EARS-Net data management survey 2014. John Stelling (USA)

WHO Collaborating Centre for Surveillance of Antimicrobial Resistance Brigham and Women’s Hospital, Boston

EARS-Net Data Management Survey 2014

John Stelling, MD, MPH Brigham and Women’s Hospital, Boston

WHO Collaborating Centre for Surveillance of Antimicrobial Resistance

[email protected]

And

John Stellinġ, Director Sustainable Health Services, Ltd. Malta!


1. You reported data for what country?

• 2014: 30 countries provided 28 survey responses • 2013: 30 countries provided 25 survey responses • 2012: 29 countries provided 27 survey responses • 2011: 28 countries provided 25 survey responses


2. What antibiotic breakpoints do you plan to use in 2014 when you submit your data?

• EUCAST – 23 countries (+2 from 2014) • CLSI – 12 countries (+2 from 2014) • BSAC – 1 country (mostly in line with EUCAST) • CA-SFM – 1 country (mostly in line with EUCAST)

• For countries using both CLSI and EUCAST, many labs

have switched over to EUCAST, gradual process


3. If you answered CLSI in Question 2, is there an eventual plan to migrate to EUCAST breakpoints in the future?

• EUCAST only already 15 • EUCAST+CLSI -> EUCAST during 2015 2 • EUCAST+CLSI -> EUCAST after 2015 1 • EUCAST+CLSI -> EUCAST slowly 4 • CLSI -> EUCAST after 2015 2 • CLSI -> EUCAST slowly 2 • Stay CLSI for some labs 2 • CA-SFM 1 • BSAC 1


4. What primary softwares do you use in your EARS-Net data management strategy?

• WHONET – 19 countries (-1) • BacLink – 8 countries (+0) • Microsoft Access – 3 countries (-2) • Microsoft Excel – 12 countries (+0) • SQL Server – 2 countries (-1) • CSVed – 3 countries (+0) • Stata – 2 countries • PostgreSQL – 2 countries


5. What do you receive from your participating laboratories?

• Paper – 10 countries (-2) • WHONET – 6 countries (+1) • Excel – 15 countries (+0) • Text – 9 countries (-1) • Web system/Electronic interface – 2 countries (+0) • XML – 2 countries (+0) • EARSS DEFS – 1 country (+0) • Access – 1 countries (-1)


6. Please indicate the MOST important mechanism for acquiring your EARS-Net data.

• Electronic data capture – 19 countries (+0) • Manual entry by national coordinators – 5 (+0) • Manual entry by labs – 3 countries (+0) • More than one method – 3 countries (+0)


7. How well did TESSy and WHONET (software and technical support)

meet your needs?

• Very well – 16 countries (-1) • Adequately – 12 countries (+2) • Poorly – 0


8. TESSy Comments and Suggestions

• “Make TESSy faster” • “How are duplicates and counting handled?” • “In TESSy we would still like to have the verification summary report

before approval, not after.” • “TESSy could be written more robustly. Example 2.000 for an Etest is

not acceptable, but this is the result of using 3 decimals in the extraction software. I had to write a lot of additional code to convert the number 2.000 to the text '2' (and 1.500 to '1.5' , etc.)”

• “I would really appreciate not having to read manuals to understand whether i should use add/replace/update when uploading, in other words: id like the text next to the option to explain what will happen if i should choose that option.”


8. WHONET Comments and Suggestions

• “I need to review WHONET version or codes because TESSy does not recognize the Acinetobacter strains.”

• “How does WHONET handle selective “encryption” of Specimen numbers?”

• “How does WHONET handle S. pneumoniae breakpoints for penicillin and third-generation cephalosporins for meningitis and non-meningitis”


WHONET and S. pneumoniae breakpoints


9. In your national EARS-Net database, you collect data on which organisms and

specimen types?

• All routine bacterial species 10 (+1) • EARS-Net organisms 16 (+2) • EARS-Net organisms plus others 1 (-1) • No response 3 (-2)


10. Do you plan on introducing any improvements in your data collection and

management strategies for next year?

• No 24 (+7) • No answer 2 (-3) • Yes with comments 4 (+2)


Conclusions

• Continued migration to EUCAST • Software platforms mostly static

– Slow move away from paper-based collection – Gradual improvements in efficiency

• Opportunities for comprehensive national surveillance with all organisms, specimen types

• Update your WHONET software once a year – Breakpoint updates – Inclusion of Acinetobacter – Guidance on S. pneumoniae breakpoints

• If you have quantitative data • If you have interpretation data


EUCAST 2015 Breakpoint changes • Relevant for EARS-Net reporting

– Enterobacteriaceae – amikacin disk diffusion • 2014: R<13, S>=16 • 2015: R<15, S>=18

• Others – Enterobacteriaceae – ceftobiprole – S. aureus – telavancin – S. pneumoniae – ceftobiprole – N. meningitidis – ciprofloxacin – M. tuberculosis – New table – delamanid, bedaquiline – PK/PD – ceftobiprofile


CLSI 2015 Breakpoint changes

• Relevant for EARS-Net reporting – None!

