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Clinical Trial Details — Status: Not yet recruiting

Administrative data

NCT number NCT05719038
Other study ID # KunmingMC
Secondary ID
Status Not yet recruiting
Phase
First received
Last updated
Start date January 30, 2023
Est. completion date December 30, 2024

Study information

Verified date February 2023
Source Kunming Medical University
Contact Yuqi Cheng, PhD
Phone (86) 087165324888-2471
Email yuqicheng@126.com
Is FDA regulated No
Health authority
Study type Observational

Clinical Trial 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.


Recruitment information / eligibility

Status Not yet recruiting
Enrollment 200
Est. completion date December 30, 2024
Est. primary completion date December 30, 2024
Accepts healthy volunteers Accepts Healthy Volunteers
Gender All
Age group 18 Years to 90 Years
Eligibility 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

Study Design


Related Conditions & MeSH terms


Intervention

Diagnostic Test:
observational study
observational study

Locations

Country Name City State
China First Affiliated Hospital of Kunming Medical University Kunming Yunnan
China The First Affiliated Hospital of Kunming Medical University Kunming Yunnan

Sponsors (1)

Lead Sponsor Collaborator
Kunming Medical University

Country where clinical trial is conducted

China, 

References & Publications (5)

Bazdyrev E, Rusina P, Panova M, Novikov F, Grishagin I, Nebolsin V. Lung Fibrosis after COVID-19: Treatment Prospects. Pharmaceuticals (Basel). 2021 Aug 17;14(8):807. doi: 10.3390/ph14080807. — View Citation

Li X, Shen C, Wang L, Majumder S, Zhang D, Deen MJ, Li Y, Qing L, Zhang Y, Chen C, Zou R, Lan J, Huang L, Peng C, Zeng L, Liang Y, Cao M, Yang Y, Yang M, Tan G, Tang S, Liu L, Yuan J, Liu Y. Pulmonary fibrosis and its related factors in discharged patient — View Citation

Sardar R, Sharma A, Gupta D. Machine Learning Assisted Prediction of Prognostic Biomarkers Associated With COVID-19, Using Clinical and Proteomics Data. Front Genet. 2021 May 20;12:636441. doi: 10.3389/fgene.2021.636441. eCollection 2021. — View Citation

Xue M, Zhang T, Chen H, Zeng Y, Lin R, Zhen Y, Li N, Huang Z, Hu H, Zhou L, Wang H, Zhang XD, Sun B. Krebs Von den Lungen-6 as a predictive indicator for the risk of secondary pulmonary fibrosis and its reversibility in COVID-19 patients. Int J Biol Sci. — View Citation

Yang J, Chen C, Chen W, Huang L, Fu Z, Ye K, Lv L, Nong Z, Zhou X, Lu W, Zhong M. Proteomics and metabonomics analyses of Covid-19 complications in patients with pulmonary fibrosis. Sci Rep. 2021 Jul 16;11(1):14601. doi: 10.1038/s41598-021-94256-8. — View Citation

Outcome

Type Measure Description Time frame Safety issue
Primary change of pulmonary fibrosis The change of pulmonary fibrosis were evaluated At the time of enrollment, The first month, the third month, the sixth month, the twelfth month
Secondary change of protein in serum Changes in plasma proteins over time At the time of enrollment, the third month
Secondary changes of Lung function Lung function over time At the time of enrollment, the third month, the sixth month, the twelfth month
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