Jensen, Anders Boeck1; Moseley, Pope3; Oprea, Tudor1; Ellesøe, Sabrina Gade4; Eriksson, Robert1; Schmock, Henriette5; Jensen, Peter Bjødstrup4; Jensen, Lars Juhl6; Brunak, Søren7
1 Department of Systems Biology, Technical University of Denmark2 Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark3 University of New Mexico4 University of Copenhagen5 Copenhagen University Hospital6 Department of Biotechnology, Technical University of Denmark7 Department of Bio and Health Informatics, Technical University of Denmark
A key prerequisite for precision medicine is the estimation of disease progression from the current patient state. Disease correlations and temporal disease progression (trajectories) have mainly been analysed with focus on a small number of diseases or using large-scale approaches without time consideration, exceeding a few years. So far, no large-scale studies have focused on defining a comprehensive set of disease trajectories. Here we present a discovery-driven analysis of temporal disease progression patterns using data from an electronic health registry covering the whole population of Denmark. We use the entire spectrum of diseases and convert 14.9 years of registry data on 6.2 million patients into 1,171 significant trajectories. We group these into patterns centred on a small number of key diagnoses such as chronic obstructive pulmonary disease (COPD) and gout, which are central to disease progression and hence important to diagnose early to mitigate the risk of adverse outcomes. We suggest such trajectory analyses may be useful for predicting and preventing future diseases of individual patients.