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

NCT ID: NCT05719038

Last Updated: 2023-02-08

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

UNKNOWN

Total Enrollment

200 participants

Study Classification

OBSERVATIONAL

Study Start Date

2023-01-30

Study Completion Date

2024-12-30

Brief Summary

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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.

Detailed Description

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Conditions

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Pulmonary Fibrosis COVID-19

Study Design

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Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Study Groups

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Healthy control group

observational study

Intervention Type DIAGNOSTIC_TEST

observational study

Pulmonary fibrosis after COVID-19 Pneumonia

observational study

Intervention Type DIAGNOSTIC_TEST

observational study

No pulmonary fibrosis after COVID-19 Pneumonia

observational study

Intervention Type DIAGNOSTIC_TEST

observational study

Interventions

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observational study

observational study

Intervention Type DIAGNOSTIC_TEST

Eligibility Criteria

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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
Minimum Eligible Age

18 Years

Maximum Eligible Age

90 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Sponsors

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Kunming Medical University

OTHER

Sponsor Role lead

Responsible Party

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Yuqi Cheng

Principal Investigator

Responsibility Role PRINCIPAL_INVESTIGATOR

Locations

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First Affiliated Hospital of Kunming Medical University

Kunming, Yunnan, China

Site Status

The First Affiliated Hospital of Kunming Medical University

Kunming, Yunnan, China

Site Status

Countries

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China

Central Contacts

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Yuqi Cheng, PhD

Role: CONTACT

(86) 087165324888-2471

Jianqing Zhang, PhD

Role: CONTACT

(86) 18988272502

Facility Contacts

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Yuqi Cheng, PhD

Role: primary

(86) 087165324888-2471 ext. Yuqi cheng

Yuqi Cheng, PhD

Role: primary

+86-0871-65324888

Yuqi Cheng

Role: backup

13888122013

References

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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.

Reference Type RESULT
PMID: 33907520 (View on PubMed)

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.

Reference Type RESULT
PMID: 34243776 (View on PubMed)

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.

Reference Type RESULT
PMID: 34272434 (View on PubMed)

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.

Reference Type RESULT
PMID: 34093642 (View on PubMed)

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.

Reference Type RESULT
PMID: 34451904 (View on PubMed)

Other Identifiers

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KunmingMC

Identifier Type: -

Identifier Source: org_study_id

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