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What are the methodological characteristics and quality of decision-analytical models used in the economic evaluation of early warning systems for chronic heart failure?
What are the methodological characteristics and quality of decision-analytical models used in the economic evaluation of early warning systems for chronic heart failure?
Current cost-effectiveness models for heart failure early warning systems have satisfactory methodological quality but require better transparency and handling of uncertainty.
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Models support cautious use in HF early warning evaluations; challenges future work to strengthen uncertainty analyses and transparency.
INTRODUCTION: Describing the general and methodological characteristics of decision-analytical models used in the economic evaluation of early warning systems for the management of chronic heart failure patients and performing a quality assessment of their methodological characteristics is expected to provide concise and useful insight to inform the future development of decision-analytical models in the field of heart failure management. AREAS COVERED: The literature on decision-analytical models for the economic evaluation of early warning systems for the management of chronic heart failure patients was systematically reviewed. Nine electronic databases were searched through the combination of synonyms for heart failure and sensitive filters for cost-effectiveness and early warning systems. EXPERT COMMENTARY: The retrieved models show some variability with regards to their general study characteristics. Overall, they display satisfactory methodological quality, even though some points could be improved, namely on the consideration and discussion of any competing theories regarding model structure and disease progression, identification of key parameters and the use of expert opinion, and uncertainty analyses. A comprehensive definition of early warning systems and further research under this label should be pursued. To improve the transparency of economic evaluation publications, authors should make available detailed technical information regarding the published models.
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Almeida et al. (2017) studied this question.
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