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European Antimicrobial Resistance Surveillance Network (EARS-Net) Belgium, isolates from 2013

EARS -Net (European Antimicrobial Resistance Survei llance Network)

Isolates from 2013, Belgium

European Antimicrobial Resistance Surveillance Network (EARS-Net) Belgium, isolates from 2013

WIV-ISP Public health & surveillance Health care associated infections & antimicrobial resistance Rue Juliette Wytsmanstraat 14 1050 Brussels E-mail : [email protected] www.nsih.be

Mathijs-Michiel Goossens August 2014; Brussels (Belgium)

European Antimicrobial Resistance Surveillance Network (EARS-Net) Belgium, isolates from 2013

1. TABLE OF CONTENT

1. TABLE OF CONTENT

2. INTRODUCTION

3. ABBREVIATION

4. ANTIMICROBIAL CODES (ECDC)

5. SUMMARY

6. ANTIMICROBIAL GROUPS

7. DISCUSSION OF ERROR & BIAS 7.1 Selection bias 7.1.1. Sample bias 7.1.2. Attrition 7.2 Measurement bias 7.2.1. incorrect AMR measurement 7.2.2. Advised breakpoints 7.2.3. Data transfer

8. RESISTANCE PATTERNS PER GERM 8.1 S. aureus 8.1.1 Relevance in AMR surveillance 8.1.2 Resistance mechanisms 8.2 S. pneumoniae 8.2.1 Relevance in AMR surveillance 8.2.2 Resistance mechanisms 8.3 E. coli 8.3.1 Relevance in AMR surveillance 8.3.2 Resistance mechanisms 8.4 K. pneumoniae 8.4.1 Relevance in AMR surveillance 8.4.2 Resistance mechanisms 8.5 E. faecalis en E. faecium 8.5.1 Relevance in AMR surveillance 8.5.2 Resistance mechanisms 8.6 P. aeruginosa 8.6.1 Relevance in AMR surveillance 8.6.2 Resistance mechanisms

9. RESULTS

10. ACKNOWLEDGEMENTS

11. FUTURE PARTICIPATION

European Antimicrobial Resistance Surveillance Network (EARS-Net) Belgium, isolates from 2013

2. INTRODUCTION

EARS-Net performs AMR (Antimicrobial resistance) surveillance for seven bacterial pathogens: • S. pneumoniae • S. aureus • E. faecalis • E. faecium • E. coli • K. pneumoniae • P. aeruginosa

More information can be found in the EARS-Net reports available at http://ecdc.europa.eu/en/publications/Publications/Forms/ECDC_DispForm.aspx?ID=998

The chapter about AMR resistance patterns has mostly been copied from an EARS-Net report published by ECDC. The results graphs were done at the WIV-ISP.

A number of things should be remembered when interpreting the results (see also chapter “Error & Bias”): • EARS-Net data are exclusively based on invasive isolates (blood or cerebrospinal fluid). This restriction pre- vents inconsistencies that arise from differences in clinical case definitions, different sampling frames or het- erogeneous healthcare utilization that would otherwise confound the data analysis if isolates from all ana- tomical sources were accepted. However, invasive isolates may for biological reasons not be representative for isolates of the same bacterial species from other sites, i.e. urinary tract infections, pneumonia, wound infections, etc. • For every patient only the first sample of the year is used in the data (per bacteria). If several samples are taken on the same day, then the sample with the least susceptible result (R>I>S) is retained.

European Antimicrobial Resistance Surveillance Network (EARS-Net) Belgium, isolates from 2013

3. ABBREVIATION

AMR: Antimicrobial resistance AST: Antimicrobial Susceptibility Testing CI: Confidence interval CLSI: Clinical and Laboratory Standards Institute (USA) CSF : Cerebrospinal fluid EARS-Net: European Antimicrobial Resistance Surveillance Network ECDC: European Centre for Disease Prevention and Control ENCFAI : E. faecium ENCFAE : E. faecalis ESCCOL : E. coli EUCAST: European Committee on Antimicrobial Susceptibility Testing (EU) KLEPNE : K. pneumoniae KUL : Katholieke Universiteit Leuven LIMS : Laboratory information management system MIC: Minimum inhibitory concentration PSEAER : P. aeruginosa S/I/R: Sensitive / Intermediary / Resistant STAAUR : S. aureus STRPNE: S. pneumoniae WIV-ISP : Wetenschappelijk Instituut Volksgezondheid - Institut Scientifique de Santé Publique

