A Patient-Level Model to Estimate Lifetime Health Outcomes of Patients With Type 1 Diabetes
AuthorTran-Duy, A; Knight, J; Palmer, AJ; Petrie, D; Lung, TWC; Herman, WH; Eliasson, B; Svensson, A-M; Clarke, PM
Source TitleDiabetes Care
PublisherAMER DIABETES ASSOC
University of Melbourne Author/sTran, An Duy; Clarke, Philip; Petrie, Dennis; Palmer, Andrew; Knight, Andrew
AffiliationMelbourne School of Population and Global Health
Document TypeJournal Article
CitationsTran-Duy, A., Knight, J., Palmer, A. J., Petrie, D., Lung, T. W. C., Herman, W. H., Eliasson, B., Svensson, A. -M. & Clarke, P. M. (2020). A Patient-Level Model to Estimate Lifetime Health Outcomes of Patients With Type 1 Diabetes. DIABETES CARE, 43 (8), pp.1741-1749. https://doi.org/10.2337/dc19-2249.
Access StatusAccess this item via the Open Access location
Open Access URLPublished version
OBJECTIVE: To develop a patient-level simulation model for predicting lifetime health outcomes of patients with type 1 diabetes and as a tool for economic evaluation of type 1 diabetes treatment based on data from a large, longitudinal cohort. RESEARCH DESIGN AND METHODS: Data for model development were obtained from the Swedish National Diabetes Register. We derived parametric proportional hazards models predicting the absolute risk of diabetes complications and death based on a wide range of clinical variables and history of complications. We used linear regression models to predict risk factor progression. Internal validation was performed, estimates of life expectancies for different age-sex strata were computed, and the impact of key risk factors on life expectancy was assessed. RESULTS: The study population consisted of 27,841 patients with type 1 diabetes with a mean duration of follow-up of 7 years. Internal validation showed good agreement between the predicted and observed cumulative incidence of death and 10 complications. Simulated life expectancy was ∼13 years lower than that of the sex- and age-matched general population, and patients with type 1 diabetes could expect to live with one or more complications for ∼40% of their remaining life. Sensitivity analysis showed the importance of preventing renal dysfunction, hypoglycemia, and hyperglycemia as well as lowering HbA1c in reducing the risk of complications and death. CONCLUSIONS: Our model was able to simulate risk factor progression and event histories that closely match the observed outcomes and to project events occurring over patients' lifetimes. The model can serve as a tool to estimate the impact of changing clinical risk factors on health outcomes to inform economic evaluations of interventions in type 1 diabetes.
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