Chen Shaochuan, Yang Jiale, Shi Xuezhi, Liu Anwei, Lin Guodong, Tong Huasheng
Department of Emergency Medicine, Huiyang Sanhe Hospital, Huizhou, China.
Guangzhou University of Chinese Medicine, Guangzhou, China.
Intern Emerg Med. 2025 Jan 28. doi: 10.1007/s11739-025-03867-w.
Multi-trauma presents significant challenges due to the complexity of injuries and high mortality rates. Early identification and intervention are crucial for improving outcomes in these critically injured patients. This retrospective study analyzed clinical data from multi-trauma patients admitted to the emergency department of Huiyang Sanhe Hospital between January 10, 2020, and September 30, 2022. Univariate and multivariate logistic regression analyses were conducted to identify independent predictors of hospital mortality. A prediction model was developed based on these prognostic markers, visualized using a nomogram, and its discriminative ability and clinical benefit were evaluated. A total of 124 multi-trauma patients were included in the study, with a hospital mortality rate of 26.7%. Univariate and multivariate logistic regression analyses identified trauma-induced coagulopathy (TIC) (OR 4.238, 95% CI 1.46-12.28), blood urea nitrogen (BUN) (OR 1.397, 95% CI 1.09-1.78), and Glasgow Coma Scale (GCS) score (OR 0.720, 95% CI 0.61-0.85) as independent factors of hospital mortality. Therefore, a nomogram incorporating TIC, BUN, and GCS score was constructed and demonstrated excellent predictive performance and clinical impact (AUC 0.898, 95% CI 0.834-0.962). The nomogram developed in this study provided a practical tool for early prediction of hospital mortality in multi-trauma patients. By focusing on TIC, BUN, and GCS score, this model may facilitate rapid bedside assessment and timely intervention. However, further multicenter, prospective studies are required to validate its performance and applicability.
由于损伤的复杂性和高死亡率，多发伤带来了重大挑战。早期识别和干预对于改善这些重伤患者的预后至关重要。这项回顾性研究分析了2020年1月10日至2022年9月30日期间惠阳三和医院急诊科收治的多发伤患者的临床数据。进行单因素和多因素逻辑回归分析以确定医院死亡率的独立预测因素。基于这些预后标志物开发了一个预测模型，用列线图进行可视化，并评估其判别能力和临床效益。该研究共纳入124例多发伤患者，医院死亡率为26.7%。单因素和多因素逻辑回归分析确定创伤性凝血病（TIC）（OR 4.238，95%CI 1.46 - 12.28）、血尿素氮（BUN）（OR 1.397，95%CI 1.09 - 1.78）和格拉斯哥昏迷量表（GCS）评分（OR 0.720，95%CI 0.61 - 0.85）为医院死亡率的独立因素。因此，构建了一个纳入TIC、BUN和GCS评分的列线图，其显示出优异的预测性能和临床影响（AUC 0.898，95%CI 0.834 - 0.962）。本研究开发的列线图为早期预测多发伤患者的医院死亡率提供了一个实用工具。通过关注TIC、BUN和GCS评分，该模型可能有助于快速床边评估和及时干预。然而，需要进一步的多中心前瞻性研究来验证其性能和适用性。
