  Predicting the risk of death for chronic patients is highly valuable for
informed medical decision-making. This paper proposes a general framework for
dynamic prediction of the risk of death of a patient given her hospitalization
history, which is generally available to physicians. Predictions are based on a
joint model for the death and hospitalization processes, thereby avoiding the
potential bias arising from selection of survivors. The framework accommodates
various submodels for the hospitalization process. In particular, we study
prediction of the risk of death in a renewal model for hospitalizations, a
common approach to recurrent event modelling. In the renewal model, the
distribution of hospitalizations throughout the follow-up period impacts the
risk of death. This result differs from prediction in the Poisson model,
previously studied, where only the number of hospitalizations matters. We apply
our methodology to a prospective, observational cohort study of 401 patients
treated for COPD in one of six outpatient respiratory clinics run by the
Respiratory Service of Galdakao University Hospital, with a median follow-up of
4.16 years. We find that more concentrated hospitalizations increase the risk
of death.
