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Improved Risk Adjustment for Comparison of Surgical Site Infection Rates

Published online by Cambridge University Press:  21 June 2016

Eveline L. P. E. Geubbels
Affiliation:
Department of Infectious Diseases Epidemiology, National Institute of Public Health and the Environment, Bilthoven, Utrecht, The Netherlands Projet Ubuzima, Kigali, Rwanda
Diederick E. Grobbee
Affiliation:
Julius Center for General Practice and Patient Oriented Research, University Medical Center Utrecht, Utrecht, The Netherlands
Christina M. J. E. Vandenbroucke-Grauls
Affiliation:
Department of Medical Microbiology and Infection Control, Vrije Universiteit Medical Center, Amsterdam, The Netherlands
Jan C. Wille*
Affiliation:
Dutch Institute for Healthcare Improvement, Utrecht, The Netherlands
Annette S. de Boer
Affiliation:
Department of Infectious Diseases Epidemiology, National Institute of Public Health and the Environment, Bilthoven, Utrecht, The Netherlands
*
Dutch Institute for Healthcare Improvement, PO Box 20064, 3502 LB, Utrecht, The Netherlands (j.wille@cbo.nl)

Abstract

Objective.

To develop prognostic models for improved risk adjustment in surgical site infection surveillance for 5 surgical procedures and to compare these models with the National Nosocomial Infection Surveillance system (NNIS) risk index.

Design.

In a multicenter cohort study, prospective assessment of surgical site infection and risk factors was performed from 1996 to 2000. In addition, risk factors abstracted from patient files, available in a national medical register, were used. The c-index was used to measure the ability of procedure-specific logistic regression models to predict surgical site infection and to compare these models with models based on the NNIS risk index. A c-index of 0.5 indicates no predictive power, and 1.0 indicates perfect predictive power.

Setting.

Sixty-two acute care hospitals in the Dutch national surveillance network for nosocomial infections.

Participants.

Patients who underwent 1 of 5 procedures for which the predictive ability of the NNIS risk index was moderate: reconstruction of the aorta (n = 875), femoropopliteal or femorotibial bypass (n = 641), colectomy (n = 1,142), primarytotal hip prosthesis (n = 13,770), and cesarean section (n = 2,962).

Results.

The predictive power of the new model versus the NNIS index was 0.75 versus 0.62 for reconstruction of the aorta (P< .01), 0.78 versus 0.58 for femoropopliteal or femorotibial bypass (P< .001), 0.69 versus 0.62 for colectomy (P< .001), 0.64 versus 0.56 for primary total hip prosthesis arthroplasty (P< .001), and 0.70 versus 0.54 for cesarean section (P< .001).

Conclusion.

Data available from hospital information systems can be used to develop models that are better at predicting the risk of surgical site infection than the NNIS risk index. Additional data collection may be indicated for certain procedures–for example, total hip prosthesis arthroplasty.

Type
Original Articles
Copyright
Copyright © The Society for Healthcare Epidemiology of America 2006

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