Journal Article

Predicting in-hospital outcomes of patients with acute kidney injury

Wang, Li, Hong, Daqing, Chen, Chunbo, Hu, Ying, Zhu, Jiajing, Liu, Bicheng, Hou, Fanfan, Tang, Ying, Chen, Zhi, Shi, Yongjun, Liu, Huafeng, Nie, Sheng, Li, Guisen, Gong, Mengchun, Xu, Gang, Li, Hua, Su, Guobin, Kong, Yaozhong, Xu, Hong, Wan, Qijun, Zha, Yan, Zhang, Yun, Liu, Yongguo, Yang, Qiongqiong, Wu, Changwei, Zhou, Yilun, Weng, Jianping

2023

Overview

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. Early prediction of AKI-related clinical events and timely intervention for high-risk patients could improve outcomes. Here, the authors show a deep learning model that can identify patients with acute kidney injury (AKI) who are at high risk of death or dialysis at certain time points.

• Publisher: Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
• Subject: 631/114/2397 / 692/308/409 / 692/4022/1585/4 / Acute Kidney Injury - diagnosis / Acute Kidney Injury - etiology / Acute Kidney Injury - therapy / Death / Deep learning / Dialysis / Hemodialysis / Hospital Mortality / Hospitals / Humanities and Social Sciences / Humans / Injuries / Kidneys / Mortality / multidisciplinary / Patients / Performance prediction / Renal Dialysis - adverse effects / Retrospective Studies / Risk / Risk Factors / Risk groups / Science / Science (multidisciplinary)