A Patient-Level Model to Estimate Lifetime Health Outcomes of Patients With Type 1 Diabetes

Abstract

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 HbA_1c 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.

Figure 1

Summary of model equations showing major risk factors and the interdependencies between events. The top part of each box indicates the event predicted by the risk factors in the body of the box, each of which is associated with the hazard ratio next to it. Each arrow indicates that the occurrence of the root event influences the subsequent occurrence of the target event. The numerical figure on each arrow indicates the hazard ratio of the target event (complication or death) in patients with a history of the root event compared with patients without a history of such an event. The reference category of BMI (kg/m²) represents BMI between 22.51 and 25.00, BMI_cat2 (BMI ≤20.00), BMI_cat3 (20.01–22.50), BMI_cat5 (27.51–30.00), BMI_cat6 (BMI >30). BP, blood pressure; HF, heart failure; wHbA_1c, time-weighted mean HbA_1c of past HbA_1c measures.

Figure 2

Observed and simulated cumulative incidence of each of 10 major diabetes-related complications and of all-cause mortality. The shaded areas in red and blue represent 95% confidence regions of the observed and simulated values, respectively. The curves were generated using Kaplan-Meier methods with a cumulative failure estimator. CV, cardiovascular; HF, heart failure.

Figure 3

Tornado plots show the impact of changing one risk factor at a time by 1 SD on life expectancy (A) and complication-free survival time (B), and the impact of changing the annual probability of one major complication at a time by 20% on life expectancy (C) and complication-free survival time (D). The cardiovascular (CV) event is a composite end point including MI, PCI, and CABG. HF, heart failure.