Format

Send to

Choose Destination
BMC Med Inform Decis Mak. 2019 May 30;19(1):103. doi: 10.1186/s12911-019-0823-y.

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

Author information

1
Division of Experimental Oncology/Unit of Urology, IRCCS Ospedale San Raffaele, Milan, Italy.
2
Department of Surgical Sciences, Uppsala University, Uppsala, Sweden.
3
King's College London, School of Cancer and Pharmaceutical Sciences, Translational Oncology & Urology Research (Tour), 3rd Floor, Bermondsey Wing, Guy's Hospital, London, SE1 9RT, UK.
4
Uppsala Clinical Research Center, Uppsala, Sweden.
5
King's College London, School of Cancer and Pharmaceutical Sciences, Translational Oncology & Urology Research (Tour), 3rd Floor, Bermondsey Wing, Guy's Hospital, London, SE1 9RT, UK. hans.garmo@kcl.ac.uk.
6
Regional Cancer Centre, Uppsala Örebro, Uppsala, Sweden. hans.garmo@kcl.ac.uk.

Abstract

BACKGROUND:

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.

METHODS:

States, state transitions, and transition probabilities were identified and estimated in Prostate Cancer data Base of Sweden (PCBaSeTraject), 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 (PCBaSeSim). 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 PCBaSeTraject.

RESULTS:

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

CONCLUSIONS:

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

KEYWORDS:

Ageing; Chronic disease; Prostate cancer; State transition

Supplemental Content

Full text links

Icon for BioMed Central Icon for PubMed Central
Loading ...
Support Center