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Imaging Progression of Chronic Obstructive Pulmonary Disease Using MRI and CT (MR-COPDII)

U

University Hospital Heidelberg

Status

Completed

Conditions

Chronic Obstructive Pulmonary Disease

Study type

Observational

Funder types

Other

Identifiers

NCT03591562
82DZLS24A1

Details and patient eligibility

About

In this follow-up trial, MRI and CT images of the lung will be acquired prospectively in a subcohort of 370 patients, three years after they successfully participated in the first COSYCONET subtrial with CT and MRI ("MR-COPD I", NCT (clinical.Trials.gov identifier) 02629432).

The objective is to obtain longitudinal data from a well-characterized collective of COPD patients in order to identify suitable image-based biomarkers to improve the prognosis of disease progression of COPD in comparison to clinical tests

Full description

There is evidence that both computed tomography (CT) and proton magnetic resonance imaging (1H-MRI) have the potential to detect changes in lung structure and function earlier and with higher sensitivity than currently available clinical tests. We state the hypothesis that the progression of regional lung alterations as detected with MRI and CT precedes the worsening of airflow limitation and clinical symptoms. Before the method can be recommended for patient stratification or for monitoring disease progression, final proof is needed that any changes over time correlate with clinical symptoms and that the quantitative parameters and biomarkers obtained with imaging are predictive for the further course of the disease. Therefore, a dedicated prospective longitudinal trial is required.

The primary end point of the study is to use changes in lung perfusion MRI (e.g. pulmonary blood volume, pulmonary blood flow) and CT (e.g. airway wall thickness, extent of emphysema, extent of air trapping) within a 3-year interval for the prediction of long-term disease progression as monitored by clinical tests (within the following 3 years; BODE index (BODE= body-mass index, airflow obstruction, dyspnea and exercise capacity index in chronic obstructive pulmonary disease). A progression of the disease is defined as an increase of the multidimensional 10-point BODE index by at least one point.

This is an exploratory study. The local two-sided type-I error rate is set to 5%.

Statistical analysis will be primarily conducted as a complete case analysis. Logistic regression models with dependent variable COPD progression will be used. Imaging biomarkers are used as independent variables. All models are adjusted for the prognostic factors age, sex, GOLD (GOLD= Global Initiative For Chronic Obstructive Lung Disease) stage and smoking status as well as the factor study center.

Enrollment

252 patients

Sex

All

Ages

40 to 100 years old

Volunteers

No Healthy Volunteers

Inclusion criteria

  • Patients enrolled into the COSYCONET main cohort (Impact of Systemic Manifestations/Comorbidities on Clinical State, Prognosis, Utilisation of Health care resources in Patients with COPD (COSYCONET), NCT01245933), having already successfully participated in the COSYCONET subtrial with CT and MRI performed between December 2013 and July 2016 (Image-Based Structural and Functional Phenotyping of the COSYCONET Cohort Using MRI and CT (MR-COPD), NCT 02629432)

Exclusion criteria

  • Insufficient quality of MRI and CT obtained at baseline (MR-COPD I)
  • Having undergone lung surgery (e.g. lung volume reduction, lung transplantation)
  • Moderate or severe exacerbation requiring antibiotic treatment within prior to appointment
  • Absence of consent
  • Inability to understand the intention of the project
  • Contraindications to MRI and/or CT

Trial design

252 participants in 1 patient group

COSYCONET COPD Subcohort
Description:
MRI and CT of the lung will be performed in a multi-centre subcohort of 370 patients having already participated in the precursor trial " Image-Based Structural and Functional Phenotyping of the COSYCONET Cohort Using MRI and CT (MR-COPD)", NCT 02629432.

Trial contacts and locations

10

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Data sourced from clinicaltrials.gov

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