| Autor: |
Mieke Van Hemelrijck, Eugenio Ventimiglia, David Robinson, Rolf Gedeborg, Lars Holmberg, Pär Stattin, Hans Garmo |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
BMC Medical Informatics and Decision Making, Vol 22, Iss 1, Pp 1-20 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
1472-6947 |
| DOI: |
10.1186/s12911-022-01766-0 |
| Popis: |
Abstract Introduction For clinical decision-making, an estimate of remaining lifetime is needed to assess benefit against harm of a treatment during the remaining lifespan. Here, we describe how to predict life expectancy based on age, Charlson Comorbidity Index (CCI) and a Drug Comorbidity Index (DCI), whilst also considering potential future changes in CCI and DCI using population-based data on Swedish men. Methods Simulations based on annual updates of vital status, CCI and DCI were used to estimate life expectancy at population level. The probabilities of these transitions were determined from generalised linear models using prostate cancer-free comparison men in PCBaSe Sweden. A simulation was performed for each combination of age, CCI, and DCI. Survival curves were created and compared to observed survival. Life expectancy was then calculated as the area under the simulated survival curve. Results There was good agreement between observed and simulated survival curves for most ages and comorbidities, except for younger men. With increasing age and comorbidity, there was a decrease in life expectancy. Cross-validation based on six regions in Sweden also showed that simulated and observed survival was similar. Conclusion Our proposed method provides an alternative statistical approach to estimate life expectancy at population level based on age and comorbidity assessed by routinely collected information on diagnoses and filled prescriptions available in nationwide health care registers. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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