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How to measure temporal changes in care pathways for chronic diseases using health care registry data
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Urology. IRCCS, Osped San Raffaele, Div Expt Oncol, Unit Urol, Milan, Italy.ORCID iD: 0000-0003-3654-1629
Kings Coll London, Sch Canc & Pharmaceut Sci, Translat Oncol & Urol Res Tour, Guys Hosp, 3rd Floor, London SE1 9RT, England.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Medicinska och farmaceutiska vetenskapsområdet, centrumbildningar mm, UCR-Uppsala Clinical Research Center.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Urology.ORCID iD: 0000-0002-8306-0687
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2019 (English)In: BMC Medical Informatics and Decision Making, ISSN 1472-6947, E-ISSN 1472-6947, Vol. 19, article id 103Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
BMC , 2019. Vol. 19, article id 103
Keywords [en]
Ageing, Chronic disease, Prostate cancer, State transition
National Category
Urology and Nephrology
Identifiers
URN: urn:nbn:se:uu:diva-386444DOI: 10.1186/s12911-019-0823-yISI: 000469777500001PubMedID: 31146754OAI: oai:DiVA.org:uu-386444DiVA, id: diva2:1330977
Funder
Swedish Research Council, 825-2008-5910Forte, Swedish Research Council for Health, Working Life and WelfareVästerbotten County CouncilThe Cancer Society in StockholmAvailable from: 2019-06-26 Created: 2019-06-26 Last updated: 2019-06-26Bibliographically approved

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Ventimiglia, EugenioLindhagen, LarsStattin, PärGarmo, Hans
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