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Clinical biomarker innovation: when is it worthwhile?

Anouck Kluytmans1, Jaap Deinum2,3, Kevin Jenniskens4

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Early health economic modeling helps prioritize biomarker innovations. A perfect diagnostic test for primary aldosteronism could be cost-effective if it has high sensitivity and specificity.

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Area of Science:

  • Biomarker discovery and development
  • Health economics
  • Diagnostic test evaluation

Background:

  • Selecting biomarker tests involves assessing diagnostic accuracy, clinical utility, and economic impact.
  • Early health economic modeling aids in evaluating biomarker innovations and guiding decision-making.

Purpose of the Study:

  • To apply early health economic modeling to assess the value of a new diagnostic test for primary aldosteronism in patients with resistant hypertension.
  • To determine the diagnostic accuracy thresholds for a cost-effective biomarker test.

Main Methods:

  • A Markov cohort state-transition model simulated patient outcomes.
  • The headroom method compared current tests to a hypothetical perfect test.
  • Threshold analyses identified minimal required diagnostic accuracy for cost-effectiveness.

Main Results:

  • A perfect diagnostic test was estimated to yield 0.027 quality-adjusted life-years (QALYs) and increase costs by €43 per patient.
  • The maximum acceptable price for a perfect test was €498 at a €20,000/QALY threshold.
  • Test value was sensitive to the current test's sensitivity; a new test requires at least 0.9 sensitivity and 0.7 specificity to be cost-effective.

Conclusions:

  • Early health economic modeling effectively evaluates the potential value of biomarker innovations before significant investment.
  • This approach supports prioritizing laboratory-based biomarker innovations by assessing economic and clinical benefits.