Schmalhofer Constanze, Otte Im Kampe Eveline, Eheberg Dirk, Sandhu Hera, Maier Martina, Perschke Alexander, Mugwagwa Tendai, Fröling Emma, Kisser Agnes
IQVIA Commercial GmbH & Co. OHG, Munich, Germany.
Pfizer Pharma GmbH, Berlin, Germany.
J Med Econ. 2025 Dec;28(1):1226-1240. doi: 10.1080/13696998.2025.2536974. Epub 2025 Jul 31.
To estimate the cost-effectiveness of Nirmatrelvir/ritonavir (NMV/r) versus best supportive care (BSC) in patients at high-risk for progression to severe COVID-19 from a German health payer perspective.
A closed cohort static model of 1,000 COVID-19 patients capturing the short-term (<1 year) decision-tree and long-term (lifetime) outcomes Markov model was used to assess the cost-effectiveness of NMV/r versus BSC. Model inputs were derived from the EPIC-HR clinical trial and published contemporary real-world data. Probabilistic and deterministic sensitivity analyses (PSA, DSA) were conducted to test the robustness of model results.
In the base case, treatment with NMV/r versus BSC reduced COVID-19 related hospitalisations (-0.042), intensive care unit admissions (-0.006) and inpatient deaths (-0.003), while increasing life-years (LY) (0.047) per patient, which results in an incremental cost-effectiveness ratio of 10,845 € per hospitalisation avoided and 9,773 € per LY gained. Sensitivity analysis suggests the magnitude of the benefits increased with increasing hospitalisation risk. NMV/r emerged as the dominant strategy in a population with a hospitalisation risk equivalent to 60 years and older. Outcomes were similar with real world effectiveness data. DSA showed the model was most sensitive to hospitalisation and inpatient mortality risk, NMV/r medication cost and efficacy/effectiveness of NMV/r in reducing hospitalisation. PSA confirmed the robustness of the model results.
As COVID-19 is a dynamic disease, caution should be taken in generalizing these results. Contemporary data is essential to ensure the model inputs and the outcomes remain relevant as there may be changes in natural disease course or effectiveness of NMV/r.
This cost-effectiveness analysis of NMV/r treatment from a German healthcare payer perspective demonstrates how by preventing progression to severe COVID-19, NMV/r reduces healthcare resource use, associated costs and preserves LY of patients. This analysis provides crucial economic rationale for decision making by policy makers.
从德国医疗支付方的角度评估奈玛特韦/利托那韦（NMV/r）与最佳支持治疗（BSC）相比，对有进展为重症 COVID-19 高风险患者的成本效益。
采用一个包含 1000 例 COVID-19 患者的封闭队列静态模型，该模型结合了短期（<1 年）决策树和长期（终身）结果的马尔可夫模型，以评估 NMV/r 与 BSC 的成本效益。模型输入数据来自 EPIC-HR 临床试验和已发表的当代真实世界数据。进行了概率性和确定性敏感性分析（PSA、DSA）以检验模型结果的稳健性。
在基础病例中，与 BSC 相比，NMV/r 治疗减少了 COVID-19 相关住院（-0.042）、重症监护病房入院（-0.006）和住院死亡（-0.003），同时增加了每位患者的生命年（LY）（0.047），这导致每避免一次住院的增量成本效益比为 10,845 欧元，每获得一个生命年为 9,773 欧元。敏感性分析表明，随着住院风险增加，获益程度也增加。在住院风险相当于 60岁及以上人群中，NMV/r 成为主导策略。使用真实世界有效性数据时结果相似。DSA 显示模型对住院和住院死亡率风险、NMV/r 药物成本以及 NMV/r 在降低住院方面的疗效/有效性最为敏感。PSA 证实了模型结果的稳健性。
由于 COVID-19 是一种动态疾病，在推广这些结果时应谨慎。当代数据对于确保模型输入和结果仍然相关至关重要，因为自然病程或 NMV/r 的有效性可能会发生变化。
从德国医疗支付方角度对 NMV/r 治疗进行的这项成本效益分析表明，通过预防进展为重症 COVID-19，NMV/r 如何减少医疗资源使用、相关成本并保留患者的生命年。该分析为政策制定者的决策提供了关键的经济依据。
