Early triage of critically ill COVID-19 patients using deep learning.

Journal: Nature communications
Published Date: Jul 15, 2020

Abstract

The sudden deterioration of patients with novel coronavirus disease 2019 (COVID-19) into critical illness is of major concern. It is imperative to identify these patients early. We show that a deep learning-based survival model can predict the risk of COVID-19 patients developing critical illness based on clinical characteristics at admission. We develop this model using a cohort of 1590 patients from 575 medical centers, with internal validation performance of concordance index 0.894 We further validate the model on three separate cohorts from Wuhan, Hubei and Guangdong provinces consisting of 1393 patients with concordance indexes of 0.890, 0.852 and 0.967 respectively. This model is used to create an online calculation tool designed for patient triage at admission to identify patients at risk of severe illness, ensuring that patients at greatest risk of severe illness receive appropriate care as early as possible and allow for effective allocation of health resources.

Authors

  • Wenhua Liang
    Department of Thoracic Surgery and Oncology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
  • Jianhua Yao
  • Ailan Chen
    China State Key Laboratory of Respiratory Disease and National Clinical Research Center for Respiratory Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
  • Qingquan Lv
    Hankou Hospital, Wuhan, China.
  • Mark Zanin
    School of Public Health, The University of Hong Kong, Hong Kong SAR, China.
  • Jun Liu
    Department of Radiology, Second Xiangya Hospital, Changsha, Hunan, China.
  • SookSan Wong
    China State Key Laboratory of Respiratory Disease and National Clinical Research Center for Respiratory Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
  • Yimin Li
    Department of Radiation Oncology, University of California Davis Medical Center, Sacramento, CA, United States.
  • Jiatao Lu
    Hankou Hospital, Wuhan, China.
  • Hengrui Liang
    China State Key Laboratory of Respiratory Disease and National Clinical Research Center for Respiratory Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
  • Guoqiang Chen
    Foshan Hospital, Foshan, China.
  • Haiyan Guo
    Foshan Hospital, Foshan, China.
  • Jun Guo
    Department of Oncology, Dongfeng Hospital, Hubei University of Medicine, Shiyan, Hubei 442008, P.R. China.
  • Rong Zhou
  • Limin Ou
    China State Key Laboratory of Respiratory Disease and National Clinical Research Center for Respiratory Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
  • Niyun Zhou
    Tencent AI Lab, Shenzhen, China.
  • Hanbo Chen
    Allen Institute for Brain Science, Seattle, WA, USA. cojoc.chen@gmail.com.
  • Fan Yang
    School of Electrical Engineering and Automation, Jiangsu Normal University, Xuzhou, China.
  • Xiao Han
    College of Chemistry, Chemical Engineering and Materials Science, Collaborative Innovation Center of Functionalized Probes for Chemical Imaging in Universities of Shandong, Key Laboratory of Molecular and Nano Probes, Ministry of Education, Shandong Provincial Key Laboratory of Clean Production of Fine Chemicals, Shandong Normal University Jinan 250014 China cyzhang@sdnu.edu.cn.
  • Wenjing Huan
    Tencent Healthcare, Shenzhen, China.
  • Weimin Tang
    Tencent Healthcare, Shenzhen, China.
  • Weijie Guan
    China State Key Laboratory of Respiratory Disease and National Clinical Research Center for Respiratory Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
  • Zisheng Chen
    China State Key Laboratory of Respiratory Disease and National Clinical Research Center for Respiratory Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
  • Yi Zhao
    Department of Biostatistics and Health Data Science, Indiana University School of Medicine.
  • Ling Sang
  • Yuanda Xu
  • Wei Wang
    State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Macau 999078, China.
  • Shiyue Li
    China State Key Laboratory of Respiratory Disease and National Clinical Research Center for Respiratory Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
  • Ligong Lu
    Zhuhai People Hospital, Zhuhai, China.
  • Nuofu Zhang
    China State Key Laboratory of Respiratory Disease and National Clinical Research Center for Respiratory Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
  • Nanshan Zhong
    Guangzhou National Laboratory, Guangzhou, China.
  • Junzhou Huang
  • Jianxing He
    Department of Thoracic Surgery and Oncology, the First Affiliated Hospital of Guangzhou Medical University, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, Guangzhou 510120, China.

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