FAIR4Health: Findable, Accessible, Interoperable and Reusable data to foster Health Research

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.This research was financially supported by the European Union’s Horizon 2020 research and innovation programme under the grant agreement No 824666 (project FAIR4Health). Also, this research has been co-supported by the Carlos III National Institute of Health, through the IMPaCT Data project (code IMP/00019), and through the Platform for Dynamization and Innovation of the Spanish National Health System industrial capacities and their effective transfer to the productive sector (code PT20/00088), both co-funded by European Regional Development Fund (FEDER) ‘A way of making Europe’.Peer reviewe

Intrathoracic malignant peripheral nerve sheath tumor with poor outcome: a case report

We report a case of intrathoracic malignant peripheral nerve sheath tumor in a 65-year old woman revealed after a few-month history of progressive dyspnea, appetite and body mass loss. The chest magnetic resonance (MR) examination revealed the presence of a large tumor occupying the mediastinum and a major portion of the right hemithorax. The diagnostic tumor sample was obtained by parasternal biopsy in local anesthesia. The surgical resection of the tumor could not be performed due to its excessive size, intrathoracic involvement and bad respiratory reserves of a patient. The chemotherapy and irradiation were performed as palliative measures. The lethal outcome appeared 10 months after the diagnosis was established

Pattern of Response to Bronchial Challenge with Histamine in Patients with Non-Atopic Cough-Variant and Classic Asthma

Background: The aim of this study was to establish whether non-atopic patients with cough variant asthma (CVA) have different pattern of response to direct bronchoconstrictors than non-atopic patients with classic asthma (CA). Method: A total of 170 patients of both sexes with stable CVA and CA were screened for the study and 153 were included. Patients with proven atopy were not included and 17 patients with worsening of their condition or with verified bronchial obstruction during screening were excluded. All included patients performed spirometry and underwent a bronchial challenge with histamine according to long-standing protocol in our laboratory. Results: Significantly higher frequency of bronchial hyper-responsiveness (BHR) was found in patients with CA than in patients with CVA (63.9% vs. 44.9%, respectively; p < 0.05). Sensitivity was significantly lower in patients with CVA (p < 0.05), while no significant difference was found in maximal response and responsiveness. Only patients with positive challenge tests were included in the analysis. Conclusion: Adult non-atopic patients with CVA and CA have a pattern of response to non-specific bronchial stimuli similar to atopic patients with same conditions, with the exception of similar maximal response, which may reflect the efficacy of previous treatment. We believe that further studies are needed to clarify the mechanisms involved in airway response to non-specific stimuli in CVA and CA, especially in non-atopic patients. Further studies should also clarify whether this response pattern has any implications on clinical presentation or on treatment options

Predicting 30-Day Readmission Risk for Patients With Chronic Obstructive Pulmonary Disease Through a Federated Machine Learning Architecture on Findable, Accessible, Interoperable, and Reusable (FAIR) Data: Development and Validation Study

BackgroundOwing 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. ObjectiveThe 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). MethodsThe 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. ResultsClinical 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. ConclusionsImplementing 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