View clinical trials related to Copd Exacerbation Acute.
Filter by:This observational study aim to characterise patients admitted to hospital with an acute respiratory condition, or acute worsening of their chronic lung condition. This will enable identification of predictors of future risk, as well as develop potential interventions targets.
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.