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Bayesian comparison of cost-effectiveness of different clinical approaches to diagnose coronary artery disease
Insights
The cost-effectiveness of diagnosing coronary artery disease depends heavily on disease prevalence. Strategies using noninvasive tests are more cost-effective for lower prevalence populations, while angiography is preferred for high prevalence scenarios.
Area of Science:
- Cardiology
- Health Economics
- Diagnostic Decision Making
Background:
- Coronary artery disease (CAD) diagnosis involves various clinical policies with differing costs and effectiveness.
- Evaluating the cost-effectiveness of these diagnostic strategies is crucial for optimal healthcare resource allocation.
Purpose of the Study:
- To compare the cost-effectiveness of four distinct clinical policies for diagnosing coronary artery disease.
- To identify the optimal diagnostic strategy based on the prevalence of CAD in the patient population.
Main Methods:
- A cost-effectiveness model was developed using Bayes' theorem and existing clinical data.
- Effectiveness was measured by the number of diagnosed CAD cases and quality-adjusted life years (QALYs) gained.
- The model analyzed four clinical policies (I-IV) for CAD diagnosis.
Main Results:
- Cost-effectiveness increased hyperbolically with rising CAD prevalence.
- All policies demonstrated poor cost-effectiveness at prevalences below 10%.
- Exercise thallium scintigraphy (Policy II) was more cost-effective than exercise ECG alone (Policy I) or combined ECG/thallium (Policy IV) at prevalences <80%.
Conclusions:
- Disease prevalence is a critical factor in selecting the most cost-effective diagnostic approach for CAD.
- Noninvasive testing is preferred for lower prevalence (<80%), while angiography is more cost-effective for high prevalence (>=80%) populations.
- The study identified an 80% prevalence threshold where the optimal diagnostic strategy shifts.
Abstract:
The objective of this study was to compare the cost-effectiveness of four clinical policies (policies I to IV) in the diagnosis of the presence or absence of coronary artery disease. A model based on Bayes' theorem and published clinical data was constructed to make these comparisons. Effectiveness was defined as either the number of patients with coronary disease diagnosed or as the number of quality-adjusted life years extended by therapy after the diagnosis of coronary disease. The following conclusions arise strictly from analysis of the model and may not necessarily be applicable to all situations. As prevalence of coronary disease in the population increased, it caused a linear increase in cost per patient tested, but a hyperbolic decrease in cost per effect, that is, increased cost-effectiveness. Thus, cost-effectiveness of all policies (I to IV) was poor in populations with a prevalence of disease below 10%, for example, asymptomatic people with no risk factors. Analysis of the model also indicates that at prevalences less than 80%, exercise thallium scintigraphy alone as a first test (policy II) is a more cost-effective initial test than is exercise electrocardiography alone as a first test (policy I) or exercise electrocardiography first combined with thallium imaging as a second test (policy IV). Exercise electrocardiography before thallium imaging (policy IV) is more cost-effective than exercise electrocardiography alone (policy I) at prevalences less than 80%. 4) Noninvasive exercise testing before angiography (policies I, II and IV) is more cost-effective than using coronary angiography as the first and only test (policy III) at prevalences less than 80%. 5) Above a threshold value of prevalence of 80% (for example patients with typical angina), proceeding to angiography as the first test (policy III) was more cost-effective than initial noninvasive exercise tests (policies I, II and IV). One advantage of this quantitative model is that it estimates a threshold value of prevalence (80%) at which the rank order of policies changes. The model also allows substitution of different values for any variable as a way of accounting for the uncertainty inherent in the data. In conclusion, it is essential to consider the prevalence of disease when selecting the most cost-effective clinical approach to making a diagnosis.