European Antimicrobial Resistance Surveillance Network (EARS-Net) Belgium, isolates from 2013

4. ANTIMICROBIAL CODES (ECDC)

Table 1 Explanation of official ECDC Antimicrobial codes

Code Name Code Name Code Name ACM Acetylmidecamycin DIT OXA AMB Amphotericin B DKB OXO AMC / DOX OXY AMK ECO Econazole PAN AMP ENR PAR AMR Amprolium ENX PAS P-Aminosalicylic acid AMX Amoxicillin EPE Eperozolid PEF APL Apalcillin EPP Epiroprim PEN G APR ERY PIM Pentisomicin APX Aspoxicillin ETH Ethambutol PIP ARB ETI Ethionamide PIR ASP Acetylspiramycin ETO Etopabat PIS Piperacillin/ AST ETP PKA Propikacin ATM FAR PNO Penicillin/ AVI Avilamycin FEP PNV Penicillin V AVO Avoparcin FLA Flavomycin POL Polymixin B AXS Amoxicillin/Sulbactam FLC PPA AZL FLE PRC Piridicillin AZM FLM PRI Pristinamycin BAC FLO PRL BAM FLR PRM Primycin BCZ Bicozamycin FLU Fluconazole PRP BDP Brodimo prim FMD Fosmidomycin PRX Premafloxacin BIA FOS PTH Prothionamide BUT Butoconazole FOX PTZ Pentizidone CAC FRM Framycetin PZA Pyrazinamide CAP Capreomycin FRZ QDA Quinup ristin/ CAR FUS RAC Ractopamine CAT GAT RIB CAZ GEH -High RID Cefaloridin CCL Cefetecol (Cefcatacol) GEM RIF Rifampin CCP GEN Gentamicin ROK CCV Ceftazidime/Clavulanic acid GRI Griseofulvin ROS CDR GRX RXT Roxithromicin CDZ HAB Habekacin SAL Salinomycin CEC HAP Cephapirin SAM Ampicillin/Sulbactam CED Cephradine HET SAR CEM HYG Hygromycin SBC CEP Cephalothin INH SDI CFB IPM SDM CFM Cefix ime ISE SIS CFP ISO Isoconazole SMX CFR ITR Itraconazole SNA Sulfasuccinamide CFS JOS SOX Sulfisoxazole CFZ KAH Kanamycin -High SPI CHE hexetil KAN Kanamycin SPT CHL KET Ketoconazole SPX CIC KIT (Leucomycin) SRX Sarmoxicillin CID LAS Lasalocid SSS Sulfonamides CIN LEX Cephalexin STH -High CIP LIN STR Streptomycin CLA Clavulanic acid LNZ SUC Sulconazole CLI LOM SUD CLO LOR SUL Sulbactam CLR LSP Linco-spectin SUM Sulfamethazine CLX LVX SUP Sulfachlorpyridazine CMX MAN SUT CMZ MCR Micromomicin SXT /Sulfamethoxazole CND MCZ Miconazole SZO CNX MEC (Amdinocillin) TAZ

European Antimicrobial Resistance Surveillance Network (EARS-Net) Belgium, isolates from 2013

