Mesure de la fragilité pour identifier les patients âgés à haut risque de complications après un remplacement valvulaire aortique chirurgical ou percutané

 

Jonathan Afilalo

Hôpital général juif

 

Domaine : santé circulatoire et respiratoire

Programme chercheurs-boursiers cliniciens - Junior 1

Concours 2013-2014

One in 22 adults will be diagnosed with significant narrowing of the aortic valve, known as aortic stenosis (AS), by the time they reach their 8th decade. Considering that this segment of the population is expected to double by 2036, forecasts indicate that the number of older adults with AS will rise exponentially. When patients with AS develop symptoms, aortic valve replacement (AVR) is the only proven treatment. Before deciding to send a patient for AVR, the doctor calculates and is guided by the predicted risk that the patient will be able to survive surgery. However, the process of risk prediction using existing risk scores is inaccurate in many complex elderly patients, in large part because these scores do not reflect the patient¿s physiologic reserves which will be called upon at the time of surgery. Frailty is a state of decreased physiologic reserves and vulnerability to stressors. Several tools exist to assess frailty, some based on physical tests and others on questionnaires, yet there is no agreement on which tool to recommend. 

In the first project, we will compare 5 frailty tools to determine which best predicts death or major complications after AVR when added to existing risk scores. In the second project, we will validate these findings in two large external datasets. In the third project, we will explore muscle mass measured by DEXA as an up-and-coming tool to assess frailty. The overall objective of these projects is to improve our ability to predict risk by measuring frailty in elderly patients undergoing AVR. By doing so, we hope to assist doctors in guiding patients suffering from AS to the appropriate therapy. Moreover, by integrating frailty into risk scores, we will provide policymakers and researchers with more accurate predictions of risk to analyze performance and conduct trials.