Su Longxiang, Liu Shengjun, Yang Yingying, Jiang Huizhen, Ye Xiangyang, Weng Li, Zhu Weiguo, Tian Xinlun, Long Yun
Department of Critical Care Medicine, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China.
Information Center, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China.
Arch Med Sci. 2024 Mar 30;20(2):464-475. doi: 10.5114/aoms/172160. eCollection 2024.
Fluid resuscitation of patients with sepsis is crucial. This study explored the role of fluid balance in the early resuscitation of sepsis patients in the intensive care unit (ICU).
A retrospective study of patients with sepsis using the Peking Union Medical College Hospital Intensive Care Medical Information System and Database from January 2014 to June 2020 was performed. Based on the survival status on day 28, the training cohort was divided into an alive group ( = 1,803) and a deceased group ( = 429). Univariate and multivariate analyses were used to identify risk factors, and the integrated learning XGBoost algorithm was used to construct a model for predicting outcomes. ROC and Kaplan-Meier survival curves were used to evaluate the effectiveness of the model. A verification cohort ( = 433) was used to verify the model.
Univariate analysis showed that fluid balance is an important covariate. Based on the scatterplot distribution, a significant difference in mortality was determined between groups stratified with a balance of 1000 ml. There were associations in the multivariate analysis between poor outcomes and sex, PO/FiO, serum creatinine, FiO, platelets, respiratory rate, SPO, temperature, and total fluid volume (1000 ml). Among these variables, total fluid balance (1000 ml) had an OR of 1.98 (CI: 1.41-2.77, < 0.001). Therefore, the model was built with these nine factors using XGBoost. Cross validation was used to verify generalizability. This model performed better than the SOFA and APACHE II models. The result was well verified in the verification cohort. A causal forest model suggested that patients with hypoxemia may suffer from positive fluid balance.
Sepsis fluid resuscitation in the ICU should be a targeted and goal-oriented treatment. A new prognostic prediction model was constructed and indicated that a 6-hour positive fluid balance after ICU initial admission is a risk factor for poor outcomes in sepsis patients. A 6-hour fluid balance above 1000 ml should be performed with caution.
脓毒症患者的液体复苏至关重要。本研究探讨了液体平衡在重症监护病房（ICU）脓毒症患者早期复苏中的作用。
利用北京协和医院重症医学信息系统和数据库对2014年1月至2020年6月的脓毒症患者进行回顾性研究。根据第28天的生存状态，将训练队列分为存活组（n = 1803）和死亡组（n = 429）。采用单因素和多因素分析确定危险因素，并使用集成学习XGBoost算法构建预测结局的模型。采用ROC和Kaplan-Meier生存曲线评估模型的有效性。使用验证队列（n = 433）对模型进行验证。
单因素分析表明液体平衡是一个重要的协变量。根据散点图分布，以1000 ml平衡量分层的组间死亡率存在显著差异。多因素分析显示不良结局与性别、PO/FiO₂、血清肌酐、FiO₂、血小板、呼吸频率、SPO₂、体温和总液体量（＞1000 ml）有关。在这些变量中，总液体平衡（＞1000 ml）的OR为1.98（CI：1.41 - 2.77，P ＜ 0.001）。因此，使用XGBoost基于这九个因素构建模型。采用交叉验证来验证模型的泛化能力。该模型的表现优于SOFA和APACHE II模型。在验证队列中结果得到了很好的验证。因果森林模型表明低氧血症患者可能存在正液体平衡。
ICU中的脓毒症液体复苏应是一种有针对性和目标导向的治疗。构建了一种新的预后预测模型，表明ICU初次入院后6小时的正液体平衡是脓毒症患者不良结局的危险因素。6小时液体平衡超过1000 ml时应谨慎操作。
