Computed Tomography Assessment of Regional Ventilation (CURVE)

NCT ID: NCT02879773

Last Updated: 2020-02-11

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

COMPLETED

Total Enrollment

131 participants

Study Classification

OBSERVATIONAL

Study Start Date

2016-01-22

Study Completion Date

2019-07-31

Brief Summary

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This study uses CT scans to assess airflow in the lung, the scan is quick, cheap and painless. The information from the scan may help doctors tell which patients are suitable to have surgery to cure early stage lung cancer. It may also help doctors tell which patients would benefit from surgery for emphysema and diagnose types of lung disease. The investigators will follow up patients who go through surgery to test how well the scan predicts the function of the lung after surgery. The investigators will follow patients being investigated for lung disease to test how accurate the scan is at the getting the diagnosis right.

Detailed Description

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CT scans during breathing in and breathing out (CTPVe) can assess regional airflow within the lung, using newly developed software as described by Aliverti et al. This provides information about the function of each part of the lung. The investigators aim to assess the feasibility of a larger trial using CTPVe to guide treatment of lung diseases and develop the statistical models needed for use in these trials.

Surgery provides the best prospect of a cure in early Non-small cell lung cancer. In the UK only half of people with early lung cancer undergo surgery and poor lung function may be a barrier to removal of part of the lung. Many patients with lung cancer also have emphysema or other lung diseases that reduce lung function. Actual post-operative lung function is often better predicted by current methods. The investigators will assess regional airflow in the lung and compare the airflow to the patients lung function both before and after surgery to assess if CTPVe can predict postoperative lung function, enabling more people to be considered suitable for curative surgery,

Severe emphysema is debilitating, some surgical treatments can improve the symptoms of emphysema; these include lung volume reduction surgery, endobronchial valves and endobronchial coils. Each of these are only suitable for certain patients and it is difficult to predict who will benefit most. The investigators will assess regional airflow in the lung and compare the airflow to the patients lung function before and after these treatments for emphysema to assess if CTPVe can predict who will benefit from surgery.

There are hundreds of subtypes of interstitial lung disease that respond to different types of treatment but diagnosing the subtype can be very difficult. Patients may need to undergo surgery to get the diagnosis of their subtype and this is associated with major risks, including death. The investigators will assess the regional airflow in the lung and compare the pattern to the final subtype of interstitial lung disease to assess if CTPVe could mean future patients do not need surgery for diagnosis.

Conditions

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Lung Neoplasms Emphysema Lung Diseases, Interstitial Pulmonary Disease, Chronic Obstructive

Study Design

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Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Study Groups

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Lung cancer

Patients undergoing thoracic surgery for suspected or confirmed lung cancer; including wedge resection, segmentectomy, lobectomy, bilobectomy or pneumonectomy. CTPVe will be modelled to predict postoperative lung function.

CTPVe

Intervention Type RADIATION

Non contrast computed tomography (CTPVe) scan during end inspiration and end expiration in supine position

Emphysema

Patients undergoing assessment of emphysema/chronic obstructive pulmonary disease (COPD) for potential surgical intervention; including lung volume reduction surgery, endobronchial valve insertion or endobronchial coil insertion. CTPVe will be modelled to predict postoperative lung function.

CTPVe

Intervention Type RADIATION

Non contrast computed tomography (CTPVe) scan during end inspiration and end expiration in supine position

Interstitial lung disease

Patients undergoing assessment or treatment of suspected or confirmed interstitial lung disease. CTPVe will be modelled to aid diagnosis of the subtype of interstitial lung disease confirmed by histological diagnosis.

CTPVe

Intervention Type RADIATION

Non contrast computed tomography (CTPVe) scan during end inspiration and end expiration in supine position

Interventions

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CTPVe

Non contrast computed tomography (CTPVe) scan during end inspiration and end expiration in supine position

Intervention Type RADIATION

Eligibility Criteria

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Inclusion Criteria

* Patients aged 18 or over
* Undergoing assessment or treatment of parenchymal lung disease which involves CT scanning
* Able to understand the study information and provide written informed consent

Exclusion Criteria

* Pregnancy
* Inability to follow breath hold instructions for CT scan
* Body size exceeding the capacity of CT scanner
* Previous chest wall resection
* Presence of implantable device that would cause artefacts on CT images including ICD, pacemaker, internal fixation of rib fracture, ventricular assist device, spinal rods/pedicle screws, shoulder replacement
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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British Lung Foundation

OTHER

Sponsor Role collaborator

Heart of England NHS Trust

OTHER

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Principal Investigators

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Babu Naidu, MBBS

Role: PRINCIPAL_INVESTIGATOR

Heart of England NHS Foundation Trust

Locations

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Heart of England NHS Foundation Trust

Birmingham, West Midlands, United Kingdom

Site Status

Countries

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United Kingdom

References

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Aliverti A, Pennati F, Salito C, Woods JC. Regional lung function and heterogeneity of specific gas volume in healthy and emphysematous subjects. Eur Respir J. 2013 May;41(5):1179-88. doi: 10.1183/09031936.00050112. Epub 2012 Aug 9.

Reference Type BACKGROUND
PMID: 22878884 (View on PubMed)

Other Identifiers

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178070

Identifier Type: -

Identifier Source: org_study_id

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