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Maintaining good cardiac function for as long as possible is a major concernfor healthcare systems worldwide and there is much interest in learning moreabout the impact of different risk factors on cardiac health. The aim of thisstudy is to analyze the impact of systolic blood pressure (SBP) on cardiacfunction while preserving the interpretability of the model using knownclinical biomarkers in a large cohort of the UK Biobank population. We proposea novel framework that combines deep learning based estimation of interpretableclinical 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. Resultson 3,600 subjects from the UK Biobank show that the proposed model allows us togain important insight into the deterioration of cardiac function withincreasing SBP, identify key interpretable factors involved in this process,and lastly exploit the model to understand patterns of positive and adverseadaptation of cardiac function.

Assessing the Impact of Blood Pressure on Cardiac Function Using Interpretable Biomarkers and Variational Autoencoders