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Applicability of Two Surgical-Site Infection Risk Indices to Risk of Sepsis in Surgical Patients

Published online by Cambridge University Press:  02 January 2015

Concepción Fariñas-Álvarez
Affiliation:
Division of Preventive Medicine and Public Health, University of Cantabria School of Medicine, Santander, Spain
M. Carmen Fariñas
Affiliation:
Infectious Diseases Unit, University Hospital “Marqués de Valdecilla,”Santander, Spain
Dolores Prieto
Affiliation:
Division of Preventive Medicine and Public Health, University of Cantabria School of Medicine, Santander, Spain
Miguel Delgado-Rodríguez*
Affiliation:
Division of Preventive Medicine and Public Health, University of Cantabria School of Medicine, Santander, Spain
*
Division of Preventive Medicine and Public Health, University of Cantabria School of Medicine, Av Cardenal Herrera Oria s/n, 39011-Santander, Spain

Abstract

Objective:

To compare the ability of the Study of the Efficacy of Nosocomial Infection Control (SENIC) and the National Nosocomial Infection Surveillance (NNIS) indices to predict the development of nosocomial sepsis in subjects undergoing surgery.

Design:

1-year prospective case-control study.

Setting:

A tertiary-care center in Spain.

Patients:

Cases were surgical patients with nosocomial sepsis defined using the criteria of the Consensus Conference on Sepsis, identified by daily prospective surveillance.

Methods:

Controls were randomly selected from the daily list of surgical inpatients. Data were prospectively collected. To determine whether either index added explanatory information to the other, two methods were used. The first method involved computing a set of residuals for both variables. Residuals and primary variables were introduced in logistic regression models. The second method evaluated both indices with the Goodman-Kruskal (G) nonparametric coefficient.

Results:

99 cases and 97 controls were included. After controlling for confounders, both the SENIC index (P<.001) and the NNIS index (P=.04) showed a significant trend. Residuals of the SENIC index added discriminating ability to the NNIS index, whereas residuals of the NNIS index did not improve the prediction ability of the SENIC index. Similar results were yielded by the G statistic: the SENIC index showed higher predictive power than the NNIS index (G=0.56 vs G=0.41).

Conclusions:

Both indices performed about equally well for discriminating risk of nosocomial sepsis. The SENIC index had a somewhat better ability than the NNIS index only when the number of discharge diagnoses (not truly a predictive factor) were involved in the calculation of the SENIC index.

Type
Original Articles
Copyright
Copyright © The Society for Healthcare Epidemiology of America 2000

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References

1. Horan, TC, Culver, DH, Gaynes, RP, Jarvis, WR, Edwards, JR, Reid, CR. Nosocomial infections in surgical patients in the United States, January 1986-June 1992. National Nosocomial Infections Surveillance (NNIS) System. Infect Control Hosp Epidemiol 1993;14:7380.CrossRefGoogle ScholarPubMed
2. Delgado-Rodríguez, M, Gómez-Ortega, A, Llorca, J, Lecuona, M, Dierssen, T, Sillero-Arenas, M, et al. Nosocomial infection, indices of intrinsic infection risk, and in-hospital mortality in general surgery. J Hosp Infect 1999;41:203211.Google Scholar
3. Haley, RW. Measuring the intrinsic risk of wound infection in surgical patients. Problems in General Surgery 1993;10:396417.Google Scholar
4. Haley, RW, Culver, DH, Morgan, WM, White, JW, Emori, TG, Hooton, TM. Identifying patients at high risk of surgical wound infection: a simple multivariate index of patient susceptibility and wound contamination. Am J Epidemiol 1985;121:206215.Google Scholar
5. Culver, DH, Horan, TC, Gaynes, RP, Martone, WJ, Jarvis, WR, Emori, TG, et al. Surgical wound infection rates by wound class, operative procedure, and patient risk index. National Nosocomial Infections Surveillance System. Am J Med 1991;91(suppl 3B):152S157S.Google Scholar
6. Delgado-Rodríguez, M, Martínez Gallego, G, Sillero-Arenas, M, Medina-Cuadros, M. Nosocomial infections in surgical patients: comparison of two measures of intrinsic patient risk. Infect Control Hosp Epidemiol 1997;18:1923.CrossRefGoogle ScholarPubMed
7. Delgado-Rodríguez, M, Medina-Cuadros, M, Martínez-Gallego, G, Sillero-Arenas, M. Usefulness of intrinsic surgical wound infection risk indices as predictors of postoperative pneumonia risk. J Hosp Infect 1997;35:269276.Google Scholar
8. Delgado-Rodríguez, M, Sillero-Arenas, M, Medina-Cuadros, M, Martínez-Gallego, G. Usefulness of intrinsic infection risk indexes as predictors of in-hospital death. Am J Infect Control 1997;25:365370.CrossRefGoogle ScholarPubMed
9. American College of Chest Physicians/Society of Critical Care Medicine Consensus Conference: definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis. Crit Care Med 1992;20:864874.Google Scholar
10. Garner, JS, Jarvis, WR, Emori, TG, Horan, TC, Hughes, JM. CDC definitions for nosocomial infections, 1988. Am J Infect Control 1988;16:128140.Google Scholar
11. Horan, TC, Gaynes, RP, Martone, WJ, Jarvis, WR, Emori, TG. CDC definitions for nosocomial surgical site infections, 1992: a modification of CDC definitions of surgical wound infections. Infect Control Hosp Epidemiol 1992;13:606608.Google Scholar
12. McCabe, WR, Jackson, GG. Gram-negative bacteremia, II: clinical, laboratory, and therapeutic observations. Arch Intern Med 1962;110:856864.Google Scholar
13. Owens, WD, Felts, JA, Spitznagel, EL Jr ASA physical status classifications: a study of consistency of ratings. Anesthesiology 1978;49:239243.Google Scholar
14. Sun, GW, Shook, TL, Kay, GL. Inappropriate use of bivariable analysis to screen risk factors for use in multivariable analysis. J Clin Epidemiol 1996;49:907916.Google Scholar
15. Mickey, RM, Greenland, S. The impact of confounder selection criteria on effect estimation. Am J Epidemiol 1989;129:125137.Google Scholar
16. Thompson, WD. Statistical analysis of case-control studies. Epidemiol Rev 1994;16:3350.Google Scholar
17. Gross, PA. Striving for benchmark infection rates: progress in control for patient mix. Am J Med 1991;91(suppl 3B):16S20S.CrossRefGoogle ScholarPubMed
18. Scheckler, WE. Surgeon-specific wound infection rates—a potentially dangerous and misleading strategy. Infect Control Hosp Epidemiol 1988;9:145146.Google ScholarPubMed
19. Haley, RW. Nosocomial infections in surgical patients: developing valid measures of intrinsic patient risk. Am J Med 1991;91(suppl 3B):145S151S.CrossRefGoogle ScholarPubMed
20. Nosocomial infection rates for interhospital comparison: limitations and possible solutions. A Report from the National Nosocomial Infections Surveillance System. Infect Control Hosp Epidemiol 1991;10:609621.Google Scholar