高级检索
当前位置: 首页 > 详情页

Predicting in-hospital outcomes of patients with acute kidney injury

文献详情

资源类型:
WOS体系:
Pubmed体系:

收录情况: ◇ SCIE ◇ 自然指数

机构: [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, 610054Chengdu, 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.
出处:
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).

基金:
语种:
WOS:
PubmedID:
中科院(CAS)分区:
出版当年[2022]版:
大类 | 1 区 综合性期刊
小类 | 1 区 综合性期刊
最新[2025]版:
大类 | 1 区 综合性期刊
小类 | 1 区 综合性期刊
JCR分区:
出版当年[2021]版:
Q1 MULTIDISCIPLINARY SCIENCES
最新[2023]版:
Q1 MULTIDISCIPLINARY SCIENCES

影响因子: 最新[2023版] 最新五年平均 出版当年[2021版] 出版当年五年平均 出版前一年[2020版] 出版后一年[2022版]

第一作者:
第一作者机构: [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
共同第一作者:
通讯作者:
推荐引用方式(GB/T 7714):
APA:
MLA:

资源点击量:2022 今日访问量:0 总访问量:648 更新日期:2024-07-01 建议使用谷歌、火狐浏览器 常见问题

版权所有©2020 广东省中医院 技术支持:重庆聚合科技有限公司 地址:广州市越秀区大德路111号