Postural orthostatic tachycardia syndrome explained using a baroreflex response model

Abstract

Patients with postural orthostatic tachycardia syndrome (POTS) experience an excessive increase in heart rate (HR) and low-frequency (∼0.1 Hz) blood pressure (BP) and HR oscillations upon head-up tilt (HUT). These responses are attributed to increased baroreflex (BR) responses modulating sympathetic and parasympathetic signalling. This study uses a closed-loop cardiovascular compartment model controlled by the BR to predict BP and HR dynamics in response to HUT. The cardiovascular model predicts these quantities in the left ventricle, upper and lower body arteries and veins. HUT is simulated by letting gravity shift blood volume (BV) from the upper to the lower body compartments, and the BR control is modelled using set-point functions modulating peripheral vascular resistance, compliance, and cardiac contractility in response to changes in mean carotid BP. We demonstrate that modulation of parameters characterizing BR sensitivity allows us to predict the persistent increase in HR and the low-frequency BP and HR oscillations observed in POTS patients. Moreover, by increasing BR sensitivity, inhibiting BR control of the lower body vasculature, and decreasing central BV, we demonstrate that it is possible to simulate patients with neuropathic and hyperadrenergic POTS.

Editorial: Data assimilation in cardiovascular medicine: Merging experimental measurements with physics-based computational models

“…Physics-based computational models, constructed based on sound theoretical principles, can give meaningful insight into the complex interrelationship among different parts of the cardiovascular system, such as the heart, vessels and valves. To date, diverse types of physics-based cardiovascular models ( Shi et al, 2018 ; Leong et al, 2019a ; Niederer et al, 2019 ; Ong et al, 2020 ; Geddes et al, 2022 ), spanning the disciplines of electrophysiology, electromechanics, solid mechanics, fluid dynamics and cardiovascular reflex, have greatly enhanced our understanding of cardiovascular diseases. However, most of these models used population-based parameters, and thus had significant model uncertainties due to huge intra- and interpatient variability.…”