The Clinical Characteristics and Prognosis Factors of Mild-Moderate Patients With COVID-19 in a Mobile Cabin Hospital: A Retrospective, Single-Center Study
Prognosis, COVID-19, clinical dynamics, novel mobile cabin hospitals, mild-moderate patients
Background: Novel mobile cabin hospitals have been built to provide more makeshift beds for patients with COVID-19 in Wuhan. However, the characteristics of these patients needed be further described.Methods: This was a retrospective, single-center study. A total of 869 patients with confirmed COVID-19 were admitted to Wuchang Mobile Cabin Hospital in Wuhan, between February 6th, 2020 and February 20th, 2020. The final date of follow-up was March 6th, 2020. Clinical characteristics and outcome data were collected and analyzed.Results: Of 869 patients, the median age was 51 years (IQR, 40–58 years), and 377 patients (377/869; 43.4%) were men. A total of 616 patients (616/869; 70.9%) were discharged, 95 patients (95/869; 10.9%) were transferred to the designated hospital due to worsening condition (endpoint), and 158 patients (158/869; 18.2%) were still in the hospital. The incidence of the main symptoms, including fever, cough, fatigue, muscle aches, and anorexia, decreased with time. However, there were no differences in outcome among the patients with different onset times. Generally, both patients aged 45 years or older and patients with comorbidities were more likely to reach the endpoint (transfer to designated high-level hospitals due to condition worsen). In the other model, patients with the lung CT feature (e.g., ground-glass opacity, reticular/linear, air bronchogram, or consolidation shadow) were more likely to reach the endpoint.Conclusion: Older age, comorbidity, special chest CT features (e.g., ground-glass opacity, reticular/linear, air bronchogram, or consolidation shadow) are associated with poor prognosis for mild-moderate patients. The initial symptoms of mild-moderate patients may become insidious, which deserves our attention.
Jing Ye, Zhen Wang, Dan Li, Jianfang Liu, Yao Xu, Shan-Shan Guo, Jun WAN, Wei Pan, Menglin Liu, Zhen Luo, Menglong Wang, Meng-Meng Zhao, Wen Ding, Di Ye, Jishou Zhang