Wenjun Wang1,2,3, Mingwang Shen4, Yusha Tao4, Christopher K Fairley4,5, Qin Zhong1,2,3, Zongren Li1,2,3, Hui Chen6,7, Jason J Ong4,5, Dawei Zhang8, Kai Zhang1, Ning Xing9, Huayuan Guo1, Enqiang Qin8, Xizhou Guan10, Feifei Yang1, Sibing Zhang6,11, Lei Zhang12,13,14, Kunlun He15,16,17. 1. Key Laboratory of Ministry of Industry and Information Technology of Biomedical Engineering and Translational Medicine, Chinese PLA General Hospital, Beijing, 100853, People's Republic of China. 2. Translational Medical Research Center, Chinese PLA General Hospital, Beijing, 100853, People's Republic of China. 3. Medical Artificial Intelligence Research Center, Chinese PLA General Hospital, Beijing, 100853, People's Republic of China. 4. China-Australia Joint Research Center for Infectious Diseases, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shanxi, 710061, People's Republic of China. 5. Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia. 6. Department of Medical Information, Huoshenshan Hospital, Wuhan, Hubei, People's Republic of China. 7. Department of Medical Information, The 940th Hospital of PLA Joint Logistics Support Force, Lanzhou, People's Republic of China. 8. Department of Infectious Disease, the Fifth Medical Center, Chinese PLA General Hospital, Beijing, 100039, People's Republic of China. 9. Department of Radiology, Chinese PLA General Hospital, Beijing, 100853, People's Republic of China. 10. Department of Pulmonary and Critical Care Medicine, Chinese PLA General Hospital, Beijing, 100853, People's Republic of China. 11. Department of Medical Administration, Chinese PLA General Hospital, Beijing, 100853, People's Republic of China. 12. China-Australia Joint Research Center for Infectious Diseases, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an, Shanxi, 710061, People's Republic of China. lei.zhang1@xjtu.edu.cn. 13. Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, VIC, Australia. lei.zhang1@xjtu.edu.cn. 14. Melbourne Sexual Health Centre, Alfred Health, Melbourne, Australia. lei.zhang1@xjtu.edu.cn. 15. Key Laboratory of Ministry of Industry and Information Technology of Biomedical Engineering and Translational Medicine, Chinese PLA General Hospital, Beijing, 100853, People's Republic of China. kunlunhe@plagh.org. 16. Translational Medical Research Center, Chinese PLA General Hospital, Beijing, 100853, People's Republic of China. kunlunhe@plagh.org. 17. Medical Artificial Intelligence Research Center, Chinese PLA General Hospital, Beijing, 100853, People's Republic of China. kunlunhe@plagh.org.
Abstract
OBJECTIVES: We aimed to identify high-risk factors for disease progression and fatality for coronavirus disease 2019 (COVID-19) patients. METHODS: We enrolled 2433 COVID-19 patients and used LASSO regression and multivariable cause-specific Cox proportional hazard models to identify the risk factors for disease progression and fatality. RESULTS: The median time for progression from mild-to-moderate, moderate-to-severe, severe-to-critical, and critical-to-death were 3.0 (interquartile range: 1.8-5.5), 3.0 (1.0-7.0), 3.0 (1.0-8.0), and 6.5 (4.0-16.3) days, respectively. Among 1,758 mild or moderate patients at admission, 474 (27.0%) progressed to a severe or critical stage. Age above 60 years, elevated levels of blood glucose, respiratory rate, fever, chest tightness, c-reaction protein, lactate dehydrogenase, direct bilirubin, and low albumin and lymphocyte count were significant risk factors for progression. Of 675 severe or critical patients at admission, 41 (6.1%) died. Age above 74 years, elevated levels of blood glucose, fibrinogen and creatine kinase-MB, and low plateleta count were significant risk factors for fatality. Patients with elevated blood glucose level were 58% more likely to progress and 3.22 times more likely to die of COVID-19. CONCLUSIONS: Older age, elevated glucose level, and clinical indicators related to systemic inflammatory responses and multiple organ failures, predict both the disease progression and the fatality of COVID-19 patients.
OBJECTIVES: We aimed to identify high-risk factors for disease progression and fatality for coronavirus disease 2019 (COVID-19) patients. METHODS: We enrolled 2433 COVID-19patients and used LASSO regression and multivariable cause-specific Cox proportional hazard models to identify the risk factors for disease progression and fatality. RESULTS: The median time for progression from mild-to-moderate, moderate-to-severe, severe-to-critical, and critical-to-death were 3.0 (interquartile range: 1.8-5.5), 3.0 (1.0-7.0), 3.0 (1.0-8.0), and 6.5 (4.0-16.3) days, respectively. Among 1,758 mild or moderate patients at admission, 474 (27.0%) progressed to a severe or critical stage. Age above 60 years, elevated levels of blood glucose, respiratory rate, fever, chest tightness, c-reaction protein, lactate dehydrogenase, direct bilirubin, and low albumin and lymphocyte count were significant risk factors for progression. Of 675 severe or critical patients at admission, 41 (6.1%) died. Age above 74 years, elevated levels of blood glucose, fibrinogen and creatine kinase-MB, and low plateleta count were significant risk factors for fatality. Patients with elevated blood glucose level were 58% more likely to progress and 3.22 times more likely to die of COVID-19. CONCLUSIONS: Older age, elevated glucose level, and clinical indicators related to systemic inflammatory responses and multiple organ failures, predict both the disease progression and the fatality of COVID-19patients.
Authors: Adrian Matysek; Aneta Studnicka; Wade Menpes Smith; Michał Hutny; Paweł Gajewski; Krzysztof J Filipiak; Jorming Goh; Guang Yang Journal: Front Med (Lausanne) Date: 2022-08-01
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