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Abnormal immunity of non-survivors with COVID-19: predictors for mortality

Overview of attention for article published in Infectious Diseases of Poverty, August 2020
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Title
Abnormal immunity of non-survivors with COVID-19: predictors for mortality
Published in
Infectious Diseases of Poverty, August 2020
DOI 10.1186/s40249-020-00723-1
Pubmed ID
Authors

Yang Zhao, Han-Xiang Nie, Ke Hu, Xiao-Jun Wu, Yun-Ting Zhang, Meng-Mei Wang, Tao Wang, Zhi-Shui Zheng, Xiao-Chen Li, Shao-Lin Zeng

Abstract

The number of coronavirus disease 2019 (COVID-19) cases has rapidly increased all over the world. Specific information about immunity in non-survivors with COVID-19 is scarce. This study aimed to analyse the clinical characteristics and abnormal immunity of the confirmed COVID-19 non-survivors. In this single-centered, retrospective, observational study, we enrolled 125 patients with COVID-19 who were died between January 13 and March 4, 2020 in Renmin Hospital of Wuhan University. A total of 414 randomly recruited patients with confirmed COVID-19 who were discharged from the same hospital during the same period served as control. The demographic, clinical characteristics and laboratory findings at admission, and treatment used in these patients were collected. The immunity-related risk factors associated with in-hospital death were tested by logistic regression models and Receiver Operating Characteristic (ROC) curve. Non-survivors (70 years, IQR: 61.5-80) were significantly older than survivors (54 years, IQR: 37-65) (P <  0.001). 56.8% of non-survivors was male. Nearly half of the patients (44.9%) had chronic medical illness. In non-survivors, hypertension (49.6%) was the most common comorbidity, followed by diabetes (20.0%) and coronary heart disease (16.0%). The common signs and symptoms at admission of non-survivors were fever (88%), followed by cough (64.8%), dyspnea (62.4%), fatigue (62.4%) and chest tightness (58.4%). Compared with survivors, non-survivors had higher white blood cell (WBC) count (7.85 vs 5.07 × 109/L), more elevated neutrophil count (6.41 vs 3.08 × 109/L), smaller lymphocyte count (0.69 vs 1.20 × 109/L) and lower platelet count (172 vs 211 × 109/L), raised concentrations of procalcitonin (0.21 vs 0.06 ng/mL) and CRP (70.5 vs 7.2 mg/L) (P < 0.001). This was accompanied with significantly decreased levels of CD3+ T cells (277 vs 814 cells/μl), CD4+ T cells (172 vs 473 cells/μl), CD8+ T cells (84 vs 262.5 cells/μl, P < 0.001), CD19+ T cells (88 vs 141 cells/μl) and CD16+ 56+ T cells (79 vs 128.5 cells/μl) (P < 0.001). The concentrations of immunoglobulins (Ig) G (13.30 vs 11.95 g/L), IgA (2.54 vs 2.21 g/L), and IgE (71.30 vs 42.25 IU/ml) were increased, whereas the levels of complement proteins (C)3 (0.89 vs 0.99 g/L) and C4 (0.22 vs 0.24 g/L) were decreased in non-survivors when compared with survivors (all P < 0.05). The non-survivors presented lower levels of oximetry saturation (90 vs 97%) at rest and lactate (2.40 vs 1.90 mmol/L) (P < 0.001). Old age, comorbidity of malignant tumor, neutrophilia, lymphocytopenia, low CD4+ T cells, decreased C3, and low oximetry saturation were the risk factors of death in patients with confirmed COVID-19. The frequency of CD4+ T cells positively correlated with the numbers of lymphocytes (r = 0.787) and the level of oximetry saturation (r = 0.295), Whereas CD4+ T cells were negatively correlated with age (r =-0.323) and the numbers of neutrophils (r = - 0.244) (all P < 0.001). Abnormal cellular immunity and humoral immunity were key features of non-survivors with COVID-19. Neutrophilia, lymphocytopenia, low CD4+ T cells, and decreased C3 were immunity-related risk factors predicting mortality of patients with COVID-19.

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Geographical breakdown

Country Count As %
Unknown 264 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 38 14%
Student > Master 27 10%
Student > Ph. D. Student 22 8%
Researcher 21 8%
Other 16 6%
Other 53 20%
Unknown 87 33%
Readers by discipline Count As %
Medicine and Dentistry 69 26%
Nursing and Health Professions 20 8%
Biochemistry, Genetics and Molecular Biology 13 5%
Pharmacology, Toxicology and Pharmaceutical Science 12 5%
Agricultural and Biological Sciences 8 3%
Other 43 16%
Unknown 99 38%