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Am J Infect Control. 2014 Dec;42(12):1291-5. doi: 10.1016/j.ajic.2014.08.013. Epub 2014 Nov 25.

Using clinical variables to guide surgical site infection detection: a novel surveillance strategy.

Author information

1
Department of Medicine, Boston VA Healthcare System, Boston, MA; Department of Healthcare Quality, Division of Infection Control, Beth Israel Deaconess Medical Center, Boston, MA; Department of Medicine, Harvard University Medical School, Boston, MA. Electronic address: westyn.branch-elliman@ucdenver.edu.
2
Department of Medicine, Boston VA Healthcare System, Boston, MA; Department of Medicine, Harvard University Medical School, Boston, MA.
3
Department of Medicine, Harvard University Medical School, Boston, MA; Department of Surgery, Boston VA Healthcare System, Boston, MA; Department of Surgery, Boston University School of Medicine, Boston, MA.
4
Department of Medicine, Boston VA Healthcare System, Boston, MA; Department of Medicine, Boston University School of Medicine, Boston, MA.

Abstract

BACKGROUND:

Surgical site infections (SSIs) are a common and expensive health care-associated infection, and are used as a health care quality benchmark. As such, SSI detection is a major focus of infection prevention programs. In an effort to improve on conventional surveillance methods, a simple algorithm for SSI detection was developed using clinical variables not traditionally included in National Healthcare Safety Network definitions.

METHODS:

A case-control study was conducted among surgeries performed at the Veterans Affairs Boston Healthcare System between January 2008 and December 2009. SSI cases were matched to controls without SSI. Clinical variables (administrative, microbiological, pharmacy, radiology) were compared between the groups to determine those that best identified SSI.

RESULTS:

A total of 70 SSIs were matched to 70 controls. On multivariable analysis, variables significantly associated with SSI identification were wound culture order, computed tomography scan/magnetic resonance imaging order, antibiotic order within 30 days after surgery, and application of a relevant International Classification of Disease, Ninth Revision code. Among patients with no SSI identifiers, 98% were correctly classified as having no SSI. Among patients with multiple SSI identifiers, 97.1% were correctly identified as having SSI. The area under the curve for this model was 0.87.

CONCLUSION:

We have derived a novel surveillance algorithm for SSI detection with excellent operating characteristics. This algorithm could be automated to streamline infection control efforts.

KEYWORDS:

Electronic tool; Post-operative care; Quality improvement

PMID:
25465259
DOI:
10.1016/j.ajic.2014.08.013
[Indexed for MEDLINE]

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