The Clinical Significance of CT-based Biomarkers in the Classification and Prognosis of Bronchiectasis
NCT ID: NCT06346938
Last Updated: 2025-08-19
Study Results
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Basic Information
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RECRUITING
300 participants
OBSERVATIONAL
2024-03-30
2027-03-26
Brief Summary
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Detailed Description
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It has been demonstrated that chronic sputum aggregation or destruction of the airway wall leads to the appearance of emphysema, mucus plugs, and pulmonary solid changes in the lung parenchyma adjacent to the diseased bronchus. Emphysema and bronchiectasis share the same pathogenesis, such as chronic inflammation and alpha-1 trypsin deficiency, etc., and to some extent a vicious circle of mutual exacerbation. Thus subjects with coexisting emphysema tend to have a higher likelihood of infection compared to subjects with bronchiectasis without manifestations of emphysema. Emphysema is also recognized as an independent risk factor for 5-year mortality in patients with bronchiectasis. Meanwhile, high mucus secretion, another major manifestation in patients with bronchiectasis, which may be visualized on imaging as the formation of mucus plugs or dendritic signs, is a contributing factor to chronic obstruction in bronchiectasis and also appears to be the best predictor of mortality in patients with bronchiectasis. It can be concluded that different imaging manifestations in patients with bronchiectasis are closely related to clinical manifestations and prognosis. In this study, we attempted to establish a new assessment system of imaging severity by analyzing the imaging manifestations of patients with bronchiectasis and conducting a 3-year follow-up of the patients with bronchiectasis to further understand the clinical significance of imaging in the staging of bronchiectasis and the prognosis of the disease.
According to the inclusion and exclusion criteria, patients with confirmed diagnosis of bronchiectasis attending Union Hospital of Tongji Medical College, Huazhong University of Science and Technology between March 31, 2024 and March 31, 2027 were included in this study. At the time of enrollment, the researchers collected the subjects' demographic information, such as gender, age, height (m), weight (kg), clinical symptoms, such as cough, sputum, hemoptysis, dyspnea, blood test results, such as red blood cell count (10\^12/L), hemoglobin (g/L), white blood cells (10\^9/L), CRP (mg/L), sputum culture, and lung function test results, such as FVC (L), FVC (L), FVC (L), FVC (L), FVC (L), CRP (M), FVC (L), and sputum culture. , such as FVC (L), FEV1 (L), FEV1/FVC (%), and lung imaging (CT, etc.), such as type of bronchiectasis; degree of bronchiectasis, number of lung segments involved, grade of lung segments involved, and thickness of tubular wall; site of emphysema, its severity, number of segments involved, and its typology; site of mucus plugs, number of lung segments involved; lung abscesses; lung solids; and lung macroglossia.
Through the collection of the above data, the investigators will score the imaging and clinical manifestations of the patients, such as Bhalla score, Reiff score, BSI, FACED, emphysema grading, mucus plug score, etc. The subjects were also followed up every six months for three years after enrollment to obtain the number of acute exacerbations, number of hospitalizations, and patient survival per year in order to derive the relationship between imaging manifestations and prognosis and staging of bronchiectasis.
METHODS: In this study, blood, sputum, lung function and lung CT results were collected from the subjects during their normal visit to the clinic without any intervention and without any additional tests performed by the subjects. Where lung CT scores were scored according to Bhalla scoring criteria, Reiff scoring criteria respectively and scored by two medical imaging physicians.
