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A Proposal of a Cost-Effectiveness Modeling Approach for Heart Failure Treatment Assessment: Considering the Short- and Long-Term Impact of Hospitalization on Event Rates

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Abstract

Background

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.

Objective

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.

Methods

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).

Results

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.

Conclusion

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.

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Authors and Affiliations

  1. University of New South Wales, Sydney, NSW, Australia

    Gian Luca Di Tanna

  2. Biogen International GmbH, Baar, Switzerland

    Michael Urbich

  3. Karolinska Institutet, Stockholm, Sweden

    Peter Lindgren

  4. The Swedish Institute for Health Economics, Lund, Sweden

    Peter Lindgren

  5. Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Thomas A. Gaziano

  6. Cerevel Therapeutics, Cambridge, MA, USA

    Gary Globe

  7. Amgen (Europe) GmbH, Economic Modeling Center of Excellence, Rotkreuz, Switzerland

    Björn Stollenwerk

  8. The George Institute for Global Health, Level 5, 1 King St, Newtown, NSW, 2042, Australia

    Gian Luca Di Tanna & Blake Angell

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  1. Gian Luca Di Tanna
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Correspondence to Gian Luca Di Tanna.

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Funding

Financial support for this study was provided entirely by Amgen Inc. The funding agreement ensured the authors’ independence in designing the study, interpreting the data, and writing and publishing the report.

Author contributions

GLDT planned and originally implemented the model proposal and wrote the paper. BA assisted with the analyses and gave writing support. MU and BS implemented the model in Microsoft Excel and gave writing support. PL, TG and GG gave expert opinion support and writing contributions. All authors participated in the critical review of the manuscript and approved the final version submitted for publication

Conflicts of interest

Björn Stollenwerk is an Amgen employee and Amgen corporate stockholder. Peter Lindgren and Thomas A. Gaziano received Amgen funds and/or honoraria. Gian Luca Di Tanna and Michael Urbich are former Amgen employees. Gian Luca Di Tanna received Amgen honoraria paid to his employer. Gary Globe is a former Amgen employee and remains an Amgen corporate stockholder. Blake Angell is supported by an National Health and Medical Research Council Emerging Leadership Grant (GNT2010055) and declares no other funding or conflicts of interests related to this work.

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Availability of data and material

The four models that we have discussed in this manuscript are available at the following Open Science Framework companion repository: https://osf.io/6wx39/ (Di Tanna, G. L. [2022, July 2]; A Proposal of Cost-effectiveness Modeling Approach for Heart Failure Treatment Assessment: Considering the Short- and Long-Term Impact of Hospitalization on Event Rates). These have been developed using the widely available Microsoft Excel suite (Microsoft Corporation, Redmond, WA, USA) but they can be programmed taking advantage of the increasingly available R packages such as heemod, hesim and mstate (The R Foundation for Statistical Computing, Vienna, Austria), among others.

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Di Tanna, G.L., Angell, B., Urbich, M. et al. A Proposal of a Cost-Effectiveness Modeling Approach for Heart Failure Treatment Assessment: Considering the Short- and Long-Term Impact of Hospitalization on Event Rates. PharmacoEconomics 40, 1095–1105 (2022). https://doi.org/10.1007/s40273-022-01174-2

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