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Infect Control Hosp Epidemiol. 2003 Oct;24(10):741-3.

Detection of postoperative surgical-site infections: comparison of health plan-based surveillance with hospital-based programs.

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  • 1Centers for Disease Control and Prevention, Eastern Massachusetts Prevention Epicenter, Boston, Massachusetts, USA.

Abstract

BACKGROUND:

Review of health plan administrative data has been shown to be more sensitive than other methods for identifying postdischarge surgical-site infections (SSIs), but there has not been a direct comparison between this method and hospital-based surveillance for all infections, including those diagnosed before discharge. We compared these two methods for identifying SSIs following coronary artery bypass graft (CABG) procedures.

METHODS:

We studied 1,352 CABG procedures performed among members of one health plan from March 1993 through June 1997. Health plan administrative records were reviewed based on claims containing diagnoses or procedures suggestive of infection or outpatient dispensing of antibiotics appropriate for SSI. Hospital-based surveillance information was also reviewed. SSI rates were calculated based on the total events identified by either mechanism.

RESULTS:

Postdischarge information was reviewed for 328 (85%) of 388 procedures. SSIs were confirmed in 167 patients (13% overall risk of confirmed SSI; range, 3% to 14% in the 5 hospitals). The overall sensitivity of hospital-based surveillance was 49.7% (83 of 167), and that of health plan data was 71.8% (120 of 167). There was no significant difference among hospitals in the sensitivity of either surveillance mechanism.

CONCLUSIONS:

Surveillance based on health plan data identified more postoperative infections, including those occurring before discharge, than did hospital-based surveillance. Screening administrative data and pharmacy activity may be an important adjunct to SSI surveillance, allowing efficient comparison of hospital-specific rates. Interpretation of differences among hospitals' infection rates requires case mix adjustment and understanding of variations in hospitals' discharge diagnosis coding practices.

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PMID:
14587934
[PubMed - indexed for MEDLINE]
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