Code Name Code Name Code Name COL MEL Meleumycin TBQ Tilbroquinol CPD MEM TCC /Clavulanic acid CPI Cefetamet pivoxil MES Mesulfamide TCY CPM MET TDC Tiodonium chloride CPO MEZ TEC CPR MFX TEM CPX Ce fpodoxime proxetil MID TET Tetroxoprim CRB MIL Miloxacin TFX CRD MNO THA Thiacetazone CRO MON Monensin sodium THI CSL Cefoperazone/Sulbacta m MOX Moxalactam () TIA CSU Cefsumide MSU Mezlocillin/Sulbactam TIC Ticarcillin CTB MTP Metioprim TIL Tilmicosin CTC /Clavulanic acid MTR TIN CTE MUP Mupirocin TIO CTF Cefotiam MXT Metioxate TLP Talmetoprim CTO Cetocycline NAF TLT CTR Clotrimazole NAL TMP Trimethoprim CTS Cefotaxime/Sulbactam NAR Narasin TMX CTT NEO TOB CTX Cefotaxime NET TRL CTZ NIC Nicarbazin TRO Trospectomycin CXA axetil NIF Nifuroquine TVA CXM Cefuroxime sodium NIT TXC Tioxacin CYC NIZ Nitrofurazone TYL CZD NOR TZP Piperacillin/Tazobactam CZL Cefetrizole NOV Novobiocin VAN CZO NTR Nitroxoline VIO Viomycin CZX NVA Norvancomycin VIR Virginiamycine DAP NYS Nystatin ZON DEM OFX DFX OLE DIC OPT Optochin DIF ORN DIR ORS Ormetropim/Sulfamethoxine

European Antimicrobial Resistance Surveillance Network (EARS-Net) Belgium, isolates from 2013

5. SUMMARY

This report covers the Belgian data (samples from 2013) for the European Antimicrobial Resistance surveillance net- work (EARS-net). Antimicrobial susceptibility profiles of 7 bacterial species ( S. aureus , S. pneumoniae , E. faecium, E. faecalis, E. coli, K. pneumoniae, P. aeruginosa ) retrieved from blood cultures and cerebrospinal fluid for a selection of antimicrobial agents are provided. Additional information is given to the potential for error and bias, as well as background on the organisms and the most important resistance mechanisms involved.

• The percentage of methicillin resistant S. aureus among invasive isolates was 17%, resistant S. aureus remained below 1%. • Vancomycin resistant enterococci is around 1%. • Penicillin and resistant S. pneumoniae was 2% and 23%, respectively. • In P. aeruginosa resistance for , for ceftazidime, and for piperacilline was around 10% (regarding PIP see comment below). • resistance in E. coli and K. pneumoniae seems below 1% but should be interpreted with caution because the EQA shows that the combinations KLEPNE-carbapenem and PSEAER-piperacillin-tazobactam suf- fer from low accuracy. The EQA shows that the results can be considered reliable for all other bug-drug combi- nations, although there are differences between bug-drug combinations.

European Antimicrobial Resistance Surveillance Network (EARS-Net) Belgium, isolates from 2013

6. ANTIMICROBIAL GROUPS

Results are not given for each separate (e.g. meropenem, imipenem, ertapenem) but for groups of antibiot- ics (e.g. carbapenems). The least susceptible result (R>I>S) is always retained for each group.

The table below shows for each group what are included. If a certain antibiotic is not in the list, it is stored in the database without being shown in the reports. If you require an analysis for these other antibiotics please send an email to [email protected]

For most groups only the percentage “R” is reported in the results, there are 4 exception where the percentages for “I or R” are given (marked in bold below).

PATHOGEN GROUP NAME ANTIBIOTIC IN THE GROUP ENCFAE/ENCFAI (I+R) AMX, AMP ENCFAE/ENCFAI High level gentamicin GEH ENCFAE/ENCFAI Glycopeptides VAN, TEC ENCFAE/ENCFAI Linezolid (I+R) LNZ

ESCCOL Aminopenicillins AMX, AMP ESCCOL/KLEPNE 3rd gen. CTX, CRO, CAZ ESCCOL/KLEPNE AMK, GEN, TOB ESCCOL/KLEPNE Fluoroquinolones CIP, OFX, LVX ESCCOL/KLEPNE Carbapenems IPM, MEM

PSEAER Piperacillin±tazobactam PIP, TZP PSEAER Ceftazidime CAZ PSEAER Aminoglycosides GEN, TOB PSEAER Amikacin AMK PSEAER Fluoroquinolones CIP, LVX PSEAER Carbapenems IPM, MEM

STAAUR MRSA MET, OXA, FOX, FLC, CLO, DIC STAAUR Fluoroquinolones CIP, OFX, LVX, NOR STAAUR Rifampin RIF STAAUR Linezolid LNZ

STRPNE (I+R) PEN, OXA STRPNE (I+R) ERY, CLR, AZM STRPNE 3rd gen. cephalosporins CTX, CRO STRPNE Fluoroquinolones CIP, OFX, LVX, NOR STRPNE Moxifloxacin MFX

European Antimicrobial Resistance Surveillance Network (EARS-Net) Belgium, isolates from 2013

7. DISCUSSION OF ERROR & BIAS

In order to correctly interpret the result, a discussion of error and bias is necessary.

