Antimicrobial resistance (AMR) is of great public health concern, causing numerous losses of lives worldwide and threatening to reverse many of the considerable strides modern medicine has made over the last century. There is a need to stratify antibiotic and alternative treatments in terms of the actual benefit for the patient, improving patient outcome and limit the impact on AMR. High quality, effective and appropriate diagnostic tests to steer appropriate use of antibiotics are available. However, implementation of these tests into daily healthcare practice is hampered due to lack of insight in the medical, technological and health economical value and limited knowledge about psychosocial, ethical, regulatory and organisational barriers to their implementation into clinical practice. VALUE-Dx will define and understand these value indicators and barriers to adoption of diagnostics of Community-Acquired Acute Respiratory Tract Infections (CA-ARTI) in order to develop and improve health economic models to generate insight in the whole value of diagnostics and develop policy and regulatory recommendations. In addition, efficient clinical algorithms and user requirement specifications of tests will be developed fuelling the medical and technological value of CA-ARTI diagnostics. The value of diagnostics will be tested and demonstrated in a unique pan-European clinical and laboratory research infrastructure allowing for innovative adaptive trial designs to evaluate novel CA-ARTI diagnostics. Close and continuous interaction with the VALUE-Dx multi-stakeholder platform provides for optimal alignment of VALUE-Dx activities with stakeholder opinions, expert knowledge and interests. A variety of dissemination and advocacy measures will promote wide-spread adoption of clinical and cost-effective innovative diagnostics to achieve more personalized, evidence-based antibiotic prescription in order to transform clinical practice, improve patient outcomes and combat AMR.

Pancreatic cancer has the lowest survival rate amongst other cancers and is responsible for 95,000 deaths every year in the EU. Its treatment is usually palliative, aiming at slowing tumour progression and at symptom management. The main hypothesis of RELEVIUM is that quality of life (QoL) of advanced pancreatic cancer patients can be significantly improved by reducing pain and cachexia through highly personalised nutrition, physical activity, and pain management strategies, in addition to chemotherapy treatment. To achieve this, RELEVIUM will empower patients with digital tools that facilitate patient-doctor communication and enable them to self-manage their disease. RELEVIUM will use (i) a multi-sensor smartwatch and an innovative remote ultrasound patch, (ii) AI algorithms for continuous remote monitoring of pain and sarcopenia, as well as for decision support, and (iii) patient and caregiver applications. Combined, these tools will provide a stream of evidence on symptom progression and will enable physicians to apply personalised care plans. RELEVIUM brings together an interdisciplinary team of experts and will also involve patients and their caregivers in an iterative co-creation process. The project will initially conduct a feasibility and data collection study (RELEVIUM-FDC, n=130). The study aims at optimizing patient adherence and compliance, and at collecting data for the development of the intervention. A five-centre randomized clinical trial (RELEVIUM-RCT) will then evaluate the efficacy of the proposed personalised care plans for advanced pancreatic cancer patients (n=132) in terms of their QoL. Several secondary outcomes will be investigated, such as the cost-effectiveness of the intervention, its potential in increasing health equity and in relieving the stress burden on the patient families. The study outcomes will result in recommendations for integrating remote monitoring and improving QoL outcomes in palliative care for advanced pancreatic cancer

Adult and paediatric malignant glioma (GBM and pHGG) remain among the most difficult-to-treat cancers with 5-year survival rates of 300, including pre-post treatment samples) with matched controls (n>300) and exceptionally long-term surviving GBM patients (n~140), in which various tumour-host niches will be studied in how they respond to I/O perturbations and lead to improved clinical outcome. This will be empowered by deploying an UNCAN-compatible data lake, to which incremental data collection will be used to further refine the machine learning models, while proposing novel treatment options. This action is part of the Cancer Mission cluster of projects on “Understanding (tumour-host interactions)".