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Decision-Analytic Models to Simulate Health Outcomes and Costs in Heart Failure

A Systematic Review

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Abstract

Chronic heart failure (CHF) is a critical public health issue with increasing effect on the healthcare budgets of developed countries. Various decisionanalytic modelling approaches exist to estimate the cost effectiveness of health technologies for CHF. We sought to systematically identify these models and describe their structures.

We performed a systematic literature review in MEDLINE/PreMEDLINE, EMBASE, EconLit and the Cost-Effectiveness Analysis Registry using a combination of search terms for CHF and decision-analytic models. The inclusion criterion required ‘use of a mathematical model evaluating both costs and health consequences for CHF management strategies’. Studies that were only economic evaluations alongside a clinical trial or that were purely descriptive studies were excluded.

We identified 34 modelling studies investigating different interventions including screening (n = 1), diagnostics (n = 1), pharmaceuticals (n = 15), devices (n = 13), disease management programmes (n = 3) and cardiac transplantation (n = 1) in CHF. The identified models primarily focused on middle-aged to elderly patients with stable but progressed heart failure with systolic left ventricular dysfunction. Modelling approaches varied substantially and included 27 Markov models, three discrete-event simulation models and four mathematical equation sets models; 19 studies reported QALYs. Three models were externally validated. In addition to a detailed description of study characteristics, the model structure and output, the manuscript also contains a synthesis and critical appraisal for each of the modelling approaches.

Well designed decision models are available for the evaluation of different CHF health technologies. Most models depend on New York Heart Association (NYHA) classes or number of hospitalizations as proxy for disease severity and progression. As the diagnostics and biomarkers evolve, there is the hope for better intermediate endpoints for modelling disease progression as those that are currently in use all have limitations.

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Acknowledgements

This work was supported by the Competence Network of Heart Failure, funded by the Federal Ministry of Education and Research (BMBF), Germany (funding no. 01GI0205).

The authors have no conflicts of interest that are directly relevant to the content of this review.

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Authors and Affiliations

  1. Institute for Technology Assessment & Cardiac MR, PET, CT Program, and Department of Radiology, Massachusetts General Hospital, Harvard Medical School, 165 Cambridge Street, Suite 400, Boston, Massachusetts, 02214, USA

    Alexander Goehler, Benjamin P. Geisler, G. Scott Gazelle & Uwe Siebert

  2. Cardiac MR, PET, CT Program, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA

    Alexander Goehler

  3. Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health, Information Systems, and Health Technology Assessment, UMIT — University for Health Sciences, Medical Informatics and Technology, Hall iT, Austria

    Alexander Goehler, Benjamin P. Geisler, Beate Jahn, Annette Conrads-Frank & Uwe Siebert

  4. Alfried Krupp von Bohlen und Halbach Foundation-Institute for Health Systems Management, University of Duisburg-Essen, Essen, Germany

    Alexander Goehler

  5. Department of Population and International Health, Harvard School of Public Health, Boston, Massachusetts, USA

    Jennifer M. Manne

  6. Center for Health Decision Science, Department of Health Policy and Management, Harvard School of Public Health, Boston, Massachusetts, USA

    G. Scott Gazelle & Uwe Siebert

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Correspondence to Alexander Goehler.

Appendix

Appendix

Search Strategies

In MEDLINE/PreMEDLINE (1966 to June 2010), we used one of the following Medical Subject Heading (MeSH) terms: ‘Technology Assessment, Biomedical’, ‘Decision Support Techniques’, ‘Models, Statistical’, ‘Markov Chains’, ‘Monte Carlo Method’, ‘Economics’, ‘Health Planning’ or ‘Quality-Adjusted Life Years’, combined with the MeSH term ‘Heart Failure’.

In EMBASE Drugs and Pharmacology (1966 to June 2010), our keywords were ‘decision analysis’, ‘decision-analytic’, ‘decision model’, ‘health care model’, ‘health care evaluation model’, ‘decision tree’, ‘Markov model’, ‘cost-effectiveness’, ‘cost-utility’, ‘cost-benefit’, ‘cost-minimi[z/s]ation’ or ‘QALY’ in combination with the term ‘heart failure’.

In EconLit and the Cost-Effectiveness Analysis Registry, we searched for ‘Heart Failure’.

In addition, a hand search was conducted. We searched the references of retrieved papers for publications that might be eligible.

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Goehler, A., Geisler, B.P., Manne, J.M. et al. Decision-Analytic Models to Simulate Health Outcomes and Costs in Heart Failure. Pharmacoeconomics 29, 753–769 (2011). https://doi.org/10.2165/11585990-000000000-00000

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