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Accounting for Incomplete Postdischarge Follow-Up During Surveillance of Surgical Site Infection by Use of the National Nosocomial Infections Surveillance System's Risk Index

Published online by Cambridge University Press:  02 January 2015

Fernando Martín Biscione ,
Renato Camargos Couto  and
Tânia M. G. Pedrosa
Fernando Martín Biscione*
Affiliation:
Health Sciences Postgraduate Course, Medicine High School, Federal University of Minas Gerais, Santa Efigênia, Belo Horizonte, Minas Gerais, Brazil
Renato Camargos Couto
Affiliation:
Health Sciences Postgraduate Course, Medicine High School, Federal University of Minas Gerais, Santa Efigênia, Belo Horizonte, Minas Gerais, Brazil
Tânia M. G. Pedrosa
Affiliation:
Health Sciences Postgraduate Course, Medicine High School, Federal University of Minas Gerais, Santa Efigênia, Belo Horizonte, Minas Gerais, Brazil
*
Health Sciences Postgraduate Course, Medicine High School, Minas Gerais, 190 Alfredo Balena Avenue, Room 7003, Santa Efigênia, Belo Horizonte, Federal University of Minas Gerais, 30-130-100, Brazil ([email protected])
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Abstract

Objective.

We examined the usefulness of a simple method to account for incomplete postdischarge follow-up during surveillance of surgical site infection (SSI) by use of the National Nosocomial Infections Surveillance (NNIS) system's risk index.

Design.

Retrospective cohort study that used data prospectively collected from 1993 through 2006.

Setting.

Five private, nonuniversity healthcare facilities in Belo Horizonte, Brazil.

Patients.

Consecutive patients undergoing the following NNIS operative procedures: 20,981 operations on the genitourinary system, 11,930 abdominal hysterectomies, 7,696 herniorraphies, 6,002 cholecystectomies, and 6,892 laparotomies.

Methods.

For each operative procedure category, 2 SSI risk models were specified. First, a model based on the NNIS system's risk index variables was specified (hereafter referred to as the NNIS-based model). Second, a modified model (hereafter referred to as the modified NNIS-based model), which was also based on the NNIS system's risk index, was specified with a postdischarge surveillance indicator, which was assigned the value of 1 if the patient could be reached during follow-up and a value of 0 if the patient could not be reached. A formal comparison of the capabilities of the 2 models to assess the risk of SSI was conducted using measures of calibration (by use of the Pearson goodness-of-fit test) and discrimination (by use of receiver operating characteristic curves). Goodman-Kruskal correlations (G) were also calculated.

Results.

The rate of incomplete postdischarge follow-up varied between 29.8% for abdominal hysterectomies and 50.5% for cholecystectomies. The modified NNIS-based model for laparotomy did not show any significant benefit over the NNIS-based model in any measure. For all other operative procedures, the modified NNIS-based model showed a significantly improved discriminatory ability and higher G statistics, compared with the NNIS-based model, with no significant impairment in calibration, except if used to assess the risk of SSI after operations on the genitourinary system or after a cholecystectomy.

Conclusions.

Compared with the NNIS-based model, the modified NNIS-based model added potentially useful clinical information regarding most of the operative procedures. Further work is warranted to evaluate this method for accounting for incomplete postdischarge follow-up during surveillance of SSI.

Type
Original Articles
Information
Infection Control & Hospital Epidemiology , Volume 30 , Issue 5 , May 2009 , pp. 433 - 439
Copyright
Copyright © The Society for Healthcare Epidemiology of America 2009

