Di Tanna Gian Luca, Chen Shuxian, Bychenkova Anna, Wirtz Heidi S, Burrows Karen L, Globe Gary
The George Institute for Global Health, Sydney, Australia.
The Comparative Health Outcomes, Policy and Economics (CHOICE) Institute, University of Washington, Seattle, WA, USA.
Pharmacoecon Open. 2020 Sep;4(3):397-401. doi: 10.1007/s41669-019-00173-y.
Various decision analytic models exist for evaluating the cost-effectiveness of pharmacological interventions for heart failure (HF). Despite this, studies that explore drivers influencing these modeling approaches remain scarce. Through a systematic review of the literature, the present study sought to identify model drivers that emerge from economic evaluations of HF pharmacological interventions. Among the 72 cost effectiveness papers evaluating HF drug interventions, the most frequently identified, top 5 ranked model drivers impacting the incremental cost-effectiveness ratio (ICER) were cost of treatment and utility, identified in 10% of studies, respectively. Other drivers that emerged as top 5 ranked drivers in > 5% of studies included treatment effect on mortality (or cardiovascular mortality), duration of treatment, and baseline cardiovascular mortality. Model drivers reported at the top of tornado diagrams were treatment effect on mortality or on cardiovascular mortality. Collectively, these observations highlight the key importance of treatment effect in driving cost-effectiveness models for HF.
存在多种用于评估心力衰竭（HF）药物干预成本效益的决策分析模型。尽管如此，探索影响这些建模方法的驱动因素的研究仍然很少。通过对文献的系统回顾，本研究旨在确定HF药物干预经济评估中出现的模型驱动因素。在72篇评估HF药物干预成本效益的论文中，最常被确定的、排名前5的影响增量成本效益比（ICER）的模型驱动因素分别是治疗成本和效用，在10%的研究中被确定。在超过5%的研究中成为排名前5的驱动因素的其他驱动因素包括对死亡率（或心血管死亡率）的治疗效果、治疗持续时间和基线心血管死亡率。在龙卷风图顶部报告的模型驱动因素是对死亡率或心血管死亡率的治疗效果。总体而言，这些观察结果突出了治疗效果在推动HF成本效益模型方面的关键重要性。
