From the Journals

Using reimbursement to drive innovation in tumor biomarker tests



Implementing a tiered, value-based reimbursement policy for tumor biomarker tests could be an important part of improving innovation and the quality of this tests.

“Precision medicine in cancer care depends on on accurate tumor biomarker tests (TBTs) to determine prognosis and guide treatment selection,” wrote Michaela Dinan, PhD, of Duke University, Durham, N.C., and colleagues. Their report is in JCO Precision Oncology.

“The true clinical utility of a TBT is often difficult to determine, initiating a vicious cycle in which TBTs are often undervalued and poorly reimbursed because of a lack of high-level evidence of clinical utility,” they continued. “An inconsistent regulatory and reimbursement environment results in unfavorable return on investment in the research and development (R&D) needed to generate the high levels of evidence (LOEs) necessary to demonstrate the clinical utility of TBTs.”

To that end, the authors have developed a tiered, value-based reimbursement policy to help spur innovation and improve the clinical utility of TBTs.

The tiering involves three categories: Opt-In, Opt-Out, and Opt-Alt, with each of these terms referring to a decision that might be made with the results of the TBT compared with decisions that would be made based on standard of care (SOC) without TBT results.

“Often in oncology, the SOC is to treat all patients in a given context, knowing that all patients will be exposed to potential risks and costs but only a fraction will benefit,” Dr. Dinan and colleagues wrote. “Opt-Out TBTs have value and the potential to be directly cost saving by reducing the use of unnecessary or ineffective yet toxic and costly therapies, either by indicating that the patient does not need additional therapy (prognosis) or that the therapy under consideration is unlikely to work.”

And example of Opt-Out TBTs are tests used to determine whether adjuvant chemotherapy should be given to patients with estrogen receptor–positive, human epidermal growth factor receptor 2–negative, and node-negative breast cancer.

For the Opt-In tier, “the SOC is to not treat, and the TBT leads to treatment,” the authors noted. “In this case, the value of the TBT is generated by turning a minimally effective or unacceptable therapy into one that is indicated, and therefore acceptable, because of an altered risk-benefit that justifies or refines treatment. An Opt-In TBT will typically increase costs, but still has value because it may result in improved long-term clinical outcomes.”

An example of an Opt-In TBT is an analysis of germline susceptibility genes, such as BRCA1 or BRCA2, which could show increased odds of developing and dying as a result of breast and/or ovarian cancer, though the risk can be cut with prophylactic mastectomy and salpingo-oophorectomy, respectively.

“In this case, these otherwise unacceptable interventions become appropriate because of the increased chances of long-term survival as a result of the intervention in a high-risk population,” the authors wrote.

The Opt-Alt TBT is a subcategory of Opt-In, where the SOC is treatment A but a positive TBT results in the selection of a different treatment.

In each teir, the minimum reimbursement of the TBT increases with the analytic validity and clinical utility of the test. For example, the highest minimum reimbursement would come with tests that have been validated as a primary endpoint in prospective randomized trials, while the lowest would be TBTs with ad hoc analyses of convenience cohorts. In the middle would be those validated ad secondary endpoints in prospective trials.

“Our tiered approach provides a clear and achievable opportunity for companies to develop high-quality TBTs with a guarantee for a commensurate return on investment,” the authors state. “Although our system does not reward a TBT for having slightly better performance than another for a similar indication, it does so indirectly. We posit that by ensuring a fertile ground for multiple high-quality competing TBTs, market forces will be allowed to then appropriately drive the use of TBTs that perform best, given current and local practices.”

The authors make no economic estimates related to the implementation of this type of system.

SOURCE: Michaela Dinan et al. JCO Precis Oncol. 2019 Sep 18. doi: 10.1200/PO.19.00210.

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