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The Development of Statistical Models for Predicting Surgical Site Infections in Japan: Toward a Statistical Model–Based Standardized Infection Ratio

Published online by Cambridge University Press:  23 December 2015

Haruhisa Fukuda  and
Manabu Kuroki
Haruhisa Fukuda*
Affiliation:
Kyushu University Graduate School of Medical Sciences, Fukuoka, Japan
Manabu Kuroki
Affiliation:
Institute of Statistical Mathematics, Tachikawa, Tokyo, Japan
*
Address correspondence to Haruhisa Fukuda, MPH, PhD, Kyushu University Graduate School of Medical Sciences, 3-1-1 Maidashi Higashi-ku, Fukuoka 812-8582, Japan (h_fukuda@hcam.med.kyushu-u.ac.jp).
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Abstract

OBJECTIVE

To develop and internally validate a surgical site infection (SSI) prediction model for Japan.

DESIGN

Retrospective observational cohort study.

METHODS

We analyzed surveillance data submitted to the Japan Nosocomial Infections Surveillance system for patients who had undergone target surgical procedures from January 1, 2010, through December 31, 2012. Logistic regression analyses were used to develop statistical models for predicting SSIs. An SSI prediction model was constructed for each of the procedure categories by statistically selecting the appropriate risk factors from among the collected surveillance data and determining their optimal categorization. Standard bootstrapping techniques were applied to assess potential overfitting. The C-index was used to compare the predictive performances of the new statistical models with those of models based on conventional risk index variables.

RESULTS

The study sample comprised 349,987 cases from 428 participant hospitals throughout Japan, and the overall SSI incidence was 7.0%. The C-indices of the new statistical models were significantly higher than those of the conventional risk index models in 21 (67.7%) of the 31 procedure categories (P<.05). No significant overfitting was detected.

CONCLUSIONS

Japan-specific SSI prediction models were shown to generally have higher accuracy than conventional risk index models. These new models may have applications in assessing hospital performance and identifying high-risk patients in specific procedure categories.

Infect. Control Hosp. Epidemiol. 2016;37(3):260–271

Type
Original Articles
Information
Infection Control & Hospital Epidemiology , Volume 37 , Supplement 3 , March 2016 , pp. 260 - 271
Copyright
© 2015 by The Society for Healthcare Epidemiology of America. All rights reserved 

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References

REFERENCES

1. Magill, SS, Edwards, JR, Bamberg, W, et al. Multistate point-prevalence survey of health care-associated infections. N Engl J Med 2014;370:1198–1208.Google Scholar
2. Fukuda, H, Morikane, K, Kuroki, M, et al. Impact of surgical site infections after open and laparoscopic colon and rectal surgeries on postoperative resource consumption. Infection 2012;40:649–659.Google Scholar
3. Fukuda, H, Lee, J, Imanaka, Y. Variations in analytical methodology for estimating costs of hospital-acquired infections: a systematic review. J Hosp Infect 2011;77:93–105.Google Scholar
4. Brandt, C, Hansen, S, Sohr, D, Daschner, F, Rüden, H, Gastmeier, P. Finding a method for optimizing risk adjustment when comparing surgical-site infection rates. Infect Control Hosp Epidemiol 2004;25:313–318.Google Scholar
5. 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
6. 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.Google Scholar
7. 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
8. Brandt, C, Sohr, D, Behnke, M, Daschner, F, Rüden, H, Gastmeier, P. Reduction of surgical site infection rates associated with active surveillance. Infect Control Hosp Epidemiol 2006;27:1347–1351.CrossRefGoogle ScholarPubMed
9. Friedman, ND, Bull, AL, Russo, PL, Gurrin, L, Richards, M. Performance of the national nosocomial infections surveillance risk index in predicting surgical site infection in Australia. Infect Control Hosp Epidemiol 2007;28:55–59.Google Scholar
10. Worth, LJ, Bull, AL, Spelman, T, Brett, J, Richards, MJ. Diminishing surgical site infections in Australia: time trends in infection rates, pathogens and antimicrobial resistance using a comprehensive Victorian surveillance program, 2002-2013. Infect Control Hosp Epidemiol 2015;36:409–416.Google Scholar
11. 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. Preventie van Ziekenhuisinfecties door Surveillance. Infect Control Hosp Epidemiol 2000;21:311–318.Google Scholar
12. 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.Google Scholar
13. Díaz-Agero Pérez, C, Robustillo Rodela, A, Pita López, MJ, López Fresneña, N, Monge Jodrá, V, Quality Control Indicator Working Group. Surgical wound infection rates in Spain: data summary, January 1997 through June 2012. Am J Infect Control 2014;42:521–524.CrossRefGoogle ScholarPubMed
14. Morikane, K, Honda, H, Yamagishi, T, Suzuki, S, Aminaka, M. Factors associated with surgical site infection in colorectal surgery: the Japan Nosocomial Infections Surveillance. Infect Control Hosp Epidemiol 2014;35:660–666.Google Scholar
15. Fukuda, H, Morikane, K, Kuroki, M, et al. Toward the rational use of standardized infection ratios to benchmark surgical site infections. Am J Infect Control 2013;41:810–814.Google Scholar
16. 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;31:64–68.Google Scholar
17. 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.Google Scholar
18. Mu, Y, Edwards, JR, Horan, TC, Berrios-Torres, SI, Fridkin, SK. Improving risk-adjusted measures of surgical site infection for the National Healthcare Safety Network. Infect Control Hosp Epidemiol 2011;32:970–986.Google Scholar
19. Gustafson, TL. Three uses of the standardized infection ratio (SIR) in infection control. Infect Control Hosp Epidemiol 2006;27:427–430.Google Scholar
20. Horan, TC, Gaynes, RP. Surveillance of nosocomial infections. In: Mayhall CG, ed., Hospital Epidemiology and Infection Control, 3rd ed. Philadelphia: Lippincott Williams & Wilkins, 2004:1659–1702.Google Scholar
21. Harrell, FE Jr, Lee, KL, Mark, DB. Multivariable prognostic models. Stat Med 1996;15:361–387.3.0.CO;2-4>CrossRefGoogle ScholarPubMed
22. Efron, B, Tibsherani, R. An Introduction to the Bootstrap. New York: Chapman and Hall, 1993.Google Scholar
23. Harrell, FE Jr. Regression Modeling Strategies. New York: Springer Science and Business, 2001.Google Scholar
24. 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.Google Scholar
25. Pavlou, M, Ambler, G, Seaman, SR, et al. How to develop a more accurate risk prediction model when there are few events. BMJ 2015;351:h3868.CrossRefGoogle ScholarPubMed
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