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References

1.Mangram, AJ, Horan, TC, Pearson, ML, Silver, LC, Jarvis, WR. Guideline for prevention of surgical site infection, 1999. Hospital Infection Control Practices Advisory Committee. Infect Control Hosp Epidemiol 1999;20:250–278.CrossRefGoogle ScholarPubMed
2.Centers for Disease Control and Prevention. The National Healthcare Safety Network (NHSN) Manual: Patient Safety Component Protocol. Division of Healthcare Quality Promotion, National Center for Infectious Diseases. Atlanta, GA. Last updated January 2008. Available at: http://www.cdc.gov/ncidod/dhqp/pdf/nhsn/NHSN_Manual_PatientSafetyProtocol_CURRENT.pdf. Accessed March 5, 2009.Google Scholar
3.National Nosocomial Infections Surveillance System. National Nosocomial Infections Surveillance (NNIS) System report, data summary from January 1992 through June 2004, issued October 2004. Am J Infect Control 2004;32:470–485.Google Scholar
4.Little, RJ, Rubin, DB. Statistical analysis with missing data. New York: John Wiley & Sons; 1987.Google Scholar
5.Baker, SG, Freedman, LS. A simple method for analyzing data from a randomized trial with a missing binary outcome. BMC Med Res Methodol 2003;3:8.Google Scholar
6.Nordheim, EV. Inference from nonrandomly missing categorical data: an example from a genetic study on Turner's syndrome. J Am Stat Assoc 1984;79:772–780.CrossRefGoogle Scholar
7.Rässler, S, Riphahn, RT. Survey item nonresponse and its treatment. All Stat Arch 2006;90:217–232.Google Scholar
8.Kenward, MG, Carpenter, J. Multiple imputation: current perspectives. Stat Methods Med Res 2007;16:199–218.CrossRefGoogle ScholarPubMed
9.Little, RJ. Regression with missing X's: a review. J Am Stat Assoc 1992;87:1227–1237.Google Scholar
10.Horan, TC, Emori, TG. Definitions of key terms used in the NNIS system. Am J Infect Control 1997;25:112–116.Google Scholar
11.Hosmer, D, Lemeshow, S. Applied logistic regression. New York: John Wiley; 1989.Google Scholar
12.Hanley, J, McNeil, B. The meaning and use of the area under a receiver-operating-characteristic curve. Radiology 1982;143:29–36.Google Scholar
13.DeLong, ER, DeLong, DM, Clarke-Pearson, DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 1988;44:837–845.CrossRefGoogle ScholarPubMed
14.Goodman, LA, Kruskal, WH. Measures of association for cross classifications. J Am Stat Assoc 1954;49:732–764.Google Scholar
15.Manniën, J, Wille, JC, Snoeren, RL, van den Hof, S. Impact of postdischarge surveillance on surgical site infection rates for several surgical procedures: results from the nosocomial surveillance network in The Netherlands. Infect Control Hosp Epidemiol 2006;27:809–816.Google Scholar
16.Wilson, AP, Hodgson, B, Liu, M, et al. Reduction in wound infection rates by wound surveillance with postdischarge follow-up and feedback. Br J Surg 2006;93:630–638.Google Scholar
17.Petrosillo, N, Drapeau, CM, Nicastri, E, et al. Surgical site infections in Italian hospitals: a prospective multicenter study. BMC Infect Dis 2008;8:34.CrossRefGoogle ScholarPubMed
18.Thibon, P, Parienti, JJ, Borgey, F, et al. Use of censored data to monitor surgical-site infections. Infect Control Hosp Epidemiol 2002;23:368–371.CrossRefGoogle ScholarPubMed
19.Rioux, C, Grandbastien, B, Astagneau, P. The standardized incidence ratio as a reliable tool for surgical site infection surveillance. Infect Control Hosp Epidemiol 2006;27:817–824.CrossRefGoogle ScholarPubMed
20.Geubbels, EL, Grobbee, DE, Vandenbroucke-Grauls, CM, Wille, JC, de Boer, AS. Improved risk adjustment for comparison of surgical site infection rates. Infect Control Hosp Epidemiol 2006;27:1330–1339.CrossRefGoogle ScholarPubMed
21.Geubbels, EL, Nagelkerke, NJ, Mintjes-De Groot, AJ, Vandenbroucke-Grauls, CM, Grobbee, DE, de Boer, AS. Reduced risk of surgical site infections through surveillance in a network. Int J Qual Health Care 2006;18:127–133.Google Scholar
22.Biscione, FM, Couto, RC, Pedrosa, TM, Neto, MC. Comparison of the risk of surgical site infection after laparoscopic cholecystectomy and open cholecystectomy. Infect Control Hosp Epidemiol 2007;28:1103–1106.Google Scholar
23.Biscione, FM, Couto, RC, Pedrosa, TM, Neto, MC. Factors influencing the risk of surgical site infection following diagnostic exploration of the abdominal cavity. J Infect 2007;55:317–323.CrossRefGoogle ScholarPubMed
24.van Houwelingen, JC, Le Cessie, S. Predictive value of statistical models. Stat Med 1990;9:1303–1325.CrossRefGoogle ScholarPubMed
25.Petherick, ES, Dalton, JE, Moore, PJ, Cullum, N. Methods for identifying surgical wound infection after discharge from hospital: a systematic review. BMC Infect Dis 2006;6:170.CrossRefGoogle ScholarPubMed
26.Reilly, J, Noone, A, Clift, A, et al. A study of telephone screening and direct observation of surgical wound infections after discharge from hospital. J Bone Joint Surg Br 2005;87:997–999.CrossRefGoogle ScholarPubMed
27.Stockley, JM, Allen, RM, Thomlinson, DF, Constantine, CE. A district general hospital's method of post-operative infection surveillance including post-discharge follow-up, developed over a five-year period. J Hosp Infect 2001;49:48–54.Google Scholar
28.Manian, FA, Meyer, L. Comparison of patient telephone survey with traditional surveillance and monthly physician questionnaires in monitoring surgical wound infections. Infect Control Hosp Epidemiol 1993;14:216–218.CrossRefGoogle ScholarPubMed