Computational Method for Estimating Cardiovascular Properties from Hemodynamic Data

This invention provides a computer technique that uses frequently obtained hemodynamic and imaging data to estimate cardiovascular characteristics. The technique makes use of a previously published, physiology-based mathematical and computational model of the cardiovascular system that can forecast quantifiable hemodynamic variables from assumed cardiovascular system parameters, such as ventricular contractility, stiffness, aortic properties, resistances, and compliances. These variables include pressures, flows, heart chamber motions, and wave reflections. This software uses statistical and optimization methods to extract underlying cardiovascular system parameters from hemodynamic readings. Because these parameters represent intrinsic myocardial properties, they provide more direct information about the biological changes occurring in heart failure, relative to other methods currently in use.

Background: 
Echocardiography, often referred to as “echo,” is a type of ultrasound imaging that provides information about the structure and function of the heart. In recent years, technological advancements in echocardiography have made it possible to measure a wide range of parameters, including the dimensions, motions, and deformations of all four heart chambers, as well as blood flow velocities in the heart valves and major blood vessels. These measurements are influenced by the complex interactions that occur between different parts of the cardiovascular system. As a result, interpreting the measured data to understand underlying heart and circulatory parameters, such as contractility, stiffness, aortic properties, vascular resistances, and compliances, is challenging. This is especially notable in conditions such as heart failure with preserved ejection fraction (HFpEF). This model can predict the values of properties that are measured noninvasively, such as echo data and blood pressures, based on assumed values of underlying cardiac parameters like wall stiffness and contractile force. It is computationally fast, enabling rapid patient-specific estimation of the underlying parameters.

Applications: 

• Clinical cardiovascular diagnostics
• Non-invasive cardiovascular function assessment
• Cardiac imaging analysis (echocardiography)

Advantages: 

• Estimates patient-specific cardiovascular properties from routinely collected non-invasive clinical data
• Quantifies uncertainty by providing confidence intervals for more reliable clinical interpretation
• Runs much faster than competing approaches based on finite-element computational methods