  Maintaining good cardiac function for as long as possible is a major concern
for healthcare systems worldwide and there is much interest in learning more
about the impact of different risk factors on cardiac health. The aim of this
study is to analyze the impact of systolic blood pressure (SBP) on cardiac
function while preserving the interpretability of the model using known
clinical biomarkers in a large cohort of the UK Biobank population. We propose
a novel framework that combines deep learning based estimation of interpretable
clinical biomarkers from cardiac cine MR data with a variational autoencoder
(VAE). The VAE architecture integrates a regression loss in the latent space,
which enables the progression of cardiac health with SBP to be learnt. Results
on 3,600 subjects from the UK Biobank show that the proposed model allows us to
gain important insight into the deterioration of cardiac function with
increasing SBP, identify key interpretable factors involved in this process,
and lastly exploit the model to understand patterns of positive and adverse
adaptation of cardiac function.
