Nature Communications (Jun 2023)
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,
- Guisen Li
Affiliations
- Changwei Wu
- Department of Nephrology and Nephrology Institute, Sichuan Provincial People’s Hospital, School of Medicine, University of Electronic Science and Technology of China
- Yun Zhang
- Knowledge and Data Engineering Laboratory of Chinese Medicine, School of Information and Software Engineering, University of Electronic Science and Technology of China
- Sheng Nie
- National Clinical Research Center for Kidney Disease, State Laboratory of Organ Failure Research, Division of Nephrology, Nanfang Hospital, Southern Medical University
- Daqing Hong
- Department of Nephrology and Nephrology Institute, Sichuan Provincial People’s Hospital, School of Medicine, University of Electronic Science and Technology of China
- Jiajing Zhu
- Knowledge and Data Engineering Laboratory of Chinese Medicine, School of Information and Software Engineering, University of Electronic Science and Technology of China
- Zhi Chen
- Knowledge and Data Engineering Laboratory of Chinese Medicine, School of Information and Software Engineering, University of Electronic Science and Technology of China
- Bicheng Liu
- Institute of Nephrology, Zhongda Hospital, Southeast University School of Medicine
- Huafeng Liu
- Key Laboratory of Prevention and Management of Chronic Kidney Disease of Zhanjiang City, Institute of Nephrology, Affiliated Hospital of Guangdong Medical University
- Qiongqiong Yang
- Department of Nephrology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University
- Hua Li
- Sir Run Run Shaw Hospital, Zhejiang University School of Medicine
- Gang Xu
- Division of Nephrology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology
- Jianping Weng
- Department of Endocrinology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China
- Yaozhong Kong
- Department of Nephrology, the First People’s Hospital of Foshan
- Qijun Wan
- The Second People’s Hospital of Shenzhen, Shenzhen University
- Yan Zha
- Guizhou Provincial People’s Hospital, Guizhou University
- Chunbo Chen
- Department of Critical Care Medicine, 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
- Huizhou Municipal Central Hospital, Sun Yat-Sen University
- Yilun Zhou
- Department of Nephrology, Beijing Tiantan Hospital, Capital Medical University
- Guobin Su
- Department of Nephrology, Guangdong Provincial Hospital of Chinese Medicine, The Second Affiliated Hospital, The Second Clinical College, Guangzhou University of Chinese Medicine
- Ying Tang
- The Third Affiliated Hospital of Southern Medical University
- Mengchun Gong
- Institute of Health Management, Southern Medical University
- Li Wang
- Department of Nephrology and Nephrology Institute, Sichuan Provincial People’s Hospital, School of Medicine, University of Electronic Science and Technology of China
- Fanfan Hou
- National Clinical Research Center for Kidney Disease, State Laboratory of Organ Failure Research, Division of Nephrology, Nanfang Hospital, Southern Medical University
- Yongguo Liu
- Knowledge and Data Engineering Laboratory of Chinese Medicine, School of Information and Software Engineering, University of Electronic Science and Technology of China
- Guisen Li
- Department of Nephrology and Nephrology Institute, Sichuan Provincial People’s Hospital, School of Medicine, University of Electronic Science and Technology of China
- DOI
- https://doi.org/10.1038/s41467-023-39474-6
- Journal volume & issue
-
Vol. 14,
no. 1
pp. 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.
