RESUMO
Bronchiectasis has been a largely overlooked disease area in respiratory medicine. This is reflected by a shortage of large-scale studies and lack of approved therapies, in turn leading to a variation of treatment across centres. BronchUK (Bronchiectasis Observational Cohort and Biobank UK) is a multicentre, prospective, observational cohort study working collaboratively with the European Multicentre Bronchiectasis Audit and Research Collaboration project. The inclusion criteria for patients entering the study are a clinical history consistent with bronchiectasis and computed tomography demonstrating bronchiectasis. Main exclusion criteria are 1) patients unable to provide informed consent, 2) bronchiectasis due to known cystic fibrosis or where bronchiectasis is not the main or co-dominant respiratory disease, 3) age <18 years, and 4) prior lung transplantation for bronchiectasis. The study is aligned to standard UK National Health Service (NHS) practice with an aim to recruit a minimum of 1500 patients from across at least nine secondary care centres. Patient data collected at baseline includes demographics, aetiology testing, comorbidities, lung function, radiology, treatments, microbiology and quality of life. Patients are followed up annually for a maximum of 5 years and, where able, blood and/or sputa samples are collected and stored in a central biobank. BronchUK aims to collect robust longitudinal data that can be used for analysis into current NHS practice and patient outcomes, and to become an integral resource to better inform future interventional studies in bronchiectasis.
RESUMO
Chronic Obstructive Pulmonary Disease (COPD) is one of the leading causes of mortality worldwide and is a major contributor to the number of emergency admissions in the UK. We introduce a modelling framework for the development of early warning systems for COPD emergency admissions. We analyse the number of COPD emergency admissions using a Poisson generalised linear mixed model. We group risk factors into three main groups, namely pollution, weather and deprivation. We then carry out variable selection within each of the three domains of COPD risk. Based on a threshold of incidence rate, we then identify the model giving the highest sensitivity and specificity through the use of exceedance probabilities. The developed modelling framework provides a principled likelihood-based approach for detecting the exceedance of thresholds in COPD emergency admissions. Our results indicate that socio-economic risk factors are key to enhance the predictive power of the model.
