Simulating Lifetime Outcomes Associated with Complications for People with Type 1 Diabetes

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Abstract

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.

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Acknowledgments

This report is independent research commissioned by the National Institute for Health Research Health Technology Assessment (NIHR HTA) programme (Project Number 08/67/03). The views expressed in this publication are those of the authors and not necessarily those of the UK NHS, the NIHR or the Department of Health (UK). In regard to other funding, P.M.C. was funded from an Australian National Health and Medical Research Council (NHMRC) Career Development Award (571122). T.W.C.L. was partially funded from a NHMRC Capacity Building Grant (571372) and an NHMRC Project Grant (1028335). A.J.H. was supported by an NHMRC Capacity Building Grant (571372). A.F. receives support from the NIHR Oxford Biomedical Research Centre.

Contributions

The authors would like to thank Andrew Palmer and Bill Herman for their comments on previous versions to the manuscript. All authors conceived and planned the work that led to the manuscript. T.W.C.L. was responsible for the analysis and development of the model, whilst P.M.C., A.J.H., R.J.S. and A.F. made substantive suggestions for revision and approved the final submitted version.

Conflicts of Interest

The authors have no competing interests that are directly relevant to the content of this article.

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Correspondence to Tom W. C. Lung.

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Lung, T.W.C., Clarke, P.M., Hayes, A.J. et al. Simulating Lifetime Outcomes Associated with Complications for People with Type 1 Diabetes. PharmacoEconomics 31, 509–518 (2013). https://doi.org/10.1007/s40273-013-0047-4

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Keywords

  • Life Expectancy Estimate
  • Albumin Creatinine Ratio
  • Conventional Treatment Group
  • Intensive Treatment Group
  • Cumulative Incidence Curve