Predicting in-hospital outcomes of patients with acute kidney injury

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, Chunbo Chen, Hong Xu, Ying Hu, Yongjun Shi, Yilun Zhou, Guobin Su, Ying Tang, Mengchun Gong, Li Wang, Fanfan Hou (), Yongguo Liu () and Guisen Li ()
Additional contact information
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
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

Nature Communications, 2023, vol. 14, issue 1, 1-9

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-39474-6

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