reality and allow the implementation of interventions more effectively. This is of utmost importance especially since the European Union (EU) directive from 2011 allows patients to seek medical treatment in any EU country. As approx. 30% of all EU citizens live in a cross-border region, this underlines the importance of a non-national-only, but a regional cross-border approach.
The Dutch-German cross-border region has been at the forefront in cooperating in the domain of AMR and infection prevention since 2005 with the support of European INTERREG programmes (www.deutschland-nederland.eu). Ever since, the projects developed within the INTERREG program have been denoted a ‘best practice’ for studying the prevalence of MDRO in a European cross-border region (Interact, European Cooperation Day, 2013). Importantly, among all cross-border regions in Europe, the Dutch-German cross-border region exhibits the most frequent exchange of citizens, with 74% of Germans and Dutch citizens living close to the border indicating to have visited the other country ^[10]^. On top of that, patient movements, exchange of patients between different healthcare institutes, across this particular border occur on a regular basis ^[9]^.
A recently published comparison of the national Dutch and German guidelines on Gram-negative MDROs urged the usage of consistent terminology and harmonised diagnostic procedures for the improvement of infection prevention, treatment and patient safety ^[11]^. Gathering and comparing regional data from both sides of the border was considered essential because of two reasons. Firstly, the EU treaty of Lisbon and directives in vigour will lead to an increasing number of patients seeking medical treatment in a neighbouring country. Secondly, particularly in cross-border regions between two high-income countries with cost-extensive, highly advanced and technological driven healthcare systems, the number of neonates, immuno-compromised and elderly patients that are seeking treatment will continue to increase ^[12]^.
With the advancements in healthcare, the demographic changes and increase in the number of multimorbidity, intensive care units (ICUs) have become the main hubs for patients in any hospital ^[13,14]^. ICUs represent a distinct hospital environment with high-frequent contact between specially trained hospital staff and critically ill patients requiring advanced technology and increased antibiotic prescription ^[15]^. Thus, ICUs are hotspots for the emergence and transmission of MDROs, frequently causing infections in these critically ill patients ^[16]^.
Therefore, the aim of this observational prospective multicentre screening study was to determine the prevalence of selected MDROs on admission to adult ICUs in the Dutch-German cross-border region based on real-time routine data and workflows and to correlate those with the existing healthcare structures.
## Methods
### Study Design
This observational prospective multicentre screening study was carried out between the 1st of September 2017 and the 18th of June 2018 in the Dutch-German cross-border region (NL-DE-BR) to determine the prevalence of MDROs on adult ICUs. All adult patients (≥18 years) were included in the study. The screening period for all hospitals lasted eight consecutive weeks (Supplementary Figure 1). A total of 23 hospitals, eight Dutch and 15 German, participated in this study. The 23 hospitals were served by ten laboratories, six on the Dutch (Dutch border region; NL-BR) and four on the German (German border region; DE-BR) side. Both regions have a similar geographical size, population density and type of hospital care (one university hospital, several secondary care hospitals). During the screening period, each participating hospital aimed at screening all patients at admission to their participating ICU for nasal carriage of MRSA and rectal carriage of VRE (both *E. faecium* and *E. faecalis*), 3GCRE and CRE. For the definition of 3GCRE, the European Centre for Disease Prevention and Control (ECDC) guideline was followed: all of cefotaxime, ceftazidime and ceftriaxone were considered. Moreover, although defined as *Enterobacteriaceae*, the present study focussed solely on *Escherichia coli* and *Klebsiella spp*. An overview of all MDRO definitions used in this study is summarised in the Supplementary Material. All samples were processed at the associated routine diagnostic laboratory, which were all ISO certified at the time of the study, following local standard operating procedures which were adapted to the study protocol when necessary (Supplementary Material Table 1). Bacterial species were confirmed by matrix-assisted laser desorption/ionisation time-of-flight (MALDI-TOF) mass spectrometry and antibiotic susceptibility was determined using VITEK 2 automated systems with EUCAST (European Committee on Antimicrobial Susceptibility Testing) clinical breakpoints ^[17]^. Moreover, data about the number of beds per hospital and ICU, hospital and ICU admissions and hospital and ICU patient days were provided by all participating hospitals for 2016.
### Statistical Analysis & Software
Data analysis was done in R using the software application RStudio and the R package AMR (R v4.0.2, RStudio v1.3.959 and AMR package v1.3.0), which are all free, open-source and publicly available ^[18]^.
Contingency tables were tested with Fisher’s exact test when the size was 2x2 and Chi2 tests otherwise. To test for equality in prevalence between countries, the exact binomial test was used. Outcomes of statistical tests were considered significant when two-sided *p* < 0.05.
### Ethics
The medical ethical committee of the University Medical Center Groningen (UMCG, The Netherlands) was informed and patients or their relatives were approached to voluntarily participate in the study. Ethical approval and informed consent were not required (METc 2015.535). All data were collected in accordance with the European Parliament and Council decisions on the epidemiological surveillance and control of communicable disease in the European Community. The board of directors of all other participating hospitals agreed to conduct the study.
## Results
### Healthcare structure of the participating hospitals
Between the 1^st^ of September 2017 and the 18^th^ of June 2018, 23 hospitals in the NL-DE-BR participated in the study, eight in the NL-BR and 15 in the DE-BR. The total number of beds from all participating ICUs was 443 beds (NL-BR: 182 [41.1%], DE-BR: 261 [58.9%]). The bed capacity of the ICUs in relation to the respective hospital bed capacity did not differ between hospitals within either country or between the two countries (NL-BR: 3.2% [IQR: 3.0-3.7%], DE-BR: 3.6% [IQR: 1.8-5.5%]). The participating hospitals are characterised by the data shown in Table 1.
<p class="tbl-caption">Table 1. Overview of the number of hospitals, laboratories, number of beds per hospital and ICU, hospital and ICU admissions, hospital and ICU patient days and average length of stay, Dutch-German cross-border region, 2016.</p>
```{r tbl10-1}
insert_graphic("images/10-t01.jpg")
```
### Study population and screening samples from ICUs
A total of 3,365 patients were screened: 1,202 (35.7%) on NL-BR and 2,163 (64.3%) on DE-BR ICUs (Table 2). The screening period per hospital lasted eight consecutive weeks (56 days, IQR: 55-58 days, Supplementary Figure 1). In both, NL-BR and DE-BR, significantly more males than females were screened (*p* < 0.001) and in NL-BR relatively less females were screened than in DE-BR (*p* < 0.01). The median age of all screened patients was 68 years (IQR: 57-77), while patients in DE-BR were significantly older than patients in the NL-BR (*p* < 0.001).
A total of 6,462 swabs were taken, 2,308 (35.7%) in NL-BR and 4,154 (64.3%) in DE-BR ICUs. Of those, 3,292 were taken from the nasopharynx and 3,170 were from the rectum. The overall screening compliance (screened for at least one MDRO group) was 60.4% (3,365 out of 5,568). For ICUs in the NL-BR this was 56.9% (1,202 out of 2,111) and for ICUs in the DE-BR this was 62.9% (2,163 out of 3,457), *p* < 0.001. The median screening compliance for all four MDRO groups (i.e., nasopharyngeal swab for MRSA, rectal swab for VRE, 3GCRE and CRE) on the other hand was in total 55.3% (3,081 out of 5,568), and 52.1% (1,100 out of 2,111) in NL-BR and 57.3% (1,981 out of 3,457) in DE-BR ICUs (*p* < 0.001). Most patients (91.5% for NL-DE-BR ICUs) that were screened while present on the ICU were screened for all MDRO groups.
In total, 3,291 patients were screened for MRSA (1,174 [35.7%] in NL-BR and 2,117 [64.3%] in DE-BR ICUs), 3,145 for VRE (1,110 [35.3%] in NL-BR and 2,035 [64.7%] in DE-BR ICUs) and 3,152 for 3GCRE (1,126 [35.7%] in NL-BR and 2,026 [64.3%] in DE-BR ICUs). Of note, in some patients multiple MDROs were found from the same or different species, meaning that some patients are included in multiple MDRO groups.
<p class="tbl-caption">Table 2. Overview of total number of patients present and screened, swabs and type of bacteria tested for in NL-BR and DE-BR, September 2017 – June 2018.</p>
```{r tbl10-2}
insert_graphic("images/10-t02.jpg")
```
### Prevalence of Gram-positive MDROs: MRSA and VRE
The overall prevalence for MRSA carriage at ICU admission was 1.3% (43 out of 3,291), and for VRE carriage 1.8% (56 out of 3,145). The prevalence was higher in DE-BR than in NL-BR ICUs, namely 1.7% (36 of 2,117) vs 0.6% (7 of 1,174) for MRSA (*p* = 0.006) and 2.7% (55 of 2,035) vs 0.1% (1 of 1,110) for VRE (*p* < 0.001), respectively (Figure 1). The prevalence ranged from 0% to 1.5% in NL-BR ICUs and from 0% to 4.1% in DE-BR ICUs for MRSA and from 0% to 0.3% in NL-BR ICUs and from 0% to 4.8% in DE-BR ICUs for VRE (Figure 1). An overview of all isolated MRSA and VRE isolates can be found in the Supplementary Table 2. Notably, all 56 cases of VRE were caused by *E. faecium*.
```{r fig10-1, fig.cap = "Prevalence of MRSA and VRE in NL-BR ICUs, in DE-BR ICUs and in both cross-border regions together (NL-DE-BR ICUs). Numbers above in squares represent the number of positive patients divided by the total number of patients screened for the respective pathogen with the calculated prevalence. Boxplots show the median prevalence in participating ICUs (thick line within each box), the first and third quartile (upper and lower border of the box, the difference is the IQR), and the whiskers with error bars represent 1.5 times the IQR denoting the normal range. The dots are outside this range. DE-BR: German cross-border region; ICU: intensive care unit; IQR: interquartile range; MRSA: methicillin-resistant *Staphylococcus aureus*; NL-BR: Dutch cross-border region; NL-DE-BR: total Dutch-German cross-border region; VRE: vancomycin-resistant enterococci."}
insert_graphic("images/10-01.jpg")
```
### Prevalence of Gram-negative MDRO: 3GCRE and CRE
The overall prevalence at ICU admission for 3GCRE carriage was 5.5% (173 out of 3,152) and 0.1% (4 out of 3,152) for CRE carriage. The prevalence for 3GCRE was significantly higher in DE-BR than in NL-BR ICUs, namely 6.6% (133 out of 2,026) vs 3.6% (40 out of 1,122), *p* < 0.001, whereas the prevalence for CRE was comparable, with 0.0% (0 out of 1,126) in NL-BR ICUs vs 0.2% (4 out of 2,026) in DE-BR ICUs (Figure 2 and Table 2). Most of the isolated 3GCRE were *E. coli* isolates, namely 166 (92.2%). Twelve isolates were *K. pneumoniae* (6.8%), one *K. variicola* (0.6%) and one *K. oxytoca* (0.6%). The four CRE isolates were found in three different DE-BR ICUs, three were *E. coli* and one was a *K. pneumoniae* isolate. The prevalence for 3GCRE differed within both countries between hospitals, ranging from 0% to 10.0% in NL-BR ICUs and from 2.3% to 15.2% in DE-BR ICUs (Figure 2). Table 2 presents an overview of the prevalence of MRSA, VRE, 3GCRE and CRE. An overview of all isolated 3GCRE and CRE isolates can be found in the Supplementary Table 2.
```{r fig10-2, fig.cap = "Prevalence of 3GCRE and CRE in NL-BR ICUs, in DE-BR ICUs and in both cross-border regions together (NL-DE-BR ICUs). Numbers above in squares represent the number of positive patients divided by the total number of patients screened for the respective pathogen with the calculated prevalence. 3GCR: third-generation cephalosporin-resistant *Enterobacteriaceae*, CRE: carbapenem-resistant *Enterobacteriaceae*; DE-BR: German cross-border region; NL-BR: Dutch cross-border region; NL-DE-BR: total Dutch-German cross-border region."}
insert_graphic("images/10-02.jpg")
```
### Prevalence of Gram-negative MDROs based on Dutch and German definitions
The national guidelines for The Netherlands and Germany differ greatly in the way Gram-negative MDROs are being defined (while definitions for MRSA and VRE are identical) ^[12,19]^. An overview of the specific Dutch and German definitions of MDROs is summarised in the Supplementary Material.
The German national infection prevention guideline classifies Gram-negative MDROs into 3MRGN and 4MRGN (German: ‘Multiresistente Gram-negative Stäbchen’, multidrug-resistant Gram-negative rods) based on phenotypic susceptibility. When the German MRGN definition is being applied to all Gram-negative isolates, the overall prevalence for 3MRGN is 2.9% (91 out of 3,152) and for 4MRGN 0.1% (4 out of 3,152). The prevalence was significantly lower in NL-BR than in DE-BR ICUs for 3MRGN, namely 1.1% (12 out of 1,126) vs 3.9% (79 out of 2,026) [*p* < 0.001], whereas the prevalence for 4MRGN was comparable, namely 0% (0 out of 1,126) vs 0.2% (4 out of 2,026) [*p* = 0.30] (Figure 3). The prevalence for 3MRGN differed within both countries between hospitals, ranging from 0% to 5.0% in NL-BR and from 1.2% to 10.9% in DE-BR ICUs. The four 4MRGN were three *E. coli* isolates and one *K. pneumoniae* isolate and originated from three different DE-BR ICUs. Of note, for the definition of 3MRGN, piperacillin results could not be included since only results for piperacillin-tazobactam were reported.
The Dutch national guideline defines exceptional resistant microorganisms as BRMO (‘Bijzonder Resistente Microorganismen’) using strict interpretation guidelines ^[20]^. When the Dutch BRMO definition is applied to all Gram-negative isolates, the overall BRMO prevalence is 5.6% (176 out of 3,152). The prevalence was lower in NL-BR than in DE-BR ICUs, namely 3.9% (44 out of 1,126) vs 6.5% (132 out of 2,026) for BRMOs [*p* = 0.002] (Figure 3). The prevalence for BRMO differed within both countries between hospitals, ranging from 0% to 10.0% in NL-BR and from 2.3% to 15.2% in DE-BR ICUs.
```{r fig10-3, fig.cap = "Prevalence of 3MRGN, 4MRGN and BRMO in NL-BR ICUs, DE-BR ICUs and both cross-border regions together (NL-DE-BR ICUs). Numbers above in squares represent the number of positive patients divided by the total number of patients screened for the respective pathogen with the calculated prevalence. 3MRGN: Multiresistente Gram-negative Stäbchen mit Resistenz gegen 3 der 4 Antibiotikagruppen (multiresistant Gram-negative rods with resistance to 3 of the 4 antibiotic groups); 4MRGN: Multiresistente Gram-negative Stäbchen mit Resistenz gegen 4 der 4 Antibiotikagruppen (multidrug-resistant Gram-negative rods with resistance to 4 of the 4 antibiotic groups); BRMO: bijzonder-resistente microorganism (particularly resistant microorganisms); NL-BR: Dutch cross-border region; NL-DE-BR: total Dutch-German cross-border region."}
insert_graphic("images/10-03.jpg")
```
### Comparison of MDRO prevalence between NL-BR and DE-BR ICUs in university and non-university hospitals
For NL-BR ICUs, the prevalence of all MDRO groups was not significantly different between the non-university and the university hospital (Figure 4). This was different for the participating DE-BR ICUs where the prevalence of 3GCRE [*p* < 0.001], 3MRGN [*p* = 0.005] and BRMO [*p* < 0.001] were significantly higher in the non-university hospitals (Figure 4). Interestingly, the prevalence of almost all investigated MDROs was not significantly different between the two university hospitals, except for the prevalence of VRE, which was significantly higher in the German university ICU [*p* < 0.001]. Comparing the prevalence of all investigated MDROs between NL-BR and DE-BR non-university hospital ICUs revealed a significant difference for VRE [*p* < 0.001], 3GCRE [*p* < 0.001], 3MRGN [*p* < 0.001] and BRMO [*p* < 0.001], whereas the difference for MRSA [*p* = 0.83] differed only slightly (Figure 4).
```{r fig10-4, fig.cap = "Comparison between prevalence of MRSA, VRE, 3GCRE, CRE, 3MRGN, 4MRGN and BRMO between non-university and university hospital ICUs in the NL-BR and DE-BR. 3MRGN: multiresistant Gram-negative rods with resistance to 3 of the 4 antibiotic groups (multiresistant Gram-negative rods with resistance to 3 of the 4 antibiotic groups); 4MRGN: multidrug-resistant Gram-negative rods with resistance to 4 of the 4 antibiotic groups (multidrug-resistant Gram-negative rods with resistance to 4 of the 4 antibiotic groups); 3GCR: third-generation cephalosporin-resistant *Enterobacteriaceae*; BRMO: bijzonder-resistente microorganisme (particularly resistant microorganisms); DE-BR: German cross-border region; ICU: intensive care unit; IQR: interquartile range; MRSA: methicillin-resistant Staphylococcus aureus; NL-BR: Dutch cross-border region; NL-DE-BR: total Dutch-German cross-border region; VRE: vancomycin-resistant enterococci, CRE: carbapenem-resistant *Enterobacteriaceae*."}
insert_graphic("images/10-04.jpg")
```
## Discussion
To the best of our knowledge this is the first prospective observational multicentre screening study focusing on ICU admission prevalence of the most common MDROs in a healthcare region that comprises a national border. This study has been performed within the Dutch-German cross-border network, which has a long-lasting experience in close cooperation in the domain of AMR and infection prevention and control ^[21,22]^. Interestingly, the Dutch and German healthcare systems differ in many aspects, creating a natural “living lab” situation to study AMR and other healthcare-related topics. One difference is the overall hospital activity, as shown in Table 1. In the NL-BR, 4.8 per 100 hospital admissions lead to an ICU admission. In contrast, in the DE-BR this is 7.7 per 100 hospital admissions. This difference can be explained by the higher ICU capacity in DE-BR hospitals, namely 261 of 5,388 (4.8%) vs 182 of 7,514 (2.4%) in NL-BR. Interestingly, the median hospital-wide length of stay (LOS) is shorter in the NL-BR than in the DE-BR (4.98 vs 6.10 days), whereas the ICU-specific LOS is longer in the NL-BR (4.06 vs 3.57 days). When comparing our data with the LOS by Eurostat from 2017, it can be observed that the hospital-wide LOS of the NL-BR is comparable to the national average (5.0 vs 4.5 days), whereas for Germany, the LOS of the DE-BR is much lower (6.1 vs 9.0) ^[19]^. Although no information was available for the present study with regard to staffing in hospitals and ICUs, it has been shown by others that the number of available staff on German ICUs is much less than on Dutch ICUs, while understaffing has been found to be inversely proportional to finding MDROs ^[15,23,24]^. Strikingly, a more recent study focussing on the Dutch-German cross-border region presented that healthcare workers of both sides of the border have a similar awareness and perception towards AMR and both struggle with the limitations to cope with the application of preventive measures ^[25]^.
The success of infection prevention and other actions to combat AMR within a hospital can be measured by the occurrence of MDROs. To this end, the ECDC reports overviews of MDRO proportions based on nationally aggregated data from blood cultures on a regular basis. On a more country-specific level, MDRO proportions are also reported by national health institutes (NHI); the Rijkinstituut voor Volgsgezondheid en Milieu (RIVM) in the Netherlands and the Robert-Koch Institut (RKI) in Germany ^[26,27]^. These MDRO proportions differ greatly from the here reported prevalence of MDRO carriage. MDRO proportions are the fraction of e.g. MRSA isolates among *S. aureus* isolates, whereas MDRO prevalence is the fraction of patients with e.g. MRSA colonisation in a certain patient population. MDRO proportions are thus based on the microorganism and the respective resistance pattern, information that can be easily extracted from any laboratory information system, whereas MDRO prevalence is based on the patient or a certain population and requires mostly active screening. While both are of high importance and serve different purposes, only MDRO prevalence informs us about the carriage or infection rate in patients.
In the present study, the overall carriage prevalence for the different MDROs was higher in the DE-BR ICUs, although some differences were marginal. Specifically, prevalence of MRSA carriage was three times higher in the DE-BR (1.7%) than in the NL-BR (0.6%). These prevalences are consistent with a recent study about all nosocomial MRSA cases in this region from 2012 until 2016 ^[22]^. For 2018, reports on the country level published by the ECDC, show that the proportion of MRSA among *S. aureus* isolates from blood cultures was 1.2% in the Netherlands and 7.6% in Germany (with regional variations as per the Dutch and German NHIs; e.g. 0.3% in the Northern Netherlands and 14.5% in Northern-West Germany in any blood culture) ^[26-28]^. Differences between proportions and prevalence are of course expected, and the higher MRSA proportions can, for example, be explained by an increased antibiotic use to foster the occurrence of MRSA. Nevertheless, the rather low prevalence of MRSA carriage on both sides of the border demonstrates that national efforts to control MRSA specifically in this cross-border region, that are continuously successful in the Netherlands since decades, have now led to a decrease on the German side of the border as well.
For VRE, the prevalence measured in this study was 0.1% in the NL-BR and also remained low in the DE-BR (2.7%), although almost 30 times higher than in the NL-BR. This difference is also reflected by different proportions of VRE among *E. faecium* from blood: 1.1% in the Netherlands vs 23.8% in Germany in 2018 as reported by the ECDC and 0.6% vs 7.6% in any blood culture in 2018 as reported by the Dutch and German NHIs, respectively ^[26-28]^. The large difference in the German VRE proportion between the data from ECDC and the German NHI cannot be explained. Moreover, Germany has seen a rapid increase in the proportion of VRE among *E. faecium*, from 1.4% in 2001 to 14.5% in 2013 and thus 23.8% in 2018 ^[28]^. The cause of this is still unknown. Probably due to the stringent infection prevention and outbreak control in the Netherlands, the proportion of VRE from blood cultures among *E. faecium* never exceeded 1.5% in the Netherlands ^[28]^.
