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Diagnostic and Prognostic Model of Pulmonary Fibrosis After COVID-19 Pneumonia and Mechanism Study

Construction of a Diagnostic and Prognostic Model of Pulmonary Fibrosis in Patients After COVID-19 Pneumonia and Study on Its Mechanism

Status
UNKNOWN
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT05719038
Enrollment
200
Registered
2023-02-08
Start date
2023-01-30
Completion date
2024-12-30
Last updated
2023-02-08

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

COVID-19, Pulmonary Fibrosis

Brief summary

The infection of COVID-19 has caused serious threat to the life and health of all mankind and increased huge economic burden. According to the current statistics, the incidence of pulmonary fibrosis after COVID-19 infection is about 27.7% -87%, 81% of severe patients and 37% of moderate patients have residual lung lesions, and 53% of patients still have residual lung abnormalities one year after infection, resulting in restrictive pulmonary dysfunction and affecting the health and life of patients. Therefore, it is very important to study the diagnostic and prognostic markers of pulmonary fibrosis after infection of COVID-19. At present, relevant studies have been carried out on imagomics and serum proteomics of pulmonary fibrosis after COVID-19 infection, and serum biomarkers and imagomics marker models for diagnosing pulmonary fibrosis after COVID-19 pneumonia have been developed. However, there are few studies combining imageomics and serum proteomics, and the mechanism of pulmonary fibrosis after COVID-19 has not been fully clarified. In this study, it is planned to recruit patients with moderate, severe and critical COVID-19 pneumonia infection, collect venous blood from subjects, and perform chest HRCT follow-up. Blood samples were screened by proteomics and verified by expanded samples to screen diagnostic and prognostic markers of pulmonary fibrosis after COVID-19 infection. At the same time, based on deep learning technology, a model was developed to predict the occurrence and prognosis of pulmonary fibrosis after infection of COVID-19 combined with clinical characteristics, serum markers and AI imagomics, so as to provide ideas for further elucidating the mechanism of occurrence and development of pulmonary fibrosis after infection of COVID-19.

Interventions

DIAGNOSTIC_TESTobservational study

observational study

Sponsors

Kunming Medical University
Lead SponsorOTHER

Study design

Observational model
COHORT
Time perspective
PROSPECTIVE

Eligibility

Sex/Gender
ALL
Age
18 Years to 90 Years
Healthy volunteers
Yes

Inclusion criteria

1. age 18-90 years 2. novel coronavirus nucleic acid or antigen confirmed novel coronavirus infection 3. Meet the diagnostic criteria for moderate/severe/severe coronavirus infection in China (Trial Tenth Edition) 4. Chest CT showed that the extent of lung lesions was greater than 50%

Exclusion criteria

1. pregnant and lactating women 2. previous severe lung disease, such as known chronic lung disease: chronic obstructive pulmonary disease, asthma, interstitial lung disease, etc. 3. severe organ dysfunction: severe liver, kidney and heart dysfunction 4. severe epidemic defects (including tumors/severe rheumatism/organs, bone marrow transplantation/HIV, etc.) 5. inappropriate enrollment judged by the investigator

Design outcomes

Primary

MeasureTime frameDescription
change of pulmonary fibrosisAt the time of enrollment, The first month, the third month, the sixth month, the twelfth monthThe change of pulmonary fibrosis were evaluated

Secondary

MeasureTime frameDescription
change of protein in serumAt the time of enrollment, the third monthChanges in plasma proteins over time
changes of Lung functionAt the time of enrollment, the third month, the sixth month, the twelfth monthLung function over time

Countries

China

Contacts

Primary ContactYuqi Cheng, PhD
yuqicheng@126.com(86) 087165324888-2471
Backup ContactJianqing Zhang, PhD
ydyyzjq@163.com(86) 18988272502

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026