Evaluation of a patient self-directed mealtime insulin titration algorithm: a US payer perspective

Area of Science:

• Health Economics
• Diabetes Management
• Pharmacoeconomics

Background:

• Type 2 diabetes management requires effective and cost-efficient insulin dosing strategies.
• Patient self-titration algorithms offer a potential avenue for optimizing treatment and reducing healthcare expenditures.
• Standard of care (SOC) insulin dosing may not fully leverage patient engagement for economic benefits.

Purpose of the Study:

• To model the economic impact of the AUTONOMY once daily (Q1D) patient self-titration mealtime insulin dosing algorithm.
• To compare the cost-effectiveness of AUTONOMY Q1D against standard of care (SOC) in US Type 2 diabetes patients.
• To assess the potential for cost reduction and savings over a 10-year time horizon.

Main Methods:

• Utilized three validated economic models: Treatment Transitions Model (TTM), Archimedes (AM), and IMS Core Diabetes Models (IMS).
• Employed real-world data from the 2012 US National Health and Nutrition Examination Survey, matched to the AUTONOMY trial cohort.
• Conducted a 10-year base-case analysis with sensitivity analyses to test model robustness.

Main Results:

• The TTM demonstrated a reduction in total costs by $1732 in the base-case scenario.
• Cost savings were predicted to materialize as early as the first year of implementation.
• Consistent cost reductions were observed across all three models and sensitivity analyses.

Conclusions:

• The AUTONOMY Q1D algorithm shows potential to significantly reduce total healthcare costs for Type 2 diabetes patients.
• Economic benefits, including cost abatement, are achievable starting from the initial year of algorithm implementation.
• Modeling suggests AUTONOMY Q1D is a cost-effective alternative to standard of care for mealtime insulin management.