7.1 Selection bias In the EARS-Net setting, the smallest units are the isolates. An important question is whether the isolates that are col- lected are representative for “all blood and CSF isolates in Belgium”. In a setting like ours there are two possible rea- sons why might not be representative: sample bias and attrition. Both of these are forms of selection bias.

If selection bias is present, this means the study population is not representative of the domain (here: all blood and CSF isolates in Belgium).

7.1.1. Sample bias Sample bias occurs if the sampling method leads to a study population that is non-representative of the domain. 1 The EARS-Net study population aims to include all (no sampling) isolates of invasive infections (blood and CSF).

The fact that only invasive samples are collected introduces an important sample bias if the goal is to draw conclusions about “all isolates”. The choice to include only blood and CSF isolates is done deliberately to avoid a situation were different hospitals or different countries have different clinical case definitions or heterogeneous healthcare utilisation. Blood and CSF sam- pling habits are likely to be more similar between countries and hospitals however even these may vary between countries and hospitals. A good example is a situation where invasive samples are only taken after failed empiric ther- apy, leading to a overestimation of resistance rate. A good indication of comparability of invasive sampling habits is a similar sampling frequency: Total number of blood culture sets per 1000 patient days . If these are similar, then sample bias can be presumed to be low. To compare this between Belgian hospitals we need the total number of blood cultures taken. These data are currently not available, the WIV-ISP is trying to get this information through analysis of reimbursement data so as not to increase the work- load for the hospitals.

The domain in EARS-Net is therefore “all invasive samples”. This allows hospitals and countries to be compared, but the data cannot be used to draw conclusions of all clinical isolates since invasive isolated may for a number of biologi- cal reasons not be representative of the same bacterial species from other sites (pneumonia, wound, etc.).

When comparing hospitals in Belgium sample bias would be an issue if the hospitals have different invasive sam- pling habits. No data is available yet, but we currently presume the differences in invasive sampling habits are lim- ited.

7.1.2. Attrition Attrition occurs if loss of study population leads to a study population that is non-representative of the domain. This can for instance be due to non-response (participation bias). Other reasons for attrition such as drop-out are not rele- vant in our setting.1

Selection bias due to non-response is a possibility because EARS-Net suffers from non-response, not all invasive iso- lates that are part of the study population are in the database. Obviously we do not know this directly, but it can be concluded from the fact that not all labs participate . Non-response does not necessarily mean the units in the study population are not representative of the domain, it merely means it must be verified. If the non-response is complete- ly at random then there is no problem for representativeness.

It is difficult to know whether the non-response in EARS-Net Belgium was random or selective. A possible method of investigation is to compare certain characteristics of the respondents with that of the study population. At the isolate level this means for instance checking whether the age or gender distribution among our responders is similar to the age or gender distribution among “all invasive samples in Belgium”.

1 D Coggon, G Rose , Barker D. Epidemiology for the uninitiated. London, BMJ, 2013. Available online at http://www.bmj.com/about-bmj/resources-readers/publications/epidemiology-uninitiated

European Antimicrobial Resistance Surveillance Network (EARS-Net) Belgium, isolates from 2013

Checking this is currently not feasible at the isolate level. What is possible is to analyse the distribution at the next level: laboratories. Table 2 shows the distribution of university versus non-university hospitals in Belgium, and com- pares it to the distribution of university and non-university hospitals that participate in EARS-Net. Because the partici- pating hospital are different per germ, we had to do this analysis per germ.

For each bacteria the distribution of type of hospital is no different from the distribution in Belgium (Chi² test, all p>0.05 ).

