Dynamic system models to noninvasively predict pulmonary vascular stiffness, pulmonary vascular resistance, flow and heart work in children with pulmonary hypertension

Pulmonary hypertension is the persistence of elevated blood pressure within the pulmonary vasculature. It is a clinically challenging health problem to diagnose, especially using noninvasive procedures. Current clinical diagnosis still requires invasive procedures to determine the hemodynamic values that ascertain the pulmonary vascular status.; Several methods of noninvasive diagnostic techniques have been investigated that can produces viable information; however, these methods have yet to prove their efficacy. In many cases, the information provided is limited in it ability to quantify more than one hemodynamic value. Many of these techniques use some form of echocardiography to obtain anatomic or hemodynamic information, either for modeling purposes or direct assessment. The most important hemodynamic factors are pulmonary vascular resistance (Rp), pulmonary vascular stiffness (PVS), mean pulmonary artery pressure (MPAP) and flow. All of these are currently tested invasively to diagnose pulmonary hypertension.; It is intended here to present a dynamic system modeling technique applied to two different types of echocardiographic data. The engineering approach should provide a novel method of evaluating hemodynamic values noninvasively while providing a better understanding of how the model parameters relate to physiological functions. This will include: (1) Development of single degree-of-freedom dynamic system models that meet the requirements of the physiological dynamics in which the model represents. (2) Use of nonlinear data fitting procedures to match the physiological system dynamics to that of the model dynamics. The result of this process is a best fit of the models motion to that of the original system, which provides model parameter values that should correlate to hemodynamic values. (3) Performing statistical analysis to obtain relationships between the invasively measured hemodynamic values to the results of the model parameter values using linear regression.; The models provide important information that can help predict pulmonary vascular resistance, pulmonary vascular stiffness, flow and heart work. The information presented should provide insight into the model's utility as an overall noninvasive indicator of cardiovascular health.