The difference in MDRO prevalence between NL-BR and DE-BR was also observed for Gram-negative MDROs. Since the Netherlands and Germany have different guidelines to classify Gram-negative bacteria as MDRO (BRMO vs 3MRGN/4MRGN) but both phenotypically test for 3rd generation cephalosporins, a comparison was made based on 3GCRE. The 3GCRE carriage prevalence in the DE-BR was almost twice as high (6.6%) as in the NL-BR (3.6%), but both were still lower than national averages. The ECDC reported proportions of 3GCRE among *E. coli* and *K. pneumoniae* from blood in 2018 as *E. coli*: 12.2% and *K. pneumoniae*: 12.9% for Germany and *E. coli*: 7.3% and *K. pneumoniae*: 11.1% for the Netherlands. The same year the NHIs reported a slightly lower prevalence with *E. coli* at 10.7% and *K. pneumoniae* at 12.0% in Germany and *E. coli* at 6.6% and *K. pneumoniae*: at 10.1% in the Netherlands ^[26-28]^. This highlights that there are important differences to be found when studying carriage in specified populations versus looking at the proportion of invasive isolates, but that the lower carriage of Gram-negative MDROs in the participating NL-DE-BR hospitals shows the importance of a regional compared to a national view. Notably, in the present study only four CRE isolates were identified, all from the DE-BR. Interestingly, when applying the country specific guidelines to the Gram-negative MDROs study isolates, the Dutch BRMO guideline yields more MDRO than the German 3MRGN/4MRGN guideline (overall BRMO: 5.6% vs overall 3MRGN/4MRGN: 2.9%/0.1%). This difference is comparable to results from a previous study where those guidelines were compared between the countries ^[12]^. Since the Dutch guideline classifies all third-generation cephalosporin-resistant *E. coli* and *Klebsiella spp*. as BRMO, while the German guideline only classifies them as MRGN if they are additionally ciprofloxacin-resistant, a higher prevalence of BRMO than MRGN was expected.
As both university and non-university hospitals participated in the study, a comparison of MDRO carriage prevalence on ICUs based on the type of hospital could be realised. In the NL-BR no significant difference for all investigated MDROs between university and non- university hospitals was observed. In the DE-BR, on the other hand, significant differences were observed for 3GCRE, 3MRGN and BRMO between university and non-university hospitals, but not for MRSA, VRE, 4MRGN and CRE. Non-university hospitals presented a significantly higher MDRO prevalence for 3GCRE, 3MRGN and BRMO at ICU admission. Explaining this observed dissimilarity requires additional studies on e.g. hospital activity, size, staff availability, hospital geography and inter-hospital distance. A recent report highlighted that a higher density of inpatient care, a higher number of hospitals, a longer length of stay and lower staffing ratios all might facilitate MDRO dissemination ^[29]^. Interestingly, when comparing the hospital types between the two border regions, the university hospitals have a very similar prevalence of all MDROs on ICUs. Our results show that ICUs in non-university hospitals in the DE-BR are being challenged more frequently with Gram-negative MDROs compared to MRSA and VRE. Especially, with respect to third-generation cephalosporin resistance, this problem seems very prominent. This contradicts the general consensus that MDROs are less prevalent in smaller hospitals. The reason for this difference and problem is unknown and requires further investigation. However, experts claim that, especially in smaller hospital settings, up to one third of all hospital-associated infections can be prevented by solely improving infection prevention ^[30]^. To investigate this, more information about the staff and patients admitted to ICUs would be required, e.g., number of staff and hours available for infection prevention, information on severity of disease, antibiotic exposure or length of hospital stay prior to ICU admission.
The limitations of this study exemplify the challenge to compare AMR prevalence rates within or between healthcare regions, especially when comprising a national border. Firstly, the median screening compliance was dissatisfying in both border regions, although significantly higher in the DE-BR (62.6%) than in the NL-BR (56.9%). Only two hospitals were equipped with sufficient staff, one each side of the border; their screening compliance was 99.3% and 83.2%, respectively. This underlines the need for more (research) guidance and/or more staffing, education and material, to implement better infection prevention and control. It also accentuates the inherently limited maximum compliance to be gained from routine wards and workflows, which is also an important point of consideration when using (inter)nationally published results. Secondly, collection of information about infection control staff, MDRO outbreaks, infections, antibiotic use and risk factors of patients was outside the scope of this study. Although this would have allowed for the analysis of origin and source of the identified MRDOs, this information was practically impossible to retrieve from the 23 different hospitals and 3,365 patients included in this study due to legislative and organizational constraints. Thirdly, the participating laboratories in this study were not homogeneous in their diagnostic test methodologies and since for most of the laboratory’s molecular confirmation (e.g., of resistance encoding genes) was not part of their standard operating procedures, it was also not included in the study protocol. Fourthly, not all hospitals conducted the screening in the same eight consecutive weeks, as this was practically unfeasible. While this might have improved comparability, others found almost no seasonality in bacterial bloodstream infections and we therefore consider this issue to be of low impact ^[31]^.
This study highlights the importance of a regional and cross-border approach in any European cross-border region, to illustrate the difference of AMR prevalence between the regions and to highlight potential differences with country-wide reports. Moreover, the focus on routine workflows in both the hospital and laboratories make this study valuable since it offers an honest perspective on the reality. To be able to emphasise on this further, attaining a deeper level of detail is a vast prerequisite, for example by collecting information about staff on the wards and infection control staff, MDRO outbreaks, infections, antibiotic use, and risk factors of patients. Standard reporting based on the Nomenclature of Territorial Units for Statistics (NUTS) on a NUTS3 or at least NUTS2 level instead of NUTS1 or the national level would also improve the resolution of the AMR prevalence within a country or healthcare region and improve the understanding thereof. Interestingly, comparisons with national data on MDRO proportions as reported by the ECDC and the respective NHIs revealed rather low numbers of submitted isolates which highlights a bottleneck of using this data source. Moreover, only a limited number of hospitals, mostly large (university) hospitals especially in Germany, actively participate in national or international surveillance systems arguing for the inclusion of small and medium-sized hospitals when determining and analysing MDRO prevalences. Additionally, generalising guidelines and definitions between countries, preferably on the European level, will improve comparability between countries which is of great importance for cross-border regions. In conclusion, geographical and political borders do not seem to be “respected” by MDROs, although healthcare systems, geographic nature and guidelines are very different between countries. Proportions of MDROs of certain pathogens, as reported on the national and international level, do not reflect MDRO prevalence in the patient or general population. This should be taken into serious consideration when interpreting reports on the country or even continental level.
## Supplementary files {-}
* Supplementary Table 1. Overview of the used media for screening in all participating laboratories in this study.
* Supplementary Table 2. Overview of all antibiotic results of all positive isolates found in this study (one isolate per row).
* Supplementary Figure 1. Screening period per hospital. All hospitals screened between September 2017 and July 2018. Hospitals #5 and #17 were university hospitals and started almost immediately after the start of the study. Hospital #7 could only start in May 2018 due to lack of available personnel.
* Supplementary Material. Overview and summary of MDRO definitions based on different national and international guidelines mentioned and used in the manuscript “A Prospective Multicentre MDRO Screening Study on ICUs in the Dutch-German Cross-Border Region (2017-2018): The Importance of Healthcare Structures”.
## Acknowledgements {-}
This study was 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. The authors would like to thank all hospitals, laboratory and ICU staff for participating in this study.
## Conflict of interest {-}
The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
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# Summary and Future Perspectives {#ch11-summary}
## Section I {-}
Where did microbial epidemiology start? How did it originate? And how does it add to the holistic approach of infection management? These questions are answered in this first section. It is subsequently outlined which important current limitations exist when applying microbial epidemiology in practice and how they could be overcome.
The general introduction of this thesis outlines in **chapter 1** that microbial epidemiology is a part of infectious disease epidemiology, which in turn is a part of clinical microbiology. Microbial epidemiology can be seen, among other things, as the scientific field for acquiring new insights about spreading microorganisms and their respective antimicrobial resistance (AMR) patterns. The advancements in information technology have brought us not only the possibilities to look beyond regional, national, and international borders to get an understanding of the spread of microorganisms and AMR, but even to observe, analyse and understand pandemics in real-time. Methods we develop and use today can be implemented on the other side of the world tomorrow. This is an important advantage in modern microbial epidemiology, which focus is increasingly becoming more data-driven.
To expedite this focus, data are the primary requirement. The data used as input for microbial epidemiological analyses are often obtained from laboratory information systems (LIS). These data consist of routine diagnostic results from laboratory tests. **Chapter 2** brings an opinionated view that diagnostics might lead to raw results, but not to a direct answer to the clinical question that a physician treating a patient might have. Providing physicians with answers requires the approach of a multidisciplinary, intertwined stewardship concept with a focus on diagnostics ^[1,2]^. This demands medical specialists in general and microbiologists, in particular, to closely interact for optimal quality of care and patient safety in successful infection management: diagnostic stewardship (DSP). The concept of stewardships, in general, has been widely used to facilitate communication and clinical decision-making, while it proved challenging to establish a clear definition of ‘stewardship’ ^[3,4]^. Moreover, diagnostics in clinical microbiology laboratories are currently advancing fast with regards to improved workflows and new technologies, such as matrix-assisted laser desorption/ionisation time-of-flight (MALDI-TOF) mass spectrometry ^[5,6]^. Yet, diagnostics in infection management is broader than this and covers many clinical areas where communication and interaction are fundamental to make the best use of knowledge and expertise, leading to all specialisms contributing to patient care. The right test at the right time for the right patient to answer the right questions and start the right treatment – this is what DSP in clinical microbiology is about. Microbial epidemiology can be utilised for a small aspect of this diagnostic entirety, by recycling the test results and subsequently bringing enrichments to the answer-generating process that DSP embodies.
This is where **chapter 3** continues, by highlighting important current limitations when applying microbial epidemiology, in particular AMR data analysis. Specifically, AMR data analysis has to be conducted in a clinically and epidemiologically sensible way ^[7]^, but is challenging since it requires expertise in (clinical) epidemiology and (clinical) microbiology, and tools to handle the AMR data analysis itself. This is further hindered by the common lack of accessibility of data stored in LIS-es, as most LIS-es are not designed with a focus on epidemiology. As an example, every LIS keeps its own taxonomic data and laboratories are responsible for their regular update. 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), this information must be correct and up to date ^[8–10]^. Unfortunately, from studying seven clinical microbiology laboratories in the Netherlands, it became apparent that all their LIS-es contained severely outdated taxonomic names. This can impact both routine result reporting and (future) epidemiological analyses. For these reasons, the `AMR` package for `R` was introduced in this chapter as a new epidemiological instrument for AMR data analysis that is free, independent, open-source, and publicly available. Developed with a team from twelve different public health organisations in seven different countries, it provides tools to simplify AMR data cleaning, transformation and analysis, as well as methods to easily incorporate (inter)national guidelines, and scientifically reliable reference data. As of May 2021, it has been downloaded at least 50,000 times from 162 different countries since its first release in 2018 ^[11]^. The results of a survey among users presented in this chapter showed that its use leads to more reproducibility of analysis results, more reliable outcomes of AMR data analyses, and new or improved insights in AMR for the users' institutions and regions. Users also stated that the `AMR` package was used to support clinical decision-making. The package solves the inconvenience of being dependent on (inter)national guidelines and reliable (reference) data, while also providing a comprehensive toolbox for the analysis itself. The `AMR` package for `R` can therefore empower any specialist in the field working with AMR data.
## Section II {-}
Following the challenges outlined in the previous section, this section introduces the `AMR` package for `R` as a new instrument to cope with these challenges. From multiple viewpoints, the `AMR` package and its advantages are put into perspective: from a technical viewpoint, from an infection management viewpoint and from a clinical viewpoint. These combined provide a common ground for understanding the explications that the `AMR` package can yield in the field and how it can set a new empowered starting point for future applications of microbial epidemiology.
The technical functionalities of the `AMR` package for `R` have been described in **chapter 4**, where it is described how the `AMR` package has been developed to standardise clean and reproducible AMR data analyses using international standardised recommendations ^[9,12]^. To facilitate this, scientifically reliable reference data are incorporated regarding valid laboratory results (as opposed to e.g., non-existing MIC values), antimicrobial agents, and the complete biological taxonomy of microorganisms. Source data should be analysed in the most reliable way, especially when for example the outcome will be used to evaluate patient treatment options. This requires reproducible and field-specific, specialised data cleaning and transforming. The `AMR` package provides a standardised and automated way of cleaning, transforming, and enhancing common LIS data, independent of the underlying data source and data accuracy. For this reason, general algorithms were developed to clean AMR test results and to validate the names of microorganisms and antimicrobial agents. The equation for taxonomic name validation takes into account the human pathogenic prevalence of microorganisms and is context-aware about other taxonomic properties such as the kingdom, phylum, order and family. To exemplify, a data value “E. coli” will be translated to the bacterium *Escherichia coli*, while informing the user that the parasite *Entamoeba coli* is also eligible but has a lower likelihood. Using convenient functions, users can quickly retrieve consistent microbial properties, such as the taxonomic kingdom, phylum, class, order, family, genus, species, subspecies, previously accepted names and even the Gram stain. Aside from information about microorganisms, the package also includes reference data about antibiotics, which comprises common laboratory information system codes, official names, ATC (Anatomical Therapeutic Chemical) codes, ATC group names, defined daily doses (DDD) and more than 5,000 trade names of 456 antimicrobial agents. Using these reference data, users can translate raw data and retrieve properties about any microorganism or antimicrobial drug. Furthermore, the `AMR` package is capable of determining multi-drug resistant organisms (MDROs) based on national and international guidelines, interpreting raw minimum inhibitory concentrations (MICs) and can determine first isolates to be used for calculating AMR of both monotherapy and combination therapies. The `AMR` package itself was meant as a comprehensive instrument for data-technical staff working in the field of AMR, although its use is not limited to this group.
To exemplify this, **chapter 5** shows that the `AMR` package was used as a backbone in an interactive open-source software app for infection management and antimicrobial stewardship, called RadaR (rapid analysis of diagnostic and antimicrobial patterns in R). Infection management in the form of antimicrobial stewardship (AMS) programs has emerged as an effective solution to address this global health problem in hospitals ^[3]^. Connecting to **chapter 2**, 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 prioritising improvement in quality of care and patient safety for any intervention ^[13,14]^. 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 specialities) even though this information might be available in the data. Therefore, the development of RadaR was intended to serve AMS teams with a user-friendly and time-saving data analysis resource, without the need for profound technical expertise. RadaR was developed for graphical exploratory (AMR) data analysis. Among others, it provides Kaplan-Meier curves about lengths of hospitals stays, time trends for the number of admissions, antimicrobial consumption, and an automated AMR data analysis for which the `AMR` package for `R` was used. RadaR was validated by 12 ESGAP members (European Society of Clinical Microbiology and Infectious Diseases Study Group for Antimicrobial Stewardship) from 9 different countries. It has the potential to be a highly useful tool for infection management and AMS teams in daily practice. Additionally, this chapter shows that the `AMR` package can be used as part of another software solution to empower integrated infection management.
Following this insight, **Chapter 6** demonstrates the effectiveness of the `AMR` package among users, by evaluating its usability and impact on clinicians’ workflows in a typical hospital scenario. Although the use of the `AMR` package in research has been demonstrated in multiple studies from different countries already ^[15–18]^, the impact on workflows for AMR data analysis and reporting in clinical settings was still pending. AMR data analysis and reporting, unfortunately, require specifically skilled personnel. Moreover, thorough and in-depth analyses can be time-consuming and sufficient resources need to be allocated for consistent and repeated reporting. To determine the impact of these facts in a clinical setting, common questions about blood culture data were formulated that had to be answered by routine clinical personnel, including clinical microbiologists, paediatricians and intensivists. In total, ten clinicians participated in the study. Additionally, participants were asked to fill in an online questionnaire capturing their backgrounds, demographics, software experience, and experience in AMR data analysis and reporting. All participants had to answer the study questions twice: the first time with their software of choice (round 1) and the second time using a newly developed web application built around the `AMR` package for `R` (round 2). The development of this web application was utilised in a highly efficient and agile workflow. The answers to the list of questions served as the basis to compare the effectiveness (solvability of each task for every user) and efficiency (time spent solving each task) between the two rounds. Not all participants were able to complete the tasks within the given time frame. Average task completion between the first and second round increased from 56% (SD: 23%) to 96% (SD: 6%). The proportion of correct answers between the first and second round increased from 38% to 98%. The mean time spent per round was reduced from 94 minutes (SD: 22 minutes) to 22 minutes (SD: 14 minutes). This chapter demonstrates the increased effectiveness, efficiency, and accuracy of using the `AMR` package for `R` for AMR data analysis compared to traditional software applications such as Microsoft Excel and SPSS.
## Section III {-}
Many clinical studies in the field of infectious diseases and microbiology rely on some form of (microbial) epidemiology. While the `AMR` package was presented in the previous section and its use in different settings was showcased, this section starts with an epidemiological research projects in the Northern Dutch region, and then extends to the Dutch-German cross-border region to better understand the occurrence and AMR patterns of pathogens on a (eu)regional level. Focusing on the regions on each side of a national border allows comparisons between two different nations on the micro level. And different nations ultimately mean different healthcare systems. What is left of ‘One Health’? What are the implications on comparison of having differences between countries in AMR test methodologies, MDRO interpretations and screening policies? This section provides answers to these questions.
**Chapter 7** zooms in on coagulase-negative staphylococci (CoNS), which are known to cause bloodstream infection (BSI) and a high mortality rate, although for years they had often been regarded as contamination ^[19–23]^. Moreover, CoNS have become increasingly associated with nosocomial infections ^[24]^. At present, the CoNS group consists of 45 different species, although determining the species level has only recently been made possible for routine diagnostic laboratories ^[25–27]^. Since 2012, MALDI-TOF mass spectrometry has become the standard for the identification of bacterial species such as CoNS. Before that, identification of CoNS was primarily done with biochemical and physiological tests, which yielded generally variable results, in particular in less prevalent species ^[27]^. AMR, and especially multi-drug resistance, is also an increasing problem in CoNS ^[28]^. Nonetheless, treatment guidelines and national surveillance programs (such as the Dutch NethMap) still gather CoNS as a whole group, lacking differentiation between species ^[29]^. Consequently, little is known about trends in occurrence and AMR in CoNS on the local and regional level. Therefore, this retrospective study shows an in-depth AMR analysis of 19,803 CoNS isolates found in all available 71,632 blood culture isolates between 2013 and 2019 in the Northern Netherlands that were determined by MALDI-TOF MS. This study followed a full-region approach by covering the whole Northern Netherlands. Through this analysis, we aimed to evaluate the differences in the occurrence of CoNS species and their AMR patterns and to assess their clinical microbiological relevance to this end. A total of 27 different species of the CoNS group were found. Major differences were observed in the occurrence of the different species: the top five species covered 97.1% of all included isolates. These were: *S. epidermidis* (48.4%), *S. hominis* (33.6%), *S. capitis* (9.3%), *S. haemolyticus* (4.1%) and *S. warneri* (1.7%), meaning that the remaining 2.9% of isolates consisted of 22 different CoNS species. The proportion of CoNS in intensive care units (ICUs) compared to other departments was also found to be significantly different between secondary care (17.5% of isolates from ICU) and tertiary care (24.4%% of isolates from ICU). As it was unknown which patients had BSI, ‘CoNS persistence’ was defined as a surrogate having at least three positive blood cultures drawn on three different days within 60 days, containing the same CoNS species, within the same patient. The relatively most common causal agent of CoNS persistence was *S. haemolyticus* (5.8% of all patients with *S. haemolyticus*), followed by *S. epidermidis* (3.7%,) and *S. lugdunensis* (3.4%). AMR analysis has shown substantial differences between CoNS species and was presented thoroughly per antibiotic class in tables and text. For example, *S. epidermidis* and *S. haemolyticus* showed 50% to 80% resistance to teicoplanin, erythromycin, ciprofloxacin, and oxacillin, while resistance to these agents remained lower than 10% in most other CoNS species. Yet, these differences are neglected on the national level such as in NethMap, which might cause the development of treatment guidelines to focus on ‘AMR-safe’ agents for treating CoNS, such as vancomycin or linezolid. Nonetheless, agents such as tetracycline, co-trimoxazole, and erythromycin could be considered viable options for some species, where according to the study results, AMR never surpassed 10%. In conclusion, a multi-year full-region approach to extensively assess the trends in both the occurrence and AMR of CoNS species was carried out, which could be used for evaluating treatment policies and understanding more about these important but still too often neglected pathogens. Furthermore, this study served as a practical research example of how the `AMR` package for `R` can be used to gain new AMR insights using epidemiologically sounds methods.
Following new insights by studying AMR test results in the Northern Netherlands, **chapter 8** provides a comparison of AMR test results and their national interpretations of MDROs in the Dutch-German cross-border region, especially concerning the practical impact on cross-border healthcare workers. Comparing AMR in general, not only MDROs, in this cross-border region is particularly interesting since both countries are characterised by highly developed but structurally different healthcare systems. AMR interpretations in patient records are transferred between healthcare facilities located in these two different countries, while the underlying definitions differ. This causes the need for clinicians and infection control personnel to understand AMR results from both sides of the border and to be able to comprehend both national MDRO interpretation guidelines. By comparing antibiograms of Gram-negative bacteria from both sides of the border, the degree of impact of these challenges was sought to determine. To this end, 35,619 antibiograms from six Dutch and four German hospitals were analysed between 2015 and 2016 of all species of *Enterobacteriaceae*, and P. aeruginosa, the A. baumannii complex and Stenotrophomonas maltophilia. MDRO recommendations and special hygiene precautions exist in this region for all of these species. On the Dutch side of the border, isolate selection was carried out using the `AMR` package. From the Dutch hospitals, 12,616 antibiograms were selected using the `AMR` package for `R` applying the Dutch MDRO interpretation guideline. Of note, other national and international guidelines, such as the German MDRO interpretation guideline, are also included in the `AMR` package for `R`. From German hospitals, 23,003 antibiograms were selected using other methods. According to the Dutch guideline, 24.5% of all isolates were an MDRO. According to the German guideline, 12.9% of all isolates were an MDRO. However, of all isolates, 73.7% were not classified as an MDRO according to either guideline. Among all carbapenem-resistant *Enterobacteriaceae* isolates, carbapenemases were detected in 27.6% with OXA-48-like genes being predominant. The remaining isolates were negative for carbapenemases (79.1%) or not tested (20.9%). When patients are transferred between hospitals, information regarding MDRO colonisation or infection must also be transferred to ensure continuous implementation of infection control measures. For cross-border healthcare, this implies that clinicians or infection control staff should be able to determine MDROs based on antibiograms according to guidelines from either of the two countries. For cross-border healthcare, the easiest solution would be to harmonise the classification rules of both countries. This would likewise solve the understandable confusion patients might experience if infection control measures are imposed in one country, but relieved after transfer to another country. As long as the harmonisation is not done, the full AMR data of Gram-negative bacteria should be transferred together with the patient to enable classification by local infection control staff.
