How to measure temporal changes in care pathways for chronic diseases using health care registry data

Disease trajectories for chronic diseases can span over several decades, with several time-dependent factors affecting treatment decisions. Thus, there is a need for long-term predictions of disease trajectories to inform patients and healthcare professionals on the long-term outcomes and provide information on the need of future health care. Here, we propose a state transition model to describe and predict disease trajectories up to 25 years after diagnosis in men with prostate cancer (PCa), as a proof of principle.

States, state transitions, and transition probabilities were identified and estimated in Prostate Cancer data Base of Sweden (PCBaSe^Traject), using nationwide population-based data from 118,743 men diagnosed with PCa. A state transition model in discrete time steps (i.e., 4 weeks) was developed and applied to capture all possible transitions (PCBaSe^Sim). Transition probabilities were estimated for changes in both treatment and comorbidity. These models combined yielded parameter estimates to run an individual-level simulation based on the state-transition model to obtain prediction estimates. Predicted estimates were then compared to real world data in PCBaSe^Traject.

PCBaSe^Sim estimates for the cumulative incidence of first and second transitions, death from PCa and death from other causes were compared to observed transitions in PCBaSe^Traject. A good agreement was found between simulated and observed estimates.

We developed a reliable and accurate simulation tool, PCBaSe^Sim that provides information on disease trajectories for subjects with a chronic disease on an individual and population-based level.