Periodic Reporting for period 3 - HarmonicSS (HARMONIzation and integrative analysis of regional, national and international Cohorts on primary Sjögren’s Syndrome (pSS) towards improved stratification, treatment and health policy making)
Reporting period: 2020-01-01 to 2020-12-31
The clinical aspects of the project, are summarized as follows: a) define distinct clinical phenotypes of the disease along with the prevalence of significant clinical, laboratory and histologic parameters in a totally harmonized cohort through integration analysis, b) explore potential associations between the histopathology of labial minor salivary gland (LMSG) biopsy and the clinical phenotype of the disease, c) validate old and discover novel biomarkers, d) study and develop lymphoma classification and prediction models, and e) employ data driven approaches contributing to the transition into the era of precision medicine.
Major progress and innovation were achieved from the technical point of view. The data sharing assessment module provides functionalities for the upload of legal and ethical documents, the evaluation of GDPR compliance of these documents and the subsequent application of beyond the state-of-the-art data curator mechanisms to enhance the quality of clinical data in terms of accuracy, relevance and completeness. The data sharing management module handles data access to the private cloud space of each data provider. The cohort data harmonization module provides functionalities for aligning the heterogeneous datasets using ontology-based mechanisms. The data mining services include several tools, which can support both local and federated learning scenarios. These functionalities have been used for clinical scenarios in order to address the clinical unmet needs of pSS. The genetic data analytics services module offers functionalities for mining association rules with pre-defined support and confidence intervals across genetic datasets towards the discovery of associations between clinical sub phenotypes and SNPs. The visual analytics module provides tools for extracting hidden patterns within the cohort data through the implementation of high-performance visualization methods. The social media analytics services module offers a single-point access to pSS-related social media posts and related content with filtering options. The health policies impact assessment services module enables the evaluation of user-defined health policy scenarios by assessing whether health impact and cost of scenarios are positive or not in the existing healthcare systems. The patient selection tool for multinational clinical trials provides functionalities for the targeted selection of patients across the harmonized cohort data for multinational clinical trials, given a specific set of pre-defined criteria. The salivary gland ultrasonography image segmentation (SGUS) module applies deep learning algorithms to distil knowledge from SGUS images towards the automated segmentation of the salivary gland and the classification of SGUS images according to a pre-defined scoring system. Finally, the training tool provides educational material to both non-clinical and clinical experts including text, image or video.
Of high importance were also the results coming from application of health policy and process evaluation. Findings from survey data show variations in access, volumes of treatments delivered to pSS patients and also their perceived quality of life and satisfaction for SS care across Europe.