Are Evidence Standards Different for Genomic- vs. Clinical-Based Precision Medicine? A Quantitative Analysis of Individualized Warfarin Therapy

How to assess pharmacogenomic tests for implementation in the NHS in England

AIMS

Pharmacogenomic testing has the potential to target medicines more effectively towards those who will benefit and avoid use in individuals at risk of harm. Health economies are actively considering how pharmacogenomic tests can be integrated into health care systems to improve use of medicines. However, one of the barriers to effective implementation is evaluation of the evidence including clinical usefulness, cost-effectiveness, and operational requirements. We sought to develop a framework that could aid the implementation of pharmacogenomic testing. We take the view from the National Health Service (NHS) in England.

METHODS

We used a literature review using EMBASE and Medline databases to identify prospective studies of pharmacogenomic testing, focusing on clinical outcomes and implementation of pharmacogenomics. Using this search, we identified key themes relating to the implementation of pharmacogenomic tests. We used a clinical advisory group with expertise in pharmacology, pharmacogenomics, formulary evaluation, and policy implementation to review data from our literature review and the interpretation of these data. With the clinical advisory group, we prioritized themes and developed a framework to evaluate proposals to implement pharmacogenomics tests.

RESULTS

Themes that emerged from review of the literature and subsequent discussion were distilled into a 10-point checklist that is proposed as a tool to aid evidence-based implementation of pharmacogenomic testing into routine clinical care within the NHS.

CONCLUSION

Our 10-point checklist outlines a standardized approach that could be used to evaluate proposals to implement pharmacogenomic tests. We propose a national approach, taking the view of the NHS in England. Using this approach could centralize commissioning of appropriate pharmacogenomic tests, reduce inequity and duplication using regional approaches, and provide a robust and evidence-based framework for adoption. Such an approach could also be applied to other health systems.

Implementation of pharmacogenomic clinical decision support for health systems: a cost-utility analysis

We constructed a cost-effectiveness model to assess the clinical and economic value of a CDS alert program that provides pharmacogenomic (PGx) testing results, compared to no alert program in acute coronary syndrome (ACS) and atrial fibrillation (AF), from a health system perspective. We defaulted that 20% of 500,000 health-system members between the ages of 55 and 65 received PGx testing for CYP2C19 (ACS-clopidogrel) and CYP2C9, CYP4F2 and VKORC1 (AF-warfarin) annually. Clinical events, costs, and quality-adjusted life years (QALYs) were calculated over 20 years with an annual discount rate of 3%. In total, 3169 alerts would be fired. The CDS alert program would help avoid 16 major clinical events and 6 deaths for ACS; and 2 clinical events and 0.9 deaths for AF. The incremental cost-effectiveness ratio was $39,477/QALY. A PGx-CDS alert program was cost-effective, under a willingness-to-pay threshold of $100,000/QALY gained, compared to no alert program.

Health Economics Tools and Precision Medicine: Opportunities and Challenges

Precision medicine - individualizing care for patients and addressing variations in treatment response - is likely to be important in improving the nation's health in a cost-effective manner. Despite this promise, widespread use of precision medicine, specifically genomic markers, in clinical care has been limited in practice to date. Lack of evidence, clear evidence thresholds, and reimbursement have been cited as major barriers. Health economics frameworks and tools can elucidate the effects of legal, regulatory, and reimbursement policies on the use of precision medicine while guiding research investments to enhance the appropriate use of precision medicine. Despite the capacity of economics to enhance the clinical and human impact of precision medicine, application of health economics to precision medicine has been limited - in part because precision medicine is a relatively new field - but also because precision medicine is complex, both in terms of its applications and implications throughout medicine and the healthcare system. The goals of this review are several-fold: (1) provide an overview of precision medicine and key policy challenges for the field; (2) explain the potential utility of economics methods in addressing these challenges; (3) describe recent research activities; and (4) summarize opportunities for cross-disciplinary research.

Are There Different Evidence Thresholds for Genomic Versus Clinical Precision Medicine? A Value of Information-Based Framework Applied to Antiplatelet Drug Therapy

BACKGROUND

The threshold of sufficient evidence for adoption of clinically- and genomically-guided precision medicine (PM) has been unclear.

OBJECTIVE

To evaluate evidence thresholds for clinically guided PM versus genomically guided PM.

METHODS

We develop an "evidence threshold criterion" (ETC), which is the time-weighted difference between expected value of perfect information and incremental net health benefit minus the cost of research, and use it as a measure of evidence threshold that is proportional to the upper bound of disutility to a risk-averse decision maker for adopting a new intervention under decision uncertainty. A larger (more negative) ETC value indicates that only decision makers with low risk aversion would adopt new intervention. We evaluated the ETC plus cost of research (ETCc), assuming the same cost of research for both interventions, over time for a pharmacogenomic (PGx) testing intervention and avoidance of a drug-drug interaction (aDDI) intervention for acute coronary syndrome patients indicated for antiplatelet therapy. We then examined how the ETC may explain incongruous decision making across different national decision-making bodies.

RESULTS

The ETCc for PGx increased over time, whereas the ETCc for aDDI decreased to a negative value over time, indicating that decision makers with even low risk aversion will have doubts in adopting PGx, whereas decision makers who are highly risk-averse will continue to have doubts about adopting aDDI. National recommendation bodies appear to be consistent over time within their own decision making, but had different levels of risk aversion.

CONCLUSION

The ETC may be a useful metric for assessing policy makers' risk preferences and, in particular, understanding differences in policy recommendations for genomic versus clinical PM.