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New Thinking on Clinical Utility: Hard Lessons for Molecular Diagnostics

John W. Peabody, MD, PhD, DTM&H, FACP; Riti Shimkhada, PhD; Kuo B. Tong, MS; and Matthew B. Zubiller, MBA
The authors describe 5 basic requirements for planning, implementing, and proving clinical utility for diagnostic tests, drawing on recent reimbursement decisions.
Arguably one of the biggest challenges is identifying and adequately capturing clinical benefits. Outcomes that by definition may manifest many years later, such as overall survival, can be completely impractical. An alternative and more feasible clinical utility study will measures clinical practice changes. Such studies might assess disease progression (Crescendo Biosciences); determine the need or lack of need for invasive testing (CardioDx); foment the avoidance of a complication (Iverson Genetics); or narrow the treatment population (Genomic Health). (See the Table for more detail on these scenarios, and those mentioned below.) Thus the key to finding the best outcome for clinical utility is to build a causal framework that links clinical action to the test and then to patient outcome.

The most common pitfalls are to use the wrong clinical proxies (as in the example of Tethys) or to not clearly define the target population (Berkeley HeartLab). Another is to not identify the clinical action or behavioral change that is clearly linked to health status (Agendia). Diagnostic test C&R failures can be further prevented with better expertise and greater investment in clinical utility design. If a company believes in a test’s clinical validity, then the urgency, today, should be to acquire capital and invest early in clinical utility to secure coverage and reimbursement. Data remain the ongoing scientific responsibility of a company developing a new test, and a steadfast commitment to examining clinical utility for the most generalizable audience is the key to clinical and financial success over the long term.

Acknowledgments: The authors would like to thank Rachel Lee (QURE) and Elaine Jeter (Palmetto GBA, MolDx) for their helpful comments regarding this manuscript.

Author Affiliations: QURE Healthcare, San Rafael, CA (JWP, RS); Global Health Sciences, University of California, San Francisco (JWP); Quorum Consulting, San Francisco, CA (KBT); McKesson Health Solutions, Newton, MA (MBZ).

Funding Source: None reported.

Author Disclosures: Dr Peabody is president of QURE Healthcare, which holds the trademark for the CPV vignettes measurement tool referenced in this paper. QURE Healthcare was contracted by Crescendo Biosciences, referenced herein, to conduct a prospective randomized clinical utility study of Vectra DA. Mr Zubiller is vice president, Decision Management, McKesson Health Solutions and McKesson Diagnostics Exchange, the system that assigns a unique 5-digit alphanumeric Z-Code Identifier to each advanced diagnostic test.

Authorship Information: Concept and design (JP, KT, MZ, RS); acquisition of data (RS, KT); analysis and interpretation of data (JP, KT, MZ, RS); drafting of the manuscript (JP, KT); critical revision of the manuscript for important intellectual content (MZ, RS); statistical analysis (RS, JP); provision of study materials or patients (RS, JP, KT); administrative, technical, or logistic support (RS); and supervision (JP).

Address correspondence to: John W. Peabody, MD, PhD, DTM&H, FACP, QURE Healthcare, 1000 Fourth St, Suite 300, San Rafael, CA 94901. E-mail:
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