Clostridium difficile surveillance: harnessing new technologies to control transmission

Abstract

Clostridium difficile surveillance allows outbreaks of cases clustered in time and space to be identified and further transmission prevented. Traditionally, manual detection of groups of cases diagnosed in the same ward or hospital, often followed by retrospective reference laboratory genotyping, has been used to identify outbreaks. However, integrated healthcare databases offer the prospect of automated real-time outbreak detection based on statistically robust methods, and accounting for contacts between cases, including those distant to the ward of diagnosis. Complementary to this, rapid benchtop whole genome sequencing, and other highly discriminatory genotyping, has the potential to distinguish which cases are part of an outbreak with high precision and in clinically relevant timescales. These new technologies are likely to shape future surveillance.

Keywords::

• Clostridium difficile
• electronic medical record
• genotyping
• integrated healthcare database
• outbreak
• surveillance
• whole genome sequencing

Financial & competing interests disclosure

This study was supported by the NIHR Oxford Biomedical Research Centre and the UKCRC Modernising Medical Microbiology Consortium, the latter funded under the UKCRC Translational Infection Research Initiative supported by Medical Research Council, Biotechnology and Biological Sciences Research Council and the National Institute for Health Research on behalf of the Department of Health (Grant G0800778) and the Wellcome Trust (Grant 087646/Z/08/Z). We acknowledge the support of Wellcome Trust core funding (Grant 090532/Z/09/Z). DW Eyre is a NIHR Doctoral Research Fellow. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

No writing assistance was utilized in the production of this manuscript.

Key issues

• • Clostridium difficile surveillance allows healthcare-associated outbreaks to be detected, and further onward transmission prevented

• • Traditionally manual review of each new case by infection control practitioners has been used to identify cases clustered in time and space; clustering in space is typically assessed based on the patient's current location or the location of testing

• • Straightforward outbreak screening definitions, such as two cases per ward in two weeks, may provide a guide, but are likely to be only partially sensitive and specific

• • The advent of integrated electronic healthcare records offers the prospect of more sophisticated screening for outbreaks, using statistically robust methods, but with calculations undertaken in the background, such that systems remain simple to use for clinicians and infection control practitioners

• • Systems integrating microbiological results and patient movement data can also account for contacts between patients occurring before diagnosis and at a different location to the place of diagnosis, for example, in cases subsequently diagnosed in the community or on a different ward to plausible transmission contacts

• • Genotyping allows transmission to be excluded between genetically distinct isolates; whole genome sequencing is able to exclude transmission with greater precision than many previous genotyping techniques

• • Whole genome sequencing in combination with epidemiological data may also assist in reconstructing transmission chains

• • Technological advances in microbial sequencing, data integration and computational power, provide an unprecedented opportunity for intelligent systems that monitor and detect infectious disease outbreaks; there is now a need for standards and integrated software solutions to deliver this promise to routine patient care