Jiang Zhizhao, Hong Sibai, Chen Yongqiang, Du Chunhong, Hong Zhiwu, Xie Rongcheng, Li Ranran, Wu Jianjun, Jiang Haibin, Lin Jiangchuan, Lin Tianlai, Yun Jiangtao, Xie Minghui, Guo Huangang, Zhu Lingyun, Zhang Shengfeng, Yang Yuqiang, Xu Liang, Yang Junhui, Zeng Qingjun, Gu Guosheng, Li Chen, Wang Peng, Shi Jianshe, Sun Xuri, Liu Yuqi
Department of Intensive Care Unit, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, People's Republic of China.
Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
Shock. 2025 May 13. doi: 10.1097/SHK.0000000000002631.
Septic shock-associated acute kidney injury (SS-AKI) is a severe complication with high mortality. This study aimed to investigate the risk factors associated with AKI in patients with septic shock and establish a nomogram to predict its occurrence.
Patients with septic shock were categorized based on the development of AKI. A binary logistic regression was employed to identify significant risk factors, which were then incorporated into a nomogram. The performance of the nomogram was evaluated using receiver operating characteristic curve analysis, calibration curve, and decision curve analysis. A validation set was used to assess the model's generalizability.
Of the 507 septic shock patients enrolled in this study, 174 (34.3%) developed AKI. The dataset was randomly partitioned into a training set (n = 355) and a validation set (n = 152) at a ratio of 7:3. The predictive factors incorporated into the nomogram included chronic kidney disease, diuretic administration, deresuscitation during vasopressor administration, mechanical ventilation, source control failure, restrictive fluid resuscitation, and SOFA scores. The developed nomogram demonstrated excellent performance in predicting the risk of AKI in patients with septic shock. The model achieved an area under the receiver operating characteristic curve of 0.788 (95% CI, 0.737-0.839) in the training set and 0.770 (95% CI, 0.693-0.846) in the validation set, indicating strong discriminatory ability. The calibration curve analysis, using the Hosmer-Lemeshow test, indicated good agreement between the predicted and observed probabilities of AKI in both the training set (p = 0.468) and the validation set (p = 0.396). The decision curve analysis further indicated that the nomogram demonstrated substantial clinical utility in both the training set (0.09-0.87) and the validation set (0.11-0.64).
The nomogram serves as an invaluable tool for clinicians to assess the risk of AKI in patients experiencing septic shock and facilitates timely intervention.
脓毒性休克相关急性肾损伤（SS-AKI）是一种死亡率很高的严重并发症。本研究旨在调查脓毒性休克患者发生急性肾损伤（AKI）的相关危险因素，并建立列线图以预测其发生。
根据是否发生AKI对脓毒性休克患者进行分类。采用二元逻辑回归确定显著的危险因素，然后将其纳入列线图。使用受试者工作特征曲线分析、校准曲线和决策曲线分析来评估列线图的性能。使用验证集评估模型的可推广性。
在本研究纳入的507例脓毒性休克患者中，174例（34.3%）发生了AKI。数据集以7:3的比例随机分为训练集（n = 355）和验证集（n = 152）。纳入列线图的预测因素包括慢性肾脏病、利尿剂使用、血管升压药使用期间的液体复苏、机械通气、源头控制失败、限制性液体复苏和序贯器官衰竭评估（SOFA）评分。所建立的列线图在预测脓毒性休克患者发生AKI的风险方面表现出色。该模型在训练集中的受试者工作特征曲线下面积为0.788（95%CI，0.737 - 0.839），在验证集中为0.770（95%CI，0.693 - 0.846），表明具有很强的鉴别能力。使用Hosmer-Lemeshow检验的校准曲线分析表明，训练集（p = 0.468）和验证集（p = 0.396）中AKI的预测概率与观察概率之间具有良好的一致性。决策曲线分析进一步表明，列线图在训练集（0.09 - 0.87）和验证集（0.11 - 0.64）中均具有显著的临床实用性。
列线图是临床医生评估脓毒性休克患者发生AKI风险的宝贵工具，有助于及时进行干预。
