Active clinical trials for "Lung Diseases, Obstructive"

Chronic obstructive pulmonary disease is a common respiratory condition that is characterized by inflammation of the large and small peripheral airways , the alveoli and adjacent capillary networks. Severe airflow obstruction that imposes a significant load on the respiratory system is a major manifestation of Chronic obstructive pulmonary disease. The reduced expiratory airflow also causes air trapping at the end of expiration , producing alveolar pressure that are higher than atmospheric pressure before the next breath. This condition is intrinsic positive end-expiratory pressure .

Patients with chronic obstructive pulmonary disease (COPD) are 2-3 times more likely to occur together with chronic gastrointestinal tract (GIT) diseases, such as inflammatory bowel disease (IBD) or irritable bowel syndrome (IBS). Similarly, despite many patients have no history of acute or chronic respiratory disease, up to 50% of IBD patients and 33% of IBS patients have pulmonary involvement, such as inflammation or impaired lung function. Increasing evidence indicated chronic gut and lung disease share key conceptual features with the disorder and dysregulation of the microbial ecosystem. However, the underlying mechanisms are not well understood. Our study is aimed to elucidate the intimate relationship between the gastrointestinal tract and respiratory tract, and uncover the mechanisms by which the gut microbiota affects the immune responses in the lungs, and vice versa.

This study aims to evaluate prevalence of sarcopenia and cachexia in patients with chronic cardiopulmonary disease. The investigators will also investigate metabolic disorders like glucose metabolism, presence of metabolic syndrome, body composition and histological changes in skeletal muscle and body fat. Finally, patients will be followed for clinical endpoints.

COPD and sleep apnea dyspnea syndrome represent two of the most common chronic lung diseases. It is reported that the prevalence of COPD with sleep apnea dyspnea syndrome is also very high. COPD and sleep apnea may have pathological or genetics interactions so that patients having both disorders tend to have worse outcomes than either condition alone. Data reflecting the prevalence, disease course and outcome of overlap syndrome is very limited in China. Therefore, the aim of the study is to describe the prevalence, treatment and outcome in Chinese patients and exploring the underlying interaction mechanism.

The purpose of this prospective cohort study is to determine factors involved in survival in new COPD patients treated by long-term oxygen therapy. Background: long-term oxygen therapy is indicated in patients with severe COPD. No studies have been performed in the past 20-25 years to examine the results of early clinical trials. Further studies are necessary to understand the utility of oxygen therapy in severe COPD.

This is a study of the lung health of the population of Vancouver and the extent of, and the risk factors for, chronic obstructive lung disease.

The purpose of this study is to establish the large COPD cohort and biological database in China, aiming for precision medicine to optimize diagnosis and treatment choices.

Chronic obstructive pulmonary disease is a serious disease . Exacerbations of Chronic obstructive pulmonary disease is an acute worsening condition of Chronic obstructive pulmonary disease, which always accompanied by clinical symptoms such as, shortness of breath and increased production of sputum. Respiratory infection (bacteria or viruses or mixed) is thought to be the main cause in most exacerbations.

Chronic Obstructive Pulmonary Disease (COPD) has been defined by international guidelines as a common preventable and treatable disease characterized by persistent air flow limitation and respiratory symptoms caused by exposure to gases or noxious particles. COPD is a major cause of morbidity and mortality worldwide. It is predicted to become the third leading cause of death and the fifth leading cause of disability by the year 2020.

Chronic obstructive pulmonary disease (COPD) is the third leading cause of death in the World since 2003. Many people suffer from this disease or its complications for many years and die prematurely. In the European Union, the total direct costs of respiratory diseases are estimated to be around 6% of the total healthcare budget, with COPD accounting for 56% (38.6 billion Euros) of the costs of respiratory diseases. In the natural history of COPD, many patients may experience acute exacerbations (AECOPD) that are described as episodes of sustained worsening of the respiratory symptoms that result in additional therapy. These episodes of exacerbation that often require been seen in the emergency department and/or a hospital admission are associated with significant morbidity and mortality; they are responsible for a significant portion of the economic burden of the disease too. The pharmacological approach used in the management of AECOPD (inhaled bronchodilators, corticosteroids, and antibiotics), has the objective to minimize the negative impact of the current exacerbation and to prevent subsequent events. Despite the collaborative effort between the European Respiratory Society, the American Thoracic Society, and others to provide clinical recommendations for the prevention of AECOPD, there is still a considerable number of patients that are prone to suffer from recurrent exacerbations and to experience a more severe impairment in health status. Based on all the above, the aim is to identify the factors potentially associated with hospital admission in patients with AECOPD in English, French, German, and Spanish, speaking countries, and to develop a predictive model that predicts the risk of hospitalization in this group of patients, by using artificial intelligence. In this study proposes to take advantage of SAVANA, a new clinical platform, created in the context of the era of electronic medical records (EMRs), to analyse the information included in the electronic medical files (i.e., big data). This clinical platform is a powerful free-text analysis engine, capable of meaningfully interpreting the contents of the EMRs, regardless of the management system in which they operate. In this context, this machine learning analytical method can be used to build a flexible, customized and automated predictive model using the information available in EMRs.