Emphysema:The distribution of emphysema can be determined by a radiologist's qualitative, visual assessment of the scanned image or quantitatively by CT imaging.The Fleischner Society has standardized the description of visually determined emphysema patterns. Specific software is available that automatically identifies the lungs, traces their contours, and determines a histogram of lung attenuation values that can be used to distinguish between non-emphysematous and emphysematous lung tissue. In healthy lungs, attenuation values are most often distributed between -750 and -850 HU, with an average attenuation of -789 HU. Negative values for emphysema on CT are usually expressed as a percentage of low attenuation areas below a given threshold, with larger values indicating more emphysema. Below -910 HU, -950 HU, and -960 HU are usually considered to be associated with pathologic emphysema. The first value is the most accepted to define mild emphysema, while the last two can be used to define severe emphysema. Large non-AATD COPD clinical studies such as COPDGene (COPD Genetic Epidemiology), SPIROMICS (Subgroups and Intermediate Outcome Indicators in COPD Studies), and ECLIPSE (Longitudinal Evaluation of COPD to Determine Predictive Surrogate Endpoints) have mostly used -950 HU as their threshold for defining emphysema. Therefore, 950 HU was used as the threshold for emphysema in this study.
Mucus plug score:The score is based on counting the number of lung segments with mucus plugs completely blocking middle-to-large-sized airways (i.e., -2-10 mm-lumen diameter) on computed tomography scans. Readers recorded the number of lung segments with mucus plugs in each lobe, with the lingula as a separate lobe. The score ranges from O (no mucus plugs seen in any lung segments) to 18 (all lung segments with mucus plugs). The employed bronchial nomenclature consists of 18 lung segments (right lung: 3, 2, and 5 lung segments in the upper, middle, and lower lobes, respectively; left lung: 2, 2, and 4 lung segments in the upper lobe,lingula, and lower lobe, respectively).
Statistical analysis and statistical methods: The data obtained during the study period were pre-organized. For continuous data, normality test was first executed. If all groups conformed to the normality values, the Student's t-test was used for comparison between groups. Otherwise, nonparametric Wilcoxon rank sum test was considered. For categorical variables, the χ2 test was used. Multifactor logistic regression analysis was performed for statistically significant data. The effect of different nutritional status on the prognosis of subjects with bronchiectasis was analyzed using the subject work characteristics (ROC) and Delong's method, and the difference was statistically significant at P\<0.05.
Statistical analysis of all data was performed by SPSS (IBM SPSS Statistics 26.0, SPSS Inc., Chicago, IL) and R language (version 4.1.3, www.R-project.org/). All statistical tests were two-sided and statistical significance was set at 0.05.
Conditions
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Study Design
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COHORT
PROSPECTIVE
Eligibility Criteria
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Inclusion Criteria
* Can cooperate with CT scan;
* Bronchiectasis confirmed by high-resolution computed tomography of the lungs;
* Can provide written informed consent
Exclusion Criteria
* Patients with lung cancer, asthma and other respiratory diseases;
* Tractive bronchiectasis caused by pulmonary fibrosis;
* Patients with malignant tumors or serious dysfunction of the heart, brain, kidney and other important systems can not cooperate with the completion of this study;
* Lack of important clinical information.
18 Years
100 Years
ALL
No
Sponsors
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Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
OTHER
Responsible Party
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Wang xiaorong
Internal medicine physician
Principal Investigators
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xiaorong Wang
Role: PRINCIPAL_INVESTIGATOR
Union Hospital, Tongji Medical College, Huazhong University of Science and Technology
Locations
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Wuhan Union Hospital
Wuhan, Hubei, China
Countries
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Central Contacts
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Facility Contacts
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References
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Quint JK, Millett ER, Joshi M, Navaratnam V, Thomas SL, Hurst JR, Smeeth L, Brown JS. Changes in the incidence, prevalence and mortality of bronchiectasis in the UK from 2004 to 2013: a population-based cohort study. Eur Respir J. 2016 Jan;47(1):186-93. doi: 10.1183/13993003.01033-2015. Epub 2015 Nov 5.
Monteagudo M, Rodriguez-Blanco T, Barrecheguren M, Simonet P, Miravitlles M. Prevalence and incidence of bronchiectasis in Catalonia, Spain: A population-based study. Respir Med. 2016 Dec;121:26-31. doi: 10.1016/j.rmed.2016.10.014. Epub 2016 Oct 25.
