Hostname: page-component-586b7cd67f-dsjbd Total loading time: 0 Render date: 2024-11-22T04:53:40.196Z Has data issue: false hasContentIssue false

Use of Antibiotic Exposure to Detect Postoperative Infections

Published online by Cambridge University Press:  02 January 2015

Deborah S. Yokoe*
Affiliation:
Channing Laboratory and Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts
Mervyn Shapiro
Affiliation:
Department of Clinical Microbiology and Infectious Diseases, Hadassah Hospital, Jerusalem, Israel
Elisheva Simchen
Affiliation:
Department of Clinical Microbiology and Infectious Diseases, Hadassah Hospital, Jerusalem, Israel
Richard Platt
Affiliation:
Channing Laboratory and Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts Department of Ambulatory Care and Prevention, Harvard Medical School and Harvard Community Health Plan, Boston, Massachusetts
*
181 Longwood Ave, Boston, MA 02115

Abstract

OBJECTIVE: To assess the utility of postoperative antibiotic exposure as an indicator of postoperative infection after coronary artery bypass graft surgery.

DESIGN: We determined an optimal antibiotic exposure threshold by creating receiver operating characteristic curves.

SETTING:Tertiary healthcare institution (United States); national sample (Israel).

PATIENTS:5,887 patients undergoing coronary artery bypass graft surgery.

RESULTS: Postoperative antibiotic exposure with at least 9 days between the first and last dates of antibiotic administration, excluding the first postoperative day, had a sensitivity of 95% (261/276) and specificity of 85% (3,944/4,628) for identifying surgical-site infection, using as a gold standard surgical-site infections identified by conventional prospective surveillance or extrapolated from review of a sample of medical records. In contrast, using the same gold standard for surgical-site infections, the sensitivity of routine prospective surveillance alone was only 60%. The predictive value positive of the defined antibiotic exposure was 28% (261/945) for surgical-site infection and 60% (563/945) for any nosocomial infection. In the Israeli cohort, the sensitivity was 87% (74/85) and the specificity was 82% (735/898).

CONCLUSION:Antibiotic exposure of sufficient duration and timing was more sensitive than conventional methods in detecting nosocomial infection and required substantially less effort to collect. Although the predictive value positive for surgical-site infection was only moderate, the majority of individuals identified this way had a nosocomial infection

Type
Original Articles
Copyright
Copyright © The Society for Healthcare Epidemiology of America 1998

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

1. Haley, R, Culver, D, White, J, Morgan, W, Emori, T, Munn, V, et al. The efficacy of infection surveillance and control programs in preventing nosocomial infections in US hospitals. Am J Epidemiol 1985;121:182205.Google Scholar
2. Olson, M, O'Connor, M, Schwartz, M. Surgical wound infections: a 5-year prospective study of 20,193 wounds at the Minneapolis VA Medical Center. Ann Surg 1984;199:253259.Google Scholar
3. Cruse, P, Foord, R. A five-year prospective study of 23,649 surgical wounds. Arch Surg 1973;107:206210.Google Scholar
4. Joint Commission on Accreditation of Healthcare Organizations. Standards: infection control. In: JCAHO, Accreditation Manual for Hospitals. Chicago, IL: JCAHO; 1990.Google Scholar
5. Feldman, L, Lamson, M, Gallelli, J, Bennett, J. Surveillance of nosocomial infections by antibiotic monitoring. JAMA 1979;241:28062807.CrossRefGoogle ScholarPubMed
6. Hirschhorn, L, Currier, J, Platt, R. Electronic surveillance of antibiotic exposure and coded discharge diagnoses as indicators of postoperative infection and other quality assurance measures. Infect Control Hosp Epidemiol 1993;14:2128.Google Scholar
7. Simchen, E, Wax, Y, Pevsner, B, Erdal, M, Michel, J, Modan, M, et al. The Israeli Study of Surgical Infections (ISSI), I: methods for developing a standardized surveillance system for a multicenter study of surgical infections. Infect Control Hosp Epidemiol 1988;9:232240.Google Scholar
8. Dorfman, D, Alf, E. Maximum likelihood estimation of parameters of signal detection theory and determination of confidence intervals. Rating method data. Journal of Mathematical Psychology 1969;6:487496.CrossRefGoogle Scholar
9. Tosteson, A, Begg, C. A general regression methodology for ROC curve estimation. Med Decis Making 1988;8:204215.CrossRefGoogle ScholarPubMed
10. Evans, R, Larsen, R, Burke, J, Gardner, R, Meier, F, Jacobson, J, et al. Computer surveillance of hospital-acquired infections and antibiotic use. JAMA 1986;256:10071011.Google Scholar