Other AMR-related cross-border challenges and differences are illustrated in **chapter 9**, which comprises a comprehensive microbial epidemiological analysis of MRSA occurrence, policies, and healthcare effects in the Dutch-German border region. MRSA is still one of the major causes of healthcare-associated infections due to AMR pathogens ^[30]^. In this study, MRSA surveillance data of five years (2012-2016) from Dutch and German cross-border region hospitals were analysed to describe temporal and spatial trends of MRSA rates and find differences between these groups of hospitals. The research setting comprised 42 hospitals located in the Dutch-German cross-border region treating approximately 620,000 admitted patients (68.0% in the German part of the study region) with 3.9 million patient days per year. All hospitals had implemented MRSA-related infection prevention control measures according to their national guidelines and recommendations, and the guideline differences between the two countries were compared. On both sides of the border, the median nasopharyngeal MRSA screening rate increased significantly between 2012 and 2016, although the median MRSA incidence remained stable over time at both sides of the border. Overall, the median screening rate was 14 times higher in the German border region (DE-BR) than in the Dutch border region (NL-BR). The median percentage of MRSA in *S. aureus* blood culture isolates decreased from 12.5% in 2012 to 5.0% in 2016 in DE-BR, while it remained stable at 0% to 1.9% in NL-BR. Nonetheless, MRSA among *S. aureus* isolates was 34 times higher in DE-BR. The in-hospital length of stay of MRSA patients was similar in both regions, while the general length of stay differed significantly. Furthermore, the number of nasopharyngeal MRSA screening swabs before or at admission to hospital per 100 inhabitants was 12.2 in DE-BR and 0.36 in NL-BR, also 34 times higher in DE-BR. The number of inpatient MRSA cases per 1,000 inhabitants was 2.52 in DE-BR and 0.14 in NL-BR. Thus, this study revealed significant differences between Dutch and German hospitals. The median MRSA incidence in DE-BR hospitals was more than seven times higher than in NL-BR hospitals. According to the European Centre of Disease Prevention and Control (ECDC), differences in the occurrence of AMR pathogens between European countries are most likely caused by differences in healthcare utilisation, antimicrobial use and infection prevention control practices ^[31]^. Concerning healthcare utilisation in our context, we found that inhabitants in the German part of the study region were almost three times as often hospitalised and had a significantly longer length of stay than patients on the Dutch part. This may be due to socioeconomic factors or a different organisation of ambulatory healthcare. This comprehensive study on MRSA covering hospitals across a European border demonstrated that routine MRSA surveillance may be helpful to monitor trends of MRSA parameters, to enable (inter)national comparisons.
The discussion of this study concluded with “cross-border surveillance should be extended to other multidrug-resistant organisms”, which is where **chapter 10** continues. Given that not only MRSA but MDROs, in general, pose a risk for healthcare, both in the community and hospitals, the study aimed to determine the prevalence of multiple MDROs in this cross-border region to understand differences and improve infection prevention based on real-time routine data and workflows. To this end, 23 hospitals in the Dutch-German cross-border region (NL-BR and DE-BR) participated between 2017 and 2018 in this prospective study by screening all patients upon admission to intensive care units (ICUs). All hospitals (8 in NL-BR, 15 in DE-BR) enrolled for eight consecutive weeks and screened patients for nasal carriage of MRSA and rectal carriage of vancomycin-resistant *Enterococcus faecium*/*E. faecalis* (VRE), third-generation cephalosporin-resistant *Enterobacteriaceae* (3GCRE) and carbapenem-resistant *Enterobacteriaceae* (CRE). A total of 3,365 patients were screened: 35.7% on NL-BR ICUs and 64.3% on DE-BR ICUs. The median age of all screened patients was 68 years (IQR: 57-77), while patients in DE-BR were significantly older than patients in the NL-BR. A total of 6,462 swabs were processed. The overall screening compliance (screened for at least one MDRO group) was 60.4%, in NL-BR 56.9% and in DE-BR 62.9%. All AMR data analyses were carried out and automated using the `AMR` package for `R`. The prevalence of MRSA was 1.7% in DE-BR ICUs and 0.6% in NL-BR ICUs. The prevalence of VRE was 2.7% in DE-BR ICUs and 0.1% in NL-BR ICUs. Notably, this prevalence ranged from 0% to 4.1% in DE-BR. All 56 cases of VRE were caused by E. faecium. The prevalence of 3GCRE was 6.6% in DE-BR ICUs and 3.6% in NL-BR ICUs, whereas the prevalence for CRE was practically non-existent on both sides of the border. The prevalence for Gram-negative MDROs differed within both countries between hospitals, ranging from 0% to 5.0% in NL-BR and from 1.2% to 10.9% in DE-BR ICUs. For NL-BR ICUs, the prevalence of all MDRO groups was not significantly different between the non-university and the university hospital. For the DE-BR ICUs however, the prevalence of Gram-negative MDROs was significantly higher in the non-university hospitals. In the NL-BR, 4.8 per 100 hospital admissions led to ICU admission. In contrast, in the DE-BR this was 7.7 per 100 hospital admissions. This difference can be explained by the higher ICU capacity in DE-BR hospitals (4.8% of all hospital beds) compared to NL-BR hospitals (2.4% of all hospital beds). The overall carriage prevalence for the different MDROs was higher in the DE-BR ICUs, although some differences were marginal. Specifically, the prevalence of MRSA carriage was three times higher in the DE-BR (1.7%) than in the NL-BR (0.6%). These prevalences are consistent with the study mentioned in **chapter 9**. The difference in MDRO prevalence between NL-BR and DE-BR was observed for all MDROs groups. Yet, the study findings were not all comparable with (inter)national averages. For example, the 3GCRE carriage prevalence in the DE-BR was almost twice as high (6.6%) as in the NL-BR (3.6%), but both were still lower than national averages. The ECDC reported 3GCRE proportions among blood culture isolates of *E. coli* and *K. pneumoniae* as 12.2% to 12.9% for Germany and 7.3% to 11.1% for the Netherlands. This highlights that there are important differences to be found when studying carriage in specified populations versus looking at the proportion of (probably) invasive isolates. Thus, this study highlights the importance of a regional and cross-border approach in any European cross-border region, to illustrate the difference in AMR prevalence between the regions and to highlight potential differences with country-wide reports. Attaining a deeper level of detail is required to be able to elaborate on this further, for example by collecting information about staff on the wards and infection control staff, MDRO outbreaks, infections, antibiotic use and risk factors of patients. In conclusion, geographical and political borders do not seem to be “respected” by MDROs, although healthcare systems, geographic nature and guidelines are very different between countries. Proportions of MDROs of certain pathogens, as reported on the national and international level, do not reflect MDRO prevalence in the patient or general population. This should be taken into serious consideration when interpreting reports on the country or even continental level.
## Future perspectives {-}
> After hearing for several decades that computers will soon be able to assist with difficult diagnoses, the practising physician may well wonder why the revolution has not occurred. Scepticism at this point is understandable. Few, if any, programs currently have active roles as consultants to physicians. The story behind these unfulfilled expectations is instructive and, we believe, offers hope for the future.
These words are from Schwartz *et al.* and, unfortunately, not very recent. It was published 34 years ago in The New England Journal of Medicine in 1987 ^[36]^. Many might find it quite disappointing that this exact quote can still apply to current times. Yet, this is not due to a lack of technological advancements – computational power and software capabilities have increased significantly over the last decades. And with them, the enablement of making optimal use of existing data to aid clinical decision-making and to support medicine as a whole. Hence, if it is not due to lack of technological advancements, what is then inhibiting the use of these advancements for clinical use? Others pointed out that the answer might be the gap in culture between the clinicians, biomedical scientists, and those skilled in computer programming ^[37,38]^. To this end, one might contemplate whether multi-disciplinarity was imbedded well enough into our integral medical field, since the differences are not only cultural. While both clinicians and biomedical scientists endure more than a decade of specialised training and education in a similar field, they often (1) do not speak each other’s language, (2) lack a common value system, even regarding knowledge and ignorance, and (3) have different sources of passion and emotional intensity ^[37]^. Scientists have to focus on asking “why?” and “how?”, whereas clinicians have to focus on acquiring practical answers to “how?” and “what?”. From a clinician’s perspective, asking “why?” distracts from the sense of mastery that comes from accumulating information and applying it in a clinical setting. Neither perspectives are wrong, they are just inherently different, and this results in a cultural gap. Unfortunately, this cultural gap hinders the translation of scientific discoveries into medical advances and may even hinder scientific progress ^[37]^.
While this gap may be existent, this thesis aims to narrow this gap for clinicians and scientists working in the fields of clinical microbiology and microbial epidemiology, by providing an instrument that can be beneficial and usable for clinicians and scientists alike. Ultimately, it could yield more collaboration, communication, and efficacy between scientists and clinicians. The `AMR` package for `R` has empowered the four studies mentioned in **SECTION III**, which were conducted in the Northern Netherlands as well as in the Dutch-German cross-border region. In these studies, the `AMR` package affected the selection of isolates, determination of MDROs, or the entire AMR data analysis. Combined with the user survey results in **chapter 3** (that also included the use of the `AMR` package by both clinicians and scientists), the proof of concept of an integrated design in **chapter 5**, and the positive effects on clinical staff working with AMR data in **chapter 6**, this indicates that this new instrument can be deployed and used in a multi-disciplinarily fashion. Many others have pointed out the challenges in AMR data analysis on (cumulative) antibiograms and, inter alia, the necessity for correcting duplicate isolates ^[7,18,39–45]^. Still, all these are theoretical and did not provide a pragmatic solution for those conducting microbial epidemiology. Hindler *et al.* presented a practical example of a data set that might require a correction for duplicate isolates (Table 1) ^[7]^. The algorithm of choice could be isolate-based, patient-based, episode-based, or phenotype-based. This choice is dependent on the type of analysis and desired outcome. Table 2 illustrates the scope of the isolates that should be included based on a chosen algorithm and, more importantly, shows how the `AMR` package for `R` can be used to accomplish this in one simple command, underlining its approachability. Some of those functions to apply the respective algorithm using the `AMR` package for `R` have been used by others ^[15–18]^.
<p class="tbl-caption">Table 1. Example AMR test results of four *Staphylococcus aureus* isolates from a single patient.</p>
```{r tbl11-1}
insert_graphic("images/11-t01.jpg")
```
<p class="tbl-caption">Table 2. Algorithms for including isolates and the accompanying function in the `AMR` package for `R` for use in the AMR data analysis. The ‘x’ in the last column denotes any data set in a similar structure as Table 1.</p>
```{r tbl11-2}
insert_graphic("images/11-t02.jpg")
```
User feedback as presented in **chapter 3** implies that usage of the `AMR` package has led to higher reproducibility, higher reliability, new AMR insights and improved clinical decision-making. From **chapter 5** until **chapter 10**, it is shown that the `AMR` package can be a sensible and reliable tool for microbial isolate selection and conducting AMR data analysis. These examples indicate that the `AMR` package for `R` has the potential to become a centrepiece in AMR data analyses, which is further supported by its use in other scientific publications ^[15–18]^. One of its most important features – enabling users to transform raw data into valuable new insights – allows data sets from any clinical source to be used. For example, data sets from different regions could be analysed every year in the same manner by reusing an automated AMR script, comparing trends in the occurrence of MDROs. This uniformity is an important advantage for gaining new AMR insights on the local, regional or national level and should be exploited to the fullest.
From an international point of view, it could be viable to achieve a common workflow with AMR interpretation guideline suppliers such as the Clinical and Laboratory Standards Institute (CLSI) and the European Committee on Antimicrobial Susceptibility Testing (EUCAST) ^[8,10]^. These organisations provide clinical microbiology laboratories around the globe with static and manually formatted Microsoft Excel and Portable Document Format (PDF) files, requiring laboratory staff to manually apply guideline updated into their LIS. Since the `AMR` package for `R` contains machine-readable files of these (often yearly) guidelines, a collaborative workflow could lead to a more seamless implementation and update process in clinical laboratories worldwide, increasing reliability and reducing the workload on laboratory staff. These possible effects have yet to be studied.
Similarly, LIS manufacturers could benefit from the freely available comprehensive reference data about antimicrobial agents and the taxonomy of microorganisms that the `AMR` package provides. The data provided in the `AMR` package are automatically updated using services from the World Health Organization Collaborating Centre for Drug Statistics Methodology, PubChem, the Catalogue of Life, the List of Prokaryotic names with Standing in Nomenclature and SNOMED CT. LIS manufacturers could provide this same automated process or the data in the `AMR` package directly to their end-users (the clinical microbiology laboratories), to ensure a continuously up to date version of the reference data about antimicrobial agents and the taxonomy of microorganisms. This would mean that laboratories could be unburdened by losing the necessity of keeping their local data up to date. Maintaining these local data is of paramount importance, as all AMR interpretation guidelines are based on these data. It would strongly optimise the quality of the output of clinical routine laboratories. Aside from this optimisation, with less manual and tedious work to conduct microbial epidemiology for data-technical staff, using this presented new instrument hopefully also leads to faster availability of higher-quality research in the field of AMR, as well as a better patient outcome in clinical settings.
A more clinical example of the possibilities of the `AMR` package is to analyse microbiological data from urinary tract infections in comparison with blood culture data. Some patients suffer from a urinary tract infection but are admitted to the hospital with urosepsis sometime after. As these are the kind of clinical complications we should thrive to prevent, a full-region analysis of these data might shed light on the reasons why these clinical complications were not or could not be prevented. Fortunately, it is not difficult anymore to select patients who had a laboratory-confirmed urinary tract infection in primary care and a positive blood culture with the same pathogen in the weeks after. The `AMR` package can be used to do so, and to calculate suggestions for more specific and probably effective antibiotic treatment. This puzzle may not be easily solved, but it is at least now possible to get the data into the right format and have them generate the answers to back our hypotheses. Research initiatives to study this clinical example have recently commenced in both our Northern Dutch region and in the Dutch-German cross-border region.
Yet, the `AMR` package could be used for even more sophisticated outcomes by combining microbial epidemiology with computational intelligence, and this is where the real potential lies. For example, empirical sepsis therapy could become more personalised or, as others call it, become precision medicine by performing in-depth analyses of blood cultures isolates ^[46]^. Blood cultures are namely the most reliable diagnostic measure for analysing microbes and their AMR, even if they are drawn from e.g. arterial catheters ^[47–49]^. Combining AMR test results from blood culture isolates with patient demographics and hospital-specific traits might enable a comprehensive and multi-angle view on the patient’s disease. To specify, by stratifying patient demographics (such as age, gender, comorbidities, history of antibiotic consumption) and comparing them with hospital-specific traits (such as geographic location, common microbial findings, infection control measures, allowed number of patients per room), AMR data analysis could show major differences between all these patient stratifications. The subsequent results could be used to calculate the likelihood of finding similar pathogens and AMR in similar cases, leading to predictive modelling for upcoming septic patients. For example, a septic 60-year-old male patient with a long antibiotic consumption history due to chronic obstructive pulmonary disease (COPD) might require different empiric antiseptic treatment than a septic 60-year-old male patient without COPD and no antibiotic consumption history. In other words, this modelling could lead to personalised empiric treatment guidelines, increasing the chance of therapeutic success. For a study to investigate this, the `AMR` package for `R` could be used to identify eligible patients, compare the antibiotic consumption histories, and calculate the AMR rates for pre-defined groups. The `AMR` package can also calculate the empiric chance of success of different monotherapies and combination therapies using different algorithms. The output of the models will most probably be different between regions and could perhaps even differ per hospital, although the model itself could be universally implementable. Using predictive modelling for treating patients opens a new way of how we make the best use of our data; data we already have and have had for many years. Better yet, new data are generated each day and their quality is constantly improving, due to technical laboratory advancements. Although this specific example of predicting therapeutic success would have been impossible to study twenty years ago, it is highly feasible now. Others have already shown similar approaches recently to predict sepsis using neutrophil-to-lymphocyte ratios, neutrophil dysregulation, or high-resolution vital signs time series ^[50–52]^. Yet, these modern approaches predict occurrence of sepsis and do not predict the likelihoods of the most effective empiric treatment if patients are already septic. Microbial epidemiology could pose an effective perspective to this end when collaborating with specialities such as acute care medicine and pharmacy, which links back to **chapter 2** about DSP: the right prediction at the right time for the right patient to (answer the right questions and) start the right predicted treatment. Still, improving empiric antiseptic treatment may feel like extensively training the goalkeeper. This may be necessary, but we should also realise that when the ball enters the penalty area, a lot has gone wrong already.
One might deduce that microbial epidemiology is not yet utilised to the fullest within clinical microbiology and this has a clear explanation. Advancements in information technology have progressed fast over the last decades, even more so over the last years. These advancements have led to improved LIS systems, enhanced software to apply complicated statistics and advanced mathematics, and even to this thesis. Thus, these advancements are quite novel, which means that they can bring new input to existing scientific fields such as clinical microbiology. Training and education are key in accelerating the required knowledge to apply these new advancements. This in turn will lead to the effect that e.g., clinical microbiologists and researchers in the field of clinical microbiology are urged to collaboratively think, develop and learn to work with these advancements. Yet, the cultural gap between clinicians and scientists as outlined earlier might inhibit progress to this end. Still, only collaborations and multi-disciplinary approaches will make sure that we can utilise the advancements in information technology up to their full potential, so patients will benefit most from our future scientific developments. For this reason, we should all strive to narrow and bridge the cultural gap. With regard to the practical labour concerning (predictive) modelling, it should perhaps become more common for research groups within our field, and probably many other research fields, to include (more) modellers and other data-technical staff.
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11b-summary-frisian.Rmd

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# Gearfetting yn Frysk {-}
**Seksje I**
Wêr is mikrobiale epidemiology begûn? Hoe is it ûntstien? En hoe kin it bydrage oan de holistyske oanpak fan ynfeksjebehear? Dy fragen wurde beäntwurde yn dizze earste seksje. Dêrnei wurdt sketst hokker wichtige hjoeddeistige beheiningen besteane yn ‘e tapassing fan mikrobiale epidemiology yn ‘e praktyk en hoe’t dy oerwûn wurde kinne.
Yn ‘e algemiene ynlieding wurdt yn **haadstik 1** sketst dat mikrobiale epidemiology diel útmakket fan ynfeksjesykte-epidemiology, dy’t op syn beurt ûnderdiel is fan klinyske mikrobiology. Under oaren kin mikrobiale epidemyology sjoen wurde as it wittenskiplike fjild foar it krijen fan nije ynsjoggen oer de ferdieling fan mikro-organismen en harren ûnderskate patroanen yn antymikrobiale resistinsje (AMR). Foarútgong yn ynformaasjetechnology hat ús net allinich de mooglikheden brocht om oer regionale, nasjonale en ynternasjonale grinzen hinne te sjen om ynsicht te krijen yn ‘e fersprieding fan mikro-organismen en AMR, mar sels ek om pandemys yn real-time wier te nimmen, te analysearjen en te begripen. Metoaden dy’t wy hjoed ûntwikkelje en brûke, kinne moarn oan ‘e oare kant fan ‘e wrâld tapast wurde. Dat is in wichtich foardiel yn moderne mikrobiale epidemiology, dêr’t de klam hieltyd mear op data komt te lizzen.
De data dy’t brûkt wyrde as ynput foar mikrobiale epidemiologyske analyzes, wurde faaks ferkrigen út laboratoariumynformaasjesystemen (LIS). Dizze data binne routine-diagnostyske resultaten fan laboratoariumtests. Yn **haadstik 2** is de eigensinnige opfetting oanfierd dat diagnostyk liedt ta rûge resultaten, mar net needsaaklikerwiis ta in direkt antwurd op de klinyske fraach dy’t in behanneljend arts fan in pasjint hawwe kin. Antwurden oan dokters fereaskje in oanpak fan in multydissiplinêr, ferweve “stewardship”-konsept mei in fokus op diagnostyk. Dat fereasket fan medyske spesjalisten yn ‘t algemien (en artsen-mikrobiolooch yn it bysûnder) in nauwe ynteraksje mei kollega’s, sadat dat soarget foar optimale kwaliteit fan soarch en feiligens fan pasjinten; dat is it saneamde Diagnostic Stewardship Program (DSP). De term “stewardship” (rintmasterskip) wurdt breed brûkt om kommunikaasje en klinyske beslútfoarming te fasilitearjen, mar it fêststellen fan in dúdlike definysje fan “stewardship” hat in útdaging bewiisd. Boppedat giet de diagnostyk yn medysk-mikrobiologyske laboratoaria op it stuit fluch foarút mei betrekking ta ferbettere workflows en nije technologyen, lykas matrix-assisted laser desorption/ionisation time-of-flight (MALDI-TOF) massaspektrometry. Dochs is diagnostyk yn ynfeksjebehear breder as dit en omfettet it in protte klinyske gebieten wêr’t kommunikaasje en ynteraksje fûneminteel binne om it bêste gebrûk te meitsjen fan kennis en saakkundigens, sadat alle spesjalismen in bydrage leverje kinne oan pasjintesoarch. De juste test op de juste tiid foar de juste pasjint om de juste fragen te beäntwurdzjen en mei de juste behanneling te begjinnen − dêr giet DSP yn medyske mikrobiology oer. Mikrobiale epidemiology kin brûkt wurde foar in lyts aspekt fan dat diagnostyske gehiel, troch de testresultaten te resirkulearjen om dêrnei ferriking oan te bringen yn it antwurd- generearjende proses dat DSP is.
**Haadstik 3** giet fierder mei it beljochtsjen fan wichtige hjoeddeistige beheiningen by it tapassen fan mikrobiale epidemiology, benammen AMR-analyze. AMR-analyze moat útfierd wurde op in klinysk en epidemiologysk sinfolle manier, mar it is útdaagjend om’t it ekspertize fereasket yn sawol (klinyske) epidemiology as (medyske) mikrobiology, en derneist de juste instruminten om de AMR-analyzes út te fieren. Dit wurdt nochris fierder yngewikkeld troch it ûntbrekken fan de tagonklikens fan LIS-data, om’t de measte LIS-en net ûntwurpen binne mei epidemiologyske analyzes yn ‘e holle. Elk LIS ûnderhâldt bygelyks syn eigen taksonomyske gegevens en laboratoaria binne sels ferantwurdlik foar it regelmjittich bywurkjen dêrfan. Sûnt AMR-rjochtlinen bot basearre binne op de mikrobiale taksonomy (guon regels jilde allinnich foar in spesifyk skaai, oare regels jilde foar in spesifike famylje), moat dizze ynformaasje akkuraat wêze en by de tiid. Spitigernôch is troch ûndersyk ûnder sân medysk-mikrobiologyske laboratoaria yn Nederlân bliken dien dat al harren LIS-en tige ferâldere taksonomyske nammen befetsje. Dit kin slimme gefolgen hawwe foar sawol de routinerapportaazje fan resultaten as foar (takomstige) epidemyologyske analyzes. Om dy redenen waard yn dit haadstik it AMR-pakket foar R yntrodusearre as in nij epidemiologysk ynstrumint foar AMR-analyze dat fergees, ûnôfhinklik, open-source en iepenbier beskikber is. It waard ûntwikkele troch in team fan tolve ferskillende iepenbiere soarchorganisaasjes út sân ferskillende lannen en biedt help om it opskjinjen, transfomearjen en analysearjen fan AMR-gegevens te ferienfâldigjen, en biedt tagelyk metoaden om maklik (ynter)nasjonale rjochtlinen en wittenskiplik betroubere referinsjegegevens ta te passen. Tsjin maaie 2021 wie it mear as 50.000 kear ynladen troch brûkers út 162 ferskillende lannen sûnt de earste release yn 2018. De resultaten fan in enkête ûnder brûkers presintearre yn dit haadstik, litte sjen dat it gebrûk liedt ta mear reprodusearberens fan analyzeresultaten, betrouberdere resultaten fan AMR-analyzes, en sawol nije as ferbettere ynsjoggen yn AMR foar de ynstellings en regio’s fan ‘e brûkers. Brûkers stelden ek dat it AMR-pakket brûkt wie om klinyske beslútfoarming te stypjen. It pakket lost it ûngemak op fan it ôfhinklik wêzen fan (ynter)nasjonale rjochtlinen en betroubere (referinsje)gegevens, wylst it ek in wiidweidige ‘toolbox’ biedt foar de analyze sels. It AMR-pakket foar R kin dêrom in help wêze foar elke spesjalist yn it fjild dy’t mei AMR-gegevens wurket.
