View clinical trials related to Copd Exacerbation Acute.
Filter by:Patients with Chronic Obstructive Pulmonary Disease (COPD) who are admitted to hospital are at high risk of readmission. While therapies have improved and there are evidence-based guidelines to reduce readmissions, there are significant challenges to implementation including 1) identifying all patients with COPD early in admission to ensure evidence-based, high value care is provided and 2) identifying those who are at high risk of readmission in order to effectively target resources. Using machine learning and natural language processing, we want to develop models to 1) identify all patients with COPD exacerbations admitted to hospital and 2) stratify them to distinguish those who are at high risk of readmission b) How will you undertake your work? From Toronto hospitals, we will develop a very large dataset of patient admissions for all medical conditions including exacerbations of COPD from the electronic health record. This data will include both structured data such as age, gender, medications, laboratory values, co-morbidities as well as unstructured data such as discharge summaries and physician notes. Using the dataset, we will train a model through natural language processing and machine learning to be able to identify people admitted with COPD exacerbation and identify those patients who will be at high risk of readmission within 30 days. We will test the ability of these models to determine our predictive accuracies. We will then test these models at other institutions.
Chronic obstructive pulmonary disease (COPD) is a lung disease characterized by respiratory problems and poor airflow with dyspnea and cough being the main symptoms. Acute exacerbations of COPD (AECOPD) are the most important events for patients with COPD that have a negative impact on patients´ quality of life, accelerate disease progression, and can result in hospital admissions and death. It is of major clinical importance to determine predictors of an AECOPD and to identify patients who are at high risk for developing an acute exacerbation and/or to detect the beginning of or prevent an ongoing acute exacerbation as early as possible. Until now, research in the field of AECOPD has gathered and analyzed data only after manifestation of AECOPD until recovery and most of them used a retrospective study design. Therefore, the aim of this prospective trial is to collect clinical data in patients prior to the first visible clinical signs of an AECOPD to investigate potential early predictors of an AECOPD.
The purpose for this study is to determine safety and effectiveness of the oxyhydrogen generator with nebulizer through a therapy for improving symptoms in patients with acute exacerbations of copd.
Chronic obstructive pulmonary disease (COPD) is the fourth leading cause of death worldwide, and over 90% of COPD-related deaths occurring in low- and middle-income countries (LMICs). Household air pollution (HAP) - from burning solid fuels such as wood, dung, agricultural crop waste, and coal for energy - is the primary risk factor for COPD in these settings. Biomass-related COPD has a distinct histopathology, phenotype and inflammatory profile when compared to tobacco mediated COPD. Despite the high global burden of biomass-related disease, little is known about the effectiveness of pharmacotherapies for biomass-related COPD; to date, no clinical trials have focused specifically on treatment of biomass-related COPD. This study proposes to assess the health impact of biomass-related COPD and test the effectiveness of low dose theophylline compared to standard therapy among adults with biomass-related COPD in Uganda with the aim to assess whether low-dose theophylline improves respiratory symptoms, decreases the inflammatory profile of serum biomarkers and whether administration attenuates the effect of HAP on lung function. The study additionally aims to assess whether low-dose theophylline is a cost-effective intervention based on the incremental cost-effectiveness ratio and a range of willingness to pay thresholds.
