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Malik A Althobiani,1,2 Bilal Khan,1 Amar J Shah,1,3 Yatharth Ranjan,4 Renata G Mendes,1,5 Amos Folarin,4 Swapna Mandal,1,3 Joanna C Porter,1,6 John R Hurst1,3 1UCL Respiratory, University College London, London, UK; 2Department of Respiratory Therapy, Faculty of Medical Rehabilitation Sciences, King Abdulaziz University, Jeddah, Saudi Arabia; 3Department of Respiratory Medicine, Royal Free London NHS Foundation Trust, London, UK; 4Department of Health Informatics and Biostatistics, Institute of Psychiatry, Psychology and Neuroscience, Kings College London, London, UK; 5Department of Physical Therapy, Cardiopulmonary Physiotherapy Laboratory, Federal University of São Carlos, São Paulo, Brazil; 6Department of Respiratory Medicine, University College London Hospital (UCLH), London, UKCorrespondence: Malik A Althobiani, UCL Respiratory, University College London, London, UK, Email malik.althobiani.20@ucl.ac.ukObjective: To investigate clinicians’ perspectives on the current use of wearable technology for detecting COPD exacerbations, and to identify potential facilitators and barriers to its adoption in clinical settings.Methods: A mixed-method survey was conducted through an online survey platform involving clinicians working with COPD patients. The questionnaires were developed by an expert panel specialising in respiratory medicine at UCL. The questionnaire evaluated clinicians’ perspectives on several aspects: the current extent of wearable technology utilisation, the perceived feasibility, and utility of these devices, as well as the potential facilitators and barriers that hinder its wider implementation.Results: Data from 118 clinicians were included in the analysis. Approximately 80% of clinicians did not currently use information from wearable devices in routine clinical care. A majority of clinicians did not have confidence in the effectiveness of wearables and their consequent impact on health outcomes. However, clinicians highlighted the potential val

Christian Kjer Heerfordt,1 Josefin Eklöf,1 Pradeesh Sivapalan,1 Truls Sylvan Ingebrigtsen,1 Tor Biering-Sørensen,2– 4 Zitta Barrella Harboe,4,5 Jesper Koefod Petersen,6,7 Christian Østergaard Andersen,8 Jonas Bredtoft Boel,9 Anne Kathrine Bock,10 Alexander G Mathioudakis,11,12 John R Hurst,13 Shailesh Kolekar,7 Sofie Lock Johansson,14 Jette Marie Bangsborg,9 Jens Otto Jarløv,9 Ram Benny Dessau,15 Christian Borbjerg Laursen,14,16 Michael Perch,4,17 Jens-Ulrik Stæhr Jensen1,4,18 1Section of Respiratory Medicine, Department of Medicine, Copenhagen University Hospital Herlev and Gentofte Hospital, Hellerup, Denmark; 2Department of Cardiology, Herlev and Gentofte Hospital, Cardiovascular Non-Invasive Imaging Research Laboratory, University of Copenhagen, Copenhagen, Denmark; 3Faculty of Biomedical Sciences, Copenhagen University, Copenhagen, Denmark; 4Department of Clinical Medicine Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; 5Department of Respiratory Medicine and Infectious Diseases, Copenhagen University Hospital, North Zealand, Denmark; 6Department of Respiratory Medicine, Zealand University Hospital Naestved, Naestved, Denmark; 7Department of Pulmonary Medicine, Zealand University Hospital, Roskilde, Denmark; 8Department of Clinical Microbiology, Hvidovre University Hospital, Hvidovre, Denmark; 9Department of Clinical Microbiology, Herlev and Gentofte Hospital, University of Copenhagen, Herlev, Denmark; 10Department of Respiratory Diseases and Allergy, Aarhus University Hospital, Aarhus, Denmark; 11The North West Lung Centre, Wythenshawe Hospital, Manchester University NHS Foundation Trust, Manchester, UK; 12Division of Infection, Immunity and Respiratory Medicine, School of Biological Sciences, The University of Manchester, Manchester Academic Health Science Centre, Manchester, UK; 13UCL Respiratory, University College London, London, UK; 14Department of Respiratory Medicine, Odense University Hospital, Odense, Denm

