University of New South Wales, Sydney, NSW, Australia.
The George Institute for Global Health, Level 5, 1 King St, Newtown, NSW, 2042, Australia.
Pharmacoeconomics. 2022 Nov;40(11):1095-1105. doi: 10.1007/s40273-022-01174-2. Epub 2022 Aug 12.
The rate of events such as recurrent heart failure (HF) hospitalization and death are known to dramatically increase directly after HF hospitalization. Furthermore, the number of HF hospitalizations is associated with irreversible long-term disease progression, which is in turn associated with increased event rates. However, cost-effectiveness models of HF treatments commonly fail to capture both the short- and long-term association between HF hospitalization and events.
The aim of this study was to provide a decision-analytic model that reflects the short- and long-term association between HF hospitalization and event rates. Furthermore, we assess the impact of omitting these associations.
We developed a life-time Markov cohort model to evaluate HF treatments, and modeled the short-term impact of HF hospitalization on event rates via a sequence of tunnel states, with transition probabilities following a parametric survival curve. The corresponding long-term impact was modeled via hazard ratios per HF hospitalization. We obtained baseline event rates and utilities from published literature. Subsequently, we assessed, for a hypothetical HF treatment, how omitting the modeled associations (through a simple two-state model) affects incremental quality-adjusted life-years (QALYs).
We developed a model that incorporates both short- and long-term impacts of HF hospitalizations. Based on an assumed treatment effect of a 20% risk reduction for HF hospitalization (and associated reductions in all-cause mortality of 15%), omitting the short-term, the long-term, or both associations resulted in a 5%, 1%, and 22% decrease in QALYs gained, respectively.
For both modeling components, i.e., the short- and long-term implications of HF hospitalization, the impact on incremental outcomes associated with treatment was substantial. Considering these aspects as proposed within this modeling approach better reflects the natural course of this progressive condition and will enhance the evaluation of future HF treatments.
众所周知，心力衰竭（HF）住院后，诸如心力衰竭复发（HF）住院和死亡等事件的发生率会急剧增加。此外，HF 住院的次数与不可逆转的长期疾病进展相关，而后者又与事件发生率的增加相关。然而，HF 治疗的成本效益模型通常无法同时捕捉 HF 住院与事件之间的短期和长期关联。
本研究旨在提供一种决策分析模型，反映 HF 住院与事件发生率之间的短期和长期关联。此外，我们评估了省略这些关联的影响。
我们开发了一个终生马尔可夫队列模型来评估 HF 治疗，并通过一系列隧道状态来模拟 HF 住院对事件发生率的短期影响，其转移概率遵循参数生存曲线。相应的长期影响通过每例 HF 住院的风险比来建模。我们从已发表的文献中获得了基线事件率和效用值。随后，我们评估了对于一种假设的 HF 治疗，通过一个简单的两状态模型省略建模关联如何影响增量质量调整生命年（QALYs）。
我们开发了一个模型，该模型既包含 HF 住院的短期影响，也包含其长期影响。基于 HF 住院风险降低 20%（并相应降低全因死亡率 15%）的假设治疗效果，省略短期、长期或两者关联会导致 QALYs 分别减少 5%、1%和 22%。
对于 HF 住院的短期和长期影响这两个建模部分，对与治疗相关的增量结果的影响是巨大的。考虑到这种建模方法中的这些方面，可以更好地反映这种进行性疾病的自然病程，并增强对未来 HF 治疗的评估。
