Haruhisa Fukuda1, Manabu Kuroki2. 1. 1Kyushu University Graduate School of Medical Sciences,Fukuoka,Japan. 2. 2Institute of Statistical Mathematics,Tachikawa,Tokyo,Japan.
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.
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.
Authors: Amin Y Noaman; Farrukh Nadeem; Abdul Hamid M Ragab; Arwa Jamjoom; Nabeela Al-Abdullah; Mahreen Nasir; Anser G Ali Journal: Biomed Res Int Date: 2017-09-20 Impact factor: 3.411