**Seksje II**
Nei de útdagings dy’t sketst binne yn ‘e foarige seksje, wurdt yn dizze seksje it AMR-pakket foar R beskreaun as in nij ynstrumint om dy útdagingen oan te pakken. Fanút ferskate perspektiven wurdt it AMR-pakket en syn foardielen beljochte: fanút in technysk perspektyf, fanút it perspektyf fan ynfeksjebehear en fanút in klinysk perspektyf. Dy kombinaasje jout in mienskiplike basis foar it begripen fan de oplossingen dy’t it AMR-pakket biede kin en hoe’t it in nij begjinpunt foarmje kin foar takomstige tapassingen fan mikrobiale epidemiology.
De technyske skaaimerken fan it AMR-pakket foar R wurde beskreaun yn **haadstik 4**, dêr’t yn beskreaun wurdt hoe’t it AMR-pakket ûntwurpen is om reprodusearbere AMR-analyzes te standerdisearjen oan ‘e hân fan ynternasjonale standert oanrikkemedaasjes. Om dat mooglik te meitsjen, wurde wittenskiplik betroubere referinsjegegevens brûkt foar de falidaasje fan laboratoariumresultaten, antymikrobiale middels en de folsleine biologyske taksonomy fan mikro-organismen. Boarnegegevens moatte analysearre wurde yn de meast betroubere wei, foaral wannear’t it resultaat, bygelyks, brûkt wurde sil om de behannelopsjes foar in psjint te evaluearjen. Dit freget reprodusearbere en spesjalisearre ferwurking fan gegevens. It AMR-pakket biedt in standerdisearre en automatisearre manier om mienskiplike LIS-data op te skjinjen, te transformearjen en te ferbetterjen, ûnôfhinklik fan de ûnderlizzende databoarne en de krektens fan ‘e data. Foar dit doel, binne algemien tapasbere algoritmen ûntwikkele, om AMR-testresultaten opskinje te kinnen en nammen fan mikro-organismen en antymikrobiale middels falidearje te kinnen. De formule foar de falidaasje fan taksonomyske nammen hâldt rekken mei it foarkommen fan siikmeitsjende mikro-organismen en is kontekstbewust oangeande oare taksonomyske skaaimerken sa as it keninkryk, fylum, oarder en famylje. Bygelyks wurdt de wearde “E. coli” oersetten nei de baktearje Escherichia coli, wylst de brûker ek ynformeare wurdt dat de parasyt Entamoeba coli ek in mooglikheid is, mar in legere kâns hat. Mei help fan handige funksjes kinne brûkers fluch konsistinte mikrobiale eigenskippen weromfine, lykas it taksonomyske keninkryk, famylje, skaai, soarte, ferâldere taksonomyske nammen en sels de Gram-kleur. Neist ynformaasje oer mikro-organismen, befettet it pakket ek referinsjegegevens oangeande antibiotika, wêrûnder in protte foarkommende LIS-koades, offisjele nammen, ATC-koades (Anatomical Therapeutic Chemical), definearre deistiche doses (defined daily doses, DDD), en mear as 5.000 hannelsnammen fan 456 antymikrobiale middels. Mei dizze referinsjegegevens kinne brûkers rauwe gegevens oersette en eigenskippen weromfine oer elk mikro-organisme of antibiotikum. Boppedat is it AMR-pakket yn steat om multiresistinte organismen (multidrug-resistant organisms, MDRO’s) te identifisearjen basearre op nasjonale en ynternasjonale rjochtlinen, minimum inhibitory concentrations (MIC’s) te ynterpretearjen, en kin it de earste isolaten bepale dy’t brûkt wurde moatte foar it berekkenjen fan AMR foar sawol monoterapy as kombinaasje-terapyen. It AMR-pakket is bedoeld om in breed helpmiddel te wêzen foar data-technysk personiel dat wurket yn it gebiet fan AMR, hoewol’t it gebrûk net beheind is ta dy groep.
As yllustraasje hjirfan wurdt yn **haadstik 5** sjen litten dat it AMR-pakket likernôch brûkt wurde kin as in soarte fan rêchbonke yn in ynteraktive open-source software-applikaasje foar ynfeksjemangement en antimicrobial stewardship, neamd RadaR (rapid analysis of diagnostic and antimicrobial patterns in R). Ynfeksjemangement yn ‘e foarm fan Antimicrobial Stewardship Programma’s (ASP), hat him ûntpopt as in effektive oplossing om it globale sûnensprobleem fan antibioatikaresistinsje yn sikehuzen oan te pakken. Dit is yn oerienstimming mei **haadstik 2**; stewarship-yntervinsjes en -aktiviteiten rjochtsje harren op yndividuele pasjinten (persoanlike genêskunde en konsultatie), mar likegoed op pasjintgroepen of klinyske syndromen, dêr’t elke yntervinsje liede moat ta ferbettering fan de kwaliteit fan ‘e soarch en de feiligens fan de pasjint. It is lykwols dreech om pasjintgroepen yn ‘e deistige praktyk te analysearjen (bgl. stratifisearre nei ôfdieling, spesifike antymikrobiale middels, of brûkte diagnostyske prosedueres). It is sels noch lestiger om fluch grutte pasjintpopulaasjes te analysearjen (bgl. ferspraat oer meardere spesjaliteiten), ek al is dizze ynformaasje beskiber yn ‘e data. Dêrom wie de ûntwikkeling fan RadaR bedoeld om ASP-teams te foarsjen fan in brûkersfreonlik en tiidsbesparjend ynstrumint, sûnder dat de djippe technyske ekspertize nedich is. RadaR biedt ûnder oaren Kaplan-Meier -curves oer de lisduur yn sikehuzen, tiidtrends foar it oantal opnames, antibiotikagebrûk, en in automatisearre AMR-data-analyze dêr't it AMR-pakket foar R foar brûkt is. RadaR is falidearre troch 12 ESGAP-leden (European Society of Clinical Microbiology and Infectious Diseases Study Group for Antimicrobial Stewardship) út 9 ferskillende lannen. It hat it potinsjeel in tige brûkber ynstrumint te wêzen yn ‘e deistige praktyk fan sawol ynfeksjebehear as ASP-teams. Dêrnjonken waard yn dit haadstik dúdlik dat it AMR-pakket brûkt wurde kin as ûnderdiel fan in oare software-oplossing om yntegrearre ynfeksjebehear mooglik te meitsjen.
Dêrút folgjend yllustrearret **haadstik 6** de effektiviteit fan it AMR-pakket ûnder brûkers, troch it beoardieljen fan de brûkberens en it effekt op de wurkstream fan dokters yn in typysk klinysk senario. Hoewol’t it AMR-pakket yn wittenskiplik ûndersyk al yn ferskate stúdzjes út ferskate lannen brûkt is, wie der noch gjin analyze fan ‘e ynfloed op AMR-analyze en -rapportaazje yn in klinyske omjouwing. De analyze en rapportaazje fan AMR-data fereaskje spitigernôch spesjaal oplaat personiel. Derneist kinne AMR-data-analyzes tiidsrôvjend wêze. Om de impact hjirfan yn in klinyske omjouwing te beoardieljen, waarden algemiene ûndersyksfragen oer bloedkultuerdata gearstald dy’t troch klinysk routinepersoniel beäntwurde wurde moasten, wêrûnder artsen-mikrobiolooch, bernedokters en yntinsivisten. Yn totaal diene tsien klinici mei oan ‘e stúdzje. Boppedat waard dielnimmers frege in online fragelist yn te foljen oer har eftergrûn, demografy (lykas leeftyd en geslacht), en eardere ûnderfining mei software en AMR-data-analyze en -rapportaazje. Alle dielnimmers moasten de fragen twa kear beäntwurdzje: de earste kear mei de software fan harren eigen kar (earste ronde) en de twadde kear mei in nij ûntwikkele webapplikaasje, boud om it AMR-pakket foar R hinne (twadde ronde). In effisjinte agile workflow waard brûkt foar de ûntwikkeling fan dizze webapplikaasje. De antwurden op de ûndersyksfragen tsjinnen as basis om de effektiviteit (antwurden op elke taak foar elke brûker) en de effisjinsje (tiid bestege oan it oplossen fan elke taak) te fergelykjen tusken de twa rondes. Net alle dielnimmers koene taken binnen it foarskreaune tiidsbestek foltôgje. De gemiddelde foltôging per taak tusken de earste en twadde ronde naam ta fan 56% nei 96% en it persintaazje goede antwurden wie tanommen fan 38% nei 98%. De gemiddelde tiid per ronde waard fermindere mei mear as in oere. Dit haadstik toant dêrmei de ferhege effektiviteit, effisjinsje en krektens fan it brûken fan it AMR-pakket foar R foar AMR-analyze yn ferliking mei tradisjonele software lykas Microsoft Excel en SPSS.
**Seksje III**
In protte klinyske stúdzjes op it gebiet fan ynfeksjesykten en medyske mikrobiology binne ôfhinklik fan ien of oare foarm fan (mikrobiale) epidemiology. Wylst yn ‘e foarige seksje it AMR-pakket yntrodusearre waard en it gebrûk yn ferskate senario’s ûndersocht waard, begjint dizze seksje mei in epidemiologysk ûndersyksprojekt yn ‘e Noard-Nederlânske regio, en wreidet dizze seksje dêrnei út nei de Nederlânsk-Dútske grinsregio om de distribúsje, it foarkommen en de AMR fan ferskate sykteferwekkende mikro-organismen op in (je)regionaal nivo better begripe te kinnen. Troch yn te zoomen op de regio’s oan beide kanten fan in lânsgrins, kinne op mikronivo ferlikings makke wurde tusken twa ferskillende naasjes. En ferskillende naasjes betsjut úteinlik ferskillende soarchstruktueren. Wat bliuwt oer fan ‘One Health‘? Wat binne de konsekwinsjes fan it hawwen fan ferskillen tusken lannen yn testtechniken, antibiotika-ynterpretaasjes en mikrobiologysk screeningsbelied? Dizze seksje jout antwurden op dy fragen.
**Haadstik 7** rjochtet him op koägulaze-negative stafylokokken (KNS), wêrfan’t bekend is dat se bloedstreamynfeksjes (BSY) en hege mortaliteit feroarsaakje kinne, hoewol’t se jierrenlang mar as ‘gewoan’ kontaminaasje beskôge waarden. Boppedat wurde KNS-en hieltyd faker assosjeare mei nosokomiale ynfeksjes. Op it stuit bestiet de KNS-groep út 45 ferskillende soarten (‘species’), hoewol it bepalen fan it soartnivo pas koartlyn mooglik makke is foar routine-diagnostyske laboratoaria. Sûnt 2012 is nammentlik MALDI-TOF massaspektrometry de standert foar de identifikaasje fan bakteriële soarten lykas KNS. Hjirfoar waard de ydentifikaasje benammen dien mei biogemyske en fysiologyske testmetoaden, dy’t fariearjende resultaten opleveren, yn it bysûnder by minder foarkommende soarten. AMR, en yn it bysûnder multyresistinsje, is in tanimmend probleem yn KNS-en. Dochs wurde KNS-en yn behannelrjochtlinen en nasjonale tafersjochprogramma’s (lykas it Nederlânske NethMap), noch hieltyd as ien groep sjoen, sûnder differinsjaasje tusken soarten. Om dizze reden is net folle bekend oer trends yn it foarkommen fan, en AMR yn, KNS-en op lokaal en regionaal nivo. Dêrom toant dizze retrospektive stúdzje in detaillearre AMR-analyze fan hast 20 tûzen KNS-isolaten dy't fûn wiene yn alle beskikbere 70 tûzen bloedkultuerisolaten tusken 2013 en 2019 yn Noard-Nederlân. Mei dizze analyze hawwe wy stribbe om ferskillen yn it foarkommen fan KNS-soarten en harren AMR-patroanen te evaluearjen en om harren klinyske mikrobiologyske relevânsje te beoardieljen. Yn totaal waarden 27 ferskillende soarten fan ‘e KNS-groep fûn. Grutte ferskillen waarden sjoen yn it foarkommen fan ‘e ferskillende soarten: de top fiif bestie út 97% fan alle isolaten (S. epidermidis, S. hominis, S. capitis, S. haemolyticus en S. warneri). It oanpart fan KNS-en op ‘e intensive care (IC) neffens oare ôfdielings wie signifikant ferskillend tusken perifeare sikehuzen en it universitêr sikehûs. Om’t net bekend wie hokker pasjinten in BSY hienen, waard “KNS-persistinsje” definieare as in surrogaat wêrfoar teminsten trije positive bloedkulturen nommen wurde moasten op trije ferskillende dagen, binnen 60 dagen, wêr’t deselde KNS yn fûn wie, by deselde pasjint. De relatyf meast foarkommende oarsaaklike ferwekker fan KNS-persistinsje wie S. haemolyticus, folge troch S. epidermidis en S. lugdunensis. AMR-analyze hat wichtige ferskillen iepenbiere tusken de KNS- soarten. Bygelyks eksposearren S. epidermidis en S. haemolyticus 50% oant 80% resistinsje tsjin de measte antibiotika, wylst de resistinsje tsjin dizze middels by ‘e measte oare KNS-en leger as 10% bleau. En dochs op nasjonaal nivo, lykas yn NethMap, wurde dizze ferskillen ferwaarleazge, wat liede kin ta de ûntwikkeling fan behannelrjochtlinen dy’t har rjochtsje op feilige en fertroude middels foar de behanneling fan KNS, lykas vancomycine of linezolid. Middels lykas tetracycline, cotrimoxazol, en erythromycine soenen as alternative opsjes beskôge wurde kinne foar guon soarten, wêr't de AMR, neffens ús ûndersyksresultaten, nea boppe de 10% útkaam is. Ta beslút kin steld wurde dat in mearjierrige regio-oerstiigjende oanpak tapast is om de ûntwikkelingen yn sawol it foarkommen as de antibioatikaresistinsje fan LNS-soarten wiidweidich te beskôgjen, om sadwaande it behannelbelied te evaluearjen en mear te begripen oer dizze wichtige, mar noch net faak genôch serieus nommen sykteferwekkers. Dêrneist tsjinne dizze stúdzje as in praktysk foarbyld fan hoe’t it AMR-pakket foar R brûkt wurde kin yn stúdzjes om nije ynsjoggen te krijen oer antibiotikaresistinsje mei epidemiologysk ûnderboude metoaden.
Nei oanlieding fan de nije befinings troch it bestudearjen fan AMR-testresultaten yn Noard-Nederlân, jout **haadstik 8** in ferliking fan nasjonale ynterpretaasjes fan MDRO’s yn de Nederlânsk-Dútske grinsregio, benammen oangeande de praktyske gefolgen foar personiel yn de sûnenssoarch dy’t ticht by de grins wurkje. It fergelykjen fan AMR yn it algemien, net allinne MDRO’s, yn dizze grinsregio is tige ynteressant om’t beide lannen karakterisearre wurde troch heech ûntwikkele, mar dochs struktureel oars ynrjochte soarchsystemen. Antibioatika-ynterpretaasjes fan pasjinten wurde oerdroegen tusken soarchynstellings yn dizze twa lannen, wylst de ûnderlizzende definysjes ferskille. Dêrtroch moatte dokters en ynfeksjeprevinsje-meiwurkers de antibioatikaresultaten fan beide kanten fan ‘e grins begripe kinne en yn steat wêze beide nasjonale MDRO-rjochtlinen tapasse te kinnen. Troch antibiogrammen fan Gram-negative baktearjes fan beide kanten fan ‘e grins mei-inoar te fergelykjen, waard besocht de omfang fan ynfloed fan dizze útdagingen te bepalen. Dêrta waarden tusken 2015 en 2016 35.619 antibiogrammen út seis Nederlânske en fjouwer Dútske sikehûzen analysearre foar alle soarten Enterobacteriaceae, en P. aeruginosa, it A. baumannii-kompleks en Stenotrophomonas maltophilia. Foar al dizze soarten besteane yn dizze regio MDRO-oanbefellings en spesjale ynfeksjeprevinsjemaatrigels. Út de Nederlânske sikehûzen waarden 12.616 antibiogrammen selekteare mei it AMR-pakket foar R, wêrmei ek de Nederlânske MDRO-rjochtline tapast wurde koe. Wichtich is dat oare nasjonale en ynternasjonale rjochtlinen, lykas de Dútske MDRO-rjochtline, ek opnommen binne yn it AMR-pakket foar R. Út Dútske sikehûzen waarden 23.003 antibiogrammen selekteare. Neffens de Nederlânske rjochtline wie 24,5% fan alle isolaten in MDRO. Neffens de Dútske rjochtline wie 12,9% fan alle isolaten in MDRO. Lykwols waard 73,7% fan alle isolaten net klassifisearre as in MDRO neffens ien fan ‘e beide rjochtlinen. By it oerdragen fan pasjinten tusken sikehûzen, moat ek ynformaasje oer MDRO-kolonisaasje of -ynfeksje oerdroegen wurde om de trochgeande útfiering fan ynfeksjeprevinsjemaatrigels te garandearjen. Foar regio-oerstiigjende sûnenssoarch betsjut dit dat klinici en ynfeksjeprevinsjemeiwurkers yn steat wêze moatte om MDRO’s te bepalen basearre op antibiogrammen neffens de rjochtlinen fan ien fan beide lannen. Foar regio-oerstiigjende sûnenssoarch soe dêrom de ienfâldichste oplossing wêze om de rjochtlinen fan beide lannen te harmonisearjen. Dat soe ek de begryplike betizing oplosse kinne dy’t pasjinten ûnderfine kinne as ynfeksjeprevinsjemaatrigels oplein wurde yn it iene lân, mar opheft wurde nei oerdracht nei it oare lân. Oant de harmonisaasje berikt is, soenen de folsleine AMR-gegevens tegearre mei de pasjint oerdroegen wurde moatte om’t klassifikaasje foar lokale ynfeksjeprevinsje-meiwurkers mooglik te meitsjen.
Oare AMR-relatearre grinsoerstiigjende útdagings en ferskillen wurde yllustrearre yn **haadstik 9**, dat in wiidweidige mikrobiale epidemiologyske analyze omfettet fan it foarkommen fan MRSA, it belied en de ynfloed op sûnenssoarch yn ‘e Nederlânsk-Dútske grinsregio. MRSA is noch altyd ien fan ‘e liedende oarsaken fan sikehûs-relatearre ynfeksjes troch resistinte baktearjes. Yn dizze stúdzje waarden MRSA-tafersjochgegevens fan fiif jier (2012-2016) út Nederlânske en Dútske sikehuzen yn ‘e grinsregio analysearre om regio-spesifike trends oer tiid fan MRSA beskriuwe te kinnen en om ferskillen tusken sikehûsgroepen fêst te stellen. De stúdzje omfette 42 sikehûzen yn ‘e Nederlânsk-Dútske grinsregio mei sawat 620.000 opnommen pasjinten (68,0% yn it Dútske diel fan ’e ûndersyksregio) mei hast fjouwer miljoen pasjintdagen per jier. Alle sikehuzen hiene MRSA-relatearre previnsjemaatrigels ymplementeare neffens harren nasjonale rjochtlinen en oanbefellings, en ferskillen yn rjochtlinen tusken de twa lannen waarden fergelike. Oan beide kanten fan ‘e grins naam it MRSA-screeningspersintaazje tusken 2012 en 2016 bot ta, hoewol de MRSA-ynsidinsje oer de tiid stabyl bleau oan beide kanten fan ‘e grins. Yn totaal wie it screeningspersintaazje yn ‘e Dútske grinsregio 14 kear heger as yn ’e Nederlânske grinsregio. It persintaazje MRSA yn bloedkultuerisolaten mei S. aureus sakke fan 13% yn 2012 nei 5% yn 2016 yn ‘e Dútske grinsregio, wylst it stabyl bleau yn ‘e Nederlânske grinsregio (0% oant 2%). Dochs wie MRSA ûnder S. aureus-isolaten 34 kear heger yn ‘e Dútske grinsregio. De listiid yn it sikehûs by MRSA-pasjinten wie yn beide regio’s lyksoartich, wylst de algemiene listiid flink fariearre. Fierder wie it oantal MRSA-útstriken foar of by sikehûsopname per 100 ynwenners 12,2 yn ‘e Dútske grinsregio en 0,36 yn ‘e Nederlânske grinsregio; 34 kear heger yn ‘e Dútske grinsregio. It oantal yntramurale MRSA-gefallen per 1.000 ynwenners wie 2,52 yn ‘e Dútske grinsregio en 0,14 yn ‘e Nederlânske grinsregio. Dizze stúdzje toande dus signifikante ferskillen oan tusken Nederlânske en Dútske sikehûzen. De MRSA-ynsidinsje yn Dútske sikehûzen wie mear as sân kear heger as yn Nederlânske sikehûzen. Neffens it European Centre of Disease Prevention and Control (ECDC) wurde ferskillen yn it foarkommen fan resistente sykteferwekkers tusken Jeropeeske lannen wierskynlik feroarsake troch ferskillen yn soarchgebrûk, antimykrobieel gebrûk en ynfeksjeprevinsjemaatrigels. Wat it soarchgebrûk yn ús kontekst oanbelanget, fûnen wy dat ynwenners yn it Dútske diel fan ‘e stúdzje hast trije kear sa faak yn it sikehûs opnommen wiene en in tige langere listiid hiene as pasjinten yn it Nederlânske diel. Dit kin wêze troch sosjaal-ekonomyske faktoaren of in oare ynrjochting fan ambulante sûnenssoarch. Dizze wiidweidige stúdzje oer MRSA yn sikehûzen rûn in Jeropeeske grins hat sjen litten dat trochgeand MRSA-tafersjoch nuttich wêze kin om trends fan MRSA te folgjen, om (ynter)nasjonale fergelikingen ta te stean.
