Syndromic surveillance of surgical site infections – A case study in coronary artery bypass graft patients

  • C. King
    Corresponding author. Institute for Global Health, 30 Guilford Street, University College London, London WC1N 1EH, UK. Tel.: +44 (0) 20 7905 2122; fax: +44 (0) 20 7404 2062.
    The National Centre for Infection Prevention and Management, Imperial College, London, UK

    Institute for Global Health, University College London, UK
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  • P. Aylin
    The National Centre for Infection Prevention and Management, Imperial College, London, UK

    Dr Foster Unit at Imperial, School of Public Health, Imperial College London, London, UK
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  • L.S.P. Moore
    The National Centre for Infection Prevention and Management, Imperial College, London, UK
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  • J. Pavlu
    Centre for Haematology, Imperial College, London, UK
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  • A. Holmes
    The National Centre for Infection Prevention and Management, Imperial College, London, UK

    Infection Prevention and Control, Imperial College Healthcare NHS Trust, London, UK
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Published:September 03, 2013DOI:



      There is a wealth of data routinely collected and stored by healthcare facilities, which are not consistently exploited for surveillance of healthcare associated infections (HCAI). Syndromic surveillance has not yet been widely applied to HCAI. This study aimed to create syndromic surveillance for surgical site infections (SSI) following coronary artery bypass graft (CABG) procedures.


      A cohort of CABG patients from Imperial College Healthcare NHS Trust was investigated. Data from the local Patient Administration System, Laboratory Information Management System, radiology department, cardiac registry and Health Protection Agency SSI surveillance were linked. This data was explored for biological markers and proxies of infection, which were used to develop syndromic surveillance algorithms; sensitivity analysis was used to determine the best algorithms.


      303 patients were included, with a SSI incidence of 6.6%. Wound culture requests, raised platelet and fibrinogen levels were all found to be good indicators of SSI. Two algorithms were generated, one to detect all SSI (sensitivity: 90%; specificity: 93.8%) and one to detect organ space infections specifically (sensitivity: 100%; specificity: 98.5%).


      Data which is routinely collected and stored in healthcare facilities can be used for syndromic surveillance of SSI, allowing for an efficient surveillance system without the need for resource intensive data collection.


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