EARS -Net (European Antimicrobial Resistance Surveillance … · 2014. 9. 5. · European Antimicrobial Resistance Surveillance Network (EARS-Net) Belgium, isolates from 2013 6. ANTIMICROBIAL

European Antimicrobial Resistance Surveillance Network (EARS-Net)

Belgium, isolates from 2013

EARS-Net (European Antimicrobial Resistance Surveillance Network)

Isolates from 2013, Belgium



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)



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



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.



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



4. ANTIMICROBIAL CODES (ECDC)

Table 1 Explanation of official ECDC Antimicrobial codes

Code Name Code Name Code Name

ACM Acetylmidecamycin DIT Cefditoren OXA Oxacillin

AMB Amphotericin B DKB Dibekacin OXO Oxolinic acid

AMC Amoxicillin/Clavulanic acid DOX Doxycycline OXY Oxytetracycline

AMK Amikacin ECO Econazole PAN Panipenem

AMP Ampicillin ENR Enrofloxacin PAR Paromomycin

AMR Amprolium ENX Enoxacin PAS P-Aminosalicylic acid

AMX Amoxicillin EPE Eperozolid PEF Pefloxacin

APL Apalcillin EPP Epiroprim PEN Penicillin G

APR Apramycin ERY Erythromycin PIM Pentisomicin

APX Aspoxicillin ETH Ethambutol PIP Piperacillin

ARB Arbekacin ETI Ethionamide PIR Piromidic acid

ASP Acetylspiramycin ETO Etopabat PIS Piperacillin/Sulbactam

AST Astromicin ETP Ertapenem PKA Propikacin

ATM Aztreonam FAR Faropenem PNO Penicillin/Novobiocin

AVI Avilamycin FEP Cefepime PNV Penicillin V

AVO Avoparcin FLA Flavomycin POL Polymixin B

AXS Amoxicillin/Sulbactam FLC Flucloxacillin PPA Pipemidic acid

AZL Azlocillin FLE Fleroxacin PRC Piridicillin

AZM Azithromycin FLM Flumequine PRI Pristinamycin

BAC Bacitracin FLO Flomoxef PRL Pirlimycin

BAM Bacampicillin FLR Florfenicol PRM Primycin

BCZ Bicozamycin FLU Fluconazole PRP Propicillin

BDP Brodimoprim FMD Fosmidomycin PRX Premafloxacin

BIA Biapenem FOS Fosfomycin PTH Prothionamide

BUT Butoconazole FOX Cefoxitin PTZ Pentizidone

CAC Cefacetrile FRM Framycetin PZA Pyrazinamide

CAP Capreomycin FRZ Furazolidone QDA Quinupristin/Dalfopristin

CAR Carumonam FUS Fusidic acid RAC Ractopamine

CAT Cefetamet GAT Gatifloxacin RIB Rifabutin

CAZ Ceftazidime GEH Gentamicin-High RID Cefaloridin

CCL Cefetecol (Cefcatacol) GEM Gemifloxacin RIF Rifampin

CCP Cefcapene GEN Gentamicin ROK Rokitamycin

CCV Ceftazidime/Clavulanic acid GRI Griseofulvin ROS Rosoxacin

CDR Cefdinir GRX Grepafloxacin RXT Roxithromicin

CDZ Cefodizime HAB Habekacin SAL Salinomycin

CEC Cefaclor HAP Cephapirin SAM Ampicillin/Sulbactam

CED Cephradine HET Hetacillin SAR Sarafloxacin

CEM Cefteram HYG Hygromycin SBC Sulbenicillin

CEP Cephalothin INH Isoniazid SDI Sulfadiazine

CFB Cefbuperazone IPM Imipenem SDM Sulfadimidine

CFM Cefixime ISE Isepamicin SIS Sisomicin

CFP Cefoperazone ISO Isoconazole SMX Sulfamethoxazole

CFR Cefadroxil ITR Itraconazole SNA Sulfasuccinamide

CFS Cefsulodin JOS Josamycin SOX Sulfisoxazole

CFZ Cefpimizole KAH Kanamycin-High SPI Spiramycin

CHE Cefotiam hexetil KAN Kanamycin SPT Spectinomycin

CHL Chloramphenicol KET Ketoconazole SPX Sparfloxacin

CIC Ciclacillin KIT Kitasamycin (Leucomycin) SRX Sarmoxicillin

CID Cefonicid LAS Lasalocid SSS Sulfonamides

CIN Cinoxacin LEX Cephalexin STH Streptomycin-High

CIP Ciprofloxacin LIN Lincomycin STR Streptomycin

CLA Clavulanic acid LNZ Linezolid SUC Sulconazole

CLI Clindamycin LOM Lomefloxacin SUD Sulfadimethoxine

CLO Cloxacillin LOR Loracarbef SUL Sulbactam

CLR Clarithromycin LSP Linco-spectin SUM Sulfamethazine

CLX Clinafloxacin LVX Levofloxacin SUP Sulfachlorpyridazine

CMX Cefmenoxime MAN Cefamandole SUT Sulfathiazole

CMZ Cefmetazole MCR Micromomicin SXT Trimethoprim/Sulfamethoxazole

CND Ceforanide MCZ Miconazole SZO Sulfamazone

CNX Cefminox MEC Mecillinam (Amdinocillin) TAZ Tazobactam



Code Name Code Name Code Name

COL Colistin MEL Meleumycin TBQ Tilbroquinol

CPD Cefpodoxime MEM Meropenem TCC Ticarcillin/Clavulanic acid

CPI Cefetamet pivoxil MES Mesulfamide TCY Tetracycline

CPM Cefpiramide MET Methicillin TDC Tiodonium chloride

CPO Cefpirome MEZ Mezlocillin TEC Teicoplanin

CPR Cefprozil MFX Moxifloxacin TEM Temocillin

CPX Cefpodoxime proxetil MID Midecamycin TET Tetroxoprim

CRB Carbenicillin MIL Miloxacin TFX Tosufloxacin

CRD Cefroxadine MNO Minocycline THA Thiacetazone

CRO Ceftriaxone MON Monensin sodium THI Thiamphenicol

CSL Cefoperazone/Sulbactam MOX Moxalactam (Latamoxef) TIA Tiamulin

CSU Cefsumide MSU Mezlocillin/Sulbactam TIC Ticarcillin