Table 2. Distribution of type of participating hospital by germ and compared to distribution of all acute care hospi- tals in Belgium.

Total Non -university ho spital University hospital N % N % N % All* labs in Belgium 110 (100%) 103 (93.7%) 7 (6.4%) Labs participating for ENCFAE 43 (100%) 41 (95.3%) 2 (4.7%) Labs participating for ENCFAI 43 (100%) 41 (95.3%) 2 (4.7%) Labs participating for ESCCOL 43 (100%) 41 (95.3%) 2 (4.7%) Labs participating for KLEPNE 43 (100%) 41 (95.3%) 2 (4.7%) Labs participating for PSEAER 43 (100%) 41 (95.3%) 2 (4.7%) Labs participating for STAAUR 43 (100%) 41 (95.3%) 2 (4.7%) Labs participating for STRPNE 91** (100%) 84 (92.4%) 7 (7.6%) * labs eligible to participate (hospital based microbiology labs that perform analysis on blood and CSF isolates) ** 2 non-eligible labs also participated (n=93) for STRPNE

We conclude that selection bias is limited but should be further investigated.

European Antimicrobial Resistance Surveillance Network (EARS-Net) Belgium, isolates from 2013

7.2 Measurement bias Measurement bias occurs when the registered results for an isolate are not valid for that isolate. In other words: the measured result does not accurately reflect the “true value” of that isolate. Since measurement bias has serious con- sequences for validity, it is important to assess it. In our setting there are 3 possibilities for measurement error: • incorrect measurement during the AST • not using advised breakpoints during AST • incorrect data transfer (for instance: in the database “S” is registered for a certain sample, while the LIMS has “R” for that sample)

7.2.1. incorrect AMR measurement The quality of measurement can be assessed in several ways: • Comparing the result with the result of a “gold standard” test • assessment of the prediction model that is created with the study measurements (a good model is an argu- ment against measurement bias but the opposite cannot be concluded) • assessment of the repeatability (bad repeatability implies there is measurement bias but the opposite cannot be concluded)

In EARS-Net the first approach is used: external quality assessment (EQA). In the EQA a lab is sent several samples that they are asked to identify and provide AMR test results for. If the identification is done correctly , the AST results are compared to the results of the gold standard (which is the result of reference labs). Two organisations organise an EQA in Belgium each year:

WIV-ISP EQA In order to have their services reimbursed by the health insurance, all labs of clinical biology need to participate in in the EQA that is organised by the WIV-ISP. This EQA is organised 3 times per year and each times includes about 4 mi- cro-organisms. The included microorganisms are not necessarily part of the EARS-Net germs. More info: https://www.wiv- isp.be/ClinBiol/bckb33/activities/external_quality/general_information/_nl/general_information.htm

The 2012 ERAS-Net Belgium report noted that there was a problem with the AST measurement for the combination KLEPNE-carbapenem . All the other bug-drug combinations have correct AST score of above 90%. More recent data from the WIV-ISP EQA could not be included in this report.

UKNEQAS EQA Each of the EARS-Net participating labs are invited to participate in the EQA organised ones per year by UKNEQAS (who are contracted by ECDC). Table 3 shows the result of the EQA performed in the late summer of 2013 (the next EQA is planned for mid-September 2014 and will be available in 2015). Six specimens were included in the 2013 EQA. More info can be found in each year’s ECDC EARS-Net report. 84 labs participated, AST results are only shown in table 3 f at least 70 labs tested that particular AB.

