Modernizing Surgical Quality: A Novel Approach to Improving Detection of Surgical Site Infections in the Veteran Population

Introduction: Surgical site infections (SSIs) are an important quality measure. Identifying SSIs often relies upon a time-intensive manual review of a sample of common surgical cases. In this study, we sought to develop a predictive model for SSI identification using antibiotic pharmacy data extracted from the electronic medical record (EMR). Methods: A retrospective analysis was performed on all surgeries at a Veteran Affair’s Medical Center between January 9, 2020 and January 9, 2022. Patients receiving outpatient antibiotics within 30 days of their surgery were identified, and chart review was performed to detect instances of SSI as defined by VA Surgery Quality Improvement Program criteria. Binomial logistic regression was used to select variables to include in the model, which was trained using k-fold cross validation. Results: Of the 8,253 surgeries performed during the study period, patients in 793 (9.6%) cases were prescribed outpatient antibiotics within 30 days of their procedure; SSI was diagnosed in 128 (1.6%) patients. Logistic regression identified time from surgery to antibiotic prescription, ordering location of the prescription, length of prescription, type of antibiotic, and operating service as important variables to include in the model. On testing, the final model demonstrated good predictive value with c-statistic of 0.81 (confidence interval: 0.71–0.90). Hosmer–Lemeshow testing demonstrated good fit of the model with p value of 0.97. Conclusion: We propose a model that uses readily attainable data from the EMR to identify SSI occurrences. In conjunction with local case-by-case reporting, this tool can improve the accuracy and efficiency of SSI identification.