Development and external validation of a machine learning model for brain injury in pediatric patients on extracorporeal membrane oxygenation.

Journal: Critical care (London, England)
PMID: 39789565

Abstract

BACKGROUND: Patients supported by extracorporeal membrane oxygenation (ECMO) are at a high risk of brain injury, contributing to significant morbidity and mortality. This study aimed to employ machine learning (ML) techniques to predict brain injury in pediatric patients ECMO and identify key variables for future research.

Authors

  • Bixin Deng
    Department of Pediatric, West China Second University Hospital, Sichuan University, Chengdu, China.
  • Zhe Zhao
    Paul G. Allen School for Global Animal Health, Washington State University, Pullman, WA, United States.
  • Tiechao Ruan
    Department of Pediatric, West China Second University Hospital, Sichuan University, Chengdu, China.
  • Ruixi Zhou
    School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China.
  • Chang'e Liu
    Department of Nutrition, The Seventh Medical Center of Chinese PLA General Hospital, Beijing, China.
  • Qiuping Li
    Newborn Intensive Care Unit, Faculty of Pediatrics, the Seventh Medical Center of PLA General Hospital, Beiing, China. zhjhospital@163.com.
  • Wenzhe Cheng
    Surgical Care Unit, Children's Hospital Affiliated to Zhengzhou University, Henan Children's Hospital, Zhengzhou, China.
  • Jie Wang
  • Feng Wang
    Department of Oncology, Binzhou Medical University Hospital, Binzhou, Shandong, China.
  • Haixiu Xie
    Center for Cardiac Intensive Care, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
  • Chenglong Li
    Beijing Advanced Innovation Center for Food Nutrition and Human Health, College of Veterinary Medicine, China Agricultural University, Beijing Key Laboratory of Detection Technology for Animal-Derived Food Safety, Beijing Laboratory for Food Quality and Safety, Beijing, 100193, People's Republic of China.
  • Zhongtao Du
    Center for Cardiac Intensive Care, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
  • Wenting Lu
    Zhujiang Hospital, Southern Medical University, 253 Gongye Road, Guangzhou, Guangdong 510280, China. Electronic address: luwenting23@163.com.
  • Xiaohong Li
    College of Pharmaceutical Science, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China.
  • Junjie Ying
    Institute of Artifical Intelligence, XiaMen University, No. 422, Siming South Road, XiaMen, 361005, Fujian, China.
  • Tao Xiong
    State Key Laboratory of Food Science & Technology, No. 235 Nanjing East Road, Nanchang, Jiangxi, 330047, PR China; School of Food Science & Technology, Nanchang University, No. 235 Nanjing East Road, Nanchang, Jiangxi, 330047, PR China. Electronic address: xiongtao0907@163.com.
  • Xiaotong Hou
    Center for Cardiac Intensive Care, Beijing Anzhen Hospital, Capital Medical University, Beijing, China. xt.hou@ccmu.edu.cn.
  • Xiaoyang Hong
    Pediatric Intensive Care Unit, Faculty of Pediatric, The Seventh Medical Center of Chinese PLA General Hospital, Beijing, China. jyhongxy@163.com.
  • Dezhi Mu
    Department of Pediatric, West China Second University Hospital, Sichuan University, Chengdu, China. mudz@scu.edu.cn.

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