Predictive Accuracy of the Complex 30-Day Model for Colon Surgical Site Infections after Traumatic Colon Injury: Any Better than a Coin Flip?
Year: 2025
Type: article
Abstract: BACKGROUND AND OBJECTIVES: Colon surgical site infections (SSIs) impose clinical and financial burden. To guide reimbursement, the Centers for Medicare and Medicaid Services utilize the National Healthcare Safety Network's (NSHN) Complex 30-Day SSI Model for Colon Procedures, which is used to compare the observed number of SSIs to the expected number of SSIs for all colon surgeries, regardless of underlying etiology. This study assessed the predictive accuracy of the Complex 30-day SSI Model for inpatients undergoing emergent colon operations for trauma. PATIENTS AND METHODS: The Trauma Quality Program Participant Use File was used to identify all patients who underwent emergent colon surgery because of trauma between 2017 and 2021 (excluding 2020). The predicted probability of colon SSI was calculated using the NSHN Complex 30-day Model using the following variables: diabetes mellitus, gender, age, body mass index, closure technique, American Society of Anesthesiology Score, and oncology hospital status. Receiver operating characteristic (ROC) curves were generated, and the area under the curve (AUC) was calculated to evaluate the model's predictive accuracy. RESULTS: In total, 292,688 patients were included. Among all patients, an SSI occurred in 0.33% (n = 1,097). For patients with complete data, the rate of SSI was 0.34%. The ROC curve for complete case analysis showed an AUC of 0.523 (95% confidence interval 0.506-0.541). CONCLUSION: The predictive accuracy of the NSHN Complex 30-Day SSI Model after colon injury is poor. The current model may not adequately capture the complexities and risk associated with trauma. An improved model is needed for more accurate risk assessment to improve patient care and accuracy of hospital reimbursement.
Source: Surgical Infections
Authors: Annmarie Butare, William Irish, Michael D. Honaker, Mark A. Newell
Institution: East Carolina University
Language: English
Subfield: Surgery
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W4415943410