The 8000 different rare diseases (RDs) affect between 3.5% to 5.9% of the general population (excluding rare cancers), resulting in 18–30 million of affected persons in EU, of which 71,9 % are caused by a genetic defect. Neuromuscular disorders (NMDs) represent a huge subgroup of RDs with an increasing number of diseases identified. The path to diagnosis for RDs is burdensome facing several misdiagnoses, and patients’ pilgrimage in order to get an accurate clinical and genetic diagnosis. This delay severely affects disease prevention, reproductive choices, timely starting personalized treatments, and clinical trials enrolment. This unacceptable situation does not meet the concept of equality for EU citizens, requires rapid, organized, systematic, and cost-effective corrective actions. Objectives of your proposal are to design new tools based on machine learning platforms providing digital diagnosis and taking advantage of databases available for rare NMD in EU, which will be used to design and carry out 4 pilot newborn screenings (NBS) in Europe (Germany, Italy, and Czech Republic) analysing about 120.000 newborn babies. We will screen four pivotal diseases, DMD, SMA, Pompe, and Limb Girdle muscular dystrophies. Objectives are: strategic overview of existing RD ressources, federation of available NBS data into a meta-data repository, co-creation of strategies for NBS to carry out the pilot NBSs, and repurpose pre-existing artificial intelligence-based algorithms to detect early onset patients by phenotypic (EHR)-based characterization of NMD patients. This will allow the design of a phenotypic Symptom Checker for efficient HCP cycling of NMDs. Pilot NBS will be versatile, based on single gene, gene panel and clinical exome approach, depending on the disease type and underlining genotype. The goal of our proposal is to shorten the time to diagnosis for NMD patients, by designing an integrated digital approach which can be adopted in all other RDs.
|Effektiv start/slut dato||01/01/2021 → 01/01/2026|