How to measure temporal changes in care pathways for chronic diseases using health care registry data
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 out...
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| Format: | Article |
| Language: | English |
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BMC
2019-05-01
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| Series: | BMC Medical Informatics and Decision Making |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12911-019-0823-y |
| _version_ | 1818161702881984512 |
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| author | Eugenio Ventimiglia Mieke Van Hemelrijck Lars Lindhagen Pär Stattin Hans Garmo |
| author_facet | Eugenio Ventimiglia Mieke Van Hemelrijck Lars Lindhagen Pär Stattin Hans Garmo |
| author_sort | Eugenio Ventimiglia |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-11T16:21:59Z |
| format | Article |
| id | doaj.art-9fd2ad04eda145a6af681ed82441763e |
| institution | Directory Open Access Journal |
| issn | 1472-6947 |
| language | English |
| last_indexed | 2024-12-11T16:21:59Z |
| publishDate | 2019-05-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj.art-9fd2ad04eda145a6af681ed82441763e2022-12-22T00:58:49ZengBMCBMC Medical Informatics and Decision Making1472-69472019-05-011911910.1186/s12911-019-0823-yHow to measure temporal changes in care pathways for chronic diseases using health care registry dataEugenio Ventimiglia0Mieke Van Hemelrijck1Lars Lindhagen2Pär Stattin3Hans Garmo4Division of Experimental Oncology/Unit of Urology, IRCCS Ospedale San RaffaeleKing’s College London, School of Cancer and Pharmaceutical Sciences, Translational Oncology & Urology Research (Tour)Uppsala Clinical Research CenterDepartment of Surgical Sciences, Uppsala UniversityKing’s College London, School of Cancer and Pharmaceutical Sciences, Translational Oncology & Urology Research (Tour)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.http://link.springer.com/article/10.1186/s12911-019-0823-yAgeingChronic diseaseProstate cancerState transition |
| spellingShingle | Eugenio Ventimiglia Mieke Van Hemelrijck Lars Lindhagen Pär Stattin Hans Garmo How to measure temporal changes in care pathways for chronic diseases using health care registry data BMC Medical Informatics and Decision Making Ageing Chronic disease Prostate cancer State transition |
| title | How to measure temporal changes in care pathways for chronic diseases using health care registry data |
| title_full | How to measure temporal changes in care pathways for chronic diseases using health care registry data |
| title_fullStr | How to measure temporal changes in care pathways for chronic diseases using health care registry data |
| title_full_unstemmed | How to measure temporal changes in care pathways for chronic diseases using health care registry data |
| title_short | How to measure temporal changes in care pathways for chronic diseases using health care registry data |
| title_sort | how to measure temporal changes in care pathways for chronic diseases using health care registry data |
| topic | Ageing Chronic disease Prostate cancer State transition |
| url | http://link.springer.com/article/10.1186/s12911-019-0823-y |
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