The aim of this multicenter, investigator-initiated, prospective, randomized, open-label, non-inferiority study is to evaluate a prednisone prescribing strategy, guided by eosinophil blood count compared to the standard (systematic) administration of corticosteroids, in patients with COPD exacerbation requiring ventilatory support. Patients fulfilling inclusion criteria and consenting to participate in the study, will be randomized through a random table generated electronically, to eosinophil-guided group or to control group. In the eosinophil-guided group, prednisone (1mg/kg/day for up to 5 days or during the hospital stay if less than 5 days) is administered only if the eosinophil count is >2%. If blood eosinophil count is ≤2%, no corticosteroids are given. In the control group: a treatment based on prednisone at a daily dose of 1 mg/kg will be routinely administered for a maximum of 5 days, or during the hospital stay, if it is less than 5 days. Corticosteroid treatment is taken in the morning in patients with NIV, and through the gastric tube in intubated patients. The hypothesis tested is a non-inferiority of the "eosinophil-guided strategy" compared to the standard strategy, with less exposure to corticosteroids. The primary endpoint is the proportion of unventilated patients at day 6 which is set to 50% in the control group. A pre-specified difference <10% would be a non-inferiority margin. Secondary endpoints are: Number of ICU days alive without ventilatory support within 28 days after recruitment, length of stay in intensive care Unit, the intubation rate in patients initially under NIV, Mortality in the ICU, Hospital mortality. Safety: New onset of diabetes or worsening of diabetes requiring the start or the increase in insulin therapy, Upper gastrointestinal bleeding (2 g drop of Hb requiring blood transfusion or fibroscopy), Uncontrolled hypertensive crisis requiring the introduction of new antihypertensives, ICU-acquired neuromyopathy, Nosocomial infection, Relapse rate / recurrence defined respectively by the rate of a new hospital consultation and/or admission in the week or the month following index hospitalization. Sample size calculation: In a non-inferiority study, with an incidence of the event (no ventilation at D6) of 50% in the control group ( with 10% of acceptable difference for non-inferiority), a power of 80% and alpha error <0.05, it would take 86 patients per arm by anticipating 2% of lost sight.
Chronic obstructive pulmonary disease (COPD) is a lung disease caused by cigarette smoke that affects millions of people. In the United States, COPD is the 3rd leading cause of death making it one of our most important public health problems. Some people with COPD get disease flares that are called acute exacerbations of COPD - or AECOPDs for short. When people get an AECOPD they experience increased shortness of breath, wheezing and cough; symptoms that often require urgent or emergent treatment by healthcare providers. In the most severe, life-threatening situations, people with AECOPDs are put on a ventilator in the emergency department and admitted to the intensive care unit. Most AECOPDs can be treated with low doses of medications called steroids. This is good because high doses of steroids can cause unwanted side effects. Unfortunately, recent studies suggest that the sickest people, those admitted to the intensive care unit needing ventilator support, need higher doses of steroids because they may have resistance to these important medications. The investigators are studying steroid resistance during very severe AECOPDs so that we can eventually develop better and safer therapies for these vulnerable people.
The aim of the study is to examine if automated oxygen delivery with O2matic allows for faster weaning from oxygen and better oxygen control than manually controlled oxygen therapy for patients admitted with an exacerbation of chronic obstructive pulmonary disease (COPD). Furthermore it will be tested if O2matic compared to manual control allows for faster discharge from hospital. Patients sense of security, anxiety and dyspnea will be evaluated by questionnaires.
The aim of the study is to examine if automated oxygen delivery with O2matic is better than manually controlled oxygen therapy for patients admitted to hospital with an exacerbation in Chronic Obstructive Pulmonary Disease (COPD). O2matic is a closed -loop system based on continuous non-invasive measurement of pulse and oxygen-saturation that is processed in an algorithm that controls the flow of oxygen to the patient. The primary hypothesis is that O2matic increases time within acceptable oxygen-saturation interval. Secondary hypotheses are that O2matic compared to manual control reduces time with severe hypoxia (SpO2 < 85 %), hypoxi (SpO2 below intended interval) and hyperoxia (SpO2 above intended interval).
When patients get an attack of COPD, one of the main treatments is regular nebulised medications called bronchodilators. These medications act by opening up the airways allowing patients to breathe easier and to reduce shortness of breath. Newer nebulisers may increase the amount of medication that gets into the lungs compared to the standard nebuliser usually used in hospital. This study is being done to assess whether increasing the amount of medication getting into the lungs using these newer nebulisers will help patients recover from a COPD exacerbation.