European Antimicrobial Resistance Surveillance Network (EARS-Net) Belgium, isolates from 2013

Table 3 Bacteria % correct ID Antimicrobial Correct AST in % Acinetobacter spp 97.6 % (n=84) Amikacin 97.3 (n =73) Ciprofloxacine 98.8 (n=83) Gentamicin 98.6 (n=74) Meropenem 98.8 (n=80) Escherichia coli 100.0% (n=84) Amikacin 98.8 (n=83) Amoxicillin-clavulanic 97.6 (n=84) acid Ampicillin 98.8 (n=80) Ceftazidime 100.0 (n=82) Ciprofloxacin 100.0 (n=76) Gentamicin 100.0 (n=75) Meropenem 100.0 (n=83) Piperacillin- 100.0 (n=82) tazobactam Klebsiella pneumoniae 100.0% (n=84) Ceftazidime 91.5 (n=82) Ciprofloxacin 98.7 (n=78) Gentamicin 100.0 (n=72) Meropenem 69.6 (n=83) Piperacillin- 97.6 (n=82) tazobactam Staphylococcus aureus 100.0% (n=84) Methicillin 100.0 (n=70) Rifampicine 97.4 (n=76) Streptococcus pneumoniae 100.0% (n=84) Erythromycin 100.0 (n=81) Penicillin 95.8 (n=72) 98.8% (n=84) Amikacin 98.8 (n=82) Cefepime 98.8 (n=81) Ceftazidime 98.8 (n=84) Ciprofloxacin 98.8 (n=81) Gentamicin 98.7 (n=75) Meropenem 97.6 (n=84) Piperacillin- 53.0 (n=83) tazobactam

The combination KLEPNE-carbapenem seems to pose a problem, as it did last year. Also the combination PSEAER- piperacillin-tazobactam suffers from low accuracy. This means the results for these combinations should be inter- preted with caution. All the other bug-drug combinations have correct AST score of above 90%.

7.2.2. Advised breakpoints WIV-ISP and BAPCOC encourage the use of EUCAST breakpoints but not every lab in Belgium uses EUCAST, approxi- mately 60% of labs use CLSI and about40% are using EUCAST. The MIC breakpoints are very close in certain cases, but can be relatively far from each other in other cases.

7.2.3. Data transfer Historically EARS-Net data transfer took place on paper, a lab would note the AMR result of an isolate on paper and send that paper to WIV-ISP where it would be entered into registration software. This leaves room for human error and is also very labour intensive which posed a burden on participation. Currently only the electronic method is used, meaning a transfer of a xml, csv or xls file. These files are created either using a GLIMS query, a query in InfoPartner or a custom made query that extracts the LIMS data into a standard format. After the data are transformed into the TESSy format (ECDC standard), the results are feedbacked to the labs in order to discover errors in ether hospital LIMS or the query (validation). After validation the data are introduced into the central database on a secure WIV-ISP server.

European Antimicrobial Resistance Surveillance Network (EARS-Net) Belgium, isolates from 2013

8. RESISTANCE PATTERNS PER GERM

8.1 S. aureus

8.1.1 Relevance in AMR surveillance The oxacillin-resistant form (MRSA) has been the most important cause of antimicrobial-resistant healthcare-associated infections worldwide. MRSA infections are added to the number of infections caused by methicillin-susceptible S. aureus . A high incidence of MRSA therefore adds to the overall burden of infections caused by S. aureus in hospitals.

8.1.2 Resistance mechanisms

8.1.2.1 Oxacillin-resistance Staphylococcus aureus acquires resistance to meticillin and all other beta-lactam antimicrobials through expression of an exogenous mecA gene that codes for a penicillin-binding protein (PBP2a) with low affinity for beta-lactams. The level of meticillin resistance, as defined by the MIC depends on the amount of PBP2’ production, which is influenced by various other genetic factors. Resistance levels of mecA-positive strains can thus range from phenotypically suscep- tible to highly resistant.

8.1.2.2 Rifampicin resistance For rifampicin, the mechanism of resistance is mutation in the rpoB-gene, leading to production of RNA polymerase with low affinity for rifampicin and other .

8.1.2.3 Fluoroquinolone resistance Resistance to fluoroquinolones is mediated by the mutations in ParC or ParE (subunits of topoisomerase IV) and/or GyrA (subunit of DNA gyrase/topoisomerase IV). Additionally, resistance may be conferred by efflux.

8.2 S. pneumoniae

8.2.1 Relevance in AMR surveillance S. pneumoniae is the most common cause of pneumonia worldwide. Morbidity and mortality are high, annually ap- proximately 3 million people are estimated to die of pneumococcal infections. Interestingly, serotypes most frequent- ly involved in pneumococcal disease or colonisation in infants are also most frequently associated with AMR. Howev- er, serotype replacement due to increased use of the pneumococcal conjugate vaccine (PCV) might change this over time.

