541 Development and External Validation of the “Global Surgical-Site Infection” (GloSSI) Predictive Model in Adult Patients Undergoing Abdominal Surgery

Abstract

Abstract Aim To develop a novel prognostic score to predict 30-day surgical-site infection (SSI) following gastrointestinal surgery, and externally validate in comparison to existing prognostic models. Method This was a secondary analysis of 3 independent prospective international cohort studies conducted on a global basis. This included adults undergoing gastrointestinal surgery. Model development was performed in the GlobalSurg-2 dataset (January-July 2016). The primary outcome was 30-day SSI, with two predictive techniques explored: penalised regression (LASSO) and machine learning (XGBOOST). Final model selection based on prognostic accuracy (Area Under the Curve [AUC], 95% confidence interval) and clinical utility. Novel and previous scores were externally validated in GlobalSurg-1 (July-November 2014), and GlobalSurg-3 (April-October 2018). Results 30,029 patients were eligible: 14,019 (SSI=12.3%) in the derivation cohort, and 8,464 (SSI=11.4%) and 7,546 (SSI=15.7%) in the validation cohorts (GlobalSurg-1 and GlobalSurg-3 respectively). The LASSO model was selected due to similar discrimination to XGBoost in the GlobalSurg-2 dataset (AUC: 0.738, 0.725-0.750 versus 0.737, 0.709-0.765), but greater explainability. The final GloSSI score included six variables: country income, ASA, diabetes, and operative contamination, approach, and duration. Discrimination remained good on external validation in GlobalSurg-1 (AUC: 0.730, 0.715-0.744), but moderate discrimination in GlobalSurg-3 (AUC: 0.606, 95% CI 0.588-0.623). Nonetheless, this demonstrated superior performance to external validation of all previous models evaluated within GlobalSurg or prior datasets. Conclusions The GloSSI score allowed accurate prediction of SSI with 6 simple variables routinely available at surgery across income settings. This can inform use of intraoperative and postoperative interventions to modify the risk of SSI and minimise associated harm.