Abstract
Early identification of sepsis in emergency department patients is critical for initiating timely interventions, highlighting the need for effective predictive scoring systems. A retrospective observational study was conducted using data from the CETAT database collected between December 2019 and October 2021. The study evaluated how well the systemic inflammatory response syndrome (SIRS), quick Sepsis-related Organ Failure Assessment (qSOFA), and National Early Warning Score (NEWS) scoring systems, along with logistic regression models, predict sepsis, and high-risk sepsis in emergency department patients. The logistic regression models were further optimized by incorporating additional features based on local data. A total of 12,799 patients were analyzed, including 1360 sepsis cases, of which 373 were classified as high-risk sepsis. The NEWS score demonstrated superior predictive performance compared to qSOFA and SIRS, with an area under the receiver operating characteristic curve (AUC-ROC) of 0.737 (95% confidence interval [CI] 0.72-0.75) for sepsis and 0.653 (95% CI 0.62-0.69) for high risk sepsis . After optimization, the NEWS-based model improved to an AUC-ROC of 0.756 (95% CI 0.74-0.77) for sepsis and 0.718 (95% CI 0.69-0.75) for high-risk sepsis. Further enhancement was observed with the inclusion of additional clinical variables, resulting in AUC-ROC values of 0.834 (95% CI 0.82-0.85) for sepsis and 0.756 (95% CI 0.73-0.78) for high-risk sepsis. Data from the Medical Information Mart for Intensive Care (MIMIC)-IV database, which included sepsis status and relevant variables for SIRS, qSOFA, and NEWS score calculations, confirmed that the optimized NEWS-based model improved the sepsis prediction AUC-ROC from 0.690 (95% CI 0.68-0.70) to 0.708 (95% CI 0.70-0.72), and consistently outperformed qSOFA and SIRS in sepsis prediction.