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@ -275,6 +275,31 @@ code span.wa { color: #60a0b0; font-weight: bold; font-style: italic; } /* Warni
<li class="chapter" data-level="" data-path="ch06-radar2.html"><a href="ch06-radar2.html#appendix-a3-task-3-sub-analysis"><i class="fa fa-check"></i>Appendix A3: Task 3 sub-analysis</a></li>
</ul></li>
</ul></li>
<li class="chapter" data-level="7" data-path="ch07-cons.html"><a href="ch07-cons.html"><i class="fa fa-check"></i><b>7</b> Trends in Occurrence and Phenotypic Resistance of Coagulase-Negative Staphylococci (CoNS) Found in Blood in the Northern Netherlands between 2013 and 2019</a>
<ul>
<li class="chapter" data-level="7.1" data-path="ch07-cons.html"><a href="ch07-cons.html#abstract-5"><i class="fa fa-check"></i><b>7.1</b> Abstract</a></li>
<li class="chapter" data-level="7.2" data-path="ch07-cons.html"><a href="ch07-cons.html#introduction-4"><i class="fa fa-check"></i><b>7.2</b> Introduction</a></li>
<li class="chapter" data-level="7.3" data-path="ch07-cons.html"><a href="ch07-cons.html#materials-methods"><i class="fa fa-check"></i><b>7.3</b> Materials &amp; methods</a>
<ul>
<li class="chapter" data-level="7.3.1" data-path="ch07-cons.html"><a href="ch07-cons.html#study-setting-and-patient-cohort"><i class="fa fa-check"></i><b>7.3.1</b> Study setting and patient cohort</a></li>
<li class="chapter" data-level="7.3.2" data-path="ch07-cons.html"><a href="ch07-cons.html#microbiological-and-demographic-data"><i class="fa fa-check"></i><b>7.3.2</b> Microbiological and demographic data</a></li>
<li class="chapter" data-level="7.3.3" data-path="ch07-cons.html"><a href="ch07-cons.html#species-determination-and-antibiotic-susceptibility-testing-ast"><i class="fa fa-check"></i><b>7.3.3</b> Species determination and antibiotic susceptibility testing (AST)</a></li>
<li class="chapter" data-level="7.3.4" data-path="ch07-cons.html"><a href="ch07-cons.html#selection-of-bacterial-isolates"><i class="fa fa-check"></i><b>7.3.4</b> Selection of bacterial isolates</a></li>
<li class="chapter" data-level="7.3.5" data-path="ch07-cons.html"><a href="ch07-cons.html#eucast-rules-and-antibiotic-resistance-analysis"><i class="fa fa-check"></i><b>7.3.5</b> EUCAST rules and antibiotic resistance analysis</a></li>
<li class="chapter" data-level="7.3.6" data-path="ch07-cons.html"><a href="ch07-cons.html#statistical-analysis"><i class="fa fa-check"></i><b>7.3.6</b> Statistical analysis</a></li>
<li class="chapter" data-level="7.3.7" data-path="ch07-cons.html"><a href="ch07-cons.html#ethical-considerations"><i class="fa fa-check"></i><b>7.3.7</b> Ethical considerations</a></li>
</ul></li>
<li class="chapter" data-level="7.4" data-path="ch07-cons.html"><a href="ch07-cons.html#results-2"><i class="fa fa-check"></i><b>7.4</b> Results</a>
<ul>
<li class="chapter" data-level="7.4.1" data-path="ch07-cons.html"><a href="ch07-cons.html#patients-and-included-isolates"><i class="fa fa-check"></i><b>7.4.1</b> Patients and included isolates</a></li>
<li class="chapter" data-level="7.4.2" data-path="ch07-cons.html"><a href="ch07-cons.html#occurrence-of-cons-species"><i class="fa fa-check"></i><b>7.4.2</b> Occurrence of CoNS species</a></li>
<li class="chapter" data-level="7.4.3" data-path="ch07-cons.html"><a href="ch07-cons.html#definition-of-cons-persistence"><i class="fa fa-check"></i><b>7.4.3</b> Definition of CoNS persistence</a></li>
<li class="chapter" data-level="7.4.4" data-path="ch07-cons.html"><a href="ch07-cons.html#antibiotic-resistance-analysis"><i class="fa fa-check"></i><b>7.4.4</b> Antibiotic resistance analysis</a></li>
</ul></li>
<li class="chapter" data-level="7.5" data-path="ch07-cons.html"><a href="ch07-cons.html#discussion-3"><i class="fa fa-check"></i><b>7.5</b> Discussion</a></li>
<li class="chapter" data-level="" data-path="ch07-cons.html"><a href="ch07-cons.html#supplementary-tables"><i class="fa fa-check"></i>Supplementary tables</a></li>
<li class="chapter" data-level="" data-path="ch07-cons.html"><a href="ch07-cons.html#references-6"><i class="fa fa-check"></i>References</a></li>
</ul></li>
<li class="divider"></li>
<li class="copyright">© 2021 Matthijs Berends</li>
@ -403,10 +428,10 @@ Figure 1.5: Geographic overview of three Euregio’s that make up most of the Du
<h2><span class="header-section-number">1.5</span> Aim of this thesis and introduction to its chapters</h2>
<p>This thesis aims to present the development of a new instrument for microbial epidemiology – a new and open method for standardised AMR data analysis – while also providing applied examples of how this new instrument has empowered AMR data analysis in regional and euregional studies.</p>
<p>This thesis is presented in four sections.</p>
