Descripción del proyecto
Biomarcadores computacionales del riesgo de infarto de miocardio
El infarto de miocardio (IM) es una de las principales causas de muerte en todo el mundo, pero las herramientas actuales de predicción de los riesgos isquémico y hemorrágico tras el tratamiento adolecen de una precisión limitada. El equipo del proyecto ORACLE, financiado por el Consejo Europeo de Investigación, pretende mejorar la predicción del riesgo utilizando datos multidimensionales procedentes de dispositivos ponibles, biomarcadores y obtención atraumática de imágenes. La idea es identificar nuevos biomarcadores computacionales de riesgo utilizando la inteligencia artificial (IA) para analizar los datos de una gran cohorte de pacientes con IM. Se espera que en el proyecto se generen algoritmos de IA guiados clínicamente que se integrarán en la práctica para tomar decisiones terapéuticas con conocimiento de causa.
Objetivo
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.
Ámbito científico
Palabras clave
Programa(s)
- HORIZON.1.1 - European Research Council (ERC) Main Programme
Régimen de financiación
HORIZON-ERC - HORIZON ERC GrantsInstitución de acogida
29590 MALAGA
España