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Performance, Revision, and Extension of the National Nosocomial Infections Surveillance System's Risk Index in Brazilian Hospitals

Published online by Cambridge University Press:  02 January 2015

Fernando Martín Biscione*
Affiliation:
Health Sciences and Tropical Medicine Postgraduate Course, Minas Gerais Federal University School of Medicine, Belo Horizonte, Minas Gerais, Brazil
Renato Camargos Couto
Affiliation:
Health Sciences and Tropical Medicine Postgraduate Course, Minas Gerais Federal University School of Medicine, Belo Horizonte, Minas Gerais, Brazil
Tânia M. G. Pedrosa
Affiliation:
Health Sciences and Tropical Medicine Postgraduate Course, Minas Gerais Federal University School of Medicine, Belo Horizonte, Minas Gerais, Brazil
*
Health Sciences and Tropical Medicine Postgraduate Course, Minas Gerais Federal University School of Medicine, 190 Alfredo Balena Avenue, Room 533, Santa Efigênia, Belo Horizonte, Minas Gerais, Brazil 30-130-100 (fernandobiscione@yahoo.com.arorfernandobiscione@med-trop.dout.ufmg.br)

Abstract

Objective.

To assess the benefit of using procedure-specific alternative cutoff points for National Nosocomial Infections Surveillance (NNIS) risk index variables and of extending surgical site infection (SSI) risk prediction models with a postdischarge surveillance indicator.

Design.

Open, retrospective, validation cohort study.

Setting.

Five private, nonuniversity Brazilian hospitals.

Patients.

Consecutive inpatients operated on between January 1993 and May 2006 (other operations of the genitourinary system [n = 20,723], integumentary system [n = 12,408], or musculoskeletal system [n = 15,714] and abdominal hysterectomy [n = 11,847]).

Methods.

For each procedure category, development and validation samples were defined nonrandomly. In the development samples, alternative SSI prognostic scores were constructed using logistic regression: (i) alternative NNIS scores used NNIS risk index covariates and cutoff points but locally derived SSI risk strata and rates, (ii) revised scores used procedure-specific alternative cutoff points, and (iii) extended scores expanded revised scores with a postdischarge surveillance indicator. Performances were compared in the validation samples using calibration, discrimination, and overall performance measures.

Results.

The NNIS risk index showed low discrimination, inadequate calibration, and predictions with high variability. The most consistent advantage of alternative NNIS scores was regarding calibration (prevalence and dispersion components). Revised scores performed slightly better than the NNIS risk index for most procedures and measures, mainly in calibration. Extended scores clearly performed better than the NNIS risk index, irrespective of the measure or operative procedure.

Conclusions.

Locally derived SSI risk strata and rates improved the NNIS risk index's calibration. Alternative cutoff points further improved the specification of the intrinsic SSI risk component. Controlling for incomplete postdischarge SSI surveillance provided consistently more accurate SSI risk adjustment.

Infect Control Hosp Epidemiol 2012;33(2):124-134

Type
Original Articles
Copyright
Copyright © The Society for Healthcare Epidemiology of America 2012