De diskusje fan dizze stúdzje waard ôfsletten mei (oersetten) “grinsoerstiigjend tafersjoch soe útwreide wurde moatte nei oare multyresistinte mikro-organismen”, wat krekt is wêr’t **haadstik 10** op trochgiet. Sûnt net allinne MRSA’s mar MDRO’s yn it algemien in risiko posearje foar de sûnenssoarch, sawol yn ‘e mienskip as yn de sikehuzen, hie dizze stúdzje ta doel om it foarkommen fan meardere MDRO’s yn dizze grinsregio fêst te stellen om sadwaande verskillen better begripe te kinnen, en om ynfeksjeprevinsje te ferbetterjen, baseare op real-time routinegegevens. Foar dat doel namen 23 sikehûzen yn ‘e Nederlânsk-Dútske grinsregio tusken 2017 en 2018 diel oan dizze prospective stúdzje troch alle pasjinten op tagong ta de intensive care (IC) te ûndersykjen. Alle sikehûzen (8 yn ‘e Nederlânske grinsregio, 15 yn ‘e Dútske grinsregio) dienen elk mei foar acht opienfolgjende wiken en ûndersochten yn dy perioade pasjinten foar dragerskip fan MRSA, vancomycine-resistinte Enterococcus faecium/E. faecalis (VRE), tredde-generaasje cefalosporine-resistinte Enterobacteriaceae (3GCRE) en carbapenem-resistinte Enterobacteriaceae (CRE). Yn totaal waarden 3.365 pasjinten ûndersocht: 35,7% op Nederlânske IC’s en 64,3% op Dútske IC’s. De mediane leeftyd fan alle screenede pasjinten wie 68 jier (IQR: 57-77), wêrby pasjinten yn ‘e Dútske grinsregio signifikant âlder wiene as pasjinten yn ‘e Nederlânske grinsregio. De algemiene screening compliance (screened foar teminsten ien MDRO-groep) wie 60%. Alle AMR-data-analyzes waarden útfierd en automatisearre mei help fan it AMR-pakket foar R. It foarkommen fan MRSA wie 1,7% op Dútske IC’s en 0,6% op Nederlânske IC’s. It foarkommen fan VRE wie 2,7% op Dútske IC’s en 0,1% op Nederlânske IC‘s. Opfallend wie dat dit foarkommen yn it Dútske grinsgebiet útienrûn fan 0% oant 4,1%. Alle 56 gefallen fan VRE waarden feroarsake troch E. faecium. It foarkommen fan 3GCRE wie 6,6% op Dútske IC’s en 3,6% op Nederlânske IC’s, wylst it foarkommen fan CRE oan beide kanten fan de grins tichtby nul bleau. It foarkommen fan Gram-negative MDRO’s ferskilden tusken sikehuzen yn beide lannen, fariearjend fan 0% oant 5,0% yn ‘e Nederlânske grinsregio en fan 1,2% oant 10,9% yn ‘e Dútske grinsregio. Foar de ynbegrepen Nederlandse IC’s wie it foarkommen fan alle MDRO-groepen net gâns oars tusken perifeare sikehuzen en it universitêr sikehûs. Op de Dútske IC’s wie it foarkommen fan Gram-negative MDRO’s lykwols heger yn perifeare sikehuzen. Yn ‘e Nederlânske grinsregio liede 4,8 per 100 sikehûsopnames ta in IC-opname. Yn ‘e Dútske grinsregio wie dit oars 7,7 per 100 sikehûsopnames. Dit ferskil kin ferklearre wurde troch de hegere IC-kapasiteit yn Dútske sikehûzen (4,8% fan alle sikehûsbêden) yn ferliking mei Nederlânske sikehûzen (2,4% fan alle sikehûsbêden). It algehiele foarkommen fan ferskillende MDRO’s wie heger op Dútske IC’s, hoewol’t guon ferskillen tige lyts wiene. Benammen it foarkommen fan MRSA wie trije kear heger yn ‘e Dútske grinsregio as yn ‘e Nederlânske grinsregio, wat konsistint is mei de ûndersyksresultaten yn **haadstik 9**. Dochs wiene de ûndersyksresultaten net konsistint mei (ynter)nasjonale gemiddelden. Bygelyks, it foarkommen fan 3GCRE wie hast twa kear sa heech yn ‘e Dútske grinsregio as yn ‘e Nederlânske grinsregio, mar beide wiene noch hieltyd leger as it nasjonale gemiddelden; de ECDC rapporteare 6% hegere 3GCRE-persintaazjes ûnder E. coli en K. pneumoniae út bloedkulturen foar Dútslân en Nederlân. Dit lit sjen dat der wichtige ferskillen binne tusken it bestudearjen fan dragerskip yn bepaalde populaasjes en it bestudearjen fan it oanpart fan (wierskynlik) invasive isolaten. Dizze stúdzje beklammet dêrom it belang fan in regionale en grinsoerstiigjende oanpak yn in Jeropeeske grinsregio, om it ferskil yn foarkommen fan AMR tusken de regio’s te yllustrearjen en om potinsjele ferskillen mei nasjonale rapporten te beljochtsjen. Om dat fierder út te wurkjen is in djipper nivo fan detail nedich, bygelyks troch ynformaasje te sammeljen oer (ynfeksjeprevinsje) personiel, MDRO-útbraken, ynfeksjes, antibiotikagebrûk en risikofaktoaren fan pasjinten. Yn konklúzje lykje geografyske en politike grinzen troch MDRO’s net “respektearre” te wurden, hoewol sûnenssoarchsystemen, geografyske lokaasje en rjochtlinen ferskille fan lân nei lân. De persintaazjes MDRO’s fan guon sykteferwekkers, lykas nasjonaal en ynternasjonaal rapportearre, reflektearje net it foarkommen fan MDRO’s yn in pasjint en/of yn ‘e algemiene befolking. Dat moat yn alle earnst beskôge wurde by it ynterpretearjen fan rapporten op nasjonaal of sels kontinintaal nivo.
**Konklúzje**
Fanút ferskate perspektiven wurdt it AMR-pakket en syn foardielen beljochte: fanút in technysk perspektyf, fanút it perspektyf fan ynfeksjebehear en fanút in klinysk perspektyf. Dy kombinaasje jout in mienskiplike basis foar it begripen fan de oplossingen dy’t it AMR-pakket biede kin en hoe’t it in nij begjinpunt foarmje kin foar takomstige tapassingen fan mikrobiale epidemiology, sawol yn klinyske omjouwings as yn wittenskiplik ûndersyk. Dit proefskrift giet yn op dizze perspektiven troch it gebrûk fan dit nije ynstrumint te yllustrearjen yn epidemiologyske stúdzjes yn ‘e Nederlânsk-Dútske grinsregio om de distribúsje, it foarkommen en de AMR fan ferskate sykteferwekkende mikro-organismen op in (je)regionaal nivo better begripe te kinnen. Ta beslút toant dit proefskrift de tafoege wearde fan in konsekwint data-analytysk ynstrumint om AMR-data foar te meitsjen en te analysearjen yn in regio-oerstiigjende oanpak, om nije ynsjoggen te krijen yn AMR-trends.

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# Samenvatting in het Nederlands {-}
**Sectie I**
Waar is de microbiële epidemiologie begonnen? Hoe is het ontstaan? En hoe draagt het bij tot de holistische benadering van infectiemanagement? Deze vragen worden in deze eerste sectie beantwoord. Vervolgens wordt geschetst welke belangrijke huidige beperkingen er bestaan bij de toepassing van microbiële epidemiologie in de praktijk en hoe deze zouden kunnen worden ondervangen.
In de algemene inleiding in **hoofdstuk 1** van dit proefschrift wordt geschetst dat microbiële epidemiologie een onderdeel is van de epidemiologie van infectieziekten, die op haar beurt weer een onderdeel is van de medische microbiologie. Microbiële epidemiologie kan onder andere worden gezien als het wetenschappelijke veld voor het verwerven van nieuwe inzichten over de verspreiding van micro-organismen en hun respectievelijke antimicrobiële resistentie (AMR). De vooruitgang in de informatietechnologie heeft ons niet alleen de mogelijkheden gebracht om over regionale, nationale en internationale grenzen heen te kijken om inzicht te krijgen in de verspreiding van micro-organismen en AMR, maar zelfs om pandemieën real-time te analyseren en te begrijpen. Methoden die we vandaag ontwikkelen en gebruiken, kunnen bij wijze van spreken morgen aan de andere kant van de wereld worden toegepast. Dit is een belangrijk voordeel van moderne microbiële epidemiologie, waarin de focus steeds meer op data komt te liggen.
De data die als input voor microbieel epidemiologische analyses worden gebruikt, worden vaak verkregen uit laboratoriuminformatiesystemen (LIS). Deze data bestaan uit routine-diagnostische resultaten van laboratoriumtests. In **hoofdstuk 2** wordt de mening naar voren gebracht dat diagnostiek wél kan leiden tot ruwe resultaten, maar níet noodzakelijkerwijs leidt tot een direct antwoord op de klinische vraag die een behandelend arts van een patiënt kan hebben. Om artsen van antwoorden te voorzien is de aanpak van een multidisciplinair, verweven “stewardship”-concept nodig met een focus op diagnostiek. Dit vraagt van medisch specialisten in het algemeen (en artsen-microbioloog in het bijzonder) een nauwe interactie voor optimale kwaliteit van zorg en patiëntveiligheid dat leidt tot succesvol infectiemanagement: diagnostisch stewardship (DSP). Het begrip “stewardship” wordt veel gebruikt om communicatie en klinische besluitvorming te vergemakkelijken, maar het is een uitdaging gebleken om een duidelijke definitie van “stewardship” vast te stellen. Bovendien boekt de diagnostiek in medisch microbiologische laboratoria momenteel snelle vooruitgang met betrekking tot verbeterde workflows en nieuwe technologieën, zoals matrix-assisted laser desorption/ionisation time-of-flight (MALDI-TOF) massaspectrometrie. Diagnostiek bij infectiemanagement is echter breder dan dit en bestrijkt veel klinische gebieden waar communicatie en onderlinge interactie van fundamenteel belang zijn om optimaal gebruik te kunnen maken van kennis en expertise, zodat alle specialismen een bijdrage kunnen leveren aan patiëntenzorg. De juiste test op het juiste moment voor de juiste patiënt om de juiste vragen te beantwoorden en de juiste behandeling te starten – dat is waar het bij DSP in de medische microbiologie om draait. Microbiële epidemiologie kan ingezet worden voor een klein aspect van het diagnostische geheel, door testresultaten te recyclen en vervolgens verrijkingen aan te brengen in het antwoord-genererende proces dat DSP belichaamt.
**Hoofdstuk 3** gaat verder met het belichten van belangrijke huidige beperkingen bij de toepassing van microbiële epidemiologie, in het bijzonder bij de analyse van AMR-data. De analyse van AMR-data moet worden verricht op een klinisch en epidemiologisch zinvolle manier, hoewel dit een uitdaging is door de vereiste expertise in (klinische) epidemiologie en (medische) microbiologie, en goede instrumenten om de AMR-data-analyse uit te voeren. Dit wordt nog eens verder bemoeilijkt door het gebrek aan de toegankelijkheid van LIS-data, aangezien de meeste LIS-en niet zijn ontworpen om epidemiologische analyses te doen. Elk LIS houdt bijvoorbeeld zijn eigen taxonomische gegevens bij en de laboratoria zijn verantwoordelijk voor de regelmatige bijwerking ervan. Aangezien AMR-richtlijnen sterk gebaseerd zijn op de microbiële taxonomie (sommige regels gelden bijv. alleen voor een specifieke genus, andere regels gelden voor een specifieke familie), moet deze informatie correct en up-to-date zijn. Helaas is uit onderzoek onder zeven medisch microbiologische laboratoria in Nederland gebleken dat al hun LIS-en sterk verouderde taxonomische namen bevatten. Dit kan gevolgen hebben voor zowel de routinematige rapportage van resultaten als voor (toekomstige) epidemiologische analyses. Om deze redenen is in dit hoofdstuk het AMR-pakket voor R (een programmeertaal voor statistische berekeningen) geïntroduceerd als een nieuw epidemiologisch instrument voor de analyse van AMR-data. Het AMR-pakket is gratis, onafhankelijk, open-source, en openbaar beschikbaar. Het is ontwikkeld met een team van twaalf verschillende gezondheidsorganisaties in zeven verschillende landen en biedt hulpmiddelen om het opschonen, transformeren en analyseren van AMR-data te vereenvoudigen, mar ook om gemakkelijk (inter)nationale richtlijnen te kunnen toepassen en wetenschappelijk betrouwbare referentiedata te kunnen gebruiken. In mei 2021 was het meer dan 50.000 keer gedownload door 162 verschillende landen sinds de eerste release in 2018. Uit de resultaten van een enquête onder gebruikers die in dit hoofdstuk worden gepresenteerd, blijkt dat het gebruik ervan leidt tot meer reproduceerbaarheid van analyseresultaten, betrouwbaardere uitkomsten van AMR-data-analyses, en nieuwe of verbeterde inzichten in AMR voor de instellingen en regio’s van de gebruikers. Gebruikers gaven ook aan dat het AMR-pakket gebruikt is om klinische besluitvorming te ondersteunen. Het pakket lost het ongemak op van het afhankelijk zijn van (inter)nationale richtlijnen en betrouwbare (referentie)data, terwijl het ook een uitgebreide ‘toolbox’ biedt voor de analyse zelf. Het AMR-pakket voor R kan daarom elke specialist in ons veld die met AMR-gegevens werkt in staat stellen zijn werk makkelijker te doen.
**Sectie II**
Na de uitdagingen die in de vorige sectie zijn geschetst, wordt in deze sectie het AMR-pakket voor R geïntroduceerd als een nieuw instrument om deze uitdagingen aan te gaan. Vanuit verschillende invalshoeken worden het AMR-pakket en zijn voordelen in perspectief geplaatst: vanuit een technisch perspectief, vanuit het perspectief van infectiemanagement en vanuit een klinisch perspectief. Deze combinatie biedt een gemeenschappelijke basis voor het begrijpen van de oplossingen die het AMR-pakket kan bieden en hoe het een nieuw startpunt kan vormen voor toekomstige toepassingen van microbiële epidemiologie.
De technische functionaliteiten van het AMR-pakket voor R zijn beschreven in **hoofdstuk 4**, waarin wordt beschreven hoe het AMR-pakket is ontwikkeld om reproduceerbare AMR-data-analyses te standaardiseren aan de hand van internationale gestandaardiseerde aanbevelingen. Om dit mogelijk te maken zijn wetenschappelijk betrouwbare referentiedata gebruikt met betrekking tot de validatie van laboratoriumresultaten, antimicrobiële middelen en de volledige biologische taxonomie van micro-organismen. Brondata moeten op de meest betrouwbare manier worden geanalyseerd, vooral wanneer het resultaat bijvoorbeeld gebruikt gaat worden om de behandelingsopties voor een patiënt te evalueren. Dit vereist een reproduceerbare en gespecialiseerde verwerking van data. Het AMR-pakket biedt een gestandaardiseerde en geautomatiseerde manier om gemeenschappelijke LIS-data op te schonen, te transformeren en te verbeteren, onafhankelijk van de onderliggende databron en de nauwkeurigheid van de data. Hiervoor zijn algemeen toepasbare algoritmen ontwikkeld, teneinde AMR-testresultaten te kunnen opschonen en namen van micro-organismen en antimicrobiële middelen te kunnen valideren. De formule voor de validatie van taxonomische namen houdt rekening met het vóórkomen van ziekteverwekkende micro-organismen en is contextbewust wat betreft andere taxonomische eigenschappen zoals het koninkrijk, het fylum, de orde en de familie. Ter illustratie: een waarde “E. coli” wordt vertaald naar de bacterie Escherichia coli, terwijl de gebruiker ook wordt geïnformeerd dat de parasiet Entamoeba coli in aanmerking komt, maar een lagere waarschijnlijkheid heeft. Met behulp van behendige functies kunnen gebruikers snel consistente microbiële eigenschappen opvragen, zoals het taxonomische koninkrijk, de familie, het geslacht, de soort, verouderde taxonomische namen en zelfs de Gram-kleur. Naast informatie over micro-organismen bevat het pakket ook referentiedata over antibiotica, waaronder veelvoorkomende LIS-codes, officiële namen, ATC-codes (Anatomical Therapeutic Chemical), gedefinieerde dagelijkse doses (defined daily doses, DDD) en meer dan 5.000 handelsnamen van 456 antimicrobiële middelen. Met behulp van deze referentiedata kunnen gebruikers ruwe data vertalen en eigenschappen ophalen over elk micro-organisme of antibioticum. Bovendien is het AMR-pakket in staat om multiresistente organismen (multidrug-resistant organisms, MDRO’s) te identificeren op basis van nationale en internationale richtlijnen, minimum inhibitory concentrations (MIC’s) te interpreteren en kan het de eerste isolaten bepalen die gebruikt zouden moeten worden voor het berekenen van AMR voor zowel monotherapie als combinatietherapieën. Het AMR-pakket is bedoeld als een uitgebreid instrument voor data-technisch personeel dat werkzaam is op het gebied van AMR, hoewel het gebruik ervan niet beperkt is tot deze groep.
Om dit te illustreren, toont **hoofdstuk 5** aan dat het AMR-pakket gebruikt kan worden als ruggengraat in een interactieve open-source software app voor infectiemanagement en antimicrobial stewardship, genaamd RadaR (rapid analysis of diagnostic and antimicrobial patterns in R). Infectiemanagement in de vorm van Antimicrobial Stewardship Programma’s (ASP) heeft zich ontpopt als een effectieve oplossing om het mondiale gezondheidsprobleem van antibioticaresistentie in ziekenhuizen aan te pakken. Dit sluit aan bij **hoofdstuk 2**; stewardship-interventies en -activiteiten richten zich zowel op individuele patiënten (gepersonaliseerde geneeskunde en consultatie) als op patiëntengroepen of klinische syndromen, waarbij bij elke interventie moet leiden tot verbetering van de kwaliteit van de zorg en de veiligheid van de patiënt. Het is echter moeilijk om in de dagelijkse praktijk patiëntengroepen te analyseren (bijv. gestratificeerd naar afdeling, specifieke antimicrobiële middelen, of gebruikte diagnostische procedures). Het is zelfs nog moeilijker om snel grote patiëntpopulaties te analyseren (bijv. verspreid over meerdere specialismen), ook al is deze informatie beschikbaar in de data. Daarom is de ontwikkeling van RadaR bedoeld om ASP-teams te voorzien van een gebruiksvriendelijk en tijdbesparend hulpmiddel voor data-analyse, zonder dat dit diepgaande technische expertise vereist. RadaR biedt onder andere Kaplan-Meier-curves over de ligduur in ziekenhuizen, tijdstrends voor het aantal opnames, antibioticaconsumptie, en een geautomatiseerde AMR-data-analyse waarvoor het AMR-pakket voor R gebruikt is. RadaR werd geëvalueerd door 12 ESGAP-leden (European Society of Clinical Microbiology and Infectious Diseases Study Group for Antimicrobial Stewardship) uit 9 verschillende landen. Het heeft de potentie om een zeer nuttig middel te zijn voor infectiemanagement en ASP-teams in de dagelijkse praktijk. Bovendien toont dit hoofdstuk aan, dat het AMR-pakket gebruikt kan worden als onderdeel van een andere softwareoplossing om geïntegreerd infectiemanagement mogelijk te maken.
Hieruit volgend, illustreert **hoofdstuk 6** de effectiviteit van het AMR-pakket onder gebruikers, door het evalueren van de bruikbaarheid en de impact op het werk van artsen in een typisch klinisch scenario. Hoewel het AMR-pakket in research al in meerdere studies uit verschillende landen gebruikt is, was er nog geen analyse naar de impact op workflows voor AMR-analyse en -rapportage in een klinische omgeving. De analyse en rapportage van AMR-data vereisen helaas specifiek opgeleid personeel. Bovendien kunnen AMR-data-analyses tijdrovend zijn. Om de impact hiervan in een klinische setting te bepalen, werden algemene vragen over bloedkweekdata opgesteld die door klinisch routinepersoneel moesten worden beantwoord, waaronder artsen-microbioloog, kinderartsen en intensivisten. In totaal namen tien clinici deel aan het onderzoek. Bovendien werd de deelnemers gevraagd een online vragenlijst in te vullen over hun achtergrond, demografische gegevens (zoals leeftijd en geslacht), software-ervaring en eerdere ervaring met AMR-data-analyse en -rapportage. Alle deelnemers moesten de onderzoeksvragen tweemaal beantwoorden: de eerste keer met de software van hun keuze (ronde 1) en de tweede keer met behulp van een nieuw ontwikkelde webapplicatie gebouwd rond het AMR-pakket voor R (ronde 2). Voor de ontwikkeling van deze webapplicatie werd gebruik gemaakt van een zeer efficiënte agile workflow. De antwoorden op de onderzoeksvragen dienden als basis om de effectiviteit (antwoorden op elke taak voor elke gebruiker) en efficiëntie (tijd besteed aan het oplossen van elke taak) tussen de twee rondes te vergelijken. Niet alle deelnemers waren in staat de taken binnen het gestelde tijdsbestek af te ronden. De gemiddelde taakvoltooiing tussen de eerste en tweede ronde steeg van 56% naar 96% en het percentage correcte antwoorden steeg van 38% naar 98%. De gemiddelde bestede tijd per ronde werd met meer dan een uur verminderd. Dit hoofdstuk demonstreert de verhoogde effectiviteit, efficiëntie en nauwkeurigheid van het gebruik van het AMR-pakket voor R voor AMR-data-analyse in vergelijking met traditionele software zoals Microsoft Excel en SPSS.
**Sectie III**
Veel klinische studies op het gebied van infectieziekten en microbiologie berusten op een of andere vorm van (microbiële) epidemiologie. Terwijl in de vorige sectie het AMR-pakket is gepresenteerd en het gebruik ervan in verschillende scenario’s is gedemonstreerd, begint deze sectie met een epidemiologisch researchproject in de Noord-Nederlandse regio, en breidt de sectie zich vervolgens uit tot de Nederlands-Duitse grensregio om het vóórkomen van ziekteverwekkers en diens AMR-patronen op een (eu)regionaal niveau beter te begrijpen. Door in te zoomen op de regio’s aan weerszijden van een landsgrens kunnen op microniveau vergelijkingen worden gemaakt tussen twee verschillende naties. En verschillende naties betekenen uiteindelijk verschillende gezondheidszorgsystemen. Wat blijft er over van ‘One Health’? Wat zijn de gevolgen van de verschillen tussen landen wat betreft AMR-testmethoden, MDRO-interpretaties en screeningsbeleid? Deze sectie geeft antwoorden op deze vragen.