8.2.2 Resistance mechanisms

8.2.2.1 Penicillin resistance Alterations in PBPs result in reduced affinity to penicillins. The mutations can cause different degrees of resistance, from low-level clinical resistance – conventionally termed intermediate – to full clinical resistance. Intermediately resistant strains are less susceptible than susceptible strains but they are often successfully treated with high doses of benzyl-penicillin or aminopenicillins as long as meningitis is absent.

8.2.2.2 MLS Macrolide, lincosamide and (MLS) antimicrobials are chemically distinct, but all bind to a ribosomal subunit inhibiting the initiation of mRNA binding and thus act as protein synthesis inhibitors. There are two predomi- nant resistance mechanisms against MLS antimicrobials in S. pneumoniae : • The acquisition of an erythromycin ribosomal methylation gene ( erm ) • The acquisition of a macrolide efflux system gene (mef (E))

European Antimicrobial Resistance Surveillance Network (EARS-Net) Belgium, isolates from 2013

8.2.2.3 Fluoroquinolone (levofloxacin and moxifloxacin) Resistance to fluoroquinolones is mediated by the mutations in ParC and/or GyrA. Additionally, resistance may be conferred by efflux.

8.3 E. coli

8.3.1 Relevance in AMR surveillance E. coli is the most frequent cause of bacteraemia and urinary tract infections (both community- and hospital- acquired).

8.3.2 Resistance mechanisms

8.3.2.1 Bèta-lactams Resistance to beta-lactams is mostly due to production of plasmid coded beta-lactamases. Surveillance has therefore focused on aminopenicillins and third-gen cephalosporins. The first ESBLs in E. coli were variants of the TEM or SHV enzymes. During the past decade, however, these enzymes have largely been replaced by the CTX-M-type ESBLs, which are now the most common ESBLs in E. coli . An important new threat that will require close surveillance is the emergence of carbapenem resistance in E. coli , providing resistance to most or all available beta-lactam agents. Carbapenem resistance is mediated by metallo-beta- lactamases (such as the VIM, IMP or NDM enzyme) or serine-carbapenemases (such as the KPC enzymes).

8.3.2.2 Fluoroquinolones Resistance to fluoroquinolones arises through stepwise mutations in the coding regions of the gyrase subunits (gyrA and gyrB) and DNA topoisomerase IV (parC). Accumulation of mutations in several of these genes increases the MIC in a stepwise manner. Low-level resistance to fluoroquinolones may also arise from lower outer membrane permeability (changes in porins) or higher efflux (upregulation of efflux pumps).

8.3.2.3 Aminoglycosides Resistance to aminoglycosides can be due to methylation of the large ribosomal subunit, or by production of enzymes that acetylate, adenylate or phosphorylate molecules thereby neutralizing it. Among E. coli isolates resistant to third-generation cephalosporins, many labs test for the presence of an ESBL-enzyme but this data is not included in the 2011 dataset.

8.3.2.4 Combination (third-generation cephalosporins, fluoroquinolones, aminoglycosides) This leaves only a few therapeutic options, mostly carbapenems, colistin, tigecyclin, temocillin.

8.4 K. pneumoniae

8.4.1 Relevance in AMR surveillance Klebsiella pneumoniae is associated with opportunistic infections in individuals with impaired immune systems, such as diabetic, alcoholic and hospitalised patients with indwelling devices. The most common sites of infection are the urinary tract and the respiratory tract. Klebsiella pneumoniae is the second most frequent cause of Gram-negative bloodstream infections after Escherichia coli .

8.4.2 Resistance mechanisms Resistance traits for K. pneumoniae are similar to the ones described in E. coli . An exception are the aminopenicillins since K. pneumoniae is intrinsically resistant to aminopenicillins due to a chromosomally encoded SHV beta-lactamase.

