Hostname: page-component-7b7567c8c-tnzcf Total loading time: 0 Render date: 2026-11-14T11:16:25.146Z Has data issue: false hasContentIssue false
  • English
  • Français

Finding a Method for Optimizing Risk Adjustment When Comparing Surgical-Site Infection Rates

Published online by Cambridge University Press:  02 January 2015

Christian Brandt,
Sonja Hansen,
Dorit Sohr,
Franz Daschner,
Henning Rüden and
Petra Gastmeier
Christian Brandt*
Affiliation:
Institut für Hygiene und Umweltmedizin, Charité - Universitätsmedizin Berlin, Freie Universität and Humboldt Universität, Berlin, Germany
Sonja Hansen
Affiliation:
Institut für Hygiene und Umweltmedizin, Charité - Universitätsmedizin Berlin, Freie Universität and Humboldt Universität, Berlin, Germany
Dorit Sohr
Affiliation:
Institut für Hygiene und Umweltmedizin, Charité - Universitätsmedizin Berlin, Freie Universität and Humboldt Universität, Berlin, Germany
Franz Daschner
Affiliation:
Institut für Umweltmedizin und Krankenhaushygiene, Albert-Ludwigs-Universität, Freiburg/Breisgau, Germany
Henning Rüden
Affiliation:
Institut für Hygiene und Umweltmedizin, Charité - Universitätsmedizin Berlin, Freie Universität and Humboldt Universität, Berlin, Germany
Petra Gastmeier
Affiliation:
Institut für Medizinische Mikrobiologie und Krankenhaushygiene, Medizinische Hochschule, Hannover, Germany
*
Institut für Hygiene und Umweltmedizin, Charite - Universitätsmedizin Berlin, Hindenburgdamm 27, 12200 Berlin, Germany
Get accessRights & Permissions [Opens in a new window]

Abstract

Objective:

To investigate whether stratification of the risk of developing a surgical-site infection (SSI) is improved when a logistic regression model is used to weight the risk factors for each procedure category individually instead of the modified NNIS System risk index.

Design and Setting:

The German Nosocomial Infection Surveillance System, based on NNIS System methodology, has 273 acute care surgical departments participating voluntarily. Data on 9 procedure categories were included (214,271 operations).

Methods:

For each of the procedure categories, the significant risk factors from the available data (NNIS System risk index variables of ASA score, wound class, duration of operation, and endoscope use, as well as gender and age) were identified by multiple logistic regression analyses with stepwise variable selection. The area under the receiver operating characteristic (ROC) curve resulting from these analyses was used to evaluate the predictive power of logistic regression models.

Results:

For most procedures, at least two of the three variables contributing to the NNIS System risk index were shown to be independent risk factors (appendectomy, knee arthroscopy, cholecystectomy, colon surgery, herniorrhaphy, hip prosthesis, knee prosthesis, and vascular surgery). The predictive power of logistic regression models (including age and gender, when appropriate) was low (between 0.55 and 0.71) and for most procedures only slightly better than that of the NNIS System risk index.

Conclusion:

Without the inclusion of additional procedure-specific variables, logistic regression models do not improve the comparison of SSI rates from various hospitals.

Information

Type
Original Articles
Information
Infection Control & Hospital Epidemiology , Volume 25 , Issue 4 , April 2004, pp. 313 - 318
Copyright
Copyright © The Society for Healthcare Epidemiology of America 2004

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.)

Article purchase

Temporarily unavailable

References

1.Merle, V, Germain, JM, Chamouni, P, et al.Assessment of prolonged hospital stay attributable to surgical site infections using appropriateness evaluation protocol. Am J Infect Control 2000;28:109–115.Google Scholar
2.Kirkland, KB, Briggs, JP, Trivette, SL, Wilkinson, WE, Sexton, DJ. The impact of surgical-site infections in the 1990s: attributable mortality, excess length of hospitalization, and extra costs. Infect Control Hosp Epidemiol 1999;20:725–730.Google Scholar
3.Haley, RW, Culver, DH, White, JW, et al.The efficacy of infection surveillance and control programs in preventing nosocomial infections in US hospitals. Am J Epidemiol 1985;121:182–205.Google Scholar
4.Olson, MM, Lee, JT Jr.Continuous, 10-year wound infection surveillance: results, advantages, and unanswered questions. Arch Surg 1990;125:794–803.Google Scholar
5.Mangram, AJ, Horan, TC, Pearson, ML, Silver, LC, Jarvis, WR. Guideline for prevention of surgical site infection, 1999: Hospital Infection Control Practices Advisory Committee. Infect Control Hosp Epidemiol 1999;20:250–278.Google Scholar
6.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;121:206–215.Google Scholar
7.Culver, DH, Horan, TC, Gaynes, RP, 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:S152–S157.Google Scholar
8.National Nosocomial Infections Surveillance (NNIS) System. National Nosocomial Infections Surveillance (NNIS) System report: data summary from January 1992-April 2000, issued June 2000. Am J Infect Control 2000;28:429–448.Google Scholar
9.Gastmeier, P, Sohr, D, Just, HM, Nassauer, A, Daschner, F, Ruden, H. How to survey nosocomial infections. Infect Control Hosp Epidemiol 2000;21:366–370.Google Scholar
10.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
11.Gaynes, RP, Culver, DH, Horan, TC, Edwards, JR, Richards, C, Tolson, JS. 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
12.Horan, TC, Edwards, JR, Culver, DH, Gaynes, RP. Risk factors for incisional surgical site infection after cesarean section: results of a 5-year multicenter study. Infect Control Hosp Epidemiol 2000;21:145. Abstract.Google Scholar
13.Geubbels, EL, Mintjes-de Groot, AJ, van den Berg, JM, de Boer, AS. An operating surveillance system of surgical site infections in The Netherlands: results of the PREZIES national surveillance network. Preventie van Ziekenhuisinfecties door Surveillance. Infect Control Hosp Epidemiol 2000;21:311–318.CrossRefGoogle Scholar
14.Geubbels, EL. Prevention of Surgical Site Infections Through Surveillance [dissertation]. Utrecht, the Netherlands: Universiteit Utrecht; 2002.Google Scholar