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Derivation and Validation of the Surgical Site Infections Risk Model Using Health Administrative Data

Published online by Cambridge University Press:  20 January 2016

Carl van Walraven ,
Timothy D. Jackson  and
Nick Daneman
Carl van Walraven*
Affiliation:
University of Ottawa, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada
Timothy D. Jackson
Affiliation:
Toronto Hospital, Toronto, Ontario, Canada
Nick Daneman
Affiliation:
Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
*
Address all correspondence to Dr. Carl van Walraven, ASB1-003 1053 Carling Ave, Ottawa, ON K1Y 4E9 (carlv@ohri.ca).
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Abstract

OBJECTIVE

Surgical site infections (SSIs) are common hospital-acquired infections. Tracking SSIs is important to monitor their incidence, and this process requires primary data collection. In this study, we derived and validated a method using health administrative data to predict the probability that a person who had surgery would develop an SSI within 30 days.

METHODS

All patients enrolled in the National Surgical Quality Improvement Program (NSQIP) from 2 sites were linked to population-based administrative datasets in Ontario, Canada. We derived a multivariate model, stratified by surgical specialty, to determine the independent association of SSI status with patient and hospitalization covariates as well as physician claim codes. This SSI risk model was validated in 2 cohorts.

RESULTS

The derivation cohort included 5,359 patients with a 30-day SSI incidence of 6.0% (n=118). The SSI risk model predicted the probability that a person had an SSI based on 7 covariates: index hospitalization diagnostic score; physician claims score; emergency visit diagnostic score; operation duration; surgical service; and potential SSI codes. More than 90% of patients had predicted SSI risks lower than 10%. In the derivation group, model discrimination and calibration was excellent (C statistic, 0.912; Hosmer-Lemeshow [H-L] statistic, P=.47). In the 2 validation groups, performance decreased slightly (C statistics, 0.853 and 0.812; H-L statistics, 26.4 [P=.0009] and 8.0 [P=.42]), but low-risk patients were accurately identified.

CONCLUSION

Health administrative data can effectively identify postoperative patients with a very low risk of surgical site infection within 30 days of their procedure. Records of higher-risk patients can be reviewed to confirm SSI status.

Infect. Control Hosp. Epidemiol. 2016;37(4):455–465

Type
Original Articles
Information
Infection Control & Hospital Epidemiology , Volume 37 , Issue 4 , April 2016 , pp. 455 - 465
Copyright
© 2016 by The Society for Healthcare Epidemiology of America. All rights reserved 

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