**Hoofdstuk 7** zoomt in op coagulase-negatieve stafylokokken (CNS), waarvan bekend is dat ze bloedbaaninfecties (BBI) en een hoog sterftecijfer veroorzaken, hoewel ze jarenlang vaak als ‘slechts’ besmettelijk werden beschouwd. Bovendien worden CNS-en steeds vaker in verband gebracht met nosocomiale infecties. Momenteel bestaat de CNS-groep uit 45 verschillende species (soorten), hoewel het bepalen van het speciesniveau pas onlangs mogelijk gemaakt is voor routinediagnostische laboratoria. Sinds 2012 is namelijk MALDI-TOF-massaspectrometrie de standaard geworden voor de identificatie van bacteriële species zoals CNS. Hiervoor gebeurde de identificatie van CNS-en hoofdzakelijk met biochemische en fysiologische tests, die doorgaans variërende resultaten opleverden, in het bijzonder bij minder prevalente species. AMR, en met name multiresistentie, is ook een toenemend probleem bij CNS-en. Niettemin worden CNS-en in behandelrichtlijnen en nationale surveillanceprogramma’s (zoals het Nederlandse NethMap) nog steeds als één groep beschouwd, zonder differentiatie tussen de species. Om deze reden is er weinig bekend over trends in het vóórkomen van, en AMR in, CNS-en op lokaal en regionaal niveau. Daarom toont deze retrospectieve studie een gedetailleerde AMR-analyse van bijna 20 duizend CNS-isolaten die gevonden waren in alle beschikbare 70 duizend bloedkweekisolaten tussen 2013 en 2019 in Noord-Nederland. Met deze analyse hebben we beoogd om de verschillen in het vóórkomen van CNS-species en hun AMR-patronen te evalueren en om hun klinisch microbiologische relevantie te beoordelen. In totaal werden 27 verschillende species van de CNS-groep gevonden. Er werden grote verschillen waargenomen in het vóórkomen van de verschillende species: de top vijf omvatte 97% van alle geïncludeerde isolaten (S. epidermidis, S. hominis, S. capitis, S. haemolyticus en S. warneri). Het aandeel van CNS-en op de intensive care (IC) in vergelijking met andere afdelingen bleek ook significant te verschillen tussen tweedelijns zorg en derdelijns zorg. Omdat onbekend was welke patiënten een BBI hadden, werd ‘CNS-persistentie’ gedefinieerd als een surrogaat waarvoor ten minste drie positieve bloedkweken afgenomen moesten zijn op drie verschillende dagen binnen 60 dagen, met dezelfde CNS, bij dezelfde patiënt. De relatief meest voorkomende veroorzaker van CNS-persistentie was S. haemolyticus, gevolgd door S. epidermidis en S. lugdunensis. AMR-analyse heeft aanzienlijke verschillen tussen CNS-species aangetoond. Zo vertoonden S. epidermidis en S. haemolyticus 50% tot 80% resistentie tegen de meeste antibiotica, terwijl de resistentie tegen deze middelen bij de meeste andere CNS-en lager dan 10% bleef. Toch worden deze verschillen op nationaal niveau, zoals in NethMap, verwaarloosd, wat ertoe zou kunnen leiden dat bij de ontwikkeling van behandelrichtlijnen de nadruk wordt gelegd op veilige en vertrouwde middelen voor de behandeling van CNS, zoals vancomycine of linezolid. Niettemin kunnen middelen zoals tetracycline, cotrimoxazol en erythromycine als alternatieve opties worden beschouwd voor sommige species, waar volgens de studieresultaten de AMR nooit boven de 10% is uitgekomen. Concluderend kan worden gesteld dat een meerjarige regio-totale benadering gebruikt is om de trends in zowel het vóórkomen als de AMR van CNS-species uitgebreid te beoordelen, wat kan worden gebruikt om het behandelingsbeleid te evalueren en meer te begrijpen over deze belangrijke maar nog steeds vaak niet serieus genomen pathogenen. Bovendien diende deze studie als een praktisch voorbeeld van hoe het AMR-pakket voor R kan worden gebruikt om nieuwe AMR-inzichten te verkrijgen met behulp van epidemiologisch onderbouwde methoden.
Als vervolg op de nieuwe inzichten door het bestuderen van AMR-testresultaten in Noord-Nederland, geeft **hoofdstuk 8** een vergelijking van nationale interpretaties van MDRO’s in de Nederlands-Duitse grensregio, vooral wat betreft de praktische gevolgen voor grenspersoneel in de gezondheidszorg. Het vergelijken van AMR in het algemeen, niet alleen MDRO’s, in deze grensoverschrijdende regio is bijzonder interessant omdat beide landen worden gekenmerkt door hoog ontwikkelde, maar desondanks structureel verschillende gezondheidszorgsystemen. AMR-interpretaties in patiëntendossiers worden overgedragen tussen zorginstellingen in deze twee verschillende landen, terwijl de onderliggende definities verschillen. Hierdoor moeten clinici en deskundigen infectiepreventie de AMR-resultaten van beide kanten van de grens begrijpen en in staat zijn om beide nationale MDRO-richtlijnen toe te kunnen passen. Door antibiogrammen van Gram-negatieve bacteriën uit beide kanten van de grens met elkaar te vergelijken, werd getracht de mate van impact van deze uitdagingen te bepalen. Hiertoe werden tussen 2015 en 2016 35.619 antibiogrammen van alle soorten Enterobacteriaceae, en P. aeruginosa, het A. baumannii-complex en Stenotrophomonas maltophilia uit zes Nederlandse en vier Duitse ziekenhuizen geanalyseerd. Voor al deze soorten bestaan in deze regio MDRO-aanbevelingen en speciale infectiepreventiemaatregelen. Uit de Nederlandse ziekenhuizen werden 12.616 antibiogrammen geselecteerd met behulp van het AMR-pakket voor R waarmee ook de Nederlandse MDRO-richtlijn toegepast kon worden. Van belang is dat andere nationale en internationale richtlijnen, zoals de Duitse MDRO-richtlijn, ook zijn opgenomen in het AMR-pakket voor R. Uit Duitse ziekenhuizen werden 23.003 antibiogrammen geselecteerd. Volgens de Nederlandse richtlijn was 25% van alle isolaten een MDRO. Volgens de Duitse richtlijn was 13% van alle isolaten een MDRO. Echter, van alle isolaten werd 74% niet geclassificeerd als een MDRO volgens een van beide richtlijnen. Wanneer patiënten tussen ziekenhuizen worden overgebracht, moet ook informatie over MDRO-kolonisatie of -infectie worden overgedragen om de continue uitvoering van infectiepreventiemaatregelen te waarborgen. Voor grensoverschrijdende gezondheidszorg houdt dit in dat clinici of deskundigen infectiepreventie in staat moeten zijn MDRO’s te bepalen op basis van antibiogrammen volgens de richtlijnen van een van beide landen. Voor grensoverschrijdende gezondheidszorg zou de eenvoudigste oplossing zijn de richtlijnen van beide landen te harmoniseren. Dit zou ook een oplossing bieden voor de begrijpelijke verwarring die patiënten zouden kunnen ondervinden wanneer infectiepreventiemaatregelen in het ene land worden opgelegd, maar na overplaatsing naar een ander land weer worden opgeheven. Zolang de harmonisatie niet is gerealiseerd, moeten de volledige AMR-gegevens samen met de patiënt worden overgedragen om classificatie voor lokale deskundigen infectiepreventie mogelijk te maken.
Andere AMR-gerelateerde grensoverschrijdende uitdagingen en verschillen worden geïllustreerd in **hoofdstuk 9**, dat een uitgebreide microbiële epidemiologische analyse omvat van het vóórkomen van MRSA, en het beleid en de gevolgen voor de gezondheidszorg in het Nederlands-Duitse grensgebied. MRSA is nog steeds een van de belangrijkste oorzaken van gezondheidszorg-geassocieerde infecties als gevolg van resistente ziekteverwekkers. In deze studie werden MRSA-surveillancegegevens van vijf jaar (2012-2016) van Nederlandse en Duitse grensoverschrijdende regioziekenhuizen geanalyseerd om regio-specifieke trends in de tijd van MRSA te beschrijven en om verschillen tussen ziekenhuizengroepen vast te stellen. De studie omvatte 42 ziekenhuizen in de Nederlands-Duitse grensregio met ongeveer 620.000 opgenomen patiënten (68,0% in het Duitse deel van de onderzoeksregio) met bijna vier miljoen patiëntdagen per jaar. Alle ziekenhuizen hadden MRSA-gerelateerde infectiepreventiemaatregelen geïmplementeerd volgens hun nationale richtlijnen en aanbevelingen, en de verschillen in richtlijnen tussen de twee landen werden vergeleken. Aan beide zijden van de grens nam het MRSA-screeningspercentage tussen 2012 en 2016 aanzienlijk toe, hoewel de MRSA-incidentie in de loop van de tijd aan beide zijden van de grens stabiel bleef. In totaal was het screeningspercentage in de Duitse grensregio 14 keer hoger dan in de Nederlandse grensregio. Het percentage MRSA’s in bloedkweekisolaten met S. aureus daalde van 13% in 2012 tot 5% in 2016 in de Duitse grensregio, terwijl het stabiel bleef in de Nederlandse grensregio (0% tot 2%). Niettemin was het ruwe aantal MRSA’s onder S. aureus-isolaten 34 keer hoger in de Duitse grensregio. De ligduur in het ziekenhuis bij MRSA-patiënten was in beide regio’s vergelijkbaar, terwijl de algemene ligduur aanzienlijk verschilde. Verder bedroeg het aantal MRSA-uitstrijken voor of bij opname in het ziekenhuis per 100 inwoners 12,2 in de Duitse grensregio en 0,36 in de Nederlandse grensregio; 34 keer zo hoog in de Duitse grensregio. Het aantal intramurale MRSA-gevallen per 1.000 inwoners bedroeg 2,52 in de Duitse grensregio en 0,14 in de Nederlandse grensregio. Dit onderzoek liet dus significante verschillen zien tussen Nederlandse en Duitse ziekenhuizen. De MRSA-incidentie in Duitse ziekenhuizen was meer dan zeven keer hoger dan in Nederlandse ziekenhuizen. Volgens het European Centre of Disease Prevention and Control (ECDC) worden verschillen in het vóórkomen van resistente ziekteverwekkers tussen Europese landen hoogstwaarschijnlijk veroorzaakt door verschillen in zorggebruik, antimicrobieel gebruik en infectiepreventiemaatregelen. Wat het zorggebruik in onze context betreft, stelden wij vast dat inwoners in het Duitse deel van het studiegebied bijna drie keer zo vaak in het ziekenhuis werden opgenomen en een aanzienlijk langere ligduur hadden dan patiënten in het Nederlandse deel. Dit kan te wijten zijn aan sociaaleconomische factoren of een andere inrichting van ambulante gezondheidszorg. Deze uitgebreide studie over MRSA in ziekenhuizen rond een Europese grens heeft aangetoond dat routinematige MRSA-surveillance nuttig kan zijn om trends van MRSA te volgen, om zodoende (inter)nationale vergelijkingen mogelijk te maken.
De discussie van deze studie werd afgesloten met (vertaald) “grensoverschrijdende surveillance moet worden uitgebreid naar andere multiresistente micro-organismen”, wat precies is waar **hoofdstuk 10** op voortborduurt. Aangezien niet alleen MRSA’s maar MDRO’s in het algemeen een risico vormen voor de gezondheidszorg, zowel in de gemeenschap als in ziekenhuizen, had deze studie tot doel de prevalentie van meerdere MDRO’s in deze grensoverschrijdende regio vast te stellen om verschillen te begrijpen en infectiepreventie te verbeteren op basis van real-time routinegegevens. Hiertoe namen 23 ziekenhuizen in de Nederlands-Duitse grensregio tussen 2017 en 2018 deel aan deze prospectieve studie door alle patiënten bij opname op de IC te screenen. Alle ziekenhuizen (8 in Nederland, 15 in Duitsland) screenden patiënten gedurende acht opeenvolgende weken op dragerschap van MRSA, vancomycineresistente Enterococcus faecium/E. faecalis (VRE), derde-generatie cefalosporine-resistente Enterobacteriaceae (3GCRE) en carbapenem-resistente Enterobacteriaceae (CRE). In totaal werden 3.365 patiënten gescreend: 36% op Nederlandse IC’s en 64% op Duitse IC’s. De mediane leeftijd van alle gescreende patiënten was 68 jaar (IQR: 57-77), waarbij patiënten in de Duitse grensregio significant ouder waren dan patiënten in de Nederlandse grensregio. De algemene screening compliance (gescreend op ten minste één MDRO-groep) was 60%. Alle AMR-data-analyses werden uitgevoerd en geautomatiseerd met behulp van het AMR-pakket voor R. De prevalentie van MRSA was 1,7% op Duitse IC’s en 0,6% op Nederlandse IC’s. De prevalentie van VRE was 2,7% op Duitse IC’s en 0,1% op Nederlandse IC’s. Opmerkelijk is dat deze prevalentie varieerde van 0% tot 4,1% in het Duitse grensgebied. Alle 56 gevallen van VRE werden veroorzaakt door E. faecium. De prevalentie van 3GCRE was 6,6% op Duitse IC’s en 3,6% op Nederlandse IC’s, terwijl de prevalentie voor CRE aan beide zijden van de grens nagenoeg nihil was. De prevalentie voor Gram-negatieve MDRO’s verschilde tussen ziekenhuizen in beide landen, variërend van 0% tot 5,0% in de Nederlandse grensregio en van 1,2% tot 10,9% in de Duitse grensregio. Voor de geïncludeerde Nederlandse IC’s was de prevalentie van alle MDRO-groepen niet significant verschillend tussen tweedelijns en derdelijns ziekenhuizen. Voor de Duitse IC’s was de prevalentie van Gram-negatieve MDRO’s echter significant hoger in de tweedelijns ziekenhuizen. In de Nederlandse grensregio leidde 4,8 per 100 ziekenhuisopnamen tot een IC-opname. In de Duitse grensregio was dit daarentegen 7,7 per 100 ziekenhuisopnames. Dit verschil kan worden verklaard door de hogere IC-capaciteit in Duitse ziekenhuizen (4,8% van alle ziekenhuisbedden) in vergelijking met Nederlandse ziekenhuizen (2,4% van alle ziekenhuisbedden). De algemene prevalentie van de verschillende MDRO’s was hoger op de Duitse IC’s, hoewel sommige verschillen marginaal waren. Met name de prevalentie van MRSA was drie keer hoger in de Duitse grensregio dan in de Nederlandse grensregio, wat consistent is met de onderzoeksresultaten in **hoofdstuk 9**. Toch waren de onderzoeksresultaten niet consistent met (inter)nationale gemiddelden. Zo was de 3GCRE-prevalentie bijna twee keer zo hoog in de Duitse grensregio als in de Nederlandse grensregio, maar beide waren nog steeds lager dan de nationale gemiddelden; de ECDC meldde 6% hogere 3GCRE-percentages onder E. coli en K. pneumoniae uit bloedkweken voor Duitsland en Nederland. Hieruit blijkt dat er belangrijke verschillen zijn tussen het bestuderen van dragerschap in bepaalde populaties en het bestuderen van het aandeel van (waarschijnlijk) invasieve isolaten. Deze studie benadrukt daarmee het belang van een regionale en grensoverschrijdende aanpak in een Europese grensregio, om het verschil in AMR-prevalentie tussen de regio’s te illustreren en om potentiële verschillen met nationale rapporten te belichten. Om dit verder te kunnen uitwerken is een dieper detailniveau nodig, bijvoorbeeld door informatie te verzamelen over (infectiepreventie)personeel, MDRO-uitbraken, infecties, antibioticagebruik en risicofactoren van patiënten. Concluderend lijken geografische en politieke grenzen door MDRO’s niet te worden “gerespecteerd”, hoewel de gezondheidszorgsystemen, de geografische aard en de richtlijnen van land tot land sterk verschillen. De percentages MDRO’s van bepaalde ziekteverwekkers, zoals gerapporteerd op nationaal en internationaal niveau, weerspiegelen niet de prevalentie van MDRO’s in de patiënt of in de algemene bevolking. Dit moet ernstig in overweging worden genomen bij de interpretatie van rapporten op nationaal of zelfs continentaal niveau.
**Conclusie**
Vanuit verschillende invalshoeken worden het AMR-pakket en zijn voordelen in perspectief geplaatst: vanuit een technisch perspectief, vanuit het perspectief van infectiemanagement en vanuit een klinisch perspectief. Deze combinatie biedt een gemeenschappelijke basis voor het begrijpen van de oplossingen die het AMR-pakket kan bieden en hoe het een nieuw startpunt kan vormen voor toekomstige toepassingen van microbiële epidemiologie, zowel in klinische settings als in wetenschappelijk onderzoek. Dit proefschrift gaat vervolgens in op deze verschillende invalshoeken door het gebruik van dit nieuwe instrument te illustreren in epidemiologische studies in de Nederlands-Duitse grensregio om het vóórkomen en de AMR-trends van micro-organismen op (eu)regionaal niveau beter te begrijpen. Concluderend toont dit proefschrift de toegevoegde waarde aan van een consistent data-analytisch instrument om AMR-data voor te bereiden en te analyseren in een regio-overstijgende benadering, om nieuwe inzichten te verkrijgen in AMR-trends.

