Overall, the HarmonicSS project after conducting extensive research at the 3 levels of analysis, successfully addressed the previously described unmet needs of SS: i) the prevalence of major and commonly used in clinical practice clinical features were estimated in 7,551 retrospectively harmonized patients and the effect of gender, early and late disease onset, presence of cryoglobulins, absolute seronegativity, geolocation and heavy inflammatory infiltration within LMSG, on the clinical expression of the disease was studied, ii) the clinical spectrum of specific subsets of pSS patients was extensively investigated including males, cryoglobulinemic and lymphoma patients, early and late disease onset patients and those with high focus score (FS), iii) cryoglobulinemia, total ESSDAI score at SS diagnosis, salivary gland enlargement, rheumatoid factors and male gender were identified as risk factors associated with lymphoma, creating a risk stratification landscape for lymphoproliferative disorders in SS, iv) older biomarkers such as CXCL13 or traditional lymphoma predictors were validated at least to some extent and new biomarkers were discovered including miRNA200b-5p in MSG biopsy specimens and serum tissue lymphopoietin serum protein (TSLP).
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.
