A patient-level model to estimate lifetime health outcomes of patients with type 1 diabetes

<b>OBJECTIVE </b> <p>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.</p> <p><b>RESEARCH DESIGN AND METHODS</b></p> <p>Data for model development were obtained from the <a>Swedish National Diabetes Register</a>. We derived parametric proportional hazards models predicting 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.</p> <p><b>RESULTS </b></p> <p>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 predicted and observed cumulative incidence of death and 10 complications. Simulated life expectancy was approximately 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 approximately 40% of their remaining life. Sensitivity analysis showed the importance of preventing renal dysfunction, hypoglycaemia and hyperglycaemia, and lowering HbA1c in reducing the risk of complications and death.</p> <p><b>CONCLUSIONS </b></p> Our model was able to simulate risk factor progression and event histories that closely match the observed outcomes, and 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.