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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
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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.

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

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