Shang Yufeng, Liu Tao, Wei Yongchang, Li Jingfeng, Shao Liang, Liu Minghui, Zhang Yongxi, Zhao Zhigang, Xu Haibo, Peng Zhiyong, Zhou Fuling, Wang Xinghuan
Department of Hematology, Zhongnan Hospital of Wuhan University, 169 Donghu Road, Wuhan 430071, PR China.
Department of Urology, Zhongnan Hospital of Wuhan University, 169 Donghu Road, Wuhan 430071, PR China.
EClinicalMedicine. 2020 Jul 3;24:100426. doi: 10.1016/j.eclinm.2020.100426. eCollection 2020 Jul.
Coronavirus disease 2019 (COVID-19) has been widely spread and caused tens of thousands of deaths, especially in patients with severe COVID-19. This analysis aimed to explore risk factors for mortality of severe COVID-19, and establish a scoring system to predict in-hospital deaths.
Patients with COVID-19 were retrospectively analyzed and clinical characteristics were compared. LASSO regression as well as multivariable analysis were used to screen variables and establish prediction model.
A total of 2529 patients with COVID-19 was retrospectively analyzed, and 452 eligible severe COVID-19 were used for finally analysis. In training cohort, the median age was 66•0 years while it was 73•0 years in non-survivors. Patients aged 60-75 years accounted for the largest proportion of infected populations and mortality toll. Anti-SARS-CoV-2 antibodies were monitored up to 54 days, and IgG levels reached the highest during 20-30 days. No differences were observed of antibody levels between severe and non-severe patients. About 60.2% of severe patients had complications. Among acute myocardial injury (AMI), acute kidney injury (AKI) and acute liver injury (ALI), the heart was the earliest injured organ, whereas the time from AKI to death was the shortest. Age, diabetes, coronary heart disease (CHD), percentage of lymphocytes (LYM%), procalcitonin (PCT), serum urea, C reactive protein and D-dimer (DD), were identified associated with mortality by LASSO binary logistic regression. Then multivariable analysis was performed to conclude that old age, CHD, LYM%, PCT and DD remained independent risk factors for mortality. Based on the above variables, a scoring system of COVID-19 (CSS) was established to divide patients into low-risk and high-risk groups. This model displayed good discrimination (AUC=0·919) and calibration (=0·264). Complications in low-risk and high-risk groups were significantly different (0·05). Use of corticosteroids in low-risk groups increased hospital stays by 4·5 days (=0·036) and durations of disease by 7·5 days (=0·012) compared with no corticosteroids.
Old age, CHD, LYM%, PCT and DD were independently related to mortality. CSS was useful for predicting in-hospital mortality and complications, and it could help clinicians to identify high-risk patients with poor prognosis.
This work was supported by the Key Project for Anti-2019 novel Coronavirus Pneumonia from the Ministry of Science and Technology, China (grant number 2020YFC0845500).
2019冠状病毒病（COVID-19）已广泛传播，导致数万人死亡，尤其是重症COVID-19患者。本分析旨在探讨重症COVID-19患者死亡的危险因素，并建立一个评分系统来预测住院死亡率。
对COVID-19患者进行回顾性分析，并比较临床特征。采用LASSO回归和多变量分析来筛选变量并建立预测模型。
共回顾性分析了2529例COVID-19患者，最终纳入452例符合条件的重症COVID-19患者进行分析。在训练队列中，患者的中位年龄为66.0岁，而死亡患者为73.0岁。60-75岁的患者在感染人群和死亡人数中占比最大。对患者的抗SARS-CoV-2抗体进行了长达54天的监测，IgG水平在20-30天达到最高。重症和非重症患者的抗体水平无差异。约60.2%的重症患者有并发症。在急性心肌损伤（AMI）、急性肾损伤（AKI）和急性肝损伤（ALI）中，心脏是最早受损的器官，而从AKI到死亡的时间最短。通过LASSO二元逻辑回归确定年龄、糖尿病、冠心病（CHD）、淋巴细胞百分比（LYM%）、降钙素原（PCT）、血清尿素、C反应蛋白和D-二聚体（DD）与死亡率相关。随后进行多变量分析得出，高龄、CHD、LYM%、PCT和DD仍然是死亡的独立危险因素。基于上述变量，建立了COVID-19评分系统（CSS），将患者分为低风险和高风险组。该模型显示出良好的区分度（AUC=0.919）和校准度（=0.264）。低风险组和高风险组的并发症有显著差异（<0.05）。与未使用糖皮质激素相比，低风险组使用糖皮质激素会使住院时间延长4.5天（=0.036），病程延长7.5天（=0.012）。
高龄、CHD、LYM%、PCT和DD与死亡率独立相关。CSS有助于预测住院死亡率和并发症，可帮助临床医生识别预后不良的高风险患者。
本研究得到了中国科学技术部抗2019新型冠状病毒肺炎重点项目（项目编号：2020YFC0845500）的支持。
