Chronic Lung Diseases

Antimicrobial resistance (AMR) refers to the ability of microorganisms like bacteria, viruses, fungi and parasites to resist the effects of antimicrobial drugs (such as antibiotics) which are widely used as treatment. AMR poses an escalating global health threat, contributing to difficult-to-treat infections associated with increased disease spread, disability and death, as well as a substantial economic burden. In chronic lung diseases, such as bronchiectasis, Cystic fibrosis or chronic obstructive lung disease (COPD), there is a higher risk of AMR due to the exposure to frequent or prolonged courses of antibiotics to treat recurrent lung infections and exacerbations (flares of the disease), to reduce lung inflammation or to control chronic infection within the lung with suppression of colonising microbes. Most data on AMR in chronic lung diseases derive from analysing pre-existing routinely collected health data collected on a national basis which is often incomplete. Hence a prospective study is crucial to better understand and address AMR in chronic lung diseases. Prospective studies follow patients forward in time, collecting data on outcomes and allowing researcher to observe the natural history of AMR development, monitor trends and evaluate interventions. This multicentre prospective study, as part of the European Respiratory Society (ERS) Clinical Research Collaboration on Antimicrobial Resistance in Lung Disease (CRC - AMR Lung), aims to investigate the patterns of AMR in chronic lung diseases through a fully anonymous registry alongside a prospective sub-cohort study tracking individuals with chronic lung disease and known colonisation with high-priority AMR pathogens (microorganisms). This study will enable analysis of prevalence and burden of AMR within chronic lung disease alongside understand the genetic drivers of resistance, the link between the microbial genotype and antimicrobial resistance and how transmission of resistance occurs in chronic lung disease.

This is a retrospective, observational study using routinely collected information collected by Alberta Health Services. The study will identify patients with chronic disease, defined by one or more of the following conditions; diabetes mellitus, heart failure, coronary artery disease, chronic kidney disease, or chronic lung disease. Adult residents of Alberta with a chronic disease of interest present upon hospital admission and who survive to hospital discharge will be included in the study cohort. The primary outcome will be the composite of hospital readmission or death within 30 days of discharge. Secondary outcomes will include components of the composite, length of stay, patient experiences related to their hospital to home transition of care, and processes of care. Multi-level interrupted time series analysis will be used to compare outcomes before versus after implementation of the Connect Care CIS.

PNEUMO-LIBS is a multicenter, non-interventional observational study designed to evaluate whether multi-element tissue imaging by Laser-Induced Breakdown Spectroscopy (LIBS), combined with an artificial intelligence-based analysis tool called Interesting Features Finder (IFF), may help physicians better understand the possible causes of chronic lung diseases. The study focuses on adult patients with chronic lung diseases for which a lung biopsy has already been performed as part of routine medical care. These diseases include diffuse interstitial lung diseases, pulmonary granulomatoses such as sarcoidosis, and emphysema, especially when an environmental or occupational exposure is suspected but not clearly demonstrated. Some chronic lung diseases may be influenced by inhaled mineral or metallic particles, such as silica, aluminum, titanium, or other metals. However, these exposures are often difficult to document at the individual patient level. Standard clinical, radiological, and pathological investigations do not usually provide direct information on the presence and distribution of such elements within lung tissue. LIBS is an imaging technique that can detect and map chemical elements directly in tissue samples, including archived formalin-fixed paraffin-embedded biopsy blocks. In this study, lung biopsy samples will be analyzed with LIBS to search for elemental signatures that may be compatible with occupational or environmental exposures. The IFF algorithm will then be used to help interpret the LIBS data and identify rare or unexpected elemental signals. The study does not require any additional biopsy, blood test, imaging examination, treatment, or hospital visit for participants. It uses tissue samples and medical data already collected during routine care. The results of LIBS and IFF analyses will be presented to the treating physician or investigator, who will complete standardized online questionnaires at three time points: before receiving the LIBS results, after receiving the LIBS report, and after receiving the IFF-assisted interpretation. The main objective is to assess the perceived clinical utility of LIBS imaging for physicians, particularly regarding etiological understanding, possible occupational disease recognition, and prevention-oriented reasoning. Secondary objectives include assessing the added value of the IFF algorithm and evaluating the feasibility of a centralized multicenter workflow for sample transfer, LIBS analysis, IFF processing, result reporting, and questionnaire completion. The study aims to include approximately 70 patients across several French hospital centers. Its results may support the development of future diagnostic, occupational health, and environmental medicine approaches, without modifying the medical care of participants during the study.