Closed-loop effects in cardiovascular clinical decision support

We have recently seen impressive methodological developments in quantitative cardiovascular physiology and pathophysiology, with novel mathematical models for the mechanical and electrophysiological processes of the heart, and fluid dynamical models to describe the pressure and flow distribution in the blood vessel network. This allows us to gain deeper insight into the state of a variety of serious cardiovascular diseases. The majority of recent research studies have focused on the forward problem: developing flexible mathematical models and robust numerical simulation procedures to match characteristics of physiological target data, and the inverse problem: inferring model parameters from cardiac physiological data with reliable uncertainty quantification. However, when connecting mathematical model predictions and statistical inference to the clinical decision process, new challenges arise. This paper briefly discusses the complications that potentially result from closed-loop effects, and the model extensions that are required to reduce the ensuing bias.