Ma Wen-Hao, Yang Ze-Yu, Fan Xing-Xing, Tian Lei, Zhang Tuo, Wang Ming-da, Gao Ji-Yuan, Xu Jian-le, Fang Wei, Hou Hui-Min, Chen Man
Department of Critical Care Medicine, Shandong Provincial Hospital, Shandong First Medical University, Jinan, 250021, China.
Department of Critical Care Medicine, Shenxian People's Hospital, Liaocheng, 252400, China.
Curr Med Sci. 2025 Jul 16. doi: 10.1007/s11596-025-00093-5.
This study aimed to develop a prediction model to assess the risk of sepsis-induced coagulopathy (SIC) in sepsis patients.
We conducted a retrospective study of septic patients admitted to the Intensive Care Units of Shandong Provincial Hospital (Central Campus and East Campus), and Shenxian People's Hospital from January 2019 to September 2024. We used Kaplan-Meier analysis to assess survival outcomes. LASSO regression identified predictive variables, and logistic regression was employed to analyze risk factors for pre-SIC. A nomogram prediction model was developed via R software and evaluated via receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA).
Among 309 patients, 236 were in the training set, and 73 were in the test set. The pre-SIC group had higher mortality (44.8% vs. 21.3%) and disseminated intravascular coagulation (DIC) incidence (56.3% vs. 29.1%) than the non-SIC group. LASSO regression identified lactate, coagulation index, creatinine, and SIC scores as predictors of pre-SIC. The nomogram model demonstrated good calibration, with an AUC of 0.766 in the development cohort and 0.776 in the validation cohort. DCA confirmed the model's clinical utility.
SIC is associated with increased mortality, with pre-SIC further increasing the risk of death. The nomogram-based prediction model provides a reliable tool for early SIC identification, potentially improving sepsis management and outcomes.
本研究旨在开发一种预测模型，以评估脓毒症患者发生脓毒症诱导的凝血病（SIC）的风险。
我们对2019年1月至2024年9月期间入住山东省立医院（中心院区和东院区）重症监护病房以及莘县人民医院的脓毒症患者进行了一项回顾性研究。我们使用Kaplan-Meier分析来评估生存结局。LASSO回归确定预测变量，采用逻辑回归分析SIC前期的危险因素。通过R软件开发列线图预测模型，并通过受试者操作特征（ROC）曲线、校准曲线和决策曲线分析（DCA）进行评估。
309例患者中，236例在训练集，73例在测试集。SIC前期组的死亡率（44.8%对21.3%）和弥散性血管内凝血（DIC）发生率（56.3%对29.1%）高于非SIC组。LASSO回归确定乳酸、凝血指标、肌酐和SIC评分是SIC前期的预测因素。列线图模型显示出良好的校准，在开发队列中的AUC为0.766，在验证队列中的AUC为0.776。DCA证实了该模型的临床实用性。
SIC与死亡率增加相关，SIC前期进一步增加死亡风险。基于列线图的预测模型为早期识别SIC提供了一种可靠工具，可能改善脓毒症的管理和结局。
