Several recent major scandals have tarnished public confidence in the evaluation and monitoring system for high-risk medical technologies (HRMD). These situations and the "Implant files" investigation, which denounced the ease with which manufacturers can obtain the right to market medical devices in Europe, highlighted the weaknesses and flaws in the health control system for placing on the market and monitoring it, in particular for HRMD. The new European regulations (EU MDR 2017/745) will come into effect in spring 2020. This new regulation sets forth reinforced rules for the generation of clinical evidence, in particular for the HRMD (current class III and implantable devices) for which clinical investigation will be compulsory. This is a big challenge for European Health SMEs (some 25,000 companies, representing 95% of the MedTech sector in Europe) to maintain their competitiveness and innovation capacity, with limited internal resources; especially in clinical trial skills. On the other hand, patients and physicians would like to ensure that the knowledge on the innovation allows for safe and efficient use of the new product. HRMD have specific features compared to drugs, such as the long-term use with unknown interactions with the human body, the means of explanting and replacing implantable devices, the user's skills, the human-machine interfaces, the management of generated data flows, etc. These specificities require specific evaluation methodologies to generate better clinical evidence. The objective of the research program will be to develop and promote methodological approaches accepted by sponsors and recognized by the evaluators. These approaches include patient involvement, mixed hybrid methods, numerical modeling, AI & Big data mining and alternative statistical methodologies, adapted to the specificities of HRMD. The goal will be to propose a new HRMD evaluation and approval process based on robust methodological approaches to the clinical data needed for the different phases of the product's life, such as clinical proof of concept, usability, premarket approval compliance, high quality clinical evidence for reimbursement, and adequate post-marketing clinical follow-up. Operational objectives - to analyze the current situation of clinical evaluation of HRMD: strengths; weaknesses; duration; indicators and criteria...It will be done by carrying out surveys and a review of the currently used investigation designs, providing a hierarchy of these approaches, identifying gaps to be filled. - by utilizing use cases, and panels of experts, develop methodologies through data-reuse, deep learning of existing medical databases, and hybrid methods for designing clinical investigation studies. - test these new methodologies for the evaluation of innovative HRMD, in close collaboration with all the involved parties (patients, academics, clinical staff, sponsors, industrials) - after this testing phase, propose new methodological references for dealing with the HRMD evaluation methodology and propose recommendations for the choice of the most powerful ones allowing to obtain sufficient evidence in terms of efficiency and safety of the HRMD - validation and implementation of proposed new approaches in close collaboration with industrial, stakeholders, networks of experts, competent authorities, and European Commission. - training program for stakeholders involved in the clinical evaluation of HRMD about the new regulatory framework with regard to clinical evidence. Within the framework of the ANR-MRSEI project, we propose to set up a consortium on the basis of the members of two involved European organizations (ECRIN and EIT-Health) and INSERM (Tech4Health network), supplemented by partners from some organizations as Notified Bodies Assembly, European Patient Associations, European Network for Health Technology Assessment (EUnetHTA), Society of European scientists and Competent Authorities for Medical Devices (CAMD).

Venous thromboembolism (VTE) affects about 1,200,000 individuals each year in Europe and is associated with a total annual cost ranging from €1.5 to 13.2 billion for the EU-28. About 50% of VTE are unprovoked and 30% will recur after stop of anticoagulant treatment. Guidelines recommend life-long treatment for most of these patients. Thus, most patients receive prolonged anticoagulation whereas their risk of recurrence is low. Available clinical rules have a high sensitivity, but a poor specificity for identifying recurrent VTE and do not allow reducing the proportion of patients receiving prolonged anticoagulant treatment. The scientific network STRATOSPHERE-VTE 2016 will help to personalize treatment duration after a first episode of unprovoked VTE and to reduce the proportion of patients receiving long-term treatment. This will include three steps. • In step 1, we will assess the predictive value of single nucleotide variants, plasma microRNA, proteomic biomarkers, humoral biomarkers and clinical data for the risk of recurrent VTE. A score for predicting recurrent VTE will be derived from available or financially secured data. • In step 2, the score will be externally validated and refined in new prospective cohorts. • In step 3, the effectiveness and medico-economic impact of the score will be evaluated against current practice in a multicenter randomized trial in 1660 patients with unprovoked VTE. By identifying patients at low risk of recurrent VTE, the STRATOSPHERE score will avoid unnecessary life-long anticoagulant treatment with its associated bleeding risk and costs in a substantial proportion of patients with VTE.

Coma induced by Out of Hospital Cardiac Arrest (OHCA) is a major global health problem. An early and accurate prognostication is the cornerstone for the clinical management of these patients, mainly because the vast majority of the related mortality observed in this setting comes from withdraw life-sustaining treatment (WLST) decisions following prognostication of a poor neurological outcome. However, currently recommended predictors, based on a multimodal assessment encompassing the use of one fluid-derived biomarker in isolation (NSE, Neuron Specific Enolase) are only informative in a minority of patients, leaving up to 50-77% of anoxo-ischemic coma patients in a ‘gray zone’ of prognostication. Moreover, a further important gap in the existing anoxic coma literature concerns the limited treatment strategies to enhance neurological recovery after OHCA. The current proposal aims to develop and validate over the course of 4 years a new mechanistically coherent battery of fluid-derived biomarkers for the early neuroprognostication and individually-tailored treatment for anoxic coma patients. As a significant paradigm shift in the field, we seek to identify the best combination of fluid-derived biomarkers related to key neuroinflammatory, neurodegenerative and neuroprotective (3N) processes known to be triggered by cardiac arrest. To this end, we will use for the first time in this setting ground-breaking and highly synergic deep 3N multiomics profiling methods that will be empowered by innovative artificial intelligence methods. Following a step-wise approach, first we will capitalize on already existing data to identify among a panel of candidate biomarkers from peripheral blood samples. Second, to increase the robustness and explainability of the predictive models, we will cross-validate the fluid biomarkers and characterize them in relation to well-established 3N hallmarks using multi-dimensional data from an additional independent prospective patient cohort. Finally, a third cohort will be gathered to ensure the prospective and external validation of the identified fluid biomarkers at a European level, aiming to guarantee the predictive model’s generazability and inform the feasibility and design of future clinical trials. The final deliverable will consist of a potential game-changer AI-based predictive classifier for anoxic coma patients based on easily accessible and inexpensive peripheral blood-derived biomarkers. The whole dataset will be made FAIR to facilitate data sharing with the broader research community.