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In this work, a novel approach is proposed for joint analysis of highdimensional time-resolved cardiac motion features obtained from segmentedcardiac MRI and low dimensional clinical risk factors to improve survivalprediction in heart failure. Different methods are evaluated to find theoptimal way to insert conventional covariates into deep prediction networks.Correlation analysis between autoencoder latent codes and covariate features isused to examine how these predictors interact. We believe that similarapproaches could also be used to introduce knowledge of genetic variants tosuch survival networks to improve outcome prediction by jointly analysingcardiac motion traits with inheritable risk factors.

Joint analysis of clinical risk factors and 4D cardiac motion for survival prediction using a hybrid deep learning network