<p>SECTION I opens with a broad introduction to the usefulness and necessity of having timely diagnostic information in chapter 2. Diagnostic stewardship programs (DSP) are a requirement to gain answers instead of results, including those from a clinical microbiology laboratory. DSP is a multidisciplinary approach to gain the most benefit for the patient by democratising different medical specialities. In chapter 3, the usefulness and necessity of having a dedicated tool for microbial epidemiology are introduced, through the AMR package for R as a new instrument. It is explained why microbial epidemiology and its effects are hindering efforts to dispose of AMR trends and how the AMR package for R can compensate for this. This chapter was primarily intended for non-data-technical professionals who work in the field of infectious diseases, such as clinical microbiologists and infectiologists.</p>
<p>SECTION II outlines the working and implementation of the AMR package for R. It starts with explaining this newly developed instrument in chapter 4. In this methodological and technical paper, the working mechanisms of the AMR package for R are thoroughly described. It is demonstrated that the AMR package enables standardised and reproducible AMR data analyses, including the application of evidence-based rules, determination of first isolates, translation of various codes for microorganisms and antimicrobial agents, determination of (multi-drug) resistant microorganisms, and calculation of antimicrobial resistance, prevalence and future trends. This chapter was primarily intended for data-technical professionals who work in the field of microbiology, such as (infectious disease) epidemiologists and biostatisticians. For chapter 5, the AMR package was implemented in a newly developed web application to present the design, development, and testing of RadaR (Rapid analysis of diagnostic and antimicrobial patterns in R), a software app for infection management, and to ascertain whether RadaR can facilitate user-friendly, intuitive, and interactive analyses of large datasets in the absence of prior in-depth software or programming knowledge. Subsequently, in chapter 6, we aimed at demonstrating and studying the usability of our developed approach and its impact on clinicians’ workflows in a typical scenario. By comparing traditional software methods such as Excel and SPSS with an online implementation of our new instrument, we tried to establish the benefit of using dedicated tools in a clinical situation.</p>
<p>SECTION III provides real-life examples of how the new instrument was used in studies that focus on AMR data analysis, in the Northern Dutch region as well as the Northern cross-border region of the Netherlands and Germany. Chapter 7 brings a thorough analysis of the occurrence and antibiotic resistance of coagulase-negative staphylococci (CoNS) in the Northern three provinces of the Netherlands, by analysing almost 20,000 antibiograms. Since 2013, all regional clinical microbiological laboratories make use of matrix-assisted laser desorption/ionisation time-of-flight (MALDI-TOF) mass spectrometry to identify microbial isolates to the species level. Using the AMR package for R, all relevant antibiotic results could be analysed for all different CoNS species that were found during the study period (2013-2019). In chapter 8, country-specific guidelines for determining MDROs in the Netherlands and Germany were compared in this border region. This was done by interpreting all isolates found on both sides of the border with the national guidelines from both countries. Major differences were observed, which also imply a strong challenge for healthcare personnel working in the border region. Isolate selection and MDRO determination on the Dutch side of the border was carried out using the AMR package. Chapter 9 outlines the euregional epidemiology of methicillin-resistant Staphylococcus aureus (MRSA) by analysing results from 42 hospitals. MRSA colonisation, infection and bacteraemia rate trends were described from the Dutch-German border region hospitals between 2012 and 2016. Although measures for MRSA cases were similar in both countries, defining patients at risk for MRSA differed. For chapter 10, twenty-three hospitals in the Dutch-German border region participated in a prospective screening study for the determination of the carriage of multi-drug resistance on admission to intensive care units (ICU), including more than 3,000 patients. The screening compliance, hospital and ICU sizes, and outcome of AMR data analysis were compared between both sides of the border.</p>
<p>SECTION IV summarises the presented work and provides future perspectives.</p>
