Engineering (Jan 2022)
Development and Validation of a Prognostic Risk Score System for COVID-19 Inpatients: A Multi-Center Retrospective Study in China
- Ye Yuan,
- Chuan Sun,
- Xiuchuan Tang,
- Cheng Cheng,
- Laurent Mombaerts,
- Maolin Wang,
- Tao Hu,
- Chenyu Sun,
- Yuqi Guo,
- Xiuting Li,
- Hui Xu,
- Tongxin Ren,
- Yang Xiao,
- Yaru Xiao,
- Hongling Zhu,
- Honghan Wu,
- Kezhi Li,
- Chuming Chen,
- Yingxia Liu,
- Zhichao Liang,
- Zhiguo Cao,
- Hai-Tao Zhang,
- Ioannis Ch. Paschaldis,
- Quanying Liu,
- Jorge Goncalves,
- Qiang Zhong,
- Li Yan
Affiliations
- Ye Yuan
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
- Chuan Sun
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
- Xiuchuan Tang
- School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
- Cheng Cheng
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
- Laurent Mombaerts
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belval L-4367, Luxembourg
- Maolin Wang
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
- Tao Hu
- Department of Emergency, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
- Chenyu Sun
- AMITA Health Saint Joseph Hospital Chicago, Chicago, IL 60657, USA
- Yuqi Guo
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
- Xiuting Li
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
- Hui Xu
- Department of Anesthesiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
- Tongxin Ren
- Huazhong University of Science and Technology-Wuxi Research Institute, Wuxi 214174, China
- Yang Xiao
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
- Yaru Xiao
- Department of Emergency, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
- Hongling Zhu
- Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
- Honghan Wu
- Institute of Health Informatics, University College London, London NW1 2DA, UK
- Kezhi Li
- Institute of Health Informatics, University College London, London NW1 2DA, UK
- Chuming Chen
- Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen 518055, China
- Yingxia Liu
- Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen 518055, China
- Zhichao Liang
- Department of Infectious Diseases, Shenzhen Key Laboratory of Pathogenic Microbiology and Immunology, National Clinical Research Center for Infectious Disease, The Third People’s Hospital of Shenzhen (Second Hospital Affiliated with the Southern University of Science and Technology), Shenzhen 518055, China
- Zhiguo Cao
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
- Hai-Tao Zhang
- School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China
- Ioannis Ch. Paschaldis
- Department of Electrical and Computer Engineering & Division of Systems Engineering & Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
- Quanying Liu
- Division of Cardiology, Department of Internal Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
- Jorge Goncalves
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Belval L-4367, Luxembourg; Department of Plant Sciences, University of Cambridge, Cambridge CB2 1TN, UK
- Qiang Zhong
- Department of Emergency, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Corresponding authors.
- Li Yan
- Department of Emergency, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China; Corresponding authors.
- Journal volume & issue
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Vol. 8
pp. 116 – 121
Abstract
Coronavirus disease 2019 (COVID-19) has become a worldwide pandemic. Hospitalized patients of COVID-19 suffer from a high mortality rate, motivating the development of convenient and practical methods that allow clinicians to promptly identify high-risk patients. Here, we have developed a risk score using clinical data from 1479 inpatients admitted to Tongji Hospital, Wuhan, China (development cohort) and externally validated with data from two other centers: 141 inpatients from Jinyintan Hospital, Wuhan, China (validation cohort 1) and 432 inpatients from The Third People’s Hospital of Shenzhen, Shenzhen, China (validation cohort 2). The risk score is based on three biomarkers that are readily available in routine blood samples and can easily be translated into a probability of death. The risk score can predict the mortality of individual patients more than 12 d in advance with more than 90% accuracy across all cohorts. Moreover, the Kaplan–Meier score shows that patients can be clearly differentiated upon admission as low, intermediate, or high risk, with an area under the curve (AUC) score of 0.9551. In summary, a simple risk score has been validated to predict death in patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); it has also been validated in independent cohorts.
