Patient-specific modelling of head-up tilt

Abstract

Short-term cardiovascular responses to head-up tilt (HUT) involve complex cardiovascular regulation in order to maintain blood pressure at homoeostatic levels. This manuscript presents a patient-specific model that uses heart rate as an input to fit the dynamic changes in arterial blood pressure data during HUT. The model contains five compartments representing arteries and veins in the upper and lower body of the systemic circulation, as well as the left ventricle facilitating pumping of the heart. A physiologically based submodel describes gravitational pooling of the blood into the lower extremities during HUT, and a cardiovascular regulation model adjusts cardiac contractility and vascular resistance to the blood pressure changes. Nominal parameter values are computed from patient-specific data and literature estimates. The model is rendered patient specific via the use of parameter estimation techniques. This process involves sensitivity analysis, prediction of a subset of identifiable parameters, and non-linear optimization. The approach proposed here was applied to the analysis of aortic and carotid HUT data from five healthy young subjects. Results showed that it is possible to identify a subset of model parameters that can be estimated allowing the model to fit changes in arterial blood pressure observed at the level of the carotid bifurcation. Moreover, the model estimates physiologically reasonable values for arterial and venous blood pressures, blood volumes and cardiac output for which data are not available.

Using Kalman Filtering to Predict Time-Varying Parameters in a Model Predicting Baroreflex Regulation During Head-Up Tilt

“…This feature is controlled via multiple mechanisms, but primarily by the baroreflex system, which senses changes in aortic and carotid blood pressure, stimulating the sympathetic and parasympathetic systems [17]. The sympathetic system induces alterations in heart rate (HR), cardiac contractility, vascular resis-tance, and compliance, while the parasympathetic system induces rapid changes in heart rate and cardiac contractility [18], [47], [57].…”

“…A number of modeling studies have been carried out to elucidate these mechanisms and their interplay [4], [24], [32], [40], [41], [44], [46], [54], [57]. Most of these studies can be separated into two categories: models predicting baroreflex regulation of HR as a function of blood pressure [4], [32], [41], [54] and models predicting blood pressure dynamics as a function of heart rate [24], [28], [40], [46], [57].…”

Structural correlation method for model reduction and practical estimation of patient specific parameters illustrated on heart rate regulation

“…The model presented here was fitted to blood pressure and heart rate measurements from multiple datasets (shown in Figure 3), one from a Dahl salt sensitive (SS) rat (from Bugenhagen et al (2010) and three from healthy young human subjects (from Williams et al (2013)). …”

“…The study by Bugenhagen et al (2010) used the model to fit data from six datasets, though these displayed significantly less variability than the human data. All five human datasets are shown in our previous study Williams et al (2013). Consequently, either the subset is not suitable to capture the intervariability in humans or the model equations themselves do not capture all relevant physiology for humans.…”

“…The human data were measured during a head-up tilt test. More details about the human datasets can be found in the study by Williams et al (2013).…”