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Predicting in-hospital outcomes of patients with acute kidney injury

Author

Listed:
  • Changwei Wu

    (University of Electronic Science and Technology of China)

  • Yun Zhang

    (University of Electronic Science and Technology of China)

  • Sheng Nie

    (Southern Medical University)

  • Daqing Hong

    (University of Electronic Science and Technology of China)

  • Jiajing Zhu

    (University of Electronic Science and Technology of China)

  • Zhi Chen

    (University of Electronic Science and Technology of China)

  • Bicheng Liu

    (Southeast University School of Medicine)

  • Huafeng Liu

    (Affiliated Hospital of Guangdong Medical University)

  • Qiongqiong Yang

    (Sun Yat-Sen University)

  • Hua Li

    (Zhejiang University School of Medicine)

  • Gang Xu

    (Huazhong University of Science and Technology)

  • Jianping Weng

    (University of Science and Technology of China)

  • Yaozhong Kong

    (the First People’s Hospital of Foshan)

  • Qijun Wan

    (Shenzhen University)

  • Yan Zha

    (Guizhou University)

  • Chunbo Chen

    (Maoming People’s Hospital)

  • Hong Xu

    (Children’s Hospital of Fudan University)

  • Ying Hu

    (The Second Affiliated Hospital of Zhejiang University School of Medicine)

  • Yongjun Shi

    (Sun Yat-Sen University)

  • Yilun Zhou

    (Capital Medical University)

  • Guobin Su

    (Guangzhou University of Chinese Medicine)

  • Ying Tang

    (The Third Affiliated Hospital of Southern Medical University)

  • Mengchun Gong

    (Southern Medical University
    DHC Technologies)

  • Li Wang

    (University of Electronic Science and Technology of China)

  • Fanfan Hou

    (Southern Medical University)

  • Yongguo Liu

    (University of Electronic Science and Technology of China)

  • Guisen Li

    (University of Electronic Science and Technology of China)

Abstract

Acute kidney injury (AKI) is prevalent and a leading cause of in-hospital death worldwide. Early prediction of AKI-related clinical events and timely intervention for high-risk patients could improve outcomes. We develop a deep learning model based on a nationwide multicenter cooperative network across China that includes 7,084,339 hospitalized patients, to dynamically predict the risk of in-hospital death (primary outcome) and dialysis (secondary outcome) for patients who developed AKI during hospitalization. A total of 137,084 eligible patients with AKI constitute the analysis set. In the derivation cohort, the area under the receiver operator curve (AUROC) for 24-h, 48-h, 72-h, and 7-day death are 95·05%, 94·23%, 93·53%, and 93·09%, respectively. For dialysis outcome, the AUROC of each time span are 88·32%, 83·31%, 83·20%, and 77·99%, respectively. The predictive performance is consistent in both internal and external validation cohorts. The model can predict important outcomes of patients with AKI, which could be helpful for the early management of AKI.

Suggested Citation

  • Changwei Wu & Yun Zhang & Sheng Nie & Daqing Hong & Jiajing Zhu & Zhi Chen & Bicheng Liu & Huafeng Liu & Qiongqiong Yang & Hua Li & Gang Xu & Jianping Weng & Yaozhong Kong & Qijun Wan & Yan Zha & Chun, 2023. "Predicting in-hospital outcomes of patients with acute kidney injury," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-39474-6
    DOI: 10.1038/s41467-023-39474-6
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