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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. Am J Med 1991;91(suppl 3B):S152S157.CrossRefGoogle ScholarPubMed
2.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;121:206215.CrossRefGoogle ScholarPubMed
3.Vandenbroucke-Grauls, C, Schultsz, C. Surveillance in infection control: are we making progress? Curr Opin Infect Dis 2002;15:415419.Google Scholar
4.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;27:13301339.Google Scholar
5.Brandt, C, Hansen, S, Sohr, D, Daschner, F, Ruden, H, Gastmeier, P. Finding a method for optimizing risk adjustment when comparing surgical-site infection rates. Infect Control Hosp Epidemiol 2004;25:313318.CrossRefGoogle ScholarPubMed
6.Barnes, S, Salemi, C, Fithian, D, et al. An enhanced benchmark for prosthetic joint replacement infection rates. Am J Infect Control 2006;34:669672.CrossRefGoogle ScholarPubMed
7.Moro, ML, Morsillo, F, Tangenti, M, et al. Rates of surgical-site infection: an international comparison. Infect Control Hosp Epidemiol 2005;26:442448.CrossRefGoogle ScholarPubMed
8.Biscione, FM, Couto, RC, Pedrosa, TM. Accounting for incomplete postdischarge follow-up during surveillance of surgical site infection by use of the National Nosocomial Infections Surveillance system's risk index. Infect Control Hosp Epidemiol 2009;30:433439.CrossRefGoogle ScholarPubMed
9.Mangram, AJ, Horan, TC, Pearson, ML, et al; Hospital Infection Control Practices Advisory Committee. Guideline for prevention of surgical site infection, 1999. Infect Control Hosp Epidemiol 1999;20:250278.CrossRefGoogle ScholarPubMed
10.National Healthcare Safety Network (NHSN). The NHSN Manual: Patient Safety Component Protocol. Atlanta: Division of Healthcare Quality Promotion, National Center for Infectious Diseases, 2008.Google Scholar
11.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;32:470485.Google Scholar
12.Edwards, JR, Peterson, KD, Mu, Y, et al. National Healthcare Safety Network (NHSN) report: data summary for 2006 through 2008, issued December 2009. Am J Infect Control 2009;37:783805.CrossRefGoogle ScholarPubMed
13.Horan, TC, Emori, TG. Definitions of key terms used in the NNIS system. Am J Infect Control 1997;25:112116.CrossRefGoogle ScholarPubMed
14.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;55:317323.Google Scholar
15.Altaian, DG, Royston, P. What do we mean by validating a prognostic model? Stat Med 2000;19:453473.3.0.CO;2-5>CrossRefGoogle Scholar
16.Justice, AC, Covinsky, KE, Berlin, JA. Assessing the generalizability of prognostic information. Ann Intern Med 1999;130:515524.CrossRefGoogle ScholarPubMed
17.Cox, DR. A note on data-splitting for the evaluation of significance levels. Biometrika 1975;62:441444.Google Scholar
18.Vergouwe, Y, Steyerberg, EW, Eijkemans, MJC, Habbema, JDF. Substantial effective sample sizes were required for external validation studies of predictive logistic regression models. J Clin Epidemiol 2005;58:475483.Google Scholar
19.Peduzzi, P, Concato, J, Kemper, E, Holford, TR, Feinstein, AR. A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol 1996;49:13731379.CrossRefGoogle ScholarPubMed
20.Kass, GV. An exploratory technique for investigating large quantities of categorical data. J R Stat Soc C 1980;29:119127.Google Scholar
21.Hosmer, DW, Lemeshow, S. Applied Logistic Regression. New York: Wiley, 1989.Google Scholar
22.Mehta, CR, Patel, NR. Exact logistic regression: theory and examples. Stat Med 1995;14:21432160.CrossRefGoogle ScholarPubMed
23.King, G, Zeng, L. Logistic regression in rare events data. Polit Anal 2001;9:137163.Google Scholar
24.Moons, KG, Harrell, FE, Steyerberg, EW. Should scoring rules be based on odds ratios or regression coefficients? J Clin Epidemiol 2002;55:10541055.CrossRefGoogle ScholarPubMed
25.Hanley, J, McNeil, B. The meaning and use of the area under a receiver-operating-characteristic curve. Radiology 1982;143:2936.CrossRefGoogle Scholar
26.DeLong, ER, DeLong, DM, Clarke-Pearson, DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 1988;44:837845.CrossRefGoogle ScholarPubMed
27.Cox, DR. Two further applications of a model for binary regression. Biometrika 1958;45:562565.CrossRefGoogle Scholar
28.Goodman, LA, Kruskal, WH. Measures of association for cross classifications. J Am Stat Assoc 1954;49:732764.Google Scholar
29.Yates, JF. External correspondence: decomposition of the mean probability score. Organ Behav Hum Perform 1982;30:132156.CrossRefGoogle Scholar
30.Cuzick, J. A Wilcoxon-type test for trend. Stat Med 1985;4:8790.CrossRefGoogle ScholarPubMed
31.van Houwelingen, JC, le Cessie, S. Predictive value of statistical models. Stat Med 1990;9:13031325.CrossRefGoogle ScholarPubMed
32.Campos, ML, Cipriano, ZM, Freitas, PF. Suitability of the NNIS index for estimating surgical-site infection risk at a small university hospital in Brazil. Infect Control Hosp Epidemiol 2001;22:268272.Google Scholar
33.Braitman, LE, Davidoff, F. Predicting clinical states in individual patients. Ann Intern Med 1996;125:406412.CrossRefGoogle ScholarPubMed
34.Geubbels, EL, Mintjes-de Groot, AJ, van den Berg, JM, de Boer, AS. An operating surveillance system of surgical site infections in the Netherlands: results of the PREZIES national surveillance network. Infect Control Hosp Epidemiol 2000;21:311318.CrossRefGoogle ScholarPubMed
35.Weiss, CA, Statz, CL, Dahms, RA, Remucal, MJ, Dunn, DL, Beilman, GJ. Six years of surgical wound infection surveillance at a tertiary care center: review of the microbiologic and epidemiological aspects of 20,007 wounds. Arch Surg 1999;134:10411048.Google Scholar
36.Nguyen, D, MacLeod, WB, Phung, DC, et al. Incidence and predictors of surgical-site infections in Vietnam. Infect Control Hosp Epidemiol 2001;22:485492.CrossRefGoogle ScholarPubMed
37.Di Leo, A, Piffer, S, Ricci, F, et al. Surgical site infections in an Italian surgical ward: a prospective study. Surg Infect (Larchmt) 2009;10:533538.Google Scholar
38.Chen, LF, Anderson, DJ, Kaye, KS, Sexton, DJ. Validating a 3-point prediction rule for surgical site infection after coronary artery bypass surgery. Infect Control Hosp Epidemiol 2010;31:6468.Google Scholar
39.Batista, R, Kaye, K, Yokoe, DS. Admission-specific chronic disease scores as alternative predictors of surgical site infection for patients undergoing coronary artery bypass graft surgery. Infect Control Hosp Epidemiol 2006;27:802808.CrossRefGoogle ScholarPubMed
40.Rioux, C, Grandbastien, B, Astagneau, P. The standardized incidence ratio as a reliable tool for surgical site infection surveillance. Infect Control Hosp Epidemiol 2006;27:817824.CrossRefGoogle ScholarPubMed