Evaluation of a patient self-directed mealtime insulin titration algorithm: a US payer perspective. (2nd June 2016)

• Record Type: Journal Article
• Authors: Curtis, Bradley H.; Curtis, Sarah; Murphy, Daniel R.; Gahn, James C.; Perk, Sinem; Smolen, Harry J.; Murray, James; Numapau, Nana; Bonner, Jeffrey S.; Liu, Rong; Johnson, Jennal; Glass, Leonard C.
• Is Part Of: Journal of medical economics. Volume 19:Number 6(2016)
• Page Start: 549
• Page End: 556
• Publication Date: 2016-06-02
• Subjects: Bolus insulin -- self-titration -- type 2 diabetes mellitus -- healthcare costs -- economic modeling; Medical care -- Cost control -- Periodicals; Medical economics -- Periodicals; 362.10941
• DOI: 10.3111/13696998.2016.1141098
• Languages: English
• ISSNs: 1369-6998

Abstract

Abstract: Objective To model the potential economic impact of implementing the AUTONOMY once daily (Q1D) patient self-titration mealtime insulin dosing algorithm vs standard of care (SOC) among a population of patients with Type 2 diabetes living in the US. Methods Three validated models were used in this analysis: The Treatment Transitions Model (TTM) was used to generate the primary results, while both the Archimedes (AM) and IMS Core Diabetes Models (IMS) were used to test the veracity of the primary results produced by TTM. Models used data from a 'real world' representative sample of patients (2012 US National Health and Nutrition Examination Survey) that matched the characteristics of US patients enrolled in the randomized controlled trial 'AUTONOMY' cohort. The base-case time horizon was 10 years. Results The modeling results from TTM demonstrated that total costs in the base-case were reduced by $1732, with savings predicted to occur as early as year 1. Results from the three models were consistent, showing a reduction in total costs for all sensitivity analyses. Limitations Data from short-term clinical trials were used to develop long-term projections. The nature of such extrapolation leads to increased uncertainty. Conclusion The results from all three models indicate that the AUTONOMY Q1D algorithm has the potential to abate total costs as early as the first year.