Prediction Models for Cardiovascular and Neurocognitive Disease Risk in the General Population

More accurate and earlier identification of people at risk of cardiovascular disease (CVD) and neurodegenerative diseases (memory, cognition, dementia) through the appropriate use of biomarkers could lead to earlier initiation of preventive therapies and potentially avoid sometimes fatal events and complications. Biomarkers are useful for determining the risk of disease, but also for establishing a diagnosis. High inter-individual variability hinders the establishment of general laws that can be- used in predictive medicine. In addition to the lack of validation, other limitations are the low participation rate in screening campaigns (regardless of disease) and the relative difficulty, accuracy, cost and time taken to perform the measurements. The perioperative period is a very good time to screen for cardiovascular and neurodegenerative pathologies for several reasons: * Patients come to their anesthesia consultation and to the operating room because they have a direct visible benefit. * the physiological data collected intraoperatively during systematic monitoring are very "rich" and of very good quality because they are not very noisy * The induction of general anesthesia or the onset of locoregional anesthesia and its maintenance represents a strong and reproducible physiological "test" for the cardiovascular and cerebral systems. * The patients are regularly re-examined postoperatively for the follow-up of their pathology and the possible complications are recorded in their file, allowing a short and medium term follow-up. The project aims to validate a biomarker predictive of cardiovascular complications, the pulse wave velocity, and a biomarker predictive of cognitive disorders, the power of the Alpha wave on the electroencephalogram, from the data usually collected during each anesthesia and during the perioperative period. The objective is to build a predictive model of cardiovascular and neurodegenerative risks, possibly combined, on a survival analysis.