Carbapenems have been widely used in many countries as an answer to the increasing rate of ESBL-producing Entero- bacteriaceae. As a consequence there has been an emergence of resistance to carbapenems, especially in K. pneu- moniae . The bla OXA-48 gene codes for an oxacillinase (OXA-48) that causes resistance to penicillin and reduces suscepti- bility to carbapenems, but not to expanded-spectrum cephalosporins. The level of resistance is often low and such strains are thus frequently missed in laboratories using automated AST systems. A combination of OXA-48-like en- zymes with ESBLs such as CTX-M15 can occur in Klebsiella spp. and can result in a highly drug-resistant phenotype.

European Antimicrobial Resistance Surveillance Network (EARS-Net) Belgium, isolates from 2013

8.5 E. faecalis en E. faecium

8.5.1 Relevance in AMR surveillance With the exception of some strains, Enterococci are regarded as commensals which can nonetheless cause invasive disease. Enterococci are also recognized as nosocomial pathogens.

8.5.2 Resistance mechanisms Enterococci are intrinsically resistant to a broad range of antimicrobials and can acquire additional resistance through the transfer of plasmids.

8.5.2.1 Beta-lactam antimicrobials Enterococci have an intrinsic low susceptibility to many beta-lactam antimicrobials as a consequence of their low- affinity PBPs. An exception is E. faecalis, which still has susceptibility and therefore remains the num- ber one choice of treatment.

8.5.2.2 High level aminoglycosides Enterococci have an intrinsic low level resistance to aminoglycosides due to the low uptake of the drug, which can be overcome with higher doses. Several aminoglycoside-modifying enzymes have been identified causing high level re- sistance. With high-level resistance, any synergistic effect between beta-lactams and glycopeptides is lost.

8.5.2.3 Glycopeptides Acquired Glycopeptide-resistance is due to the synthesis of modified precursors that have a decreased affini- ty for glycopeptides. Two phenotypes are of clinical importance: VanA; variable level of resistance to teicoplanin, and a high-level resistance to vancomycin VanB: variable level of resistance to vancomycin.

8.6 P. aeruginosa

8.6.1 Relevance in AMR surveillance P. aeruginosa is an opportunistic pathogen which is difficult to control in hospitals due to its intrinsic tolerance to many detergents, disinfectants and antimicrobial compounds.

8.6.2 Resistance mechanisms Pseudomonas aeruginosa is intrinsically resistant to the majority of antimicrobial agents due to its ability to exclude various molecules from penetrating its outer membrane. The antimicrobial classes that remain active include the fol- lowing: • certain fluoroquinolones • aminoglycosides • piperacillin • carbapenems • colistin

European Antimicrobial Resistance Surveillance Network (EARS-Net) Belgium, isolates from 2013

9. RESULTS

The results are presented in the graphs of the corresponding bug-drug combination. The absolute numbers can be found in the horizontal axis of every graph. If a lab did not send a certain bug-drug combination (zero cases of AB not tested) then it is not in the graph of that combination. Confidence intervals (CI) are necessary even though a lab sends all of its data. This can easily be illustrated with a hy- pothetical example of a lab that has 1 patient with Streptococcus , with results “R” in penicillin susceptibility. The re- sult is 100% resistance. Obviously this 100% has a large margin of error, due to the low sample size.

CI are not given in the graph, because the graphs become difficult to read in that case, should a hospital wish to have more in-depth analysis the WIV-ISP will be happy to help.

Please remember, as we mentioned earlier: EARS-Net examines only blood and CSF samples, only the first sample of the year is taken into account for each patient. In case of multiple samples on the same day or in case of multiple an- tibiotics of the same group: priority is always giving to the “worst” AMR test result: R>I>S.

European Antimicrobial Resistance Surveillance Network (EARS-Net) Belgium, isolates from 2013

10. ACKNOWLEDGEMENTS

We wish to thank all labs who have sent us their data and look forward to receiving your feedback on how to im- prove data collection and analyses to best fit your needs.

11. FUTURE PARTICIPATION

If you do not yet participate for all 7 germs, more information is available: [email protected]

Data is transferred by making an extraction from your LIMS and sending this file to the WIV-ISP. This happens once every year. Until 2012 it was also possible to participate by filling in a form per patient, this method is no longer used after 2012.