DEVELOPMENT AND VALIDATION OF A PREDICTION MODEL FOR SEPTIC SHOCK-ASSOCIATED ACUTE KIDNEY INJURY: A MULTICENTER STUDY USING NOMOGRAM MODELING.

Journal: Shock (Augusta, Ga.)
Published Date:

Abstract

Background: 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. Methods: Patients with septic shock were categorized based on the development of AKI. A binary logistic regression was used 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. Results: 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 Sequential Organ Failure Assessment 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% confidence interval, 0.737-0.839) in the training set and 0.770 (95% confidence interval, 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). Conclusions: The nomogram serves as an invaluable tool for clinicians to assess the risk of AKI in patients experiencing septic shock and facilitates timely intervention.

Authors

  • Zhizhao Jiang
    Department of Intensive Care Unit, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, People's Republic of China.
  • Sibai Hong
    Department of Intensive Care Unit, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, People's Republic of China.
  • Yongqiang Chen
    Department of Computer Science and Engineering, The Chinese University of Hong Kong, New Territories, 999077, Hong Kong, China.
  • Chunhong Du
    Department of Intensive Care Unit, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, People's Republic of China.
  • Zhiwu Hong
    Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
  • Rongcheng Xie
    Fudan University Zhongshan Hospital Xiamen Branch, Xiamen, China.
  • Ranran Li
    The 909th Hospital, School of Medicine, Xiamen University, Zhangzhou, China.
  • Jianjun Wu
    College of Information Technology and Communication, Hexi University, Zhangye 734000, China.
  • Haibin Jiang
    College of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning 116024, China.
  • Jiangchuan Lin
    Dehua County Hospital, Quanzhou, China.
  • Tianlai Lin
    Department of Critical Care Medicine, Quanzhou First Hospital Affiliated to Fujian Medical University, Quanzhou, Fujian, China.
  • Jiangtao Yun
    The Affiliated Hospital of Jiangsu University, Zhenjiang, China.
  • Minghui Xie
    The Second Affiliated Hospital of Chinese University of Hong Kong, Shenzhen, China.
  • Huangang Guo
    Shishi City Hospital of Fujian Province, Shishi, China.
  • Lingyun Zhu
    Department of Biology and Chemistry, College of Science, National University of Defense Technology, 410073 Changsha, Hunan, China.
  • Shengfeng Zhang
    The People's Hospital of Guangxi Zhuang Autonomous Region, Guangxi Academy of Medical Sciences, Nanning, China.
  • Yuqiang Yang
    The First Affiliated Hospital of Xiamen University, Xiamen, China.
  • Liang Xu
  • Junhui Yang
    Zhejiang Key Laboratory of Intelligent Cancer Biomarker Discovery and Translation, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
  • Qingjun Zeng
    Yueyang Central Hospital, Yueyang, China.
  • Guosheng Gu
    School of Computer Science, Guangdong University of Technology, Guangzhou 510006, China.
  • Chen Li
    School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
  • Peng Wang
    Neuroengineering Laboratory, School of Biomedical Engineering and Technology, Tianjin Medical University, Tianjin, China.
  • Jianshe Shi
    Department of General Surgery, Huaqiao University Affiliated Strait Hospital, Quanzhou, Fujian 362000, China.
  • Xuri Sun
    Department of Intensive Care Unit, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, People's Republic of China.
  • Yuqi Liu
    School of Chemistry and Chemical Engineering, Guangdong Pharmaceutical University, Guangzhou, 510006, China.