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Predicting in-hospital outcomes of patients with acute kidney injury

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收录情况： ◇ SCIE ◇ 自然指数

作者：
Wu Changwei[1] * ; Zhang Yun[2] ; Nie Sheng[3] ; Hong Daqing[1] ; Zhu Jiajing[2] ; Chen Zhi[2] ; Liu Bicheng[4] ; Liu Huafeng[5] ; Yang Qiongqiong[6] ; Li Hua[7] ; Xu Gang[8] ; Weng Jianping[9] ; Kong Yaozhong[10] ; Wan Qijun[11] ; Zha Yan[12] ; Chen Chunbo[13] ; Xu Hong[14] ; Hu Ying[15] ; Shi Yongjun[16] ; Zhou Yilun[17] ; Su Guobin[18] ; Tang Ying[19] ; Gong Mengchun[20,21] ; Wang Li[1] ; Hou Fanfan[22] ; Liu Yongguo[23] ; Li Guisen[24] ;
单位： [1]Department of Nephrology and Nephrology Institute, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, 610072, Chengdu, China. [2]Knowledge and Data Engineering Laboratory of Chinese Medicine, School of Information and Software Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, China. [3]National Clinical Research Center for Kidney Disease, State Laboratory of Organ Failure Research, Division of Nephrology, Nanfang Hospital, Southern Medical University, 510515, Guangzhou, China. [4]Institute of Nephrology, Zhongda Hospital, Southeast University School of Medicine, 210000, Nanjing, China. [5]Key Laboratory of Prevention and Management of Chronic Kidney Disease of Zhanjiang City, Institute of Nephrology, Affiliated Hospital of Guangdong Medical University, 524000, Zhanjiang, China. [6]Department of Nephrology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, 510515, Guangzhou, China. [7]Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, 310000, Hangzhou, China. [8]Division of Nephrology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 430000, Wuhan, China. [9]Department of Endocrinology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, 230000, Hefei, China. [10]Department of Nephrology, the First People's Hospital of Foshan, 528000, Foshan, China. [11]The Second People's Hospital of Shenzhen, Shenzhen University, 518000, Shenzhen, China. [12]Guizhou Provincial People's Hospital, Guizhou University, 550000, Guiyang, China. [13]Department of Critical Care Medicine, Maoming People's Hospital, 525000, Maoming, China. [14]Children's Hospital of Fudan University, 200000, Shanghai, China. [15]The Second Affiliated Hospital of Zhejiang University School of Medicine, 310000, Hangzhou, China. [16]Huizhou Municipal Central Hospital, Sun Yat-Sen University, 516000, Huizhou, China. [17]Department of Nephrology, Beijing Tiantan Hospital, Capital Medical University, 100000, Beijing, China. [18]Department of Nephrology, Guangdong Provincial Hospital of Chinese Medicine, The Second Affiliated Hospital, The Second Clinical College, Guangzhou University of Chinese Medicine, 510000, Guangzhou, China. [19]The Third Affiliated Hospital of Southern Medical University, 510000, Guangzhou, China. [20]Institute of Health Management, Southern Medical University, 510000, Guangzhou, China. [21]DHC Technologies, 100000, Beijing, China. [22]National Clinical Research Center for Kidney Disease, State Laboratory of Organ Failure Research, Division of Nephrology, Nanfang Hospital, Southern Medical University, 510515, Guangzhou, China. ffhouguangzhou@163.com. [23]Knowledge and Data Engineering Laboratory of Chinese Medicine, School of Information and Software Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, China. ygliu_uestc@163.com. [24]Department of Nephrology and Nephrology Institute, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, 610072, Chengdu, China. guisenli@163.com.
出处：
DOI： 10.1038/s41467-023-39474-6
ISSN：

摘要：
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.© 2023. The Author(s).

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大类 | 1 区 综合性期刊
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大类 | 1 区 综合性期刊
小类 | 1 区 综合性期刊
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Q1 MULTIDISCIPLINARY SCIENCES
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Q1 MULTIDISCIPLINARY SCIENCES

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第一作者：
第一作者单位： [1]Department of Nephrology and Nephrology Institute, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, 610072, Chengdu, China.
推荐引用方式(GB/T 7714)：
Wu Changwei,Zhang Yun,Nie Sheng,et al.Predicting in-hospital outcomes of patients with acute kidney injury[J].NATURE COMMUNICATIONS.2023,14(1):3739.doi:10.1038/s41467-023-39474-6.
APA：
Wu Changwei,Zhang Yun,Nie Sheng,Hong Daqing,Zhu Jiajing...&Li Guisen.(2023).Predicting in-hospital outcomes of patients with acute kidney injury.NATURE COMMUNICATIONS,14,(1)
MLA：
Wu Changwei,et al."Predicting in-hospital outcomes of patients with acute kidney injury".NATURE COMMUNICATIONS 14..1(2023):3739

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