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Electronic Measures of Surgical Site Infection: Implications for Estimating Risks and Costs

Published online by Cambridge University Press:  02 January 2015

Christopher S. Hollenbeak*
Affiliation:
Division of Outcomes Research and Quality, Department of Surgery, Penn State Milton S. Hershey Medical Center, Hershey, Pennsylvania Department of Surgery, Penn State Milton S. Hershey Medical Center, Hershey, Pennsylvania Department of Public Health Sciences, Penn State College of Medicine, Hershey, Pennsylvania
Melissa M. Boltz
Affiliation:
Division of Outcomes Research and Quality, Department of Surgery, Penn State Milton S. Hershey Medical Center, Hershey, Pennsylvania
Lucas E. Nikkel
Affiliation:
Penn State College of Medicine, Hershey, Pennsylvania
Eric Schaefer
Affiliation:
Division of Biostatistics, Department of Public Health Sciences, Penn State College of Medicine, Hershey, Pennsylvania
Gail Ortenzi
Affiliation:
Division of Outcomes Research and Quality, Department of Surgery, Penn State Milton S. Hershey Medical Center, Hershey, Pennsylvania
Peter W. Dillon
Affiliation:
Department of Surgery, Penn State Milton S. Hershey Medical Center, Hershey, Pennsylvania
*Corresponding
Department of Surgery, Penn State College of Medicine, 600 Centerview Drive, A210, Hershey, PA 17033 (chollenbeak@psu.edu)

Abstract

Objective.

Electronic measures of surgical site infections (SSIs) are being used more frequently in place of labor-intensive measures. This study compares performance characteristics of 2 electronic measures of SSIs with a clinical measure and studies the implications of using electronic measures to estimate risk factors and costs of SSIs among surgery patients.

Methods.

Data included 1,066 general and vascular surgery patients at a single academic center between 2007 and 2008. Clinical data were from the National Surgical Quality Improvement Program (NSQIP) database, which includes a nurse-derived measure of SSI. We compared the NSQIP SSI measure with 2 electronic measures of SSI: MedMined Nosocomial Infection Marker (NIM) and International Classification of Diseases, Ninth Revision (ICD-9) coding for SSIs. We compared infection rates for each measure, estimated sensitivity and specificity of electronic measures, compared effects of SSI measures on risk factors for mortality using logistic regression, and compared estimated costs of SSIs for measures using linear regression.

Results.

SSIs were observed in 8.8% of patients according to the NSQIP definition, 2.6% of patients according to the NIM definition, and 5.8% according to the ICD-9 definition. Logistic regression for each SSI measure revealed large differences in estimated risk factors. NIM and ICD-9 measures overestimated the cost of SSIs by 134% and 33%, respectively.

Conclusions.

Caution should be taken when relying on electronic measures for SSI surveillance and when estimating risk and costs attributable to SSIs. Electronic measures are convenient, but in this data set they did not correlate well with a clinical measure of infection.

Type
Original Article
Copyright
Copyright © The Society for Healthcare Epidemiology of America 2011

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