OBJECTIVES: Clinical trials have become shorter and sometimes do not allow to measure hemoglobin A1c (HbA1c) changes. Continuous glucose monitoring devices measure glycemic control with time in range (TIR) of 70-180 mg/dL. However, risk equations predicting cardiovascular outcomes based on TIR are not available. The aim of this study was to find an adequate TIR-to-HbA1c change conversion algorithm in the literature, and the implantation of that approach in the IQVIA Core Diabetes Model v10.0.

METHODS: We performed a target literature search on publications that presented paired TIR and HbA1c changes in randomized trials, for both type 1 (T1D) and type 2 diabetes and compared them with available conversion algorithms. T1D analyses were performed in two cohorts (A: baseline HbA1c=8%; B: HbA1c=9%), comparing two treatment effects on TIR (+10% and +20%) to no effect, and to the equivalent effects on HbA1c.

RESULTS: Literature showed that the algorithm presented by Beck et al (2017) provided best results in both T1D and T2D.

Without treatment, predicted discounted life years (LY) and quality-adjusted LY (QALY) were 28.19 and 19.15 for cohort A, and 26.85 and 17.68 for cohort B.

For cohort A, the 10% TIR effect increased LY and QALY by 0.42 and 0.51. With an additional 10% TIR increase (10% to 20%), LY and QALY increased further by 0.32 and 0.38.

For cohort B, 10% TIR effect increased LY and QALY by 0.61 and 0.63. With an additional 10% TIR increase, LY and QALY increased further by 0.37 and 0.41.

The same change in TIR provides a higher impact on LY and QALY in the cohort with the highest baseline HbA1c.

Same results were predicted when using equivalent effects on HbA1c.

CONCLUSIONS: The initial increase in TIR is more important than further increases. And the impact is higher in less controlled populations.