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Development of a fully automated surgical site infection detection algorithm for use in cardiac and orthopedic surgery research.

Suzuki H, Clore GS, Perencevich EN, Hockett-Sherlock SM, Goto M, Nair R, Branch-Elliman W, Richardson KK, Gupta K, Beck BF, Alexander B, Balkenende EC, Schweizer ML. Development of a fully automated surgical site infection detection algorithm for use in cardiac and orthopedic surgery research. Infection control and hospital epidemiology. 2021 Oct 1; 42(10):1215-1220.

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Abstract:

OBJECTIVE: To develop a fully automated algorithm using data from the Veterans'' Affairs (VA) electrical medical record (EMR) to identify deep-incisional surgical site infections (SSIs) after cardiac surgeries and total joint arthroplasties (TJAs) to be used for research studies. DESIGN: Retrospective cohort study. SETTING: This study was conducted in 11 VA hospitals. PARTICIPANTS: Patients who underwent coronary artery bypass grafting or valve replacement between January 1, 2010, and March 31, 2018 (cardiac cohort) and patients who underwent total hip arthroplasty or total knee arthroplasty between January 1, 2007, and March 31, 2018 (TJA cohort). METHODS: Relevant clinical information and administrative code data were extracted from the EMR. The outcomes of interest were mediastinitis, endocarditis, or deep-incisional or organ-space SSI within 30 days after surgery. Multiple logistic regression analysis with a repeated regular bootstrap procedure was used to select variables and to assign points in the models. Sensitivities, specificities, positive predictive values (PPVs) and negative predictive values were calculated with comparison to outcomes collected by the Veterans'' Affairs Surgical Quality Improvement Program (VASQIP). RESULTS: Overall, 49 (0.5%) of the 13,341 cardiac surgeries were classified as mediastinitis or endocarditis, and 83 (0.6%) of the 12,992 TJAs were classified as deep-incisional or organ-space SSIs. With at least 60% sensitivity, the PPVs of the SSI detection algorithms after cardiac surgeries and TJAs were 52.5% and 62.0%, respectively. CONCLUSIONS: Considering the low prevalence rate of SSIs, our algorithms were successful in identifying a majority of patients with a true SSI while simultaneously reducing false-positive cases. As a next step, validation of these algorithms in different hospital systems with EMR will be needed.





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