Central hemodynamic quantities, such as cardiac output and central aortic pressure, have been generally shown to be more powerful predictors of clinical outcomes than corresponding measurements obtained in the peripheral arteries such as the radial, femoral or brachial arteries. But despite the diagnostic importance of central measurements, their clinical use is severely hampered by their invasive nature or cost and need of special equipment. Peripheral measurements such as cuff-based peripheral pressure, on the other hand, are noninvasive and can be monitored by any clinician on a regular basis.
Accordingly, our work aims at developing novel biomedical tools for estimating central indices (e.g., central pressure, cardiac output, cardiac contractility indices) by combining insights from both cardiovascular modelling and data assimilation methodology. Our endpoint is to provide clinicians with versatile, cost-efficient tools to achieve efficient patient monitoring and disease diagnosis.
