Hostname: page-component-78c5997874-g7gxr Total loading time: 0 Render date: 2024-11-14T04:30:37.215Z Has data issue: false hasContentIssue false
  • English
  • Français

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

Published online by Cambridge University Press:  02 January 2015

Christopher S. Hollenbeak ,
Melissa M. Boltz ,
Lucas E. Nikkel ,
Eric Schaefer ,
Gail Ortenzi  and
Peter W. Dillon
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
*
Department of Surgery, Penn State College of Medicine, 600 Centerview Drive, A210, Hershey, PA 17033 (chollenbeak@psu.edu)
Get access Rights & Permissions [Opens in a new window]

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
Information
Infection Control & Hospital Epidemiology , Volume 32 , Issue 8 , August 2011 , pp. 784 - 790
Copyright
Copyright © The Society for Healthcare Epidemiology of America 2011

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.)

References

1.Centers for Disease Control and Prevention. Surgical site infection event. http://www.cdc.gov/nhsn/PDFs/pscManual/9pscSSIcurrent.pdf. Published 2009. Accessed January 7, 2010.Google Scholar
2.Nguyen, GT, Proctor, SE, Sinkowitz-Cochran, RL, Garrett, DO, Jarvis, WR. Status of infection surveillance and control programs in the United States, 1992–1996. Association for Professionals in Infection Control and Epidemiology. Am J Infect Control 2000;28:392–400.CrossRefGoogle ScholarPubMed
3.Sherman, ER, Heydon, KH, St John, KH, et al.Administrative data fail to accurately identify cases of healthcare-associated infection. Infect Control Hosp Epidemiol 2006;27:332–337.CrossRefGoogle ScholarPubMed
4.Cadwallader, HL, Toohey, M, Linton, S, Dyson, A, Riley, TV. A comparison of two methods for identifying surgical site infections following orthopaedic surgery. J Hosp Infect 2001;48:261–266.Google Scholar
5. American College of Surgeons National Surgical Quality Improvement Program. https://acsnsqip.org. Published 2008. Accessed January 7, 2010.Google Scholar
6.Hall, BL, Richards, K, Ingraham, A, Ko, CY. New approaches to the National Surgical Quality Improvement Program: the American College of Surgeons experience. Am J Surg 2009;198(suppl):S56–S62.CrossRefGoogle Scholar
7.Khuri, SF, Daley, J, Henderson, W, et al.The Department of Veterans Affairs' NSQIP: the first national, validated, outcome-based, risk-adjusted, and peer-controlled program for the measurement and enhancement of the quality of surgical care. National VA Surgical Quality Improvement Program. Ann Surg 1998;228:491–507.CrossRefGoogle ScholarPubMed
8.Brossette, SE, Hacek, DM, Gavin, PJ, et al.A laboratory-based, hospital-wide, electronic marker for nosocomial infection: the future of infection control surveillance? Am J Clin Pathol 2006;125:34–39.CrossRefGoogle ScholarPubMed
9.Kilgore, M, Brossette, S. Cost of bloodstream infections. Am J Infect Control 2008;36(S172):e171–e173.Google Scholar
10.Shwartz, M, Young, DW, Siegrist, R. The ratio of costs to charges: how good a basis for estimating costs? Inquiry Winter 1995;32:476–481.Google Scholar
11.National Nosocomial Infections Surveillance report, data summary from October 1986–April 1996, issued May 1996. A report from the National Nosocomial Infections Surveillance System. Am J Infect Control 1996;24:380–388.Google Scholar
12.Leal, J, Laupland, KB. Validity of electronic surveillance systems: a systematic review. J Hosp Infect 2008;69:220–229.Google Scholar
13.Emori, TG, Edwards, JR, Culver, DH, et al.Accuracy of reporting nosocomial infections in intensive-care-unit patients to the National Nosocomial Infections Surveillance System: a pilot study. Infect Control Hosp Epidemiol 1998;19:308–316.Google Scholar
14.Wright, SB, Huskins, WC, Dokholyan, RS, Goldmann, DA, Platt, R. Administrative databases provide inaccurate data for surveillance of long-term central venous catheter-associated infections. Infect Control Hosp Epidemiol 2003;24:946–949.Google Scholar
15.World Health Organization. International Statistical Classification of Diseases and Related Health Problems. 10th revision, vol 2, 2nd ed. 2004.Google Scholar
16.Glenister, HM, Taylor, LJ, Bartlett, CL, Cooke, EM, Sedgwick, JA, Mackintosh, CA. An evaluation of surveillance methods for detecting infections in hospital inpatients. J Hosp Infect 1993;23:229–242.CrossRefGoogle ScholarPubMed