Economic evaluation of dapagliflozin added to standard therapy in chronic heart failure in Argentina: a cost-utility analysis

Figures

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  Fig. 1. a. Markov model structure for DAPA-HF. b. Markov model structure for DELIVER.

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  Fig. 2. Tornado diagrams showing the individual impact of variations in key model parameters on the ICER of dapagliflozin vs standard therapy in the DAPA-HF population (a) and the DELIVER population (b). Each bar represents the range of ICER values obtained by replacing the base-case value of each parameter with its lower and upper bounds, while holding all other model assumptions constant. Parameters are ordered according to their relative influence on the ICER. Abbreviations: CV, cardiovascular; HF, heart failure; HR, hazard ratio; ICER, incremental cost-effectiveness ratio; USD, United States dollars.

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  Fig. 3. Cost-effectiveness planes from the probabilistic sensitivity analysis for DAPA-HF (a) and DELIVER (b). Each point represents one iteration of the probabilistic sensitivity analysis, showing the incremental costs (USD) and incremental QALYs of dapagliflozin compared with standard therapy. The diagonal lines correspond to willingness-to-pay thresholds equivalent to 0.5 times GDP per capita (blue line) and 1 GDP per capita (green line), at USD 6929 and USD 13,858 per QALY, respectively. The dispersion of points reflects the joint uncertainty of the model parameters. Abbreviations: GDP, gross domestic product; QALY, quality-adjusted life-year; USD, United States dollars.

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  Fig. 4. Cost-effectiveness acceptability curves for DAPA-HF (a) and DELIVER (b). The curves depict the probability that dapagliflozin is cost-effective across a range of willingness-to-pay thresholds, based on the probabilistic sensitivity analysis. Abbreviations: QALY, quality-adjusted life-year; USD, United States dollars.