A cost-effectiveness model of genetic testing for the evaluation of families with hypertrophic cardiomyopathy.

BACKGROUND: Traditional management of families with hypertrophic cardiomyopathy (HCM) involves periodic lifetime clinical screening of family members, an approach that does not identify all gene carriers owing to incomplete penetrance and significant clinical heterogeneity. Limitations in availability and cost have meant genetic testing is not part of routine clinical management for many HCM families. OBJECTIVE: To determine the cost-effectiveness of the addition of genetic testing to HCM family management, compared with clinical screening alone. METHODS: A probabilistic Markov decision model was used to determine cost per quality-adjusted life-year and cost for each life-year gained when genetic testing is included in the management of Australian families with HCM, compared with the conventional approach of periodic clinical screening alone. RESULTS: The incremental cost-effectiveness ratio (ICER) was $A785 (£510 or €587) per quality-adjusted life-year gained, and $A12 720 (£8261 or €9509) per additional life-year gained making genetic testing a very cost-effective strategy. Sensitivity analyses showed that the cost of proband genetic testing was an important variable. As the cost of proband genetic testing decreased, the ICER decreased and was cost saving when the cost fell below $A248 (£161 or €185). In addition, the mutation identification rate was also important in reducing the overall ICER, although even at the upper limits, the ICER still fell well within accepted willingness to pay bounds. CONCLUSIONS: The addition of genetic testing to the management of HCM families is cost-effective in comparison with the conventional approach of regular clinical screening. This has important implications for the evaluation of families with HCM, and suggests that all should have access to specialised cardiac genetic clinics that can offer genetic testing.

DNA testing for hypertrophic cardiomyopathy: a cost-effectiveness model.

Aims To explore the cost-effectiveness of alternative methods of screening family members for hypertrophic cardiomyopathy (HCM), the most common monogenic cardiac disorder and the most frequent cause of sudden cardiac death (SCD) in young people. Methods and results Economic decision model comparing cascade screening by genetic, as opposed to clinical methods. The incremental cost per life year saved was 14,397 euro for the cascade genetic compared with the cascade clinical approach. Genetic diagnostic strategies are more likely to be cost-effective than clinical tests alone. The costs for cascade molecular genetic testing were slightly higher than clinical testing in the short run, but this was largely because the genetic approach is more effective and identifies more individuals at risk. Conclusion The use of molecular genetic information in the diagnosis and management of HCM is a cost-effective approach to the primary prevention of SCD in these patients.

Eliciting expert opinion for economic models: an applied example.

OBJECTIVES: Expert opinion is considered as a legitimate source of information for decision-analytic modeling where required data are unavailable. Our objective was to develop a practical computer-based tool for eliciting expert opinion about the shape of the uncertainty distribution around individual model parameters. METHODS: We first developed a prepilot survey with departmental colleagues to test a number of alternative approaches to eliciting opinions on the shape of the uncertainty distribution around individual parameters. This information was used to develop a survey instrument for an applied clinical example. This involved eliciting opinions from experts to inform a number of parameters involving Bernoulli processes in an economic model evaluating DNA testing for families with a genetic disease, hypertrophic cardiomyopathy. The experts were cardiologists, clinical geneticists, and laboratory scientists working with cardiomyopathy patient populations and DNA testing. RESULTS: Our initial prepilot work suggested that the more complex elicitation techniques advocated in the literature were difficult to use in practice. In contrast, our approach achieved a reasonable response rate (50%), provided logical answers, and was generally rated as easy to use by respondents. The computer software user interface permitted graphical feedback throughout the elicitation process. The distributions obtained were incorporated into the model, enabling the use of probabilistic sensitivity analysis. CONCLUSION: There is clearly a gap in the literature between theoretical elicitation techniques and tools that can be used in applied decision-analytic models. The results of this methodological study are potentially valuable for other decision analysts deriving expert opinion.