Semiautomated surveillance of deep surgical site infections after colorectal surgeries: A multicenter external validation of two surveillance algorithms

Abstract

Objective: Automated surveillance methods increasingly replace or support conventional (manual) surveillance; the latter is labor intensive and vulnerable to subjective interpretation. We sought to validate 2 previously developed semiautomated surveillance algorithms to identify deep surgical site infections (SSIs) in patients undergoing colorectal surgeries in Dutch hospitals. Design: Multicenter retrospective cohort study. Methods: From 4 hospitals, we selected colorectal surgery patients between 2018 and 2019 based on procedure codes, and we extracted routine care data from electronic health records. Per hospital, a classification model and a regression model were applied independently to classify patients into low- or high probability of having developed deep SSI. High-probability patients need manual SSI confirmation; low-probability records are classified as no deep SSI. Sensitivity, positive predictive value (PPV), and workload reduction were calculated compared to conventional surveillance. Results: In total, 672 colorectal surgery patients were included, of whom 28 (4.1%) developed deep SSI. Both surveillance models achieved good performance. After adaptation to clinical practice, the classification model had 100% sensitivity and PPV ranged from 11.1% to 45.8% between hospitals. The regression model had 100% sensitivity and 9.0%-14.9% PPV. With both models, <25% of records needed review to confirm SSI. The regression model requires more complex data management skills, partly due to incomplete data. Conclusions: In this independent external validation, both surveillance models performed well. The classification model is preferred above the regression model because of source-data availability and less complex data-management requirements. The next step is implementation in infection prevention practices and workflow processes.