Economic value of exome sequencing for suspected monogenic disorders

To the Editor:

Schofield and colleagues recently published a cost-effectiveness analysis of exome sequencing (ES) as a replacement test for single- and multigene panel tests and other complex or invasive diagnostics for suspected monogenic disorders in infants aged 0–2 years.1 The authors employed decision modeling of counterfactual outcomes, i.e., comparing expected quality-adjusted life years (QALYs) and costs for a hypothetical scenario of ES versus standard of care testing, using data from a single-center cohort of 80 infants who underwent ES and usual diagnostic care in parallel.2 Estimates of differences in costs and QALYs across the two diagnostic strategies indicated that the use of ES versus usual diagnostic care was borderline cost-effective, with an incremental cost-effectiveness ratio (ICER) of AU$31,144/QALY. This ICER was obtained due to a gain in QALYs of 0.09 per patient (7.39 additional QALYs within 80 probands) with ES against an increase in costs of AU$2878 per patient (AU$31,144 within 80 probands). Cost-effectiveness of ES improved when including further health benefits following cascade testing of first degree relatives and when parental reproductive outcomes were considered.

The analysis by Schofield et al. is valuable because it contributes to a better understanding of the incremental value of ES in pediatrics in two ways. First, it uses the QALY concept for measuring health outcomes. The QALY is a comprehensive health outcome measure incorporating both longevity and quality of life effects.3 Second, it accounts for a long-term time horizon capturing many relevant outcomes including those from reanalysis of ES, cases with medically actionable results followed by changes in subsequent treatment, and cascade testing of first degree relatives in those with a positive finding.

However, this study also has important limitations that offer the opportunity for improved economic evaluations of ES in future work. First, uncertainty around the incremental diagnostic yield of ES—the intervention efficacy—was not explicitly evaluated. The estimated cumulative gain of 7.39 QALYs across the 80 probands stemmed from just three cases in which ES diagnosis led to altered clinical management. Given the small study sample, the expected changes in QALYs and costs following implementation of ES in the whole Australian patient population quite possibly are different, potentially warranting larger studies before definitive conclusions about cost-effectiveness can be drawn. It would be useful to evaluate the impact of a range of likely values of diagnostic yield and proportion of medically actionable cases on the cost-effectiveness of ES, also in the context of uncertainty of other important parameters such as quality of life values and costs. Second, only differences in the costs of diagnostic testing were estimated for calculating ICERs, and changes in the long-term costs of subsequent treatments following the medically actionable results were ignored, potentially biasing the reported ICERs in a direction that is difficult to predict. Finally, potential improvements of quality of life beyond those estimated in affected patients—spillover effects4 in parents—were not considered.5

Assessment of the cost-effectiveness of ES and other next-generation sequencing interventions in the diagnostic work-up of infants with suspected inherited disorders will be increasingly important to support their large scale use, insurance coverage, and equitable access. The analysis by Schofield and colleagues has certainly educated clinicians and the research community about how important it is to integrate outcomes beyond the diagnostic pathway. Well-designed cost-effectiveness studies based on more representative data from various settings and explicit modeling of uncertain data inputs are however needed.


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Correspondence to Bart S. Ferket MD, PhD.

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Ferket, B.S., Veenstra, D.L. Economic value of exome sequencing for suspected monogenic disorders. Genet Med (2020).

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Further reading

  • Response to Ferket et al.

    • Deborah Schofield
    • , Luke Rynehart
    • , Rupendra Shresthra
    • , Susan M. White
    •  & Zornitza Stark

    Genetics in Medicine (2020)