Diana R Tamondong-Lachica,1 Neil Skolnik,2 John R Hurst,3 Nathaniel Marchetti,4 Adrian Paul J Rabe,5,6 Maria Montes de Oca,7 Bartolome R Celli8 1College of Medicine, University of the Philippines Manila, Manila, Philippines; 2Sidney Kimmel Medical College, Thomas Jefferson University, Abington, Philadelphia, PA, USA; 3UCL Respiratory, University College London, London, UK; 4Department of Thoracic Medicine and Surgery, Temple University, Philadelphia, PA, USA; 5Department of Primary Care and Public Health, Imperial College London, London, UK; 6Biopharmaceuticals Medical, AstraZeneca, Cambridge, UK; 7Pulmonary and Thoracic Surgery Department, Universidad Central de Venezuela, School of Medicine, Centro Médico de Caracas, Caracas, Venezuela; 8Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USACorrespondence: Diana R Tamondong-Lachica, College of Medicine, University of the Philippines Manila, 547 Pedro Gil Street, Ermita, Manila, 1000 Metro Manila, Philippines, Tel +63285264170, Email drtamondonglachica@up.edu.phAbstract: In 2022, over 3 million people died of chronic obstructive pulmonary disease (COPD) and the global burden of the disease is expected to increase over the coming decades. Recommendations for the treatment and management of patients with COPD are published by the Global Initiative for Chronic Obstructive Lung Disease, and updated annually with scientific evidence-based recommendations. The 2023 updates, published in November 2022, contain key changes to recommendations for diagnosis and treatment of COPD that are anticipated to have a significant impact on clinical practice for patients with COPD. Updates to how COPD is defined and diagnosed, including the expansion of contributing factors beyond tobacco use, have the potential to lead to the diagnosis of more patients and to allow for the implementation of early interventions for patients during early stages of the disease. Simplifi

Background: Inhaled corticosteroids (ICS) are associated with an increased risk of clinical pneumonia among patients with chronic obstructive pulmonary disease (COPD). It is unknown whether the risk of microbiologically verified pneumonia such as pneumococcal pneumonia is increased in ICS users. Methods: The study population consists of all COPD patients followed in outpatient clinics in eastern Denmark during 2010–2017. ICS use was categorized into four categories based on accumulated use. A Cox proportional hazard regression model was used adjusting for age, body mass index, sex, airflow limitation, use of oral corticosteroids, smoking, and year of cohort entry. A propensity score matched analysis was performed for sensitivity analyses. Findings: A total of 21,438 patients were included. Five hundred and eighty-two (2.6%) patients acquired a positive lower airway tract sample with S. pneumoniae during follow-up. In the multivariable analysis ICS-use was associated with a dose-dependent risk of S. pneumoniae as follows: low ICS dose: HR 1.11, 95% CI 0.84 to 1.45, p = 0.5; moderate ICS dose: HR 1.47, 95% CI 1.13 to 1.90, p = 0.004; high ICS dose: HR 1.77, 95% CI 1.38 to 2.29, p < 0.0001, compared to no ICS use. Sensitivity analyses confirmed these results. Interpretation: Use of ICS in patients with severe COPD was associated with an increased and dose-dependent risk of acquiring S. pneumoniae, but only for moderate and high dose. Caution should be taken when administering high dose of ICS to patients with COPD. Low dose of ICS seemed not to carry this risk.

The morphology and distribution of airway tree abnormalities enables diagnosis and disease characterisation across a variety of chronic respiratory conditions. In this regard, airway segmentation plays a critical role in the production of the outline of the entire airway tree to enable estimation of disease extent and severity. In this study, we propose a data-centric deep learning technique to segment the airway tree. The proposed technique utilises interpolation and image split to improve data usefulness and quality. Then, an ensemble learning strategy is implemented to aggregate the segmented airway trees at different scales. In terms of segmentation performance (dice similarity coefficient), our method outperforms the baseline model by 2.5% on average when a combined loss is used. Further, our proposed technique has a low GPU usage and high flexibility enabling it to be deployed on any 2D deep learning model.