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Early prediction of disease progression in COVID-19 pneumonia patients with chest CT and clinical characteristics

Author

Listed:
  • Zhichao Feng

    (Central South University)

  • Qizhi Yu

    (First Hospital of Changsha
    Changsha Public Health Treatment Center)

  • Shanhu Yao

    (Central South University)

  • Lei Luo

    (Central South University)

  • Wenming Zhou

    (First Hospital of Yueyang)

  • Xiaowen Mao

    (Central Hospital of Shaoyang)

  • Jennifer Li

    (University of Sydney)

  • Junhong Duan

    (Central South University)

  • Zhimin Yan

    (Central South University)

  • Min Yang

    (Central South University)

  • Hongpei Tan

    (Central South University)

  • Mengtian Ma

    (Central South University)

  • Ting Li

    (Central South University)

  • Dali Yi

    (Central South University)

  • Ze Mi

    (Central South University)

  • Huafei Zhao

    (Central South University)

  • Yi Jiang

    (Central South University)

  • Zhenhu He

    (Central South University)

  • Huiling Li

    (Central South University)

  • Wei Nie

    (Central South University)

  • Yin Liu

    (Central South University)

  • Jing Zhao

    (Central South University)

  • Muqing Luo

    (Central South University)

  • Xuanhui Liu

    (Second People’s Hospital of Hunan)

  • Pengfei Rong

    (Central South University
    Central South University)

  • Wei Wang

    (Central South University
    Central South University)

Abstract

The outbreak of coronavirus disease 2019 (COVID-19) has rapidly spread to become a worldwide emergency. Early identification of patients at risk of progression may facilitate more individually aligned treatment plans and optimized utilization of medical resource. Here we conducted a multicenter retrospective study involving patients with moderate COVID-19 pneumonia to investigate the utility of chest computed tomography (CT) and clinical characteristics to risk-stratify the patients. Our results show that CT severity score is associated with inflammatory levels and that older age, higher neutrophil-to-lymphocyte ratio (NLR), and CT severity score on admission are independent risk factors for short-term progression. The nomogram based on these risk factors shows good calibration and discrimination in the derivation and validation cohorts. These findings have implications for predicting the progression risk of COVID-19 pneumonia patients at the time of admission. CT examination may help risk-stratification and guide the timing of admission.

Suggested Citation

  • Zhichao Feng & Qizhi Yu & Shanhu Yao & Lei Luo & Wenming Zhou & Xiaowen Mao & Jennifer Li & Junhong Duan & Zhimin Yan & Min Yang & Hongpei Tan & Mengtian Ma & Ting Li & Dali Yi & Ze Mi & Huafei Zhao &, 2020. "Early prediction of disease progression in COVID-19 pneumonia patients with chest CT and clinical characteristics," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-18786-x
    DOI: 10.1038/s41467-020-18786-x
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    Cited by:

    1. Hu Li & Huarui Gong & Tsz Hung Wong & Jingkun Zhou & Yuqiong Wang & Long Lin & Ying Dou & Huiling Jia & Xingcan Huang & Zhan Gao & Rui Shi & Ya Huang & Zhenlin Chen & Wooyoung PARK & Ji Yu Li & Hongwe, 2023. "Wireless, battery-free, multifunctional integrated bioelectronics for respiratory pathogens monitoring and severity evaluation," Nature Communications, Nature, vol. 14(1), pages 1-13, December.

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