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The Standardized Incidence Ratio as a Reliable Tool for Surgical Site Infection Surveillance

Published online by Cambridge University Press:  21 June 2016

Christophe Rioux ,
Bruno Grandbastien  and
Pascal Astagneau
Christophe Rioux*
Affiliation:
Interregional Center for the Coordination of Nosocomial Infection Control (C-CLIN Paris Nord), Paris, France
Bruno Grandbastien
Affiliation:
Interregional Center for the Coordination of Nosocomial Infection Control (C-CLIN Paris Nord), Paris, France
Pascal Astagneau*
Affiliation:
Interregional Center for the Coordination of Nosocomial Infection Control (C-CLIN Paris Nord), Paris, France
*
CHU Chenevier-Mondor, 51 avenue du marechal de Lattre de Tassigny, 94010 Créteil, France, ([email protected]or, [email protected])
Paris Nord, Institut des Cordeliers, 15 Rue de l'Ecole de Medecine, 75006 Paris, France, ([email protected])
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Abstract

Objective.

To evaluate whether the standardized incidence ratio (SIR) is a more reliable tool for comparing rates and temporal trends of surgical site infection (SSI) in surgery wards than the incidence rate among patients with an National Nosocomial Infections Surveillance system (NNIS) risk index category of 0.

Design.

Observational, prospective cohort study in a sequential SSI surveillance system.

Setting.

Volunteer surgery wards in a surveillance network in northern France that annually conducted SSI surveillance for 3 months from 1998 to 2000.

Methods.

The incidence rate was the number of SSIs divided by the number of patients included, stratified by the NNIS risk index category. SIR was the observed number of SSIs divided by the expected number computed using a multiple regression model.

Results.

Overall, 26,904 patients in 67 surgery wards were enrolled. Between 1998 and 2000, the SSI incidence rate among patients with NNIS risk index category 0 decreased from 2.1% to 1.4%, which was a 33% reduction (P = .002). The SIR decreased from 1.2 (95% confidence interval [CI], 1.1-1.3) to 0.8 (95% CI, 0.7-0.9), which was a 20% decrease per year and an overall 33% reduction. The number of SSIs was significantly higher than expected in 17 of 201 surveillance periods over the 3 years. The classification of the wards according to the 2 indicators over the 3 years showed that wards with a high SIR did not consistently have the highest SSI incidence rate among patients with NNIS risk index category 0, partly because the type of surgical procedure and the duration of follow-up are not taken into account in the NNIS risk index.

Conclusion.

SIR should be considered a reliable indicator to estimate the reduction in SSI incidence that results from implementation of infection control policies and for comparison of SSI rates between wards.

Type
Original Articles
Information
Infection Control & Hospital Epidemiology , Volume 27 , Issue 8 , August 2006 , pp. 817 - 824
Copyright
Copyright © The Society for Healthcare Epidemiology of America 2006

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References

1. Mangram, AJ, Horan, TC, Pearson, ML, et al. Guideline for prevention of surgical site infection, 1999. Hospital Infection Control Practices Advisory Committee. Infect Control Hosp Epidemiol 1999; 20:250–278.CrossRefGoogle ScholarPubMed
2. Prevalence of nosocomial infections in France: results of the nationwide survey in 1996. The French Prevalence Survey Study Group. J Hosp Infect 2000; 46:186–193.Google Scholar
3. Culver, DH, Horan, TC, Gaynes, RP, et al. Surgical wound infection rates by wound class, operative procedure, and patient risk index. Am J Med 1991; 91:152S–157S.CrossRefGoogle ScholarPubMed
4. Haley, RW, Culver, DH, Morgan, WM, et al. Identifying patients at high risk of surgical wound infection: a simple multivariate index of patient susceptibility and wound contamination. Am J Epidemiol 1985; 121: 206–215.Google Scholar
5. Gaynes, RP, Culver, DH, Horan, TC, et al. Surgical site infection (SSI) rates in the United States, 1992-1998: the National Nosocomial Infections Surveillance system basic SSI risk index. Clin Infect Dis 2001; 33(Suppl 2):S69–S77.CrossRefGoogle ScholarPubMed
6. Garibaldi, R, Cushing, D, Lerer, T. Risk factors for postoperative infection. Am J Med 1991; 91:158S–163S.Google Scholar
7. Astagneau, P, Rioux, C, Golliot, F, et al. Morbidity and mortality associated with surgical site infections: results from the 1997-1999 INCISO surveillance. J Hosp Infect 2001; 48:267–274.CrossRefGoogle ScholarPubMed
8. Astagneau, P, Brucker, G. Organization of hospital-acquired infection control in France. J Hosp Infect 2001; 47:84–87.CrossRefGoogle ScholarPubMed
9. Keats, AS. The ASA classification of physical status: a recapitulation. Anesthesiology 1978; 49:233–236.Google Scholar
10. Berard, F, Gandon, J. Postoperative wound infections: the influence of ultraviolet irradiation of the operating room and of various other factors. Ann Surg 1964; 160(Suppl 1):1–192.Google ScholarPubMed
11. Horan, TC, Gaynes, RP, Martone, WJ, et al. CDC definitions of nosocomial surgical site infections, 1992 : a modification of CDC definitions of surgical wound infections. Am J Infect Control 1992; 20:271–274.Google Scholar
12. Report of the French National Network for Investigation and Surveillance of Nosocomial Infections [in French]. Institut de Veille Sanitaire; 2003. Available at: http://www.invs.sante.fr/publications/2003/raisin_2002/rai-sin_2002_vf.pdf. Accessed July 18, 2006.Google Scholar
13. Hosmer, D, Lemeshow, S. Applied logistic regression. New York: Wiley; 1989.Google Scholar
14. Hanley, JA, McNeil, BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 1982; 143:29–36.Google Scholar
15. Lemeshow, S, Hosmer, DW. A review of goodness of fit statistics for use in the development of logistic regression models. Am J Epidemiol 1982; 115:92–106.Google Scholar
16. Wasson, HW, Sox, HC, Neff, RK, et al. Clinical prediction rules: applications and methodological standards. N Engl J Med 1985; 313:793–799.CrossRefGoogle ScholarPubMed
17. Hosmer, DW, Lemeshow, S. Confidence interval estimates of an index of quality performance based on logistic regression model. Stat Med 1995; 14:2161–2172.Google Scholar
18. Armitage, P. Tests for linear trend in proportions and frequencies. Biometrics 1955; 11:375–386.Google Scholar
19. Cruse, PJE, Foord, R. A five-year prospective study of 23,649 surgical wounds. Arch Surg 1973; 107:206–210.Google Scholar
20. Thibon, P, Parienti, J, Borgey, F, et al. Use of censured data to monitor surgical-site infections. Infect Control Hosp Epidemiol 2002; 23:368–371.Google Scholar
21. Culver, D, Horan, T, Gaynes, R et al. Results of a multicenter Study on Risk Factors for Surgical Site Infections following C-section (abstract). SHEA annual meeting, Washington, DC, 1996, April 21-23. Infect Control Hosp Epidemiol 1996;17(Suppl):19.Google Scholar
22. Russo, PL, Spelman, DW. A new surgical-site infection risk index using risk factors identified by multivariate analysis for patients undergoing coronary artery bypass graft surgery. Infect Control Hosp Epidemiol 2002; 23:372–6.Google Scholar