| Literature DB >> 35743736 |
Eugenio De Corso1, Gianluca Furneri2, Daria Salsi3, Francesca Fanelli4, Gianluca Ronci4, Giovanna Sala4, Rossella Bitonti2, Domenico Cuda5.
Abstract
The objective of this analysis was to estimate the incremental cost-utility ratio (ICUR) of dupilumab as an add-on treatment to best supportive care (BSC), versus BSC alone, in Italy for severe uncontrolled chronic rhinosinusitis with nasal polyps (CRSwNP). A simulation of outcomes and costs was undertaken using a 1-year decision tree, followed by a lifetime horizon Markov model. Clinical data were derived from a pooled analysis of two studies (SINUS-24 NCT02912468 and SINUS-52 NCT02898454). The Italian National Healthcare Service (NHS) perspective was considered. Model robustness was tested through sensitivity analyses. In the base-case analysis, treatment with dupilumab + BSC resulted in an increase in quality of life-adjusted survival (+1.02 quality-adjusted life years (QALY-gained)), compared to the BSC alone. The resulted ICUR was €21,817 per QALY-gained and it is below the acceptability threshold commonly used in Italy. Both one-way deterministic and probabilistic sensitivity analyses confirmed the robustness of base-case results. The cost-utility analysis showed that dupilumab, as an add-on treatment to BSC, is a cost-effective therapeutic alternative to BSC in the treatment of patients with severe uncontrolled chronic rhinosinusitis with nasal polyps, confirming that it is economically sustainable.Entities:
Keywords: CRSwNP; cost–utility analysis; dupilumab
Year: 2022 PMID: 35743736 PMCID: PMC9225649 DOI: 10.3390/jpm12060951
Source DB: PubMed Journal: J Pers Med ISSN: 2075-4426
Figure 1Model structure: (a) Decision tree; (b) Markov model. Note: BSC, best supportive care; CRSwNP, chronic rhinosinusitis with nasal polyps; DUP, dupilumab.
Figure 2Response rate at 24 and 52 weeks.
Clinical inputs for the “Inadequately controlled disease” and “Post-operative” health states.
| Parameters | Value | Source |
|---|---|---|
| Clinical input for “Inadequately Controlled Disease” state | ||
| Percentage of patients who are ineligible for surgery (%) | 15.1% | Pooled analysis of the SINUS-24 and SINUS-52 studies [ |
| Maximum age eligible for surgery (years) | 70.0 | Assumption |
| Annual transition probability of eligible patients moving to “Surgery” health state (%) | 1.9% | PMSI, 2019 [ |
| Annual transition probability of eligible patients moving to “Surgery” health state (%) | 98.1% | Calculated |
| Clinical input for Post-operative period | ||
| Annual transition probability of moving to “Post-operative controlled” health state from “Surgery” health state (%) | 100.0% | Pooled analysis of the SINUS-24 and SINUS-52 studies [ |
| Annual transition probability of remaining in the “Post-operative controlled” health state (%) | 60.0% | Assumption |
| Annual transition probability of moving to “Post-operative uncontrolled” health state from “Post-operative controlled” (%) | 40.0% | Assumption |
| Annual transition probability of moving to “Surgery” health state from “Post-operative uncontrolled” health state | 1.9% | PMSI, 2019 [ |
| Annual transition probability of remaining in the “Post-operative uncontrolled” health state (%) | 98.1% | Calculated |
Utility data included in the model.
| Utility | DUP | BSC | Description |
|---|---|---|---|
| Decision tree | |||
| Week 0–12, baseline | 0.769 | 0.769 | Baseline utility from pooled analysis SINUS-24 and SINUS-52 studies [ |
| Week 12–24, regardless of response | 0.875 | 0.810 | Treatment specific utility at 24 weeks [ |
| After week 25, patient responders | 0.891 | 0.891 | Utility at 24 weeks, all treatments |
| After week 25, patient non-responders | 0.808 | 0.808 | Utility at 24 weeks, non-responder patients [ |
| Markov model | |||
| Controlled disease | 0.913 | 0.913 | Utility at 52 weeks, responder patients [ |
| Inadequately controlled disease | 0.776 | 0.776 | Utility at 52 weeks, non-responder patients [ |
| Surgery | 0.820 | 0.820 | Estimated based on utility gain from surgery with inclusion of short-term disutility [ |
| Post-operative controlled disease | 0.827 | 0.827 | Assumed to be 0.051 higher than the utility of “Inadequately controlled disease” (0.776) [ |
| Post-operative uncontrolled disease | 0.760 | 0.760 | Assumed equal to baseline utility of patients with two previous surgeries |
Note: DUP, dupilumab; BSC, best supportive care.
