Severity-onset prediction of COVID-19 via artificial-intelligence analysis of multivariate factors
Yu Fu,
Lijiao Zeng,
Pilai Huang,
Mingfeng Liao,
Jialu Li,
Mingxia Zhang,
Qinlang Shi,
Zhaohua Xia,
Xinzhong Ning,
Jiu Mo,
Ziyuan Zhou,
Zigang Li,
Jing Yuan,
Lifei Wang,
Qing He,
Qikang Wu,
Lei Liu,
Yuhui Liao,
Kun Qiao
Affiliations
Yu Fu
Department of Infectious Diseases, Department of Thoracic Surgery, Department of Radiology, National Clinical Research Center for Infectious Disease, The Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen Third People's Hospital, Shenzhen, China
Lijiao Zeng
Department of Infectious Diseases, Department of Thoracic Surgery, Department of Radiology, National Clinical Research Center for Infectious Disease, The Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen Third People's Hospital, Shenzhen, China
Pilai Huang
Department of Infectious Diseases, Department of Thoracic Surgery, Department of Radiology, National Clinical Research Center for Infectious Disease, The Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen Third People's Hospital, Shenzhen, China
Mingfeng Liao
Department of Infectious Diseases, Department of Thoracic Surgery, Department of Radiology, National Clinical Research Center for Infectious Disease, The Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen Third People's Hospital, Shenzhen, China
Jialu Li
Department of Biostatistics, HuaJia Biomedical Intelligence, Shenzhen, China
Mingxia Zhang
Department of Infectious Diseases, Department of Thoracic Surgery, Department of Radiology, National Clinical Research Center for Infectious Disease, The Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen Third People's Hospital, Shenzhen, China
Qinlang Shi
Department of Infectious Diseases, Department of Thoracic Surgery, Department of Radiology, National Clinical Research Center for Infectious Disease, The Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen Third People's Hospital, Shenzhen, China
Zhaohua Xia
Department of Infectious Diseases, Department of Thoracic Surgery, Department of Radiology, National Clinical Research Center for Infectious Disease, The Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen Third People's Hospital, Shenzhen, China
Xinzhong Ning
Department of Infectious Diseases, Department of Thoracic Surgery, Department of Radiology, National Clinical Research Center for Infectious Disease, The Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen Third People's Hospital, Shenzhen, China
Jiu Mo
Department of Biostatistics, HuaJia Biomedical Intelligence, Shenzhen, China
Ziyuan Zhou
Department of Infectious Diseases, Department of Thoracic Surgery, Department of Radiology, National Clinical Research Center for Infectious Disease, The Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen Third People's Hospital, Shenzhen, China
Zigang Li
Pingshan Translational Medicine Center, Shenzhen Bay Laboratory, and State Key Laboratory of Chemical Oncogenomics, School of Chemical Biology and Biotechnology, Peking University Shenzhen Graduate School, Shenzhen, China
Jing Yuan
Department of Infectious Diseases, Department of Thoracic Surgery, Department of Radiology, National Clinical Research Center for Infectious Disease, The Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen Third People's Hospital, Shenzhen, China
Lifei Wang
Department of Infectious Diseases, Department of Thoracic Surgery, Department of Radiology, National Clinical Research Center for Infectious Disease, The Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen Third People's Hospital, Shenzhen, China
Qing He
Department of Infectious Diseases, Department of Thoracic Surgery, Department of Radiology, National Clinical Research Center for Infectious Disease, The Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen Third People's Hospital, Shenzhen, China
Qikang Wu
Department of Clinical Laboratory, The First People's Hospital of Foshan, Foshan, China; Corresponding author.
Lei Liu
Department of Infectious Diseases, Department of Thoracic Surgery, Department of Radiology, National Clinical Research Center for Infectious Disease, The Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen Third People's Hospital, Shenzhen, China; Corresponding authors.
Yuhui Liao
Molecular Diagnosis and Treatment Center for Infectious Diseases, Dermatology Hospital, Southern Medical University, Guangzhou, China; Corresponding authors.
Kun Qiao
Department of Infectious Diseases, Department of Thoracic Surgery, Department of Radiology, National Clinical Research Center for Infectious Disease, The Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen Third People's Hospital, Shenzhen, China; Corresponding author.
Progression to a severe condition remains a major risk factor for the COVID-19 mortality. Robust models that predict the onset of severe COVID-19 are urgently required to support sensitive decisions regarding patients and their treatments. In this study, we developed a multivariate survival model based on early-stage CT images and other physiological indicators and biomarkers using artificial-intelligence analysis to assess the risk of severe COVID-19 onset. We retrospectively enrolled 338 adult patients admitted to a hospital in China (severity rate, 31.9%; mortality rate, 0.9%). The physiological and pathological characteristics of the patients with severe and non-severe outcomes were compared. Age, body mass index, fever symptoms upon admission, coexisting hypertension, and diabetes were the risk factors for severe progression. Compared with the non-severe group, the severe group demonstrated abnormalities in biomarkers indicating organ function, inflammatory responses, blood oxygen, and coagulation function at an early stage. In addition, by integrating the intuitive CT images, the multivariable survival model showed significantly improved performance in predicting the onset of severe disease (mean time-dependent area under the curve = 0.880). Multivariate survival models based on early-stage CT images and other physiological indicators and biomarkers have shown high potential for predicting the onset of severe COVID-19.
