  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 time 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 the transitions between clusters we construct an age-dependent multilayer
network of disease clusters. Random walks on this multilayer 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. Our results
can be used to identify decisive events that potentially determine the future
disease trajectory of a patient. We find that for elderly patients the cluster
network consists of regions of low, medium and high in-hospital mortality.
Diagnoses of diabetes and hypertension are found to strongly increase the
likelihood for patients to subsequently move into the high-mortality region
later in life.
