OBJECTIVE: Our goal was to estimate the performance statistics of an electronic surveillance system for surgical site infections (SSIs), generally applicable in French hospitals. METHODS: Three detection algorithms using 2 different data sources were tested retrospectively on 9 types of surgical procedures performed between January 2010 and December 2011 in the University Hospital of Nantes. The first algorithm was based on administrative codes, the second was based on bacteriological data, and the third used both data sources. For each algorithm, sensitivity, specificity, and positive and negative predictive values (PPV and NPV) were calculated. The reference method was the hospital's routine surveillance: a comprehensive review of the computerized medical charts of the patients who underwent one of the targeted procedures during the study period. SETTING: A 3,000-bed teaching hospital in western France. POPULATION: We analyzed 4,400 targeted surgical procedures. RESULTS: Sensitivity results varied significantly between the three algorithms, from 25% (95% confidence interval, 17-33) when using only administrative codes to 87% (80%-93%) with the bacteriological data and 90% (85%-96%) with the combined algorithm. Fewer variations were observed for specificity (91%-98%), PPV (21%-25%), and NPV (98% to nearly 100%). Overall, performance statistics were higher for deep SSIs than for superficial infections. CONCLUSIONS: A reliable computer-enhanced SSI surveillance can easily be implemented in French hospitals using common data sources. This should allow infection control professionals to spend more time on prevention and education duties. However, a multicenter study should be conducted to assess the generalizability of this method.
OBJECTIVE: Our goal was to estimate the performance statistics of an electronic surveillance system for surgical site infections (SSIs), generally applicable in French hospitals. METHODS: Three detection algorithms using 2 different data sources were tested retrospectively on 9 types of surgical procedures performed between January 2010 and December 2011 in the University Hospital of Nantes. The first algorithm was based on administrative codes, the second was based on bacteriological data, and the third used both data sources. For each algorithm, sensitivity, specificity, and positive and negative predictive values (PPV and NPV) were calculated. The reference method was the hospital's routine surveillance: a comprehensive review of the computerized medical charts of the patients who underwent one of the targeted procedures during the study period. SETTING: A 3,000-bed teaching hospital in western France. POPULATION: We analyzed 4,400 targeted surgical procedures. RESULTS: Sensitivity results varied significantly between the three algorithms, from 25% (95% confidence interval, 17-33) when using only administrative codes to 87% (80%-93%) with the bacteriological data and 90% (85%-96%) with the combined algorithm. Fewer variations were observed for specificity (91%-98%), PPV (21%-25%), and NPV (98% to nearly 100%). Overall, performance statistics were higher for deep SSIs than for superficial infections. CONCLUSIONS: A reliable computer-enhanced SSI surveillance can easily be implemented in French hospitals using common data sources. This should allow infection control professionals to spend more time on prevention and education duties. However, a multicenter study should be conducted to assess the generalizability of this method.
Authors: Maaike S M van Mourik; Pleun Joppe van Duijn; Karel G M Moons; Marc J M Bonten; Grace M Lee Journal: BMJ Open Date: 2015-08-27 Impact factor: 2.692