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<li class="chapter" data-level="" data-path="ch09-changing-epidemiology.html"><a href="ch09-changing-epidemiology.html#funding-1"><i class="fa fa-check"></i>Funding</a></li>
<li class="chapter" data-level="" data-path="ch09-changing-epidemiology.html"><a href="ch09-changing-epidemiology.html#references-8"><i class="fa fa-check"></i>References</a></li>
</ul></li>
<li class="chapter" data-level="10" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html"><i class="fa fa-check"></i><b>10</b> A Prospective Multicentre MDRO Screening Study on ICUs in the Dutch-German Cross-Border Region (2017-2018): The Importance of Healthcare Structures</a>
<ul>
<li class="chapter" data-level="" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#abstract-8"><i class="fa fa-check"></i>Abstract</a></li>
<li class="chapter" data-level="10.1" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#introduction-7"><i class="fa fa-check"></i><b>10.1</b> Introduction</a></li>
<li class="chapter" data-level="10.2" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#methods-4"><i class="fa fa-check"></i><b>10.2</b> Methods</a>
<ul>
<li class="chapter" data-level="10.2.1" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#study-design"><i class="fa fa-check"></i><b>10.2.1</b> Study Design</a></li>
<li class="chapter" data-level="10.2.2" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#statistical-analysis-software"><i class="fa fa-check"></i><b>10.2.2</b> Statistical Analysis &amp; Software</a></li>
<li class="chapter" data-level="10.2.3" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#ethics"><i class="fa fa-check"></i><b>10.2.3</b> Ethics</a></li>
</ul></li>
<li class="chapter" data-level="10.3" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#results-5"><i class="fa fa-check"></i><b>10.3</b> Results</a>
<ul>
<li class="chapter" data-level="10.3.1" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#healthcare-structure-of-the-participating-hospitals"><i class="fa fa-check"></i><b>10.3.1</b> Healthcare structure of the participating hospitals</a></li>
<li class="chapter" data-level="10.3.2" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#study-population-and-screening-samples-from-icus"><i class="fa fa-check"></i><b>10.3.2</b> Study population and screening samples from ICUs</a></li>
<li class="chapter" data-level="10.3.3" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#prevalence-of-gram-positive-mdros-mrsa-and-vre"><i class="fa fa-check"></i><b>10.3.3</b> Prevalence of Gram-positive MDROs: MRSA and VRE</a></li>
<li class="chapter" data-level="10.3.4" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#prevalence-of-gram-negative-mdro-3gcre-and-cre"><i class="fa fa-check"></i><b>10.3.4</b> Prevalence of Gram-negative MDRO: 3GCRE and CRE</a></li>
<li class="chapter" data-level="10.3.5" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#prevalence-of-gram-negative-mdros-based-on-dutch-and-german-definitions"><i class="fa fa-check"></i><b>10.3.5</b> Prevalence of Gram-negative MDROs based on Dutch and German definitions</a></li>
<li class="chapter" data-level="10.3.6" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#comparison-of-mdro-prevalence-between-nl-br-and-de-br-icus-in-university-and-non-university-hospitals"><i class="fa fa-check"></i><b>10.3.6</b> Comparison of MDRO prevalence between NL-BR and DE-BR ICUs in university and non-university hospitals</a></li>
</ul></li>
<li class="chapter" data-level="10.4" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#discussion-6"><i class="fa fa-check"></i><b>10.4</b> Discussion</a></li>
<li class="chapter" data-level="" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#supplementary-files"><i class="fa fa-check"></i>Supplementary files</a></li>
<li class="chapter" data-level="" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#acknowledgements-5"><i class="fa fa-check"></i>Acknowledgements</a></li>
<li class="chapter" data-level="" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#conflict-of-interest-1"><i class="fa fa-check"></i>Conflict of interest</a></li>
<li class="chapter" data-level="" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#references-9"><i class="fa fa-check"></i>References</a></li>
</ul></li>
<li class="chapter" data-level="11" data-path="ch11-summary.html"><a href="ch11-summary.html"><i class="fa fa-check"></i><b>11</b> Summary and Future Perspectives</a>
<ul>
<li class="chapter" data-level="" data-path="ch11-summary.html"><a href="ch11-summary.html#section-i"><i class="fa fa-check"></i>Section I</a></li>
<li class="chapter" data-level="" data-path="ch11-summary.html"><a href="ch11-summary.html#section-ii"><i class="fa fa-check"></i>Section II</a></li>
<li class="chapter" data-level="" data-path="ch11-summary.html"><a href="ch11-summary.html#section-iii"><i class="fa fa-check"></i>Section III</a></li>
<li class="chapter" data-level="" data-path="ch11-summary.html"><a href="ch11-summary.html#future-perspectives"><i class="fa fa-check"></i>Future perspectives</a></li>
<li class="chapter" data-level="" data-path="ch11-summary.html"><a href="ch11-summary.html#references-10"><i class="fa fa-check"></i>References</a></li>
</ul></li>
<li class="chapter" data-level="" data-path="gearfetting-yn-frysk.html"><a href="gearfetting-yn-frysk.html"><i class="fa fa-check"></i>Gearfetting yn Frysk</a></li>
<li class="chapter" data-level="" data-path="samenvatting-in-het-nederlands.html"><a href="samenvatting-in-het-nederlands.html"><i class="fa fa-check"></i>Samenvatting in het Nederlands</a></li>
<li class="chapter" data-level="" data-path="zusammenfassung-auf-deutsch.html"><a href="zusammenfassung-auf-deutsch.html"><i class="fa fa-check"></i>Zusammenfassung auf Deutsch</a></li>
<li class="divider"></li>
<li class="copyright">© 2021 Matthijs Berends</li>

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<li class="chapter" data-level="" data-path="ch09-changing-epidemiology.html"><a href="ch09-changing-epidemiology.html#funding-1"><i class="fa fa-check"></i>Funding</a></li>
<li class="chapter" data-level="" data-path="ch09-changing-epidemiology.html"><a href="ch09-changing-epidemiology.html#references-8"><i class="fa fa-check"></i>References</a></li>
</ul></li>
<li class="chapter" data-level="10" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html"><i class="fa fa-check"></i><b>10</b> A Prospective Multicentre MDRO Screening Study on ICUs in the Dutch-German Cross-Border Region (2017-2018): The Importance of Healthcare Structures</a>
<ul>
<li class="chapter" data-level="" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#abstract-8"><i class="fa fa-check"></i>Abstract</a></li>
<li class="chapter" data-level="10.1" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#introduction-7"><i class="fa fa-check"></i><b>10.1</b> Introduction</a></li>
<li class="chapter" data-level="10.2" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#methods-4"><i class="fa fa-check"></i><b>10.2</b> Methods</a>
<ul>
<li class="chapter" data-level="10.2.1" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#study-design"><i class="fa fa-check"></i><b>10.2.1</b> Study Design</a></li>
<li class="chapter" data-level="10.2.2" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#statistical-analysis-software"><i class="fa fa-check"></i><b>10.2.2</b> Statistical Analysis &amp; Software</a></li>
<li class="chapter" data-level="10.2.3" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#ethics"><i class="fa fa-check"></i><b>10.2.3</b> Ethics</a></li>
</ul></li>
<li class="chapter" data-level="10.3" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#results-5"><i class="fa fa-check"></i><b>10.3</b> Results</a>
<ul>
<li class="chapter" data-level="10.3.1" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#healthcare-structure-of-the-participating-hospitals"><i class="fa fa-check"></i><b>10.3.1</b> Healthcare structure of the participating hospitals</a></li>
<li class="chapter" data-level="10.3.2" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#study-population-and-screening-samples-from-icus"><i class="fa fa-check"></i><b>10.3.2</b> Study population and screening samples from ICUs</a></li>
<li class="chapter" data-level="10.3.3" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#prevalence-of-gram-positive-mdros-mrsa-and-vre"><i class="fa fa-check"></i><b>10.3.3</b> Prevalence of Gram-positive MDROs: MRSA and VRE</a></li>
<li class="chapter" data-level="10.3.4" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#prevalence-of-gram-negative-mdro-3gcre-and-cre"><i class="fa fa-check"></i><b>10.3.4</b> Prevalence of Gram-negative MDRO: 3GCRE and CRE</a></li>
<li class="chapter" data-level="10.3.5" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#prevalence-of-gram-negative-mdros-based-on-dutch-and-german-definitions"><i class="fa fa-check"></i><b>10.3.5</b> Prevalence of Gram-negative MDROs based on Dutch and German definitions</a></li>
<li class="chapter" data-level="10.3.6" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#comparison-of-mdro-prevalence-between-nl-br-and-de-br-icus-in-university-and-non-university-hospitals"><i class="fa fa-check"></i><b>10.3.6</b> Comparison of MDRO prevalence between NL-BR and DE-BR ICUs in university and non-university hospitals</a></li>
</ul></li>
<li class="chapter" data-level="10.4" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#discussion-6"><i class="fa fa-check"></i><b>10.4</b> Discussion</a></li>
<li class="chapter" data-level="" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#supplementary-files"><i class="fa fa-check"></i>Supplementary files</a></li>
<li class="chapter" data-level="" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#acknowledgements-5"><i class="fa fa-check"></i>Acknowledgements</a></li>
<li class="chapter" data-level="" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#conflict-of-interest-1"><i class="fa fa-check"></i>Conflict of interest</a></li>
<li class="chapter" data-level="" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#references-9"><i class="fa fa-check"></i>References</a></li>
</ul></li>
<li class="chapter" data-level="11" data-path="ch11-summary.html"><a href="ch11-summary.html"><i class="fa fa-check"></i><b>11</b> Summary and Future Perspectives</a>
<ul>
<li class="chapter" data-level="" data-path="ch11-summary.html"><a href="ch11-summary.html#section-i"><i class="fa fa-check"></i>Section I</a></li>
<li class="chapter" data-level="" data-path="ch11-summary.html"><a href="ch11-summary.html#section-ii"><i class="fa fa-check"></i>Section II</a></li>
<li class="chapter" data-level="" data-path="ch11-summary.html"><a href="ch11-summary.html#section-iii"><i class="fa fa-check"></i>Section III</a></li>
<li class="chapter" data-level="" data-path="ch11-summary.html"><a href="ch11-summary.html#future-perspectives"><i class="fa fa-check"></i>Future perspectives</a></li>
<li class="chapter" data-level="" data-path="ch11-summary.html"><a href="ch11-summary.html#references-10"><i class="fa fa-check"></i>References</a></li>
</ul></li>
<li class="chapter" data-level="" data-path="gearfetting-yn-frysk.html"><a href="gearfetting-yn-frysk.html"><i class="fa fa-check"></i>Gearfetting yn Frysk</a></li>
<li class="chapter" data-level="" data-path="samenvatting-in-het-nederlands.html"><a href="samenvatting-in-het-nederlands.html"><i class="fa fa-check"></i>Samenvatting in het Nederlands</a></li>
<li class="chapter" data-level="" data-path="zusammenfassung-auf-deutsch.html"><a href="zusammenfassung-auf-deutsch.html"><i class="fa fa-check"></i>Zusammenfassung auf Deutsch</a></li>
<li class="divider"></li>
<li class="copyright">© 2021 Matthijs Berends</li>

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<li class="chapter" data-level="" data-path="ch09-changing-epidemiology.html"><a href="ch09-changing-epidemiology.html#funding-1"><i class="fa fa-check"></i>Funding</a></li>
<li class="chapter" data-level="" data-path="ch09-changing-epidemiology.html"><a href="ch09-changing-epidemiology.html#references-8"><i class="fa fa-check"></i>References</a></li>
</ul></li>
<li class="chapter" data-level="10" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html"><i class="fa fa-check"></i><b>10</b> A Prospective Multicentre MDRO Screening Study on ICUs in the Dutch-German Cross-Border Region (2017-2018): The Importance of Healthcare Structures</a>
<ul>
<li class="chapter" data-level="" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#abstract-8"><i class="fa fa-check"></i>Abstract</a></li>
<li class="chapter" data-level="10.1" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#introduction-7"><i class="fa fa-check"></i><b>10.1</b> Introduction</a></li>
<li class="chapter" data-level="10.2" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#methods-4"><i class="fa fa-check"></i><b>10.2</b> Methods</a>
<ul>
<li class="chapter" data-level="10.2.1" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#study-design"><i class="fa fa-check"></i><b>10.2.1</b> Study Design</a></li>
<li class="chapter" data-level="10.2.2" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#statistical-analysis-software"><i class="fa fa-check"></i><b>10.2.2</b> Statistical Analysis &amp; Software</a></li>
<li class="chapter" data-level="10.2.3" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#ethics"><i class="fa fa-check"></i><b>10.2.3</b> Ethics</a></li>
</ul></li>
<li class="chapter" data-level="10.3" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#results-5"><i class="fa fa-check"></i><b>10.3</b> Results</a>
<ul>
<li class="chapter" data-level="10.3.1" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#healthcare-structure-of-the-participating-hospitals"><i class="fa fa-check"></i><b>10.3.1</b> Healthcare structure of the participating hospitals</a></li>
<li class="chapter" data-level="10.3.2" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#study-population-and-screening-samples-from-icus"><i class="fa fa-check"></i><b>10.3.2</b> Study population and screening samples from ICUs</a></li>
<li class="chapter" data-level="10.3.3" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#prevalence-of-gram-positive-mdros-mrsa-and-vre"><i class="fa fa-check"></i><b>10.3.3</b> Prevalence of Gram-positive MDROs: MRSA and VRE</a></li>
<li class="chapter" data-level="10.3.4" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#prevalence-of-gram-negative-mdro-3gcre-and-cre"><i class="fa fa-check"></i><b>10.3.4</b> Prevalence of Gram-negative MDRO: 3GCRE and CRE</a></li>
<li class="chapter" data-level="10.3.5" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#prevalence-of-gram-negative-mdros-based-on-dutch-and-german-definitions"><i class="fa fa-check"></i><b>10.3.5</b> Prevalence of Gram-negative MDROs based on Dutch and German definitions</a></li>
<li class="chapter" data-level="10.3.6" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#comparison-of-mdro-prevalence-between-nl-br-and-de-br-icus-in-university-and-non-university-hospitals"><i class="fa fa-check"></i><b>10.3.6</b> Comparison of MDRO prevalence between NL-BR and DE-BR ICUs in university and non-university hospitals</a></li>
</ul></li>
<li class="chapter" data-level="10.4" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#discussion-6"><i class="fa fa-check"></i><b>10.4</b> Discussion</a></li>
<li class="chapter" data-level="" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#supplementary-files"><i class="fa fa-check"></i>Supplementary files</a></li>
<li class="chapter" data-level="" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#acknowledgements-5"><i class="fa fa-check"></i>Acknowledgements</a></li>
<li class="chapter" data-level="" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#conflict-of-interest-1"><i class="fa fa-check"></i>Conflict of interest</a></li>
<li class="chapter" data-level="" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#references-9"><i class="fa fa-check"></i>References</a></li>
</ul></li>
<li class="chapter" data-level="11" data-path="ch11-summary.html"><a href="ch11-summary.html"><i class="fa fa-check"></i><b>11</b> Summary and Future Perspectives</a>
<ul>
<li class="chapter" data-level="" data-path="ch11-summary.html"><a href="ch11-summary.html#section-i"><i class="fa fa-check"></i>Section I</a></li>
<li class="chapter" data-level="" data-path="ch11-summary.html"><a href="ch11-summary.html#section-ii"><i class="fa fa-check"></i>Section II</a></li>
<li class="chapter" data-level="" data-path="ch11-summary.html"><a href="ch11-summary.html#section-iii"><i class="fa fa-check"></i>Section III</a></li>
<li class="chapter" data-level="" data-path="ch11-summary.html"><a href="ch11-summary.html#future-perspectives"><i class="fa fa-check"></i>Future perspectives</a></li>
<li class="chapter" data-level="" data-path="ch11-summary.html"><a href="ch11-summary.html#references-10"><i class="fa fa-check"></i>References</a></li>
</ul></li>
<li class="chapter" data-level="" data-path="gearfetting-yn-frysk.html"><a href="gearfetting-yn-frysk.html"><i class="fa fa-check"></i>Gearfetting yn Frysk</a></li>
<li class="chapter" data-level="" data-path="samenvatting-in-het-nederlands.html"><a href="samenvatting-in-het-nederlands.html"><i class="fa fa-check"></i>Samenvatting in het Nederlands</a></li>
<li class="chapter" data-level="" data-path="zusammenfassung-auf-deutsch.html"><a href="zusammenfassung-auf-deutsch.html"><i class="fa fa-check"></i>Zusammenfassung auf Deutsch</a></li>
<li class="divider"></li>
<li class="copyright">© 2021 Matthijs Berends</li>

36
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<li class="chapter" data-level="" data-path="ch09-changing-epidemiology.html"><a href="ch09-changing-epidemiology.html#funding-1"><i class="fa fa-check"></i>Funding</a></li>
<li class="chapter" data-level="" data-path="ch09-changing-epidemiology.html"><a href="ch09-changing-epidemiology.html#references-8"><i class="fa fa-check"></i>References</a></li>
</ul></li>
<li class="chapter" data-level="10" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html"><i class="fa fa-check"></i><b>10</b> A Prospective Multicentre MDRO Screening Study on ICUs in the Dutch-German Cross-Border Region (2017-2018): The Importance of Healthcare Structures</a>
<ul>
<li class="chapter" data-level="" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#abstract-8"><i class="fa fa-check"></i>Abstract</a></li>
<li class="chapter" data-level="10.1" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#introduction-7"><i class="fa fa-check"></i><b>10.1</b> Introduction</a></li>
<li class="chapter" data-level="10.2" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#methods-4"><i class="fa fa-check"></i><b>10.2</b> Methods</a>
<ul>
<li class="chapter" data-level="10.2.1" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#study-design"><i class="fa fa-check"></i><b>10.2.1</b> Study Design</a></li>
<li class="chapter" data-level="10.2.2" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#statistical-analysis-software"><i class="fa fa-check"></i><b>10.2.2</b> Statistical Analysis &amp; Software</a></li>
<li class="chapter" data-level="10.2.3" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#ethics"><i class="fa fa-check"></i><b>10.2.3</b> Ethics</a></li>
</ul></li>
<li class="chapter" data-level="10.3" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#results-5"><i class="fa fa-check"></i><b>10.3</b> Results</a>
<ul>
<li class="chapter" data-level="10.3.1" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#healthcare-structure-of-the-participating-hospitals"><i class="fa fa-check"></i><b>10.3.1</b> Healthcare structure of the participating hospitals</a></li>
<li class="chapter" data-level="10.3.2" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#study-population-and-screening-samples-from-icus"><i class="fa fa-check"></i><b>10.3.2</b> Study population and screening samples from ICUs</a></li>
<li class="chapter" data-level="10.3.3" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#prevalence-of-gram-positive-mdros-mrsa-and-vre"><i class="fa fa-check"></i><b>10.3.3</b> Prevalence of Gram-positive MDROs: MRSA and VRE</a></li>
<li class="chapter" data-level="10.3.4" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#prevalence-of-gram-negative-mdro-3gcre-and-cre"><i class="fa fa-check"></i><b>10.3.4</b> Prevalence of Gram-negative MDRO: 3GCRE and CRE</a></li>
<li class="chapter" data-level="10.3.5" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#prevalence-of-gram-negative-mdros-based-on-dutch-and-german-definitions"><i class="fa fa-check"></i><b>10.3.5</b> Prevalence of Gram-negative MDROs based on Dutch and German definitions</a></li>
<li class="chapter" data-level="10.3.6" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#comparison-of-mdro-prevalence-between-nl-br-and-de-br-icus-in-university-and-non-university-hospitals"><i class="fa fa-check"></i><b>10.3.6</b> Comparison of MDRO prevalence between NL-BR and DE-BR ICUs in university and non-university hospitals</a></li>
</ul></li>
<li class="chapter" data-level="10.4" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#discussion-6"><i class="fa fa-check"></i><b>10.4</b> Discussion</a></li>
<li class="chapter" data-level="" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#supplementary-files"><i class="fa fa-check"></i>Supplementary files</a></li>
<li class="chapter" data-level="" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#acknowledgements-5"><i class="fa fa-check"></i>Acknowledgements</a></li>
<li class="chapter" data-level="" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#conflict-of-interest-1"><i class="fa fa-check"></i>Conflict of interest</a></li>
<li class="chapter" data-level="" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#references-9"><i class="fa fa-check"></i>References</a></li>
</ul></li>
<li class="chapter" data-level="11" data-path="ch11-summary.html"><a href="ch11-summary.html"><i class="fa fa-check"></i><b>11</b> Summary and Future Perspectives</a>
<ul>
<li class="chapter" data-level="" data-path="ch11-summary.html"><a href="ch11-summary.html#section-i"><i class="fa fa-check"></i>Section I</a></li>
<li class="chapter" data-level="" data-path="ch11-summary.html"><a href="ch11-summary.html#section-ii"><i class="fa fa-check"></i>Section II</a></li>
<li class="chapter" data-level="" data-path="ch11-summary.html"><a href="ch11-summary.html#section-iii"><i class="fa fa-check"></i>Section III</a></li>
<li class="chapter" data-level="" data-path="ch11-summary.html"><a href="ch11-summary.html#future-perspectives"><i class="fa fa-check"></i>Future perspectives</a></li>
<li class="chapter" data-level="" data-path="ch11-summary.html"><a href="ch11-summary.html#references-10"><i class="fa fa-check"></i>References</a></li>
</ul></li>
<li class="chapter" data-level="" data-path="gearfetting-yn-frysk.html"><a href="gearfetting-yn-frysk.html"><i class="fa fa-check"></i>Gearfetting yn Frysk</a></li>
<li class="chapter" data-level="" data-path="samenvatting-in-het-nederlands.html"><a href="samenvatting-in-het-nederlands.html"><i class="fa fa-check"></i>Samenvatting in het Nederlands</a></li>
<li class="chapter" data-level="" data-path="zusammenfassung-auf-deutsch.html"><a href="zusammenfassung-auf-deutsch.html"><i class="fa fa-check"></i>Zusammenfassung auf Deutsch</a></li>
<li class="divider"></li>
<li class="copyright">© 2021 Matthijs Berends</li>

36
docs/ch05-radar.html

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<li class="chapter" data-level="" data-path="ch09-changing-epidemiology.html"><a href="ch09-changing-epidemiology.html#funding-1"><i class="fa fa-check"></i>Funding</a></li>
<li class="chapter" data-level="" data-path="ch09-changing-epidemiology.html"><a href="ch09-changing-epidemiology.html#references-8"><i class="fa fa-check"></i>References</a></li>
</ul></li>
<li class="chapter" data-level="10" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html"><i class="fa fa-check"></i><b>10</b> A Prospective Multicentre MDRO Screening Study on ICUs in the Dutch-German Cross-Border Region (2017-2018): The Importance of Healthcare Structures</a>
<ul>
<li class="chapter" data-level="" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#abstract-8"><i class="fa fa-check"></i>Abstract</a></li>
<li class="chapter" data-level="10.1" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#introduction-7"><i class="fa fa-check"></i><b>10.1</b> Introduction</a></li>
<li class="chapter" data-level="10.2" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#methods-4"><i class="fa fa-check"></i><b>10.2</b> Methods</a>
<ul>
<li class="chapter" data-level="10.2.1" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#study-design"><i class="fa fa-check"></i><b>10.2.1</b> Study Design</a></li>
<li class="chapter" data-level="10.2.2" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#statistical-analysis-software"><i class="fa fa-check"></i><b>10.2.2</b> Statistical Analysis &amp; Software</a></li>
<li class="chapter" data-level="10.2.3" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#ethics"><i class="fa fa-check"></i><b>10.2.3</b> Ethics</a></li>
</ul></li>
<li class="chapter" data-level="10.3" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#results-5"><i class="fa fa-check"></i><b>10.3</b> Results</a>
<ul>
<li class="chapter" data-level="10.3.1" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#healthcare-structure-of-the-participating-hospitals"><i class="fa fa-check"></i><b>10.3.1</b> Healthcare structure of the participating hospitals</a></li>
<li class="chapter" data-level="10.3.2" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#study-population-and-screening-samples-from-icus"><i class="fa fa-check"></i><b>10.3.2</b> Study population and screening samples from ICUs</a></li>
<li class="chapter" data-level="10.3.3" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#prevalence-of-gram-positive-mdros-mrsa-and-vre"><i class="fa fa-check"></i><b>10.3.3</b> Prevalence of Gram-positive MDROs: MRSA and VRE</a></li>
<li class="chapter" data-level="10.3.4" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#prevalence-of-gram-negative-mdro-3gcre-and-cre"><i class="fa fa-check"></i><b>10.3.4</b> Prevalence of Gram-negative MDRO: 3GCRE and CRE</a></li>
<li class="chapter" data-level="10.3.5" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#prevalence-of-gram-negative-mdros-based-on-dutch-and-german-definitions"><i class="fa fa-check"></i><b>10.3.5</b> Prevalence of Gram-negative MDROs based on Dutch and German definitions</a></li>
