Aims: Our aim was to explore optimal treatment decisions for HbA1c control for type 2 diabetes mellitus patients and assess the impact on potential improvements in quality of life compared with current guidelines.
Methods: We analyzed a large dataset of HbA1c levels, diabetes-related key risk factors and medication dispensed to 70,069 patients with type 2 diabetes from polyclinics and a large public hospital in Singapore during January 1, 2008, to December 31, 2015. A Markov decision process (MDP) model was developed to determine the optimal treatment policy concerning medication management for glycemic control over a long-term treatment period. We assessed the model performance by comparing quality-adjusted life years (QALYs) gained by the model with those derived by a conventional Markov model informed by current clinical guidelines.
Results: Numerical results showed that optimal treatment strategies derived by the MDP model could increase the total expected QALYs by as much as 0.27 years for patients at higher risk such as old age, high HbA1c levels and smokers. In particular, the improvements in QALYs gained for patients with HbA1c levels of 9% (75 mmol/mol) and above were higher than those with lower HbA1c levels. However, the potential improvements appeared to be marginal for patients at lower risk compared with current guidelines.
Conclusions: Use of data-driven prescriptive analytics would help clinicians make evidence-based treatment decisions for HbA1c control for patients with type 2 diabetes, in particular for those at high risk.
Keywords: Glycemic control; Markov decision process; Treatment strategy; Type 2 diabetes.