Journal Article

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

van Mourik, Maaike S.M., Verberk, Janneke D.M., Hetem, David J., van der Kooi, Tjallie I.I., Oostdam, Nicolette E.W.M., de Greeff, Sabine C., Bonten, Marc J.M., Noordergraaf, Mieke

2023

Overview

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. Multicenter retrospective cohort study. 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. 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. 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.

• Publisher: Cambridge University Press
• Subject: Algorithms / Automation / Classification / Clinical medicine / Colorectal Neoplasms / Colorectal surgery / Data collection / Data management / Digestive System Surgical Procedures - adverse effects / Electronic health records / Hospitals / Humans / Length of stay / Microbiology / Onsite / Original Article / Patients / Population / Questionnaires / Retrospective Studies / Surgical site infections / Surgical Wound Infection - epidemiology / Surveillance / Workloads