Objectives: Changes in body mass index (BMI) is one of the key drivers of costeffectiveness in diabetes modeling via its impact on utility. Most models are using a fixed disutility per point increase of BMI. However, it can be questioned whether decreasing BMI has the same impact on utility in overweighed and non-overweighed patients. IQVIA Core Diabetes Model (CDM) version 9.5 includes two disutility approaches: 1) fixed disutility per unit gain of BMI above a BMI of 25kg/m2 (already available in version 9.0); 2) polynomial model based on a regression analysis with a variable disutility based on the current BMI (Soltoft F, 2009). The aim of this study was to assess the impact of the two approaches on QALYs and ICER. Methods: The observational EDGE study comparing metformin+vildagliptin (M+V) versus metfor-min+sulphonylurea (M+S) was used as a base case. Basal insulin rescue therapy was applied to both arms when at an HbA1c threshold level of 7.5% was reached. Baseline characteristics and treatments effects from the published EDGE study were applied. HbA1c reductions and change in body weight were applied -0.99% -1.19% and -0.3kg -1.6kg for M+S and M+V respectively. Fixed disutility per point of BMI was the NICE approved CDM default value (0.0061). UK costs (2018) were used and annual discounting of 3.5% was applied on costs and QALYs. Results: With the polynomial approach, the lifetime QALYs were 8.147 and 8.119 for M+V and M+S respectively. When using the static approach, QALY were 7.940 and 7.896 respectively. With the new approach the ICER increased from 12,061 GBP/QALY to 19,655 GBP/QALY. Conclusions: The polynomial approach is more conservative resulting in higher total QALY in both arms, lower incrementals and thus higher ICERs compared to the static approach.
Objectives: Changes in body mass index (BMI) is one of the key drivers of costeffectiveness in diabetes modeling via its impact on utility. Most models are using a fixed disutility per point increase of BMI. However, it can be questioned whether decreasing BMI has the same impact on utility in overweighed and non-overweighed patients. IQVIA Core Diabetes Model (CDM) version 9.5 includes two disutility approaches: 1) fixed disutility per unit gain of BMI above a BMI of 25kg/m2 (already available in version 9.0); 2) polynomial model based on a regression analysis with a variable disutility based on the current BMI (Soltoft F, 2009). The aim of this study was to assess the impact of the two approaches on QALYs and ICER. Methods: The observational EDGE study comparing metformin+vildagliptin (M+V) versus metfor-min+sulphonylurea (M+S) was used as a base case. Basal insulin rescue therapy was applied to both arms when at an HbA1c threshold level of 7.5% was reached. Baseline characteristics and treatments effects from the published EDGE study were applied. HbA1c reductions and change in body weight were applied -0.99% -1.19% and -0.3kg -1.6kg for M+S and M+V respectively. Fixed disutility per point of BMI was the NICE approved CDM default value (0.0061). UK costs (2018) were used and annual discounting of 3.5% was applied on costs and QALYs. Results: With the polynomial approach, the lifetime QALYs were 8.147 and 8.119 for M+V and M+S respectively. When using the static approach, QALY were 7.940 and 7.896 respectively. With the new approach the ICER increased from 12,061 GBP/QALY to 19,655 GBP/QALY. Conclusions: The polynomial approach is more conservative resulting in higher total QALY in both arms, lower incrementals and thus higher ICERs compared to the static approach.
Objectives: Effectiveness of antidiabetic drugs is related to adherence to the drug. Due to the design of clinical trials, adherence tends to be better compared to real life. In health economic models mostly, the efficacy of clinical trials is used. In the new version (9.5) of the IQVIA-Core-Diabetes-Model (CDM) an adherence module (AM) is included. The current study aims to explain how this functionality operates and how its use influences health economic outcomes. Methods: The AM requires three inputs per treatment 1) adherence rate, 2) HbA1c change per 10% change in adherence rate, 3) BMI change in Kg/m2 per 10% change in adherence rate. Adherence rate directly impacts treatment costs. All inputs influence the first-year treatment effect of each treatment independent of the treatment line. Baseline characteristics and treatment effects from the observational EDGE-study comparing metformin+vildagliptin (M+V) with metformin+sulphonylurea (M+S) were employed. Two adherence rates (100% and 80%) were tested. The impact of adherence per 10% change on HbA1c (-0.08%points) and BMI (DPP4= -0.0576 Kg/m ˇ 2 ; SU: 0.0072 Kg/m ˇ 2 ) was taken from literature. The economic analysis employed UK 2018 costs and applied a 3.5% annual discount rate was on costs and outcomes. Results: Diminishing adherence from 100% to 80% hardly changed life expectancy. Quality adjusted life years reduced from 6.93 to 6.91 in M+V and increased from 6.87 to 6.88 in M+S (due to more BMI decrease with lower adherence). Linked to the adherence rate, treatment costs diminish accordingly, however complication costs increase. Impact on ICER was minimal (slight increase). Conclusions: Lower adherence did not impact the ICER significantly, because of opposite effects on BMI, the same adherence rates in M+V and M+S arms, and patients switch to next line therapy quickly. Applying different adherence rates and other BMI effects, would result in considerable impact on final outcomes.
A747markers and estimation of their long-term impact via predictive risk equations. The evidence base from cardiovascular outcomes trials (CVOTs) in T2DM is growing rapidly, due to FDA requirements for new treatments to demonstrate CV safety versus placebo as part of standard care. This new generation of CVOTs may require a new approach for associated cost-effectiveness models in T2DM. Methods: A targeted literature review was conducted to identify opportunities for future modelling of CVOT evidence from approaches taken in other therapy areas. Current NICE clinical and public health guidelines for CV conditions were reviewed to identify methods employed in de novo economic models to assess the impact of comparator interventions on CV outcomes. Results: Across 22 guidelines for treatment of hypertension, lipid modification, myocardial infarction (MI), stroke and other CV conditions, a total of 21 cost-effectiveness models were identified that explicitly modelled at least one CV outcome: MI, stroke, angina, revascularisation, peripheral arterial disease (PAD), heart failure and/or CV mortality. The majority of evaluations utilised lifetime (n= 19) cohort-level (n= 20) Markov (n= 16) modelling approaches; the only patientlevel evaluation utilised time-to-event simulation. Intervention-specific relative risks derived from meta-analyses were commonly applied to baseline risks of CV events, obtained from clinical trials and observational studies, including audit data. Surrogate markers were rarely modelled and the use of published risk equations limited to the estimation of baseline CV risk: Framingham (n= 4), QRisk2 (n= 1) and UKPDS (n= 1) in the only T2DM-specific evaluation. ConClusions: When modelling the outcomes of CVOTs in T2DM patients, alternative modelling methods may be more appropriate than typical T2DM approaches; a more suitable framework, consistent with the approach taken in CV modelling, may be to assess the comparative evidence via the application of relative risks.
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