Background: Airway stenting (AS) commenced in Europe circa 1987 with the first placement of a dedicated silicone airway stent. Subsequently, over the last 3 decades, AS was spread throughout Europe, using different insertion techniques and different types of stents. Objectives: This study is an international survey conducted by the European Association of Bronchology and Interventional Pulmonology (EABIP) focusing on AS practice within 26 European countries. Methods: A questionnaire was sent to all EABIP National Delegates in February 2015. National delegates were responsible for obtaining precise and objective data regarding the current AS practice in their country. The deadline for data collection was February 2016. Results: France, Germany, and the UK are the 3 leading countries in terms of number of centres performing AS. These 3 nations represent the highest ranked nations within Europe in terms of gross national income. Overall, pulmonologists perform AS exclusively in 5 countries and predominately in 12. AS is performed almost exclusively in public hospitals. AS performed under general anaesthesia is the rule for the majority of institutions, and local anaesthesia is an alternative in 9 countries. Rigid bronchoscopy techniques are predominant in 20 countries. Amongst commercially available stents, both Dumon and Ultraflex are by far the most commonly deployed. Finally, 11 countries reported that AS is an economically viable activity, while 10 claimed that it is not. Conclusion: This EABIP survey demonstrates that there is significant heterogeneity in AS practice within Europe. Therapeutic bronchoscopy training and economic issues/reimbursement for procedures are likely to be the primary reasons explaining these findings.

At the cut-off value of 1505 pg/mL NT-proBNP could be used as a diagnostic marker for LVSD in acute exacerbation of COPD.

Background Owing to the nature of health data, their sharing and reuse for research are limited by legal, technical, and ethical implications. In this sense, to address that challenge and facilitate and promote the discovery of scientific knowledge, the Findable, Accessible, Interoperable, and Reusable (FAIR) principles help organizations to share research data in a secure, appropriate, and useful way for other researchers. Objective The objective of this study was the FAIRification of existing health research data sets and applying a federated machine learning architecture on top of the FAIRified data sets of different health research performing organizations. The entire FAIR4Health solution was validated through the assessment of a federated model for real-time prediction of 30-day readmission risk in patients with chronic obstructive pulmonary disease (COPD). Methods The application of the FAIR principles on health research data sets in 3 different health care settings enabled a retrospective multicenter study for the development of specific federated machine learning models for the early prediction of 30-day readmission risk in patients with COPD. This predictive model was generated upon the FAIR4Health platform. Finally, an observational prospective study with 30 days follow-up was conducted in 2 health care centers from different countries. The same inclusion and exclusion criteria were used in both retrospective and prospective studies. Results Clinical validation was demonstrated through the implementation of federated machine learning models on top of the FAIRified data sets from different health research performing organizations. The federated model for predicting the 30-day hospital readmission risk was trained using retrospective data from 4.944 patients with COPD. The assessment of the predictive model was performed using the data of 100 recruited (22 from Spain and 78 from Serbia) out of 2070 observed (records viewed) patients during the observational prospective study, which was executed from April 2021 to September 2021. Significant accuracy (0.98) and precision (0.25) of the predictive model generated upon the FAIR4Health platform were observed. Therefore, the generated prediction of 30-day readmission risk was confirmed in 87% (87/100) of cases. Conclusions Implementing a FAIR data policy in health research performing organizations to facilitate data sharing and reuse is relevant and needed, following the discovery, access, integration, and analysis of health research data. The FAIR4Health project proposes a technological solution in the health domain to facilitate alignment with the FAIR principles.

Due to the nature of health data, its sharing and reuse for research are limited by ethical, legal and technical barriers. The FAIR4Health project facilitated and promoted the application of FAIR principles in health research data, derived from the publicly funded health research initiatives to make them Findable, Accessible, Interoperable, and Reusable (FAIR). To confirm the feasibility of the FAIR4Health solution, we performed two pathfinder case studies to carry out federated machine learning algorithms on FAIRified datasets from five health research organizations. The case studies demonstrated the potential impact of the developed FAIR4Health solution on health outcomes and social care research. Finally, we promoted the FAIRified data to share and reuse in the European Union Health Research community, defining an effective EU-wide strategy for the use of FAIR principles in health research and preparing the ground for a roadmap for health research institutions. This scientific report presents a general overview of the FAIR4Health solution: from the FAIRification workflow design to translate raw data/metadata to FAIR data/metadata in the health research domain to the FAIR4Health demonstrators’ performance.