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Updated: May 10, 2026
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Cardiac Magnetic Resonance for the Evaluation of Suspected Cardiac Thrombus: Conventional and Emerging Techniques
Published on: June 11, 2019
Cost-effectiveness analysis for imaging techniques with a focus on cardiovascular magnetic resonance
Sanjeev A Francis1, Caroline Daly, Bobak Heydari
1Department of Medicine, Brigham and Women's Hospital, Cardiovascular Division, Boston, MA, USA.
Summary
Demonstrating the cost-effectiveness of new diagnostic tests presents unique challenges. Their value depends on performance, disease prevalence, and treatment effectiveness, influencing patient management indirectly.
Area of Science:
- Health Economics
- Medical Technology Assessment
Background:
- Healthcare systems face pressure for cost containment, increasing scrutiny of new diagnostic technologies.
- Evaluating the cost-effectiveness of diagnostic tests is complex due to their indirect impact on patient outcomes.
Purpose of the Study:
- To review the principles and theories of cost-effectiveness analysis (CEA) specifically for diagnostic tests.
- To discuss the limitations and strengths of CEA for diagnostic technologies in various contexts.
Main Methods:
- Review of cost-effectiveness analysis (CEA) concepts applied to diagnostic tests.
- Examination of factors influencing the benefits of diagnostic technologies.
- Analysis of existing literature on CEA of cardiovascular magnetic resonance (CMR) compared to alternatives.
Main Results:
- Diagnostic tests indirectly affect patient outcomes by informing management decisions, unlike therapeutics.
- The cost-effectiveness of a diagnostic test is influenced by its performance, disease prevalence, and treatment efficacy.
- Existing CEA studies on CMR have varying strengths and weaknesses.
Conclusions:
- Cost-effectiveness analysis for diagnostic tests requires careful consideration of indirect outcome pathways.
- The value of diagnostic technology is context-dependent, influenced by disease epidemiology and therapeutic options.
- Further research is needed to refine CEA methodologies for diagnostic innovations.
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