Search

Background The coronavirus disease 2019 (COVID-19) pandemic has put pressure on healthcare services, forcing the reorganisation of traditional care pathways. We investigated how physicians taking care of severe asthma patients in Europe reorganised care, and how these changes affected patient satisfaction, asthma control and future care. Methods In this European-wide cross-sectional study, patient surveys were sent to patients with a physician-diagnosis of severe asthma, and physician surveys to severe asthma specialists between November 2020 and May 2021. Results 1101 patients and 268 physicians from 16 European countries contributed to the study. Common physician-reported changes in severe asthma care included use of video/phone consultations (46%), reduced availability of physicians (43%) and change to home-administered biologics (38%). Change to phone/video consultations was reported in 45% of patients, of whom 79% were satisfied or very satisfied with this change. Of 709 patients on biologics, 24% experienced changes in biologic care, of whom 92% were changed to home-administered biologics and of these 62% were satisfied or very satisfied with this change. Only 2% reported worsening asthma symptoms associated with changes in biologic care. Many physicians expect continued implementation of video/phone consultations (41%) and home administration of biologics (52%). Conclusions Change to video/phone consultations and home administration of biologics was common in severe asthma care during the COVID-19 pandemic and was associated with high satisfaction levels in most but not all cases. Many physicians expect these changes to continue in future severe asthma care, though satisfaction levels may change after the pandemic.

[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.

Zorica Lazic,1 Ivana Stankovic,2 Branislava Milenkovic,3 Biljana Zvezdin,4 Sanja Hromis,4 Slobodan Jankovic,5 Vojislav Cupurdija1 1Faculty of Medical Sciences, University of Kragujevac, and Clinic for Pulmonology, Clinical Centre, Kragujevac, Serbia; 2Faculty of Medicine, University of Niš, and Clinic for Lung Diseases, Clinical Centre, Niš, Serbia; 3Faculty of Medicine, University of Belgrade, and Clinic for Pulmonology, Clinical Centre of Serbia, Belgrade, Serbia; 4Faculty of Medicine, University of Novi Sad, and the Institute for Pulmonary Diseases of Vojvodina, Novi Sad, Serbia; 5Faculty of Medical Sciences, University of Kragujevac, and Clinical Pharmacology Department, Clinical Centre, Kragujevac, SerbiaCorrespondence: Slobodan JankovicFaculty of Medical Sciences, University of Kragujevac, Svetozara Markovića Street, 69, Kragujevac, 34000, SerbiaEmail slobnera@gmail.comBackground: Establishing a regional/national/international registry of patients suffering from chronic obstructive pulmonary disease (COPD) is essential for both research and healthcare, because it enables collection of comprehensive real-life data from a large number of individuals.Objective: The aim of this study was to describe characteristics of COPD patients from the Serbian patient registry, and to investigate actual differences of those characteristics among the COPD phenotypes.Methods: The Serbian registry of patients with COPD was established in 2018 at University of Kragujevac, Faculty of Medical Sciences, based on an online platform. Entry in the Registry was allowed for patients who were diagnosed with COPD according to the following criteria: symptoms of dyspnea, chronic cough or sputum production, history of risk factors for COPD and any degree of persistent airflow limitation diagnosed at spirometry.Results: In the Serbian COPD registry B and D GOLD group were dominant, while among the COPD phenotypes, the most prevalent were non-exacerbators (49.4%) and the