Objective: Automated surveillance of  healthcare-associated infections reduces workload and improves  standardization, but it has not yet been adopted widely. In this study,  we assessed the performance and feasibility of an easy implementable  framework to develop algorithms for semiautomated surveillance of deep  incisional and organ-space surgical site infections (SSIs) after  orthopedic, cardiac, and colon surgeries.

Design: Retrospective cohort study in multiple countries.

Methods: European  hospitals were recruited and selected based on the availability of  manual SSI surveillance data from 2012 onward (reference standard) and  on the ability to extract relevant data from electronic health records. A  questionnaire on local manual surveillance and clinical practices was  administered to participating hospitals, and the information collected  was used to pre-emptively design semiautomated surveillance algorithms  standardized for multiple hospitals and for center-specific application.  Algorithm sensitivity, positive predictive value, and reduction of  manual charts requiring review were calculated. Reasons for  misclassification were explored using discrepancy analyses.

Results: The  study included 3 hospitals, in the Netherlands, France, and Spain.  Classification algorithms were developed to indicate procedures with a  high probability of SSI. Components concerned microbiology, prolonged  length of stay or readmission, and reinterventions. Antibiotics and  radiology ordering were optional. In total, 4,770 orthopedic procedures,  5,047 cardiac procedures, and 3,906 colon procedures were analyzed.  Across hospitals, standardized algorithm sensitivity ranged between 82%  and 100% for orthopedic surgery, between 67% and 100% for cardiac  surgery, and between 84% and 100% for colon surgery, with 72%-98%  workload reduction. Center-specific algorithms had lower sensitivity.

Conclusions: Using  this framework, algorithms for semiautomated surveillance of SSI can be  successfully developed. The high performance of standardized algorithms  holds promise for large-scale standardization.