Abstract

To evaluate the cost-effectiveness of empagliflozin when added to standard of care (SoC) in patients with type 2 diabetes (T2D) and established cardiovascular (CV) disease in China. A validated discrete event simulation model, based on time-dependent parametric survival analyses of the EMPA-REG OUTCOME trial data, was used to project individual clinical and economic outcomes of patients receiving empagliflozin plus SoC compared with SoC alone. The model was adapted to the Chinese setting with local cost data from a healthcare system perspective. The background mortalities of the model cohort were adjusted with China-specific parameters. Model extrapolated outcomes included numbers and rates of diabetes-related events, life years (LYs), quality-adjusted life years (QALYs), costs as well as incremental cost-effectiveness ratios (ICERs). The main analysis simulated 5,000 patients over a lifetime horizon. An annual discount rate of 3.5% was applied to both future costs and QALYs. Deterministic and probabilistic sensitivity analyses were conducted to test the robustness of the model results. Compared with SoC alone, empagliflozin plus SoC was predicted to result in longer mean survivals (14.75 LYs vs 12.36 LYs) and reduced rates of clinical events, including CV death (4.11 vs. 5.78, per 100-patient years), heart failure (2.08 vs. 3.11), nonfatal myocardial infarction (1.94 vs 2.20), renal injury (1.02 vs. 1.56) and renal failure (0.33 vs. 0.56). This resulted in a predicted additional 1.01 QALYs with empagliflozin (8.05 QALYs vs. 7.04 QALYs with SoC) at an incremental cost of ¥58,166 per patient. The generated ICER was ¥57,971 per QALY gained, which was considered highly cost-effective in China given a willingness-to-pay threshold of ¥161,940 per QALY gained (three times GDP per capita). Deterministic and probabilistic sensitivity analyses indicated that the results are robust. Empagliflozin added to SoC is a highly cost-effective treatment strategy for patients with T2D and established CV disease in China.

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