May 10, 2019 | 15:00—16:00
Multimorbidity, the co-occurrence of two or more chronic diseases such as diabetes, obesity or cardiovascular diseases in one patient, is a frequent phenomenon.
To make care more efficient, it is of relevance to understand how different diseases condition each other over the life course of a patient. However, most of our current knowledge on such patient careers is either confined to narrow time spans or specific (sets of) diseases. Here, we present a population-wide analysis of long-term patient trajectories by clustering them according to their disease history observed over 17 years.
When patients acquire new diseases, their cluster assignment might change. A health trajectory can then be described by a temporal sequence of disease clusters. From these cluster transitions we construct an age-dependent multiplex network of disease clusters. Random walks on this multiplex network provide a more precise model for the time evolution of multimorbid health states when compared to models that cluster patients based on single diseases. We find that for elderly patients the cluster network consists of two different regions, one for clusters with low and one with high in-hospital mortality.
Our results can be used to identify the crucial events that potentially determine the future disease trajectory of a patient.