CTB Ceftibuten MTP Metioprim TIL Tilmicosin

CTC Cefotaxime/Clavulanic acid MTR Metronidazole TIN Tinidazole

CTE Chlortetracycline MUP Mupirocin TIO Ceftiofur

CTF Cefotiam MXT Metioxate TLP Talmetoprim

CTO Cetocycline NAF Nafcillin TLT Telithromycin

CTR Clotrimazole NAL Nalidixic acid TMP Trimethoprim

CTS Cefotaxime/Sulbactam NAR Narasin TMX Temafloxacin

CTT Cefotetan NEO Neomycin TOB Tobramycin

CTX Cefotaxime NET Netilmicin TRL Troleandomycin

CTZ Cefatrizine NIC Nicarbazin TRO Trospectomycin

CXA Cefuroxime axetil NIF Nifuroquine TVA Trovafloxacin

CXM Cefuroxime sodium NIT Nitrofurantoin TXC Tioxacin

CYC Cycloserine NIZ Nitrofurazone TYL Tylosin

CZD Cefazedone NOR Norfloxacin TZP Piperacillin/Tazobactam

CZL Cefetrizole NOV Novobiocin VAN Vancomycin

CZO Cefazolin NTR Nitroxoline VIO Viomycin

CZX Ceftizoxime NVA Norvancomycin VIR Virginiamycine

DAP Daptomycin NYS Nystatin ZON Cefuzonam

DEM Demeclocycline OFX Ofloxacin

DFX Danofloxacin OLE Oleandomycin

DIC Dicloxacillin OPT Optochin

DIF Difloxacin ORN Ornidazole

DIR Dirithromycin ORS Ormetropim/Sulfamethoxine



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%, rifampicin resistant S. aureus

remained below 1%.

• Vancomycin resistant enterococci is around 1%.

• Penicillin and macrolide resistant S. pneumoniae was 2% and 23%, respectively.

• In P. aeruginosa resistance for carbapenems, for ceftazidime, and for piperacilline was around 10% (regarding

PIP see comment below).

• Carbapenem 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.



6. ANTIMICROBIAL GROUPS

Results are not given for each separate antibiotic (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 antibiotics 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 Aminopenicillins (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. cephalosporins CTX, CRO, CAZ

ESCCOL/KLEPNE Aminoglycosides 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 Penicillins (I+R) PEN, OXA

STRPNE Macrolides (I+R) ERY, CLR, AZM

STRPNE 3rd gen. cephalosporins CTX, CRO

STRPNE Fluoroquinolones CIP, OFX, LVX, NOR

STRPNE Moxifloxacin MFX



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



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 hospital 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.



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.



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

acid

97.6 (n=84)

Ampicillin 98.8 (n=80)

Ceftazidime 100.0 (n=82)

Ciprofloxacin 100.0 (n=76)



Piperacillin-

tazobactam

100.0 (n=82)

Klebsiella pneumoniae 100.0% (n=84) Ceftazidime 91.5 (n=82)




Piperacillin-

tazobactam

97.6 (n=82)

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)

Pseudomonas aeruginosa 98.8% (n=84) Amikacin 98.8 (n=82)

Cefepime 98.8 (n=81)

Ceftazidime 98.8 (n=84)




Piperacillin-

tazobactam

53.0 (n=83)

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.



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 rifamycins.

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


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.


8.2.2.1 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 streptogramin (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))



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


E. coli is the most frequent cause of bacteraemia and urinary tract infections (both community- and hospital-

acquired).


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 aminoglycoside 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


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.


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 blaOXA-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.



8.5 E. faecalis en E. faecium


With the exception of some strains, Enterococci are regarded as commensals which can nonetheless cause invasive

disease. Enterococci are also recognized as nosocomial pathogens.


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 aminopenicillin 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 cell wall 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


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.


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



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.



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.

Documents

EARS -Net (European Antimicrobial Resistance Surveillance … · 2014. 9. 5. · European Antimicrobial Resistance Surveillance Network (EARS-Net) Belgium, isolates from 2013 6. ANTIMICROBIAL