<li class="chapter" data-level="10.3.6" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#comparison-of-mdro-prevalence-between-nl-br-and-de-br-icus-in-university-and-non-university-hospitals"><i class="fa fa-check"></i><b>10.3.6</b> Comparison of MDRO prevalence between NL-BR and DE-BR ICUs in university and non-university hospitals</a></li>
</ul></li>
<li class="chapter" data-level="10.4" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#discussion-6"><i class="fa fa-check"></i><b>10.4</b> Discussion</a></li>
<li class="chapter" data-level="" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#supplementary-files"><i class="fa fa-check"></i>Supplementary files</a></li>
<li class="chapter" data-level="" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#acknowledgements-5"><i class="fa fa-check"></i>Acknowledgements</a></li>
<li class="chapter" data-level="" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#conflict-of-interest-1"><i class="fa fa-check"></i>Conflict of interest</a></li>
<li class="chapter" data-level="" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#references-9"><i class="fa fa-check"></i>References</a></li>
</ul></li>
<li class="chapter" data-level="11" data-path="ch11-summary.html"><a href="ch11-summary.html"><i class="fa fa-check"></i><b>11</b> Summary and Future Perspectives</a>
<ul>
<li class="chapter" data-level="" data-path="ch11-summary.html"><a href="ch11-summary.html#section-i"><i class="fa fa-check"></i>Section I</a></li>
<li class="chapter" data-level="" data-path="ch11-summary.html"><a href="ch11-summary.html#section-ii"><i class="fa fa-check"></i>Section II</a></li>
<li class="chapter" data-level="" data-path="ch11-summary.html"><a href="ch11-summary.html#section-iii"><i class="fa fa-check"></i>Section III</a></li>
<li class="chapter" data-level="" data-path="ch11-summary.html"><a href="ch11-summary.html#future-perspectives"><i class="fa fa-check"></i>Future perspectives</a></li>
<li class="chapter" data-level="" data-path="ch11-summary.html"><a href="ch11-summary.html#references-10"><i class="fa fa-check"></i>References</a></li>
</ul></li>
<li class="chapter" data-level="" data-path="gearfetting-yn-frysk.html"><a href="gearfetting-yn-frysk.html"><i class="fa fa-check"></i>Gearfetting yn Frysk</a></li>
<li class="chapter" data-level="" data-path="samenvatting-in-het-nederlands.html"><a href="samenvatting-in-het-nederlands.html"><i class="fa fa-check"></i>Samenvatting in het Nederlands</a></li>
<li class="chapter" data-level="" data-path="zusammenfassung-auf-deutsch.html"><a href="zusammenfassung-auf-deutsch.html"><i class="fa fa-check"></i>Zusammenfassung auf Deutsch</a></li>
<li class="divider"></li>
<li class="copyright">© 2021 Matthijs Berends</li>

36
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<li class="chapter" data-level="" data-path="ch09-changing-epidemiology.html"><a href="ch09-changing-epidemiology.html#funding-1"><i class="fa fa-check"></i>Funding</a></li>
<li class="chapter" data-level="" data-path="ch09-changing-epidemiology.html"><a href="ch09-changing-epidemiology.html#references-8"><i class="fa fa-check"></i>References</a></li>
</ul></li>
<li class="chapter" data-level="10" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html"><i class="fa fa-check"></i><b>10</b> A Prospective Multicentre MDRO Screening Study on ICUs in the Dutch-German Cross-Border Region (2017-2018): The Importance of Healthcare Structures</a>
<ul>
<li class="chapter" data-level="" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#abstract-8"><i class="fa fa-check"></i>Abstract</a></li>
<li class="chapter" data-level="10.1" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#introduction-7"><i class="fa fa-check"></i><b>10.1</b> Introduction</a></li>
<li class="chapter" data-level="10.2" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#methods-4"><i class="fa fa-check"></i><b>10.2</b> Methods</a>
<ul>
<li class="chapter" data-level="10.2.1" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#study-design"><i class="fa fa-check"></i><b>10.2.1</b> Study Design</a></li>
<li class="chapter" data-level="10.2.2" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#statistical-analysis-software"><i class="fa fa-check"></i><b>10.2.2</b> Statistical Analysis &amp; Software</a></li>
<li class="chapter" data-level="10.2.3" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#ethics"><i class="fa fa-check"></i><b>10.2.3</b> Ethics</a></li>
</ul></li>
<li class="chapter" data-level="10.3" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#results-5"><i class="fa fa-check"></i><b>10.3</b> Results</a>
<ul>
<li class="chapter" data-level="10.3.1" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#healthcare-structure-of-the-participating-hospitals"><i class="fa fa-check"></i><b>10.3.1</b> Healthcare structure of the participating hospitals</a></li>
<li class="chapter" data-level="10.3.2" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#study-population-and-screening-samples-from-icus"><i class="fa fa-check"></i><b>10.3.2</b> Study population and screening samples from ICUs</a></li>
<li class="chapter" data-level="10.3.3" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#prevalence-of-gram-positive-mdros-mrsa-and-vre"><i class="fa fa-check"></i><b>10.3.3</b> Prevalence of Gram-positive MDROs: MRSA and VRE</a></li>
<li class="chapter" data-level="10.3.4" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#prevalence-of-gram-negative-mdro-3gcre-and-cre"><i class="fa fa-check"></i><b>10.3.4</b> Prevalence of Gram-negative MDRO: 3GCRE and CRE</a></li>
<li class="chapter" data-level="10.3.5" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#prevalence-of-gram-negative-mdros-based-on-dutch-and-german-definitions"><i class="fa fa-check"></i><b>10.3.5</b> Prevalence of Gram-negative MDROs based on Dutch and German definitions</a></li>
<li class="chapter" data-level="10.3.6" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#comparison-of-mdro-prevalence-between-nl-br-and-de-br-icus-in-university-and-non-university-hospitals"><i class="fa fa-check"></i><b>10.3.6</b> Comparison of MDRO prevalence between NL-BR and DE-BR ICUs in university and non-university hospitals</a></li>
</ul></li>
<li class="chapter" data-level="10.4" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#discussion-6"><i class="fa fa-check"></i><b>10.4</b> Discussion</a></li>
<li class="chapter" data-level="" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#supplementary-files"><i class="fa fa-check"></i>Supplementary files</a></li>
<li class="chapter" data-level="" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#acknowledgements-5"><i class="fa fa-check"></i>Acknowledgements</a></li>
<li class="chapter" data-level="" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#conflict-of-interest-1"><i class="fa fa-check"></i>Conflict of interest</a></li>
<li class="chapter" data-level="" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#references-9"><i class="fa fa-check"></i>References</a></li>
</ul></li>
<li class="chapter" data-level="11" data-path="ch11-summary.html"><a href="ch11-summary.html"><i class="fa fa-check"></i><b>11</b> Summary and Future Perspectives</a>
<ul>
<li class="chapter" data-level="" data-path="ch11-summary.html"><a href="ch11-summary.html#section-i"><i class="fa fa-check"></i>Section I</a></li>
<li class="chapter" data-level="" data-path="ch11-summary.html"><a href="ch11-summary.html#section-ii"><i class="fa fa-check"></i>Section II</a></li>
<li class="chapter" data-level="" data-path="ch11-summary.html"><a href="ch11-summary.html#section-iii"><i class="fa fa-check"></i>Section III</a></li>
<li class="chapter" data-level="" data-path="ch11-summary.html"><a href="ch11-summary.html#future-perspectives"><i class="fa fa-check"></i>Future perspectives</a></li>
<li class="chapter" data-level="" data-path="ch11-summary.html"><a href="ch11-summary.html#references-10"><i class="fa fa-check"></i>References</a></li>
</ul></li>
<li class="chapter" data-level="" data-path="gearfetting-yn-frysk.html"><a href="gearfetting-yn-frysk.html"><i class="fa fa-check"></i>Gearfetting yn Frysk</a></li>
<li class="chapter" data-level="" data-path="samenvatting-in-het-nederlands.html"><a href="samenvatting-in-het-nederlands.html"><i class="fa fa-check"></i>Samenvatting in het Nederlands</a></li>
<li class="chapter" data-level="" data-path="zusammenfassung-auf-deutsch.html"><a href="zusammenfassung-auf-deutsch.html"><i class="fa fa-check"></i>Zusammenfassung auf Deutsch</a></li>
<li class="divider"></li>
<li class="copyright">© 2021 Matthijs Berends</li>

36
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<li class="chapter" data-level="" data-path="ch09-changing-epidemiology.html"><a href="ch09-changing-epidemiology.html#funding-1"><i class="fa fa-check"></i>Funding</a></li>
<li class="chapter" data-level="" data-path="ch09-changing-epidemiology.html"><a href="ch09-changing-epidemiology.html#references-8"><i class="fa fa-check"></i>References</a></li>
</ul></li>
<li class="chapter" data-level="10" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html"><i class="fa fa-check"></i><b>10</b> A Prospective Multicentre MDRO Screening Study on ICUs in the Dutch-German Cross-Border Region (2017-2018): The Importance of Healthcare Structures</a>
<ul>
<li class="chapter" data-level="" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#abstract-8"><i class="fa fa-check"></i>Abstract</a></li>
<li class="chapter" data-level="10.1" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#introduction-7"><i class="fa fa-check"></i><b>10.1</b> Introduction</a></li>
<li class="chapter" data-level="10.2" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#methods-4"><i class="fa fa-check"></i><b>10.2</b> Methods</a>
<ul>
<li class="chapter" data-level="10.2.1" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#study-design"><i class="fa fa-check"></i><b>10.2.1</b> Study Design</a></li>
<li class="chapter" data-level="10.2.2" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#statistical-analysis-software"><i class="fa fa-check"></i><b>10.2.2</b> Statistical Analysis &amp; Software</a></li>
<li class="chapter" data-level="10.2.3" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#ethics"><i class="fa fa-check"></i><b>10.2.3</b> Ethics</a></li>
</ul></li>
<li class="chapter" data-level="10.3" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#results-5"><i class="fa fa-check"></i><b>10.3</b> Results</a>
<ul>
<li class="chapter" data-level="10.3.1" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#healthcare-structure-of-the-participating-hospitals"><i class="fa fa-check"></i><b>10.3.1</b> Healthcare structure of the participating hospitals</a></li>
<li class="chapter" data-level="10.3.2" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#study-population-and-screening-samples-from-icus"><i class="fa fa-check"></i><b>10.3.2</b> Study population and screening samples from ICUs</a></li>
<li class="chapter" data-level="10.3.3" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#prevalence-of-gram-positive-mdros-mrsa-and-vre"><i class="fa fa-check"></i><b>10.3.3</b> Prevalence of Gram-positive MDROs: MRSA and VRE</a></li>
<li class="chapter" data-level="10.3.4" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#prevalence-of-gram-negative-mdro-3gcre-and-cre"><i class="fa fa-check"></i><b>10.3.4</b> Prevalence of Gram-negative MDRO: 3GCRE and CRE</a></li>
<li class="chapter" data-level="10.3.5" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#prevalence-of-gram-negative-mdros-based-on-dutch-and-german-definitions"><i class="fa fa-check"></i><b>10.3.5</b> Prevalence of Gram-negative MDROs based on Dutch and German definitions</a></li>
<li class="chapter" data-level="10.3.6" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#comparison-of-mdro-prevalence-between-nl-br-and-de-br-icus-in-university-and-non-university-hospitals"><i class="fa fa-check"></i><b>10.3.6</b> Comparison of MDRO prevalence between NL-BR and DE-BR ICUs in university and non-university hospitals</a></li>
</ul></li>
<li class="chapter" data-level="10.4" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#discussion-6"><i class="fa fa-check"></i><b>10.4</b> Discussion</a></li>
<li class="chapter" data-level="" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#supplementary-files"><i class="fa fa-check"></i>Supplementary files</a></li>
<li class="chapter" data-level="" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#acknowledgements-5"><i class="fa fa-check"></i>Acknowledgements</a></li>
<li class="chapter" data-level="" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#conflict-of-interest-1"><i class="fa fa-check"></i>Conflict of interest</a></li>
<li class="chapter" data-level="" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#references-9"><i class="fa fa-check"></i>References</a></li>
</ul></li>
<li class="chapter" data-level="11" data-path="ch11-summary.html"><a href="ch11-summary.html"><i class="fa fa-check"></i><b>11</b> Summary and Future Perspectives</a>
<ul>
<li class="chapter" data-level="" data-path="ch11-summary.html"><a href="ch11-summary.html#section-i"><i class="fa fa-check"></i>Section I</a></li>
<li class="chapter" data-level="" data-path="ch11-summary.html"><a href="ch11-summary.html#section-ii"><i class="fa fa-check"></i>Section II</a></li>
<li class="chapter" data-level="" data-path="ch11-summary.html"><a href="ch11-summary.html#section-iii"><i class="fa fa-check"></i>Section III</a></li>
<li class="chapter" data-level="" data-path="ch11-summary.html"><a href="ch11-summary.html#future-perspectives"><i class="fa fa-check"></i>Future perspectives</a></li>
<li class="chapter" data-level="" data-path="ch11-summary.html"><a href="ch11-summary.html#references-10"><i class="fa fa-check"></i>References</a></li>
</ul></li>
<li class="chapter" data-level="" data-path="gearfetting-yn-frysk.html"><a href="gearfetting-yn-frysk.html"><i class="fa fa-check"></i>Gearfetting yn Frysk</a></li>
<li class="chapter" data-level="" data-path="samenvatting-in-het-nederlands.html"><a href="samenvatting-in-het-nederlands.html"><i class="fa fa-check"></i>Samenvatting in het Nederlands</a></li>
<li class="chapter" data-level="" data-path="zusammenfassung-auf-deutsch.html"><a href="zusammenfassung-auf-deutsch.html"><i class="fa fa-check"></i>Zusammenfassung auf Deutsch</a></li>
<li class="divider"></li>
<li class="copyright">© 2021 Matthijs Berends</li>

36
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<li class="chapter" data-level="" data-path="ch09-changing-epidemiology.html"><a href="ch09-changing-epidemiology.html#funding-1"><i class="fa fa-check"></i>Funding</a></li>
<li class="chapter" data-level="" data-path="ch09-changing-epidemiology.html"><a href="ch09-changing-epidemiology.html#references-8"><i class="fa fa-check"></i>References</a></li>
</ul></li>
<li class="chapter" data-level="10" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html"><i class="fa fa-check"></i><b>10</b> A Prospective Multicentre MDRO Screening Study on ICUs in the Dutch-German Cross-Border Region (2017-2018): The Importance of Healthcare Structures</a>
<ul>
<li class="chapter" data-level="" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#abstract-8"><i class="fa fa-check"></i>Abstract</a></li>
<li class="chapter" data-level="10.1" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#introduction-7"><i class="fa fa-check"></i><b>10.1</b> Introduction</a></li>
<li class="chapter" data-level="10.2" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#methods-4"><i class="fa fa-check"></i><b>10.2</b> Methods</a>
<ul>
<li class="chapter" data-level="10.2.1" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#study-design"><i class="fa fa-check"></i><b>10.2.1</b> Study Design</a></li>
<li class="chapter" data-level="10.2.2" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#statistical-analysis-software"><i class="fa fa-check"></i><b>10.2.2</b> Statistical Analysis &amp; Software</a></li>
<li class="chapter" data-level="10.2.3" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#ethics"><i class="fa fa-check"></i><b>10.2.3</b> Ethics</a></li>
</ul></li>
<li class="chapter" data-level="10.3" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#results-5"><i class="fa fa-check"></i><b>10.3</b> Results</a>
<ul>
<li class="chapter" data-level="10.3.1" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#healthcare-structure-of-the-participating-hospitals"><i class="fa fa-check"></i><b>10.3.1</b> Healthcare structure of the participating hospitals</a></li>
<li class="chapter" data-level="10.3.2" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#study-population-and-screening-samples-from-icus"><i class="fa fa-check"></i><b>10.3.2</b> Study population and screening samples from ICUs</a></li>
<li class="chapter" data-level="10.3.3" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#prevalence-of-gram-positive-mdros-mrsa-and-vre"><i class="fa fa-check"></i><b>10.3.3</b> Prevalence of Gram-positive MDROs: MRSA and VRE</a></li>
<li class="chapter" data-level="10.3.4" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#prevalence-of-gram-negative-mdro-3gcre-and-cre"><i class="fa fa-check"></i><b>10.3.4</b> Prevalence of Gram-negative MDRO: 3GCRE and CRE</a></li>
<li class="chapter" data-level="10.3.5" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#prevalence-of-gram-negative-mdros-based-on-dutch-and-german-definitions"><i class="fa fa-check"></i><b>10.3.5</b> Prevalence of Gram-negative MDROs based on Dutch and German definitions</a></li>
<li class="chapter" data-level="10.3.6" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#comparison-of-mdro-prevalence-between-nl-br-and-de-br-icus-in-university-and-non-university-hospitals"><i class="fa fa-check"></i><b>10.3.6</b> Comparison of MDRO prevalence between NL-BR and DE-BR ICUs in university and non-university hospitals</a></li>
</ul></li>
<li class="chapter" data-level="10.4" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#discussion-6"><i class="fa fa-check"></i><b>10.4</b> Discussion</a></li>
<li class="chapter" data-level="" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#supplementary-files"><i class="fa fa-check"></i>Supplementary files</a></li>
<li class="chapter" data-level="" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#acknowledgements-5"><i class="fa fa-check"></i>Acknowledgements</a></li>
<li class="chapter" data-level="" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#conflict-of-interest-1"><i class="fa fa-check"></i>Conflict of interest</a></li>
<li class="chapter" data-level="" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#references-9"><i class="fa fa-check"></i>References</a></li>
</ul></li>
<li class="chapter" data-level="11" data-path="ch11-summary.html"><a href="ch11-summary.html"><i class="fa fa-check"></i><b>11</b> Summary and Future Perspectives</a>
<ul>
<li class="chapter" data-level="" data-path="ch11-summary.html"><a href="ch11-summary.html#section-i"><i class="fa fa-check"></i>Section I</a></li>
<li class="chapter" data-level="" data-path="ch11-summary.html"><a href="ch11-summary.html#section-ii"><i class="fa fa-check"></i>Section II</a></li>
<li class="chapter" data-level="" data-path="ch11-summary.html"><a href="ch11-summary.html#section-iii"><i class="fa fa-check"></i>Section III</a></li>
<li class="chapter" data-level="" data-path="ch11-summary.html"><a href="ch11-summary.html#future-perspectives"><i class="fa fa-check"></i>Future perspectives</a></li>
<li class="chapter" data-level="" data-path="ch11-summary.html"><a href="ch11-summary.html#references-10"><i class="fa fa-check"></i>References</a></li>
</ul></li>
<li class="chapter" data-level="" data-path="gearfetting-yn-frysk.html"><a href="gearfetting-yn-frysk.html"><i class="fa fa-check"></i>Gearfetting yn Frysk</a></li>
<li class="chapter" data-level="" data-path="samenvatting-in-het-nederlands.html"><a href="samenvatting-in-het-nederlands.html"><i class="fa fa-check"></i>Samenvatting in het Nederlands</a></li>
<li class="chapter" data-level="" data-path="zusammenfassung-auf-deutsch.html"><a href="zusammenfassung-auf-deutsch.html"><i class="fa fa-check"></i>Zusammenfassung auf Deutsch</a></li>
<li class="divider"></li>
<li class="copyright">© 2021 Matthijs Berends</li>

42
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@ -32,7 +32,7 @@
<link rel="prev" href="ch08-defining-mdr.html"/>
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<script src="libs/header-attrs-2.11/header-attrs.js"></script>
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@ -337,6 +337,42 @@ code span.wa { color: #60a0b0; font-weight: bold; font-style: italic; } /* Warni
<li class="chapter" data-level="" data-path="ch09-changing-epidemiology.html"><a href="ch09-changing-epidemiology.html#funding-1"><i class="fa fa-check"></i>Funding</a></li>
<li class="chapter" data-level="" data-path="ch09-changing-epidemiology.html"><a href="ch09-changing-epidemiology.html#references-8"><i class="fa fa-check"></i>References</a></li>
</ul></li>
<li class="chapter" data-level="10" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html"><i class="fa fa-check"></i><b>10</b> A Prospective Multicentre MDRO Screening Study on ICUs in the Dutch-German Cross-Border Region (2017-2018): The Importance of Healthcare Structures</a>
<ul>
<li class="chapter" data-level="" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#abstract-8"><i class="fa fa-check"></i>Abstract</a></li>
<li class="chapter" data-level="10.1" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#introduction-7"><i class="fa fa-check"></i><b>10.1</b> Introduction</a></li>
<li class="chapter" data-level="10.2" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#methods-4"><i class="fa fa-check"></i><b>10.2</b> Methods</a>
<ul>
<li class="chapter" data-level="10.2.1" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#study-design"><i class="fa fa-check"></i><b>10.2.1</b> Study Design</a></li>
<li class="chapter" data-level="10.2.2" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#statistical-analysis-software"><i class="fa fa-check"></i><b>10.2.2</b> Statistical Analysis &amp; Software</a></li>
<li class="chapter" data-level="10.2.3" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#ethics"><i class="fa fa-check"></i><b>10.2.3</b> Ethics</a></li>
</ul></li>
<li class="chapter" data-level="10.3" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#results-5"><i class="fa fa-check"></i><b>10.3</b> Results</a>
<ul>
<li class="chapter" data-level="10.3.1" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#healthcare-structure-of-the-participating-hospitals"><i class="fa fa-check"></i><b>10.3.1</b> Healthcare structure of the participating hospitals</a></li>
<li class="chapter" data-level="10.3.2" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#study-population-and-screening-samples-from-icus"><i class="fa fa-check"></i><b>10.3.2</b> Study population and screening samples from ICUs</a></li>
<li class="chapter" data-level="10.3.3" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#prevalence-of-gram-positive-mdros-mrsa-and-vre"><i class="fa fa-check"></i><b>10.3.3</b> Prevalence of Gram-positive MDROs: MRSA and VRE</a></li>
<li class="chapter" data-level="10.3.4" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#prevalence-of-gram-negative-mdro-3gcre-and-cre"><i class="fa fa-check"></i><b>10.3.4</b> Prevalence of Gram-negative MDRO: 3GCRE and CRE</a></li>
<li class="chapter" data-level="10.3.5" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#prevalence-of-gram-negative-mdros-based-on-dutch-and-german-definitions"><i class="fa fa-check"></i><b>10.3.5</b> Prevalence of Gram-negative MDROs based on Dutch and German definitions</a></li>
<li class="chapter" data-level="10.3.6" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#comparison-of-mdro-prevalence-between-nl-br-and-de-br-icus-in-university-and-non-university-hospitals"><i class="fa fa-check"></i><b>10.3.6</b> Comparison of MDRO prevalence between NL-BR and DE-BR ICUs in university and non-university hospitals</a></li>
</ul></li>
<li class="chapter" data-level="10.4" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#discussion-6"><i class="fa fa-check"></i><b>10.4</b> Discussion</a></li>
<li class="chapter" data-level="" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#supplementary-files"><i class="fa fa-check"></i>Supplementary files</a></li>
<li class="chapter" data-level="" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#acknowledgements-5"><i class="fa fa-check"></i>Acknowledgements</a></li>
<li class="chapter" data-level="" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#conflict-of-interest-1"><i class="fa fa-check"></i>Conflict of interest</a></li>
<li class="chapter" data-level="" data-path="ch10-multi-mdro-screening.html"><a href="ch10-multi-mdro-screening.html#references-9"><i class="fa fa-check"></i>References</a></li>
</ul></li>
<li class="chapter" data-level="11" data-path="ch11-summary.html"><a href="ch11-summary.html"><i class="fa fa-check"></i><b>11</b> Summary and Future Perspectives</a>
<ul>
<li class="chapter" data-level="" data-path="ch11-summary.html"><a href="ch11-summary.html#section-i"><i class="fa fa-check"></i>Section I</a></li>
<li class="chapter" data-level="" data-path="ch11-summary.html"><a href="ch11-summary.html#section-ii"><i class="fa fa-check"></i>Section II</a></li>
<li class="chapter" data-level="" data-path="ch11-summary.html"><a href="ch11-summary.html#section-iii"><i class="fa fa-check"></i>Section III</a></li>
<li class="chapter" data-level="" data-path="ch11-summary.html"><a href="ch11-summary.html#future-perspectives"><i class="fa fa-check"></i>Future perspectives</a></li>
<li class="chapter" data-level="" data-path="ch11-summary.html"><a href="ch11-summary.html#references-10"><i class="fa fa-check"></i>References</a></li>
</ul></li>
<li class="chapter" data-level="" data-path="gearfetting-yn-frysk.html"><a href="gearfetting-yn-frysk.html"><i class="fa fa-check"></i>Gearfetting yn Frysk</a></li>
<li class="chapter" data-level="" data-path="samenvatting-in-het-nederlands.html"><a href="samenvatting-in-het-nederlands.html"><i class="fa fa-check"></i>Samenvatting in het Nederlands</a></li>
<li class="chapter" data-level="" data-path="zusammenfassung-auf-deutsch.html"><a href="zusammenfassung-auf-deutsch.html"><i class="fa fa-check"></i>Zusammenfassung auf Deutsch</a></li>
<li class="divider"></li>
<li class="copyright">© 2021 Matthijs Berends</li>
@ -535,8 +571,8 @@ Table 5. Calculated parameters in the patient catchment area of all study hospit
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