Active clinical trials for "Pulmonary Disease, Chronic Obstructive"

This single and repeat increasing dose study will collect information on safety, tolerability and drug levels in the body of the CCI15106 inhalation powder. The study will also look at the level of CCI15106 that will be released into the air and may be found in the blood of the people standing around the person inhaling it (bystanders). This is a two-part study in which Part 1 will enroll healthy subjects and look at environmental and bystander exposure and Part 2 will enroll subjects with moderate COPD. Approximately 36 healthy subjects and approximately 22 subjects with COPD will be randomized in this study for dosing. The total study duration will be 82 days for Cohort A Part 1; 75 days for Cohort B Part 1 and Cohort C Part 1; 77 days for Cohort A Part 2; and 90 days for Cohort B Part 2.

Patients with COPD (chronic bronchitis and/or emphysema) are known to be at an increased risk of heart disease and death due to heart attacks. There are several possible reasons for this, one of which is an increased tendency of the blood to clot, that can give rise to blood clot formation in the coronary arteries, and lead to heart attack. Medications such as Aspirin and another new blood thinning tablet called Ticagrelor are already used for patients with heart attacks. Given that patients with COPD are at higher risk of heart attack, the investigators wish to see if these tablets that can prevent blood clot formation in heart arteries might also prevent heart attacks happening in COPD patients. The investigators hope to understand the effects by measuring clotting and inflammation in the blood. All patients will be followed up for 6-months. In addition the investigators wish to study COPD patients who do not have a high risk of developing future heart problems using the QRISK score to study their well being over a 1 year period to see if they might also benefit from blood thinning medications.

The purpose of this project is to validate quantitative lung structure assessment using an automated analysis software (VIDA), for application to low dose computed tomography (LDCT) acquired for lung cancer screening. Currently the software runs on standard dose CT data. In addition, it is the plan to incorporate algorithms into the software to address assessment of any identified pulmonary lesions.

The study hypothesis is that symptomatic current and former smokers with spirometric values within the normal range (post-bronchodilator FEV1/FVC≥0.70 and post-BD FVC ≥ 70% predicted will still derive symptomatic benefit from long-acting bronchodilator therapy even though they are excluded from current GOLD guideline recommendations.

The aim of this interventional, randomized, double-blind, monocentric, cross-over study is to quantify the possible deleterious effect on the cardiac autonomic nervous system control of two long-acting anticholinergic bronchodilatators (tiotropium and glycopyrronium) and one beta-2 agonist long-acting bronchodilatator (indacaterol ) in patients with mild COPD.

Structural changes in skeletal muscles of patients with chronic obstructive pulmonary disease (COPD) have been linked to impaired muscle function, reduced exercise capacity, and increased mortality associated with this disease. Muscle dysfunction also contributes to dyspnea intensity and the ability to sustain exercise, making aerobic exercise training intolerable at the intensity and/or volume required to achieve clinically important changes. Resistance training (RT) is an attractive exercise modality because it is efficacious and more tolerable initially. No work has examined whether a short-term RT program can reduce exertional symptoms and improve exercise tolerance (dyspnea and leg fatigue) in patients with COPD.

To assess the effect of treatment with Glycopyronnium (GP) MDI administered twice daily (BID) and Formoterol Fumarate (FF) MDI administered BID on specific image-based airway volumes and resistance in subjects with moderate to severe chronic obstructive pulmonary disease (COPD) following chronic dosing after approximately two weeks treatment.

Background: Non-invasive ventilation (NIV) reduces respiratory load and demands on peripheral muscles. Methods: This study aims to evaluate the acute effects of bi-level NIV on peripheral muscle function during isokinetic exercise and aerobic performance in chronic obstructive pulmonary disease (COPD) patients. This is a pilot crossover study performed with a non-probabilistic sample of 14 moderate to very severe COPD patients. Procedures carried out in two days. Dyspnea, quality of life, lung function, respiratory muscle strength, functional capacity (6-minute walk test - 6MWT), and isokinetic assessment of the quadriceps were assessed. Blood samples (lactate, lactate dehydrogenase, and creatine kinase concentration) were also collected. Right after, NIV was performed for 30 minutes (bi-level or placebo, according to randomization) followed by new blood sample collection, 6MWT, and isokinetic dynamometer tests. Before and after evaluations, the subjective perception of dyspnea and fatigue in the lower limbs was quantified. After a wash-out period of seven days, participants returned, and all assessments were performed again.

the study aims to investigate effectiveness of thoracic cage mobilizations on the chronic obstructive pulmonary diseases.

Idiopathic pulmonary fibrosis (IPF), non-specific interstitial pneumonia (NSIP), and chronic obstructive pulmonary disease (COPD) are severe, progressive, irreversibly incapacitating pulmonary disorders with modest response to therapeutic interventions and poor prognosis. Prompt and accurate diagnosis is important to enable patients to receive appropriate care at the earliest possible stage to delay disease progression and prolong survival. Artificial intelligence (AI)-assisted digital lung auscultation could constitute an alternative to conventional subjective operator-related auscultation to accurately and earlier diagnose these diseases. Moreover, lung ultrasound (LUS), a relevant gold standard for lung pathology, could also benefit from automation by deep learning.