Risk Identification of Long-term Complications in the Recover Patients With Severe COVID-19
试验速览
- 阶段
- 不适用
- 状态
- Enrolling By Invitation
- 发起方
- 入组人数
- 500
- 试验地点
- 1
- 主要终点
- Lung function
研究概览
简要总结
The investigators retrospectively analyze the clinical characteristics of severe COVID-19 in our hospital, and then establish a prediction model for long-term complications in patients with severe COVID-19, and strengthen follow-up to improve the prognosis of patients.
详细描述
At present, there is a lack of prediction models for the long-term complications of severe COVID-19. Therefore, the investigators used the hospital big data platform to retrospectively analyze the clinical characteristics of severe COVID-19 in our hospital, and conducted cohort follow-up of the changes in lung function including FEV1, FVC，FEV1% and DLCO, etc and and high-resolution CT of patients after discharge. COX model and other statistical methods were used to establish a prediction model for long-term complications of severe COVID-19, and early identification and intervention, strengthen follow-up, and improve the prognosis of patients.
研究设计
- 研究类型
- Observational
- 观察模型
- Case Only
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 80 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •The patient met the diagnostic criteria for severe COVID-19
排除标准
- •Pregnant women Patients who died of COVID-19 Patients younger than 18 years of age without pulmonary CT
结局指标
主要结局
Lung function
时间窗: 1 year
Pulmonary function indicators improved gradually
Imaging of the lung
时间窗: 1 year
The residual lesions in the lung were gradually absorbed
次要结局
未报告次要终点