Disease management costs.
| Decision Tree | ||||
|---|---|---|---|---|
| Response state | Therapy costs (€) | Medical costs (€) | ||
| Weeks | Weeks | Weeks | Weeks | |
| Responder patients | €780 | €910 | €3345 | €3903 |
| Non-responder patients | €842 | €982 | €6790 | €7922 |
| Markov model | ||||
| Health state | Total costs (€) | |||
| Year 1 | Year 2+ | |||
| Controlled disease | €8937 | €8937 | ||
| Inadequately controlled disease | €16,536 | €16,536 | ||
| Post-operative controlled | €8937 | €8937 | ||
| Post-operative uncontrolled | €16,536 | €16,536 | ||
Adverse events’ incidence rates and costs.
| Adverse Event | Incidence Rate (N/Year) [ | Unit Costs (€) | Source of Costs | |
|---|---|---|---|---|
| Dupilumab | BSC | |||
| Injection site reaction | 0.395 | 0.000 | 20.66 | Code 89.7 [ |
| Nasopharyngitis | 0.275 | 0.287 | 16.31 | Garattini et al. inflated to January 2022 [ |
| Epistaxis | 0.106 | 0.114 | 16.31 | Garattini et al. inflated to January 2022 [ |
| Headache | 0.092 | 0.181 | 20.66 | Code 89.7 [ |
| Asthma | 0.058 | 0.173 | 326.48 | Access to the emergency room, inflated to January 2022 [ |
Note: BSC, best supportive care.
Results of the cost–utility analysis.
| Results of the Cost-Utility Analysis | Dupilumab + BSC | BSC | Difference |
|---|---|---|---|
| Outcome | |||
| Years of life adjusted for quality (QALYs) | 17.15 | 16.13 | 1.02 |
| Direct costs (€) | |||
| Drug acquisition costs | €76,383 | €0 | €76,383 |
| Disease management costs * | €275,517 | €329,367 | −€53,850 |
| Adverse events costs | €1083 | €1333 | −€249 |
| TOTAL direct costs | €352,983 | €330,700 | €22,283 |
| Incremental cost–utility ratio (ICUR) | |||
| ICUR (dupilumab vs. BSC) (€/QALY) | €21,817 | ||
Note: * They include the costs of surgery. BSC: best supportive care; ICUR: incremental cost–utility ratio; QALY: quality adjusted life year.
Figure 3Tornado graph (deterministic sensitivity analysis): dupilumab + BSC vs. BSC (base-case). Note: BSC, best supportive care; DUP, dupilumab; ICUR, incremental cost–utility ratio; QALY, quality adjusted life year.
Figure 4Cost–utility acceptability curve: dupilumab + BSC vs. BSC (base-case; N = 1000 simulations; ICUR in base-case: €21,817/QALY-gained).
Figure 5Results of the probabilistic sensitivity analysis.
Results of the scenario analysis 1.
| Results of the Cost-Utility Analysis | Dupilumab + BSC | BSC | Difference |
|---|---|---|---|
| Outcome | |||
| Years of life adjusted for quality (QALYs) | 17.15 | 16.13 | 1.02 |
| Costs (€) | |||
| Direct costs | €352,983 | €330,700 | €22,283 |
| Indirect costs | €163 | €483 | −€320 |
| Total costs | €353,146 | €331,183 | €21,963 |
| Incremental cost–utility ratio (ICUR) | |||
| ICUR (dupilumab vs. BSC) (€/QALY) | €21,503 | ||
Note: BSC, best supportive care; ICUR, incremental cost–utility ratio; QALY, quality adjusted life year.