Jinghua Gao1,2, Li Zhong3, Ming Wu4, Jingjing Ji2, Zheying Liu2, Conglin Wang2, Qifeng Xie2, Zhifeng Liu5,6,7. 1. The First School of Clinical Medicine, Southern Medical University, Guangzhou, 510010, China. 2. Department of Critical Care Medicine, General Hospital of Southern Theater Command of PLA, Guangzhou, 510010, China. 3. Department of Critical Care Medicine, The First Affiliated Hospital, Guizhou University of Chinese Medicine, Guiyang, 550001, China. 4. Department of Critical Care Medicine and Infection Prevention and Control, Health Science Center, The Second People's Hospital of Shenzhen & First Affiliated Hospital of Shenzhen University, Shenzhen, 518035, China. 5. The First School of Clinical Medicine, Southern Medical University, Guangzhou, 510010, China. Zhifengliu7797@163.com. 6. Department of Critical Care Medicine, General Hospital of Southern Theater Command of PLA, Guangzhou, 510010, China. Zhifengliu7797@163.com. 7. Key Laboratory of Hot Zone Trauma Care and Tissue Repair of PLA, General Hospital of Southern Theater Command of PLA, Guangzhou, 510010, China. Zhifengliu7797@163.com.
Abstract
BACKGROUND: Coronavirus disease 2019 (COVID-19) has spread around the world, until now, the number of positive and death cases is still increasing. Therefore, it remains important to identify risk factors for death in critically patients. METHODS: We collected demographic and clinical data on all severe inpatients with COVID-19. We used univariable and multivariable Cox regression methods to determine the independent risk factors related to likelihood of 28-day and 60-day survival, performing survival curve analysis. RESULTS: Of 325 patients enrolled in the study, Multi-factor Cox analysis showed increasing odds of in-hospital death associated with basic illness (hazard ratio [HR] 6.455, 95% Confidence Interval [CI] 1.658-25.139, P = 0.007), lymphopenia (HR 0.373, 95% CI 0.148-0.944, P = 0.037), higher Sequential Organ Failure Assessment (SOFA) score on admission (HR 1.171, 95% CI 1.013-1.354, P = 0.033) and being critically ill (HR 0.191, 95% CI 0.053-0.687, P = 0.011). Increasing 28-day and 60-day mortality, declining survival time and more serious inflammation and organ failure were associated with lymphocyte count < 0.8 × 109/L, SOFA score > 3, Acute Physiology and Chronic Health Evaluation II (APACHE II) score > 7, PaO2/FiO2 < 200 mmHg, IL-6 > 120 pg/ml, and CRP > 52 mg/L. CONCLUSIONS: Being critically ill and lymphocyte count, SOFA score, APACHE II score, PaO2/FiO2, IL-6, and CRP on admission were associated with poor prognosis in COVID-19 patients.
BACKGROUND:Coronavirus disease 2019 (COVID-19) has spread around the world, until now, the number of positive and death cases is still increasing. Therefore, it remains important to identify risk factors for death in critically patients. METHODS: We collected demographic and clinical data on all severe inpatients with COVID-19. We used univariable and multivariable Cox regression methods to determine the independent risk factors related to likelihood of 28-day and 60-day survival, performing survival curve analysis. RESULTS: Of 325 patients enrolled in the study, Multi-factor Cox analysis showed increasing odds of in-hospital death associated with basic illness (hazard ratio [HR] 6.455, 95% Confidence Interval [CI] 1.658-25.139, P = 0.007), lymphopenia (HR 0.373, 95% CI 0.148-0.944, P = 0.037), higher Sequential Organ Failure Assessment (SOFA) score on admission (HR 1.171, 95% CI 1.013-1.354, P = 0.033) and being critically ill (HR 0.191, 95% CI 0.053-0.687, P = 0.011). Increasing 28-day and 60-day mortality, declining survival time and more serious inflammation and organ failure were associated with lymphocyte count < 0.8 × 109/L, SOFA score > 3, Acute Physiology and Chronic Health Evaluation II (APACHE II) score > 7, PaO2/FiO2 < 200 mmHg, IL-6 > 120 pg/ml, and CRP > 52 mg/L. CONCLUSIONS: Being critically ill and lymphocyte count, SOFA score, APACHE II score, PaO2/FiO2, IL-6, and CRP on admission were associated with poor prognosis in COVID-19patients.
Authors: Manu Shankar-Hari; Gary S Phillips; Mitchell L Levy; Christopher W Seymour; Vincent X Liu; Clifford S Deutschman; Derek C Angus; Gordon D Rubenfeld; Mervyn Singer Journal: JAMA Date: 2016-02-23 Impact factor: 56.272
Authors: Timotius Ivan Hariyanto; Karunia Valeriani Japar; Felix Kwenandar; Vika Damay; Jeremia Immanuel Siregar; Nata Pratama Hardjo Lugito; Margaret Merlyn Tjiang; Andree Kurniawan Journal: Am J Emerg Med Date: 2020-12-30 Impact factor: 2.469
Authors: J Zhang; X Wang; X Jia; J Li; K Hu; G Chen; J Wei; Z Gong; C Zhou; H Yu; M Yu; H Lei; F Cheng; B Zhang; Y Xu; G Wang; W Dong Journal: Clin Microbiol Infect Date: 2020-04-15 Impact factor: 8.067
Authors: Jie Li; Daniel Q Huang; Biyao Zou; Hongli Yang; Wan Zi Hui; Fajuan Rui; Natasha Tang Sook Yee; Chuanli Liu; Sanjna Nilesh Nerurkar; Justin Chua Ying Kai; Margaret Li Peng Teng; Xiaohe Li; Hua Zeng; John A Borghi; Linda Henry; Ramsey Cheung; Mindie H Nguyen Journal: J Med Virol Date: 2020-08-25 Impact factor: 20.693
Authors: Carlos Jiménez-Cortegana; Flora Sánchez-Jiménez; Antonio Pérez-Pérez; Nerissa Álvarez; Alberto Sousa; Luisa Cantón-Bulnes; Teresa Vilariño-García; Sandra Fuentes; Salomón Martín; Marta Jiménez; Antonio León-Justel; Luis de la Cruz-Merino; José Garnacho-Montero; Víctor Sánchez-Margalet Journal: Front Immunol Date: 2022-01-28 Impact factor: 7.561
Authors: Zachary R Bergman; Michael Usher; Andrew Olson; Jeffrey G Chipman; Melissa E Brunsvold; Greg Beilman; Christopher Tignanelli; Elizabeth R Lusczek Journal: JAMA Netw Open Date: 2022-03-01