Tiddens HAWM, Meerburg JJ, van der Eerden MM, Ciet P. The radiological diagnosis of bronchiectasis: what's in a name? Eur Respir Rev. 2020 Jun 17;29(156):190120. doi: 10.1183/16000617.0120-2019. Print 2020 Jun 30.
Bonavita J, Naidich DP. Imaging of bronchiectasis. Clin Chest Med. 2012 Jun;33(2):233-48. doi: 10.1016/j.ccm.2012.02.007.
Aliberti S, Goeminne PC, O'Donnell AE, Aksamit TR, Al-Jahdali H, Barker AF, Blasi F, Boersma WG, Crichton ML, De Soyza A, Dimakou KE, Elborn SJ, Feldman C, Tiddens H, Haworth CS, Hill AT, Loebinger MR, Martinez-Garcia MA, Meerburg JJ, Menendez R, Morgan LC, Murris MS, Polverino E, Ringshausen FC, Shteinberg M, Sverzellati N, Tino G, Torres A, Vandendriessche T, Vendrell M, Welte T, Wilson R, Wong CA, Chalmers JD. Criteria and definitions for the radiological and clinical diagnosis of bronchiectasis in adults for use in clinical trials: international consensus recommendations. Lancet Respir Med. 2022 Mar;10(3):298-306. doi: 10.1016/S2213-2600(21)00277-0. Epub 2021 Sep 24.
Wu FJ, Qi Q, Hu Q, Xu LS. [Correlation between chest CT features and clinical characteristics of patients with bronchiectasis]. Zhonghua Yi Xue Za Zhi. 2019 Oct 15;99(38):2982-2988. doi: 10.3760/cma.j.issn.0376-2491.2019.38.004. Chinese.
Chassagnon G, Brun AL, Bennani S, Chergui N, Freche G, Revel MP. [Bronchiectasis imaging]. Rev Pneumol Clin. 2018 Oct;74(5):299-314. doi: 10.1016/j.pneumo.2018.09.009. Epub 2018 Oct 19. French.
Flume PA, Chalmers JD, Olivier KN. Advances in bronchiectasis: endotyping, genetics, microbiome, and disease heterogeneity. Lancet. 2018 Sep 8;392(10150):880-890. doi: 10.1016/S0140-6736(18)31767-7.
Gao L, Qin KR, Li T, Wang HL, Pang M. The clinical phenotype of bronchiectasis and its clinical guiding implications. Exp Biol Med (Maywood). 2021 Feb;246(3):275-280. doi: 10.1177/1535370220972324. Epub 2020 Nov 26.
Martinez-Garcia MA, Olveira C, Maiz L, Giron RM feminine, Prados C, de la Rosa D, Blanco M, Agusti A. Bronchiectasis: A Complex, Heterogeneous Disease. Arch Bronconeumol (Engl Ed). 2019 Aug;55(8):427-433. doi: 10.1016/j.arbres.2019.02.024. Epub 2019 Apr 18. English, Spanish.
Pasteur MC, Bilton D, Hill AT; British Thoracic Society Bronchiectasis non-CF Guideline Group. British Thoracic Society guideline for non-CF bronchiectasis. Thorax. 2010 Jul;65 Suppl 1:i1-58. doi: 10.1136/thx.2010.136119.
Dicker AJ, Lonergan M, Keir HR, Smith AH, Pollock J, Finch S, Cassidy AJ, Huang JTJ, Chalmers JD. The sputum microbiome and clinical outcomes in patients with bronchiectasis: a prospective observational study. Lancet Respir Med. 2021 Aug;9(8):885-896. doi: 10.1016/S2213-2600(20)30557-9. Epub 2021 May 4.
Martinez-Garcia MA, Miravitlles M. Bronchiectasis in COPD patients: more than a comorbidity? Int J Chron Obstruct Pulmon Dis. 2017 May 11;12:1401-1411. doi: 10.2147/COPD.S132961. eCollection 2017.
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Other Identifiers
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2024WHXH001
Identifier Type: -
Identifier Source: org_study_id
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