<p><strong>SECTION I</strong> opens with a broad introduction to the usefulness and necessity of having timely diagnostic information in <strong>chapter 2</strong>. Diagnostic stewardship programs (DSP) are a requirement to gain answers instead of results, including those from a clinical microbiology laboratory. DSP is a multidisciplinary approach to gain the most benefit for the patient by democratising different medical specialities. In <strong>chapter 3</strong>, the usefulness and necessity of having a dedicated tool for microbial epidemiology are introduced, through the AMR package for R as a new instrument. It is explained why microbial epidemiology and its effects are hindering efforts to dispose of AMR trends and how the AMR package for R can compensate for this. This chapter was primarily intended for non-data-technical professionals who work in the field of infectious diseases, such as clinical microbiologists and infectiologists.</p>
<p><strong>SECTION II</strong> outlines the working and implementation of the AMR package for R. It starts with explaining this newly developed instrument in <strong>chapter 4</strong>. In this methodological and technical paper, the working mechanisms of the AMR package for R are thoroughly described. It is demonstrated that the AMR package enables standardised and reproducible AMR data analyses, including the application of evidence-based rules, determination of first isolates, translation of various codes for microorganisms and antimicrobial agents, determination of (multi-drug) resistant microorganisms, and calculation of antimicrobial resistance, prevalence and future trends. This chapter was primarily intended for data-technical professionals who work in the field of microbiology, such as (infectious disease) epidemiologists and biostatisticians. For <strong>chapter 5</strong>, the AMR package was implemented in a newly developed web application to present the design, development, and testing of RadaR (Rapid analysis of diagnostic and antimicrobial patterns in R), a software app for infection management, and to ascertain whether RadaR can facilitate user-friendly, intuitive, and interactive analyses of large datasets in the absence of prior in-depth software or programming knowledge. Subsequently, in <strong>chapter 6</strong>, we aimed at demonstrating and studying the usability of our developed approach and its impact on clinicians’ workflows in a typical scenario. By comparing traditional software methods such as Excel and SPSS with an online implementation of our new instrument, we tried to establish the benefit of using dedicated tools in a clinical situation.</p>
<p><strong>SECTION III</strong> provides real-life examples of how the new instrument was used in studies that focus on AMR data analysis, in the Northern Dutch region as well as the Northern cross-border region of the Netherlands and Germany. <strong>Chapter 7</strong> brings a thorough analysis of the occurrence and antibiotic resistance of coagulase-negative staphylococci (CoNS) in the Northern three provinces of the Netherlands, by analysing almost 20,000 antibiograms. Since 2013, all regional clinical microbiological laboratories make use of matrix-assisted laser desorption/ionisation time-of-flight (MALDI-TOF) mass spectrometry to identify microbial isolates to the species level. Using the AMR package for R, all relevant antibiotic results could be analysed for all different CoNS species that were found during the study period (2013-2019). In <strong>chapter 8</strong>, country-specific guidelines for determining MDROs in the Netherlands and Germany were compared in this border region. This was done by interpreting all isolates found on both sides of the border with the national guidelines from both countries. Major differences were observed, which also imply a strong challenge for healthcare personnel working in the border region. Isolate selection and MDRO determination on the Dutch side of the border was carried out using the AMR package. <strong>Chapter 9</strong> outlines the euregional epidemiology of methicillin-resistant Staphylococcus aureus (MRSA) by analysing results from 42 hospitals. MRSA colonisation, infection and bacteraemia rate trends were described from the Dutch-German border region hospitals between 2012 and 2016. Although measures for MRSA cases were similar in both countries, defining patients at risk for MRSA differed. For <strong>chapter 10</strong>, twenty-three hospitals in the Dutch-German border region participated in a prospective screening study for the determination of the carriage of multi-drug resistance on admission to intensive care units (ICU), including more than 3,000 patients. The screening compliance, hospital and ICU sizes, and outcome of AMR data analysis were compared between both sides of the border.</p>
<p><strong>SECTION IV</strong> summarises the presented work and provides future perspectives.</p>
</div>
<div id="references" class="section level2 unnumbered">
<h2>References</h2>