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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 ,
Diederick E. Grobbee ,
Christina M. J. E. Vandenbroucke-Grauls ,
Jan C. Wille  and
Annette S. de Boer
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 ([email protected])
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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
Information
Infection Control & Hospital Epidemiology , Volume 27 , Issue 12 , December 2006 , pp. 1330 - 1339
Copyright
Copyright © The Society for Healthcare Epidemiology of America 2006

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References

1. Culver, DH, Horan, TC, Gaynes, RP, et al. Surgical wound infection rates by wound class, operative procedure, and patient risk index. National Nosocomial Infections Surveillance System. Am J Med 1991; 91:152S–157S.Google Scholar
2. Coello, R, Gastmeier, P, de Boer, AS. Surveillance of hospital-acquired infection in England, Germany, and The Netherlands: will international comparison of rates be possible? Infect Control Hosp Epidemiol 2001; 22:393–397.CrossRefGoogle ScholarPubMed
3. Haley, RW, Culver, DH, Morgan, WM, White, JW, Emori, TG, Hooton, TM. Identifying patients at high risk of surgical wound infection: a simple multivariate index of patient susceptibility and wound contamination. Am J Epidemiol 1985; 121:206–215.Google Scholar
4. Altemeier, WA, Burke, JF, Pruitt, BA, Sandusky, WR. Manual on control of infection in surgical patients. 2nd ed. Philadelphia, PA: JB Lippincott, 1984.Google Scholar
5. American Society of Anesthesiologists. New classification of physical status. Anesthesiology 1963; 24:11.Google Scholar
6. Gaynes, RP. Surgical-site infections and the NNIS SSI Risk Index: room for improvement. Infect Control Hosp Epidemiol 2000; 21:184–185.CrossRefGoogle ScholarPubMed
7. Gaynes, RP. Surgical-site infections (SSI) and the NNIS Basic SSI Risk Index, part II: room for improvement. Infect Control Hosp Epidemiol 2001;22:266–267.CrossRefGoogle ScholarPubMed
8. Roy, MC, Herwaldt, LA, Embrey, R, Kuhns, K, Wenzel, RP, Perl, TM. Does the Centers for Disease Control's NNIS system risk index stratify patients undergoing cardiothoracic operations by their risk of surgical-site infection? Infect Control Hosp Epidemiol 2000; 21:186–190.CrossRefGoogle ScholarPubMed
9. Killian, CA, Graffunder, EM, Vinciguerra, TJ, Venezia, RA. Risk factors for surgical-site infections following cesarean section. Infect Control Hosp Epidemiol 2001; 22:613–617.Google Scholar
10. Tran, TS, Jamulitrat, S, Chongsuvivatwong, V, Geater, A. Risk factors for postcesarean surgical site infection. Obstet Gynecol 2000; 95:367–371.Google Scholar
11. Tang, R, Chen, HH, Wang, YL, et al. Risk factors for surgical site infection after elective resection of the colon and rectum: a single-center prospective study of 2,809 consecutive patients. Ann Surg 2001; 234:181–189.Google Scholar
12. Geubbels, ELPE. Prevention of surgical site infections through surveillance. Utrecht, The Netherlands: Utrecht University, 2002.Google Scholar
13. Garner, JS, Jarvis, WR, Emori, TG, Horan, TC, Hughes, JM. CDC definitions for nosocomial infections, 1988. Am J Infect Control 1988; 16:128–140.Google Scholar
14. Horan, TC, Gaynes, RP, Martone, WJ, Jarvis, WR, Emori, TG. CDC definitions of nosocomial surgical site infections, 1992: a modification of CDC definitions of surgical wound infections. Infect Control Hosp Epidemiol 1992; 13:606–608.CrossRefGoogle ScholarPubMed
15. Sands, K, Vineyard, G, Platt, R. Surgical site infections occurring after hospital discharge. J Infect Dis 1996; 173:963–970.Google Scholar
16. Hanley, JA, McNeil, BJ. The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 1982; 143:29–36.Google Scholar
17. Harrell, FE Jr, Lee, KL, Mark, DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med 1996; 15:361–387.Google Scholar
18. Hanley, JA, McNeil, BJ. A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology 1983; 148:839–843.Google Scholar
19. Ash, AS, Shwartz, M. Evaluating the performance of risk-adjustment methods: dichotomous outcomes. In: Iezzoni, LI, ed. Risk-adjustment for measuring healthcare outcomes. 2nd ed. Chicago: Health Administration Press, 1997:460.Google Scholar
20. Hosmer, DW, Lemeshow, S. Applied logistic regression. 1st ed. New York: John Wiley & Sons, 1989.Google Scholar
21. Manniën, J, van der Zeeuw, AE, van den Hof, S, Wille, JC. Validation of surgical site infection surveillance in the Netherlands. Infect Control Hosp Epidemiol 2005; 27:XXX-XX (in this issue).Google Scholar
22. Bunt, TJ. Vascular graft infections: an update. Cardiovasc Surg 2001; 9:225–233.Google Scholar
23. Richet, HM, Chidiac, C, Prat, A, et al. Analysis of risk factors for surgical wound infections following vascular surgery. Am J Med 1991; 91: 170S–172S.Google Scholar
24. Edwards, WH Jr, Kaiser, AB, Tapper, S, et al. Cefamandole versus cefazolin in vascular surgical wound infection prophylaxis: cost-effectiveness and risk factors. J Vasc Surg 1993; 18:470–475; discussion 475-476.Google Scholar
25. Miransky, J, Ruo, L, Nicoletta, S, et al. Impact of a surgeon-trained observer on accuracy of colorectal surgical site infection rates. Dis Colon Rectum 2001;44:1100–1105.CrossRefGoogle ScholarPubMed
26. Siegman-Igra, Y, Rozin, R, Simchen, E. Determinants of wound infection in gastrointestinal operations: the Israeli study of surgical infections. J Clin Epidemiol 1993; 46:133–140.Google Scholar
27. Poss, R, Thornhill, TS, Ewald, FC, Thomas, WH, Batte, NJ, Sledge, CB. Factors influencing the incidence and outcome of infection following total joint arthroplasty. Clin Orthop 1984; 182:117–126.Google Scholar
28. Wong, ES. Surgical site infections. In: Mayhall, CG, ed. Hospital epidemiology and infection control. Baltimore: Williams & Wilkins, 1996:154–175.Google Scholar
29. Haley, RW, Culver, DH, White, JW, et al. The efficacy of infection surveillance and control programs in preventing nosocomial infections in US hospitals. Am J Epidemiol 1985; 121:182–205.Google Scholar