Clinical trial NCT05719038: Diagnostic and Prognostic Model of Pulmonary Fibrosis After COVID-19 Pneumonia and Mechanism Study
Official title: Construction of a Diagnostic and Prognostic Model of Pulmonary Fibrosis in Patients After COVID-19 Pneumonia and Study on Its Mechanism
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Pulmonary Fibrosis
COVID-19
Lead sponsor: Kunming Medical University
Sponsor class: OTHER
First posted: 2023-02-08
Enrollment: 200
Sex: ALL
Minimum age (years): 18
Maximum age (years): 90
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients with newly diagnosed COVID-19 pneumonia were classified as moderate, severe and critical according to clinical classification, and underwent chest CT imaging follow-up and blood protein measurement. In the later stage, patients were divided into groups with pulmonary fibrosis and without pulmonary fibrosis according to whether pulmonary fibrosis occurred; Healthy people as healthy control groups.

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.


Eligibility criteria: 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

Primary outcomes:
measure: change of pulmonary fibrosis
timeFrame: At the time of enrollment, The first month, the third month, the sixth month, the twelfth month
Secondary outcomes:
measure: change of protein in serum
timeFrame: At the time of enrollment, the third month
measure: changes of Lung function
timeFrame: At the time of enrollment, the third month, the sixth month, the twelfth month
Other outcomes:
Locations:
city: Kunming
country: China
facility: First Affiliated Hospital of Kunming Medical University
state: Yunnan
city: Kunming
country: China
facility: The First Affiliated Hospital of Kunming Medical University
state: Yunnan
Study officials:
References:
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. 2021 Apr 10;17(6):1565-1573. doi: 10.7150/ijbs.58825. eCollection 2021.
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 patients with new corona virus pneumonia: a cohort study. Respir Res. 2021 Jul 9;22(1):203. doi: 10.1186/s12931-021-01798-6.
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.
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.
citation: 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.