Townsend Alford (gunchard60)

fered during their medical visit. The prevexair application is useful in monitoring COPD patients at high risk, in order to a better assessment of exacerbations of COPD during medical visits. Further research must be carried out to evaluate this strategy in clinical practice. The presence of cardiovascular (CV) risk factors and CV disease in patients with chronic obstructive pulmonary disease (COPD) leads to worse outcomes. A number of tools are currently available to stratify the risk of adverse outcomes in these patients with COPD. This post hoc analysis evaluated the Summit Lab Score for validation as a predictor of the first episode of moderate-to-severe acute exacerbation of chronic obstructive pulmonary disease (AECOPD) and other outcomes, in patients with COPD and high arterial pulse wave velocity (aPWV). Data from a multicenter, randomized, placebo-controlled, double-blind study were retrospectively analyzed to evaluate treatment effects of once-daily fluticasone furoate/vilanterol 100/25 μg in patients with COPD and an elevated CV risk (aPWV≥11m/s) over 24 weeks. The previously derived Summit Lab Score and, secondarily, the Intermountain Risk Score (IMRS) were computed for each patient, with patients then stratified into tertiles for each score. Risk of moderate-to-setowards differences in the risk of AECOPD, which was not statistically significant. There have been calls for more knowledge of activities of daily living (ADL) performance in order to address interventions in pulmonary rehabilitation effectively. Everyday technology (ET) has become an integrated dimension of ADL, impacting the ways in which ADL is performed. To improve everyday functioning and quality of life, the use of ADL and ET use needs to be evaluated and addressed effectively in interventions. Therefore, the aim of this study was twofold 1) to explore the quality of ADL performance, and 2) to investigate the relationship between observation and self-reported ADL performance and ability to use everyday technologies in people living with COPD. This cross-sectional study involved 84 participants aged 46-87 years. Participants were recruited through healthcare centres in the Northern Region of Denmark using a convenience sampling procedure. Data were collected using standardized assessments that investigated different ADL perspectives self-reported ADL tasks and ET use, observed mototant to evaluate and target pulmonary rehabilitation. Overall, the knowledge from the present study is valuable for focusing interventions that address challenging ADL performance and ET use through relevant and realistic activities. The ability to use ET is important to evaluate and target pulmonary rehabilitation. Chronic obstructive pulmonary disease (COPD), the third leading cause of death worldwide, is often underdiagnosed. To develop machine learning methods to predict COPD using chest radiographs and a convolutional neural network (CNN) trained with near-concurrent pulmonary function test (PFT) data. Comparison is made to natural language processing (NLP) of the associated radiologist text reports. This IRB-approved single-institution retrospective study uses 6749 two-view chest radiograph exams (2012-2017, 4436 unique subjects, 54% female, 46% male), same-day associated radiologist text reports, and PFT exams acquired within 180 days. The Image Model (Resnet18 pre-trained with ImageNet CNN) is trained using frontal and lateral radiographs and PFTs with 10% of the subjects for validation and 19% for testing. The NLP Model is trained using radiologist text reports and PFTs. The primary metric of model comparison is the area under the receiver operating characteristic curve (AUC). The Image Model achieves an AUC of 0.814 for prediction of obstructive lung disease (FEV1/FVC <0.7) from chest radiographs and performs better t