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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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