• Others – Salmonella Typhi: azithromycin – Salmonella spp.: pefloxacin – P. acnes: Epidemiological cut-off value for vancomycin

• Comment on “Susceptible Dose-Dependent” - SDD


WHONET Software development • WHONET 5.6

– Updated annually, but name stays the same – Breakpoint updates, new outbreak detection features,

other small enhancements and fixes

• WHONET 2015 – Desktop version – Far advanced, beta-testing soon – First version – English only, no graphics

• WHONET 2015 – Web version – Used to support CDC NHSN reporting – Manual data entry being piloted in Argentina – Data analysis – by April 15, 2015


WHONET Web-based data entry


New NIH Software development grant • Data content

– Microbial subtyping: MALDI-TOF, NGS and WGS – Infection control needs for NHSN – Geo-reference data: Latitude/longitude

• Data analysis – Use of new typing methods to improve strain recognition,

tracking, and outbreak detection – New cluster detection algorithms, e.g. purely geographic

clustering – Integration or R, EpiInfo, and Weka: advanced statistics

and data mining


New NIH Software development grant

• Data display – Web interface – Maps – Mobile devices

• Collaborators – Martin Kulldorff – Harvard Pilgrim Healthcare – David Aanensen – Imperical College London, Sanger Insitute – Pilot sites: Argentina, Norway, United States (Boston), United

States (Department of Defense)


WHONET and Cluster detection

• Patient location – Latitude/longitude – Medical ward (or groups of wards) – Medical service (or groups of services)

• Microbial characteristics – Species, Serotype, Phagetype – Resistance phenotype – Biochemical phenotype – ? MALDI-TOF, Sequence types


MIDAS: Shigella outbreaks in Argentina Reported to MOH

1,17,19

2

3,6,9,10

4

5,14

7

8

11

12,13,16 15,18

Suggested by SaTScan

B

C

D E

F

A


PFGE Confirmation of WHONET-SaTScan signals detected by resistance phenotype


Random patient first isolates

6607734753164010

6605734653164410 4607735777565350 2607734753564010 6607734773164010 6607735453164010 6607734653164000 4607734753564010 6607734671164010 6607734753564010 6605734653160010

6607735553164010 6607735553164010 6607735453164010 6607734653164010 6607734653164010 6607735553164010 6607735553164010

Bionumbers of Klebsiella pnumoniae isolates

Cluster patient first isolates

27/10 = 2.7 variations per bionumber

5/6 = .83 variations per bionumber


Biochemical markers and cluster detection Klebsiella pneumoniae and Vitek bionumbers

• Overall findings – In a recent year, there were 1226 isolates of K. pneumoniae isolated from 803 patients – These isolates represented 377 distinct biochemical phenotypes

• SaTScan findings – We ran a retrospective SaTScan analysis which detected a statistical cluster for isolates

with adjusted bionumber: 1101100001111101110011101011011101001000100

– In the entire year, there were only 13 patients with this particular bionumber, and 8 of these patients occurred in a 25-day period, p-value = 0.00000803

– 6 isolates had identical or nearly identical resistance phenotypes (susceptible to all drugs tested except for AMP). 1 isolate differed in SXT and NIT, 1 isolate differed in several antibiotics.

– Mixture of outpatient, EW, and oncology unit isolates

• SaTScan conclusion – Perhaps this was an unrecognized outbreak of a mostly susceptible strain; or – Perhaps this represents normal fluctuation, previously unappreciated, in

bionumber populations


Protein phenotyping with Mass Spectrometry: MALDI-TOF

• MALDI-TOF = Matrix-Assisted Laser Desorption/Ionization • Spectrum generation

www.biomerieux-industry.com/servlet/srt/bio/industry-microbiology dynPage?doc=NDY_IND_BPA_PRD_G_PRD_NDY_6

• Spectrum interpretation –1300 signals “bins” of variable intensity www.anagnostec.eu/products-services/saramis.html


MALDI-TOF – N. gonorrhoeae Comparison of signal profiles

Isolate 1 Isolate 2

Consensus peaks


Detection of clusters in space with latitude and longitude


S. aureus spa types in Europe ST 084

ST 041

ST 067

ST 003

http://www.spatialepidemiology.net/SRL-Maps

Green = MSSA

Red = MRSA


London School of Hygiene and Tropical Medicine

• 2014 – Argentina: Selali Fiamanya. Exploring the value of matrix assisted laser

desorption/ionisation time of flight (MALDI-TOF) technologies for microbial subtyping and outbreak detection

– China: Sara Haine. Antibiotic Resistance and Outbreak Detection in China – Italy. Alessandra Natale. Application of WHONET software to simulated prospective

real-time surveillance and analysis of antimicrobial resistance in Italy – United States: Hannah Lishman. Outbreak detection of antimicrobial-resistant

healthcare associated infections at the hospital network level

• 2015 – Argentina: Further exploration of MALDI-TOF – Singapore: Epidemiology of carbapenemase-producing vs. non-carbapenemase-

producing CRE (Risk factors, clinical outcomes, cluster detection, CRE definitions) – United States-Boston: Statistical detection of geographic clusters – United States-New York City: Epidemiological and molecular assessment and

investigation of WHONET-detected cluster signals


Welcome to Malta!

EARS-Net data management survey 2014. John Stelling (USA)

Health & Medicine

Transcript of EARS-Net data management survey 2014. John Stelling (USA)