Wang Wenwen, Wang Kaipeng, Wang Yueguo, Liu Qingyuan, Sun Jian, Shi Ronghua, Liu Sicheng, Wang Huanli, Yuan Yuan, Xu Jun, Jin Kui, Zhang Yixin
Department of Emergency Medicine, the First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, China.
School of Mathematics and Statistics, Nanjing University of Science and Technology, China.
Biomol Biomed. 2025 May 8;25(7):1470-1478. doi: 10.17305/bb.2024.11134.
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.
在急诊科患者中早期识别脓毒症对于及时开展干预措施至关重要，这凸显了有效预测评分系统的必要性。一项回顾性观察研究利用了2019年12月至2021年10月期间从CETAT数据库收集的数据。该研究评估了全身炎症反应综合征（SIRS）、快速脓毒症相关器官功能衰竭评估（qSOFA）和国家早期预警评分（NEWS）评分系统以及逻辑回归模型在预测急诊科患者脓毒症和高危脓毒症方面的效果。通过纳入基于本地数据的其他特征，对逻辑回归模型进行了进一步优化。总共分析了12799例患者，其中包括1360例脓毒症病例，其中373例被归类为高危脓毒症。与qSOFA和SIRS相比，NEWS评分显示出更好的预测性能，脓毒症的受试者工作特征曲线下面积（AUC-ROC）为0.737（95%置信区间[CI]0.72-0.75），高危脓毒症为0.653（95%CI0.62-0.69）。优化后，基于NEWS的模型在脓毒症预测方面的AUC-ROC提高到0.756（95%CI0.74-0.
