OBJECTIVES: The aim of this study was to develop a discrete-time simulation model for people with type 1 diabetes mellitus, to estimate and compare mean life expectancy and quality-adjusted life-years (QALYs) over a lifetime between intensive and conventional blood glucose treatment groups. METHODS: We synthesized evidence on type 1 diabetes patients using several published sources. The simulation model was based on 13 equations to estimate risks of events and mortality. Cardiovascular disease (CVD) risk was obtained from results of the DCCT (diabetes control and complications trial). Mortality post-CVD event was based on a study using linked administrative data on people with diabetes from Western Australia. Information on incidence of renal disease and the progression to CVD was obtained from studies in Finland and Italy. Lower-extremity amputation (LEA) risk was based on the type 1 diabetes Swedish inpatient registry, and the risk of blindness was obtained from results of a German-based study. Where diabetes-specific data were unavailable, information from other populations was used. We examine the degree and source of parameter uncertainty and illustrate an application of the model in estimating lifetime outcomes of using intensive and conventional treatments for blood glucose control. RESULTS: From 15 years of age, male and female patients had an estimated life expectancy of 47.2 (95 % CI 35.2-59.2) and 52.7 (95 % CI 41.7-63.6) years in the intensive treatment group. The model produced estimates of the lifetime benefits of intensive treatment for blood glucose from the DCCT of 4.0 (95 % CI 1.2-6.8) QALYs for women and 4.6 (95 % CI 2.7-6.9) QALYs for men. Absolute risk per 1,000 person-years for fatal CVD events was simulated to be 1.37 and 2.51 in intensive and conventional treatment groups, respectively. CONCLUSIONS: The model incorporates diabetic complications risk data from a type 1 diabetes population and synthesizes other type 1-specific data to estimate long-term outcomes of CVD, end-stage renal disease, LEA and risk of blindness, along with life expectancy and QALYs. External validation was carried out using life expectancy and absolute risk for fatal CVD events. Because of the flexible and transparent nature of the model, it has many potential future applications.
OBJECTIVES: The aim of this study was to develop a discrete-time simulation model for people with type 1 diabetes mellitus, to estimate and compare mean life expectancy and quality-adjusted life-years (QALYs) over a lifetime between intensive and conventional blood glucose treatment groups. METHODS: We synthesized evidence on type 1 diabetespatients using several published sources. The simulation model was based on 13 equations to estimate risks of events and mortality. Cardiovascular disease (CVD) risk was obtained from results of the DCCT (diabetes control and complications trial). Mortality post-CVD event was based on a study using linked administrative data on people with diabetes from Western Australia. Information on incidence of renal disease and the progression to CVD was obtained from studies in Finland and Italy. Lower-extremity amputation (LEA) risk was based on the type 1 diabetes Swedish inpatient registry, and the risk of blindness was obtained from results of a German-based study. Where diabetes-specific data were unavailable, information from other populations was used. We examine the degree and source of parameter uncertainty and illustrate an application of the model in estimating lifetime outcomes of using intensive and conventional treatments for blood glucose control. RESULTS: From 15 years of age, male and female patients had an estimated life expectancy of 47.2 (95 % CI 35.2-59.2) and 52.7 (95 % CI 41.7-63.6) years in the intensive treatment group. The model produced estimates of the lifetime benefits of intensive treatment for blood glucose from the DCCT of 4.0 (95 % CI 1.2-6.8) QALYs for women and 4.6 (95 % CI 2.7-6.9) QALYs for men. Absolute risk per 1,000 person-years for fatal CVD events was simulated to be 1.37 and 2.51 in intensive and conventional treatment groups, respectively. CONCLUSIONS: The model incorporates diabetic complications risk data from a type 1 diabetes population and synthesizes other type 1-specific data to estimate long-term outcomes of CVD, end-stage renal disease, LEA and risk of blindness, along with life expectancy and QALYs. External validation was carried out using life expectancy and absolute risk for fatal CVD events. Because of the flexible and transparent nature of the model, it has many potential future applications.
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