Joint data imputation and mechanistic modelling for simulating heart-brain interactions in incomplete datasets

The use of mechanistic models in clinical studies is limited by the lack of multi-modal patients data representing different anatomical and physiological processes. For example, neuroimaging datasets do not provide a sufficient representation of heart features for the modeling of cardiovascular factors in brain disorders. To tackle this problem we introduce a probabilistic framework for joint cardiac data imputation and personalisation of cardiovascular mechanistic models, with application to brain studies with incomplete heart data. Our approach is based on a variational framework for the joint inference of an imputation model of cardiac information from the available features, along with a Gaussian Process emulator that can faithfully reproduce personalised cardiovascular dynamics. Experimental results on UK Biobank show that our model allows accurate imputation of missing cardiac features in datasets containing minimal heart information, e.g. systolic and diastolic blood pressures only, while jointly estimating the emulated parameters of the lumped model. This allows a novel exploration of the heart-brain joint relationship through simulation of realistic cardiac dynamics corresponding to different conditions of brain anatomy

Non-Invasive Pressure Estimation in Patients with Pulmonary Arterial Hypertension: Data-driven or Model-based?

International audienceRight heart catheterisation is considered as the gold standard for the assessment of patients with suspected pulmonary hyper-tension. It provides clinicians with meaningful data, such as pulmonary capillary wedge pressure and pulmonary vascular resistance, however its usage is limited due to its invasive nature. Non-invasive alternatives, like Doppler echocardiography could present insightful measurements of right heart but lack detailed information related to pulmonary vascu-lature. In order to explore non-invasive means, we studied a dataset of 95 pulmonary hypertension patients, which includes measurements from echocardiography and from right-heart catheterisation. We used data extracted from echocardiography to conduct cardiac circulation model per-sonalisation and tested its prediction power of catheter data. Standard machine learning methods were also investigated for pulmonary artery pressure prediction. Our preliminary results demonstrated the potential prediction power of both data-driven and model-based approaches

Large Scale Cardiovascular Model Personalisation for Mechanistic Analysis of Heart and Brain Interactions

International audienceCerebrovascular diseases have been associated with a variety of heart diseases like heart failure or atrial fibrillation, however the mech-anistic relationship between these pathologies is largely unknown. Until now, the study of the underlying heart-brain link has been challenging due to the lack of databases containing data from both organs. Current large data collection initiatives such as the UK Biobank provide us with joint cardiac and brain imaging information for thousands of individuals , and represent a unique opportunity to gain insights about the heart and brain pathophysiology from a systems medicine point of view. Research has focused on standard statistical studies finding correlations in a phenomenological way. We propose a mechanistic analysis of the heart and brain interactions through the personalisation of the parameters of a lumped cardiovascular model under constraints provided by brain-volumetric parameters extracted from imaging, i.e: ventricles or white matter hyperintensities volumes, and clinical information such as age or body surface area. We applied this framework in a cohort of more than 3000 subjects and in a pathological subgroup of 53 subjects diagnosed with atrial fibrillation. Our results show that the use of brain feature constraints helps in improving the parameter estimation in order to identify significant differences associated to specific clinical conditions