Risk Identification of Long-term Complications in the Recover Patients With Severe COVID-19
Overview
- Phase
- Not Applicable
- Intervention
- Not specified
- Conditions
- Severe COVID-19
- Sponsor
- Wuhan Central Hospital
- Enrollment
- 500
- Locations
- 1
- Primary Endpoint
- Lung function
- Status
- Enrolling By Invitation
- Last Updated
- last year
Overview
Brief Summary
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.
Detailed Description
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.
Investigators
Eligibility Criteria
Inclusion Criteria
- •The patient met the diagnostic criteria for severe COVID-19
Exclusion Criteria
- •Pregnant women Patients who died of COVID-19 Patients younger than 18 years of age without pulmonary CT
Outcomes
Primary Outcomes
Lung function
Time Frame: 1 year
Pulmonary function indicators improved gradually
Imaging of the lung
Time Frame: 1 year
The residual lesions in the lung were gradually absorbed