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

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posted on 12.06.2020 by An Tran-Duy, Josh Knight, Andrew J Palmer, Dennis Petrie, Tom WC Lung, William H Herman, Björn Eliasson, Ann-Marie Svensson, Philip M Clarke
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 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 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.

CONCLUSIONS

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.

Funding

The Swedish Association of Local Authorities and Regions funds the NDR. This study was supported by: the National Health and Medical Research Council (NHMRC; grant number 1028335), the Australian Research Council’s Discovery Early Career Researcher Awards scheme (DECRA; grant number DE150100309), and the Australian Research Council’s Centre of Excellence in Population Ageing Research (CEPAR; grant number CE170100005 awarded to Prof Philip Clarke). The funders had no role in study design, data analysis, preparation of the manuscript, or decision to publish the manuscript.

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