Description du projet
Biomarqueurs informatiques du risque d’infarctus du myocarde
L’infarctus du myocarde (IM) est une cause majeure de décès dans le monde, mais les outils actuels de prédiction des risques ischémiques et hémorragiques après traitement manquent de précision. Financé par le Conseil européen de la recherche, le projet ORACLE vise à améliorer la prédiction des risques en utilisant des données multidimensionnelles provenant de dispositifs portables, de biomarqueurs et d’imagerie non invasive. L’idée est d’identifier de nouveaux biomarqueurs computationnels du risque en utilisant l’IA pour analyser les données d’une grande cohorte de patients ayant subi un infarctus du myocarde. Le projet devrait permettre de générer des algorithmes d’IA guidés cliniquement et de les intégrer dans la pratique afin de prendre des décisions thérapeutiques éclairées.
Objectif
Myocardial infarction (MI) is a leading cause of death worldwide. After MI, long-term antithrombotic therapy is crucial to prevent recurrent events, but increases bleeding, that also impacts morbidity and mortality. Giving these competing risks prediction tools to forecast ischemic and bleeding are of paramount importance to inform clinical decisions, but their current precision is limited. Improve events prediction, by discovering novel and innovative markers of risk would have a tremendous impact on therapeutic decisions and patients’ outcome. I hypothesize that using innovative multidimensional information from wearable devices, biomarkers, behavioral patterns and non-invasive imaging, integrated through artificial intelligence computation, we may discover novel “computational biomarkers” of risk and improve current standards of risk prediction. In this project, I will enroll a large cohort of MI patients, whereby prospective collection of consolidated and innovative potential risk predictors will take place, in order to generate a comprehensive and multidimensional dataset. I will collect data from state-of-the-art non-invasive imaging, blood biomarkers, wearable medical devices of continuous heart electrical activity, sweat, mobility and behavioral patterns to create a large physiological time series allowing patients’ deep phenotyping. We will therefore analyze data leveraging artificial intelligence computation to find relevant associations with clinical outcomes, and compare new algorithms with current risk prediction tools. This research will increase our knowledge on bleeding and ischemic risk factors, enabling enhanced capability predictions models. In the near future, we hypothesize that our clinically-guided Artificial Intelligence algorithm might be integrated in clinical practice, helping clinicians to inform treatment decisions, patients to better understand their risk profile, finally setting a common ground for shared patient/physician decisions.
Champ scientifique
Mots‑clés
Programme(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Thème(s)
Régime de financement
HORIZON-ERC - HORIZON ERC GrantsInstitution d’accueil
29590 MALAGA
Espagne