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detected by all media. A total of 40, 48, 47 and 43 CPE were detected

on MacI, MacD, MacC ESBL and TSB broth respectively. Sensitivity

was highest for MacD (96%) (94%). Specificity was maximum for

MacI (97%).

The performance of media varied with carbapenem MIC and

carbapenemase gene associated with CPE isolate.

Discussion and/or Conclusion(s):

No single method is 100% sensitive

and specific for CPE detection. The choice depends on type of carba-

penemase gene prevalent locally. Combination of media increases the

sensitivity and specificity.

ID: 5033

Spread of hospital-acquired infections: a comparison of patient

transfer networks

Narimane Nekkab


, Pascal Astagneau


, Pascal Crépey





National des Arts et Metiers, Ecole des Hautes Etudes en Sante Publique,


Centre de Coordination pour la Lutte Contre les Infections Associées aux



Ecole des Hautes Etudes en Santé Publique


Hospital-acquired infections (HAIs), including emerging

multi-drug resistance organisms, continue to threaten all healthcare



To better understand how to reduce the scale

of HAI epidemic spread we compared and explored the characteristics

of patient transfer patterns in the French healthcare network to

identify their potential role in epidemic spread of HAIs within the



Three patient transfer networks were compared: twowith

only HAI related patients (a specific and a more sensitive definition),

and all transferred patients. We assessed network characteristics,

computed centrality, community measures, and compared these

networks to Erdos-Renyi random models and to networks with

randomly selected patients.


More than 10 million patient transfers were recorded in

France in 2014 for 2.3 million patients, building a network of 2063

hospitals and 50026 connections. The network of specific-HAI patients

was composed of 1266 hospitals and 3722 connections for 13627

patient transfers, while the suspected-HAI network was composed of

1975 hospitals and 18812 connections for 128681 patient transfers.

These networks displayed a scale-free structure for hospital

s number

of connections (in and out), and transfers. They also feature a small-

world characteristic. Communities within the networks had small

average inner distances between 22km to 88km, depending on the

modularity of the community detection algorithms.

Discussion and/or Conclusion(s):

These characteristics may have a

tremendous effect on the diffusion of nosocomial pathogens. In

addition, different patient inclusion criteria could impact network

characteristics and the identification of key hospital centres, patient

flow trajectories, and regional clustering that may serve as a basis for

novel wide-scale HAI control strategies.

ID: 5035

Use of ICD-10 coding in the hospital setting for infectious disease

and antimicrobial resistance surveillance

Meghan Perry


, Catriona Parker


, Oliver Koch


, Mark Woolhouse




NHS Lothian and University of Edinburgh,


NHS Lothian,


University of



Databases of hospital admissions using the International

Classification of Disease codes could be a valuable source of infectious

diseases surveillance.


This study

s aim was to assess the accuracy of

ICD-10 codes for infectious diseases diagnoses and recording of

antimicrobial resistance.


The ICD-10 codes from 100 discharges in the Regional

Infectious Diseases Unit, Western General Hospital, Edinburgh starting

on June 1


2015 were recorded. Corresponding discharge letters were

reviewed to verify the accuracy of the codes. Microbiological records of

the admission were checked to confirm that all relevant information

had been recorded on the discharge letter.


Sixty-eight discharges had appropriate ICD-10 codes relating

to the discharge letter. Of the 32 inappropriate codes 21 were incom-

plete, 8 were inaccurate, 2 were unconfirmed diagnosis and one

diagnosis had not been coded. On reviewing the microbiology 16

results were unrecorded in discharge letters that would impacted the

ICD-10 codes. Sixteen of the pathogens identified were resistant to one

or more antibiotics. However, only 2 of the discharge diagnoses

mentioned antimicrobial resistance and no ICD-10 resistance codes

were used.

Discussion and/or Conclusion(s):

Although a small sample size, this

audit highlights that the use of hospital ICD-10 databases for

epidemiological surveillance of infectious diseases diagnoses and

antimicrobial resistance would not currently give an accurate picture.

There are areas where the ICD code format could be improved to allow

for adequate representations of infectious diseases diagnoses includ-

ing recording of the clinical impact of antimicrobial resistance.

Increased awareness in doctors of the coding process and education

of the coders is recommended.

ID: 5036

Computer-assisted risk assessment of hospital infections: a

preliminary implementation in Polish hospitals

Andrzej Jarynowski


, Damian Marchewka


, Andrzej Grabowski




Jagiellonian University,


Interdisciplinary Research Institute in Wroclaw,



National Research Institute in Warsaw


We create an intuitive and functional desktop

application (free of charge, open licensed) to support the work of

the hospital epidemiologists in preventing and containing hospital



The goal of the application is reconstructing the

most likely paths of infection and classifying places and individuals

into different risk groups. We study the time-varying structure of

contacts reconstructed from the data sets collected in Polish and

Swedish hospitals. We will apply theory of temporal networks to

improve computer-assisted hospital infection analyses.


The algorithm processes data from the register of patient

admissions and discharges from each hospital unit (wards, clinics,

etc.), microbiological laboratory test results and medical staff

register. The patients

structure of contacts (persons who had

visited the same care facility) and functional paths are reconstructed.

Epidemiological models are implemented on a temporary network

of contacts (where each link can provide a path for the pathogen



In a preliminary setup, we analysed only one alarm pathogen:

MRSA. With simulated infection paths, we were able to compute

network measures for patients. We obtained the risk of getting

infected, based on the patient

s incoming connections, and the risk of

spreading infections resulting from outgoing connections (both

analogous to Google algorithm).

Discussion and/or Conclusion(s):

We provide a costless preventive

computer-assisted tool against HAI. The biggest effort from hospital

administration is mapping hospital topology, entered manually by a

hospital employee. Later on, data is imported from hospital informa-

tion system. The results of the algorithms are presented



an hospital epidemiologist for interpretation and applying preventive


ID: 5058

Invasive pneumococcal disease in the North East of England: Rising

incidence in 2014/2015 and 2015/2016

Kate Houseman, Gareth J Hughes, Kaye Chapman, Russell Gorton,

Deb Wilson.

Public Health England


A substantial and sustained increased incidence of

invasive pneumococcal disease (IPD) has been observed across the

North East of England (NEE) fromApril 2014, reversing the decline that

Abstracts of FIS/HIS 2016

Poster Presentations / Journal of Hospital Infection 94S1 (2016) S24