The Value of Genetic and Genomic Technologies Workshop Summary (2010) / Chapter Skim
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3 Pharmacogenomic Testing to Guide Warfarin Dosing
3 Pharmacogenomic Testing to Guide Warfarin Dosing
Pages 19-34
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WARFARIN PHARMACOGENOMICS Warfarin (known also by the brand name Coumadin) is an anticoagulant used for the prevention of thromboembolic events.
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Warfarin is generally underutilized, particularly in the elderly, because of concerns about bleeding events. It is well known that certain clinical and demographic factors influence warfarin dose requirements, including age, race, sex, co-morbidities, concomitant medications, and diet.
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using findings from nine different countries and based on the relationship between dose requirements and the known clinical and genetic factors. The consortium found that estimating a starting dose using the pharmacogenetic algorithm resulted in a more accurate starting dose which was closer to the required stable therapeutic dose than starting doses estimated using clinical factors alone.
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Indeed, the 2006 ACMG report concluded that no study had shown testing to be effective in reducing high INRs, shortening the time to a stable INR, or limiting the number of serious bleeding events. A more recent systematic review by researchers at the University of California, San Francisco did not find sufficient evidence to support the use of genetic testing to guide warfarin therapy (Kangelaris et al., 2009)
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recently issued a coverage decision for warfarin pharmacogenomic testing based on the current evidence available. The decision states that CMS will only reimburse testing if the patient is enrolled in a randomized controlled trial with sufficient power to detect major bleeding and thromboembolic events.
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Thus, in order for pharmacogenomic testing to be useful for this patient base, the price would have to drop significantly, she said. Although such tests are not available, Garret said it would be a real benefit to have genetic testing that could identify those patients who are more likely to have adverse drug interactions (e.g., an exaggerated reaction to warfarin and amiodarone, or an adverse response to the combination of warfarin and acetaminophen or ciprofloxacin)
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Private Practice Perspective Dennis Salisbury, a family physician at the Rocky Mountain Clinic in southwest Montana, addressed how doctors in private practice make the decision whether to recommend pharmacogenomic testing to patients. Reiterating some of the points he made when discussing the Lynch syndrome example, Salisbury said that it is important that there be a clear association between a genomic test and a condition and that there should also be relevant information about the incidence and prevalence of the condition, the severity of the illness's impact, and the potential benefits of the genomic testing.
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Pharmacogenomic testing to guide warfarin dosing is different from these cases in that it involves patients with no particular disease, so coverage is not straightforward based on Medicare law. However, Lurvey said, if the government were to decide that testing is related to a particular condition, then perhaps it could be covered under existing precedence.
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The other aspect they must consider is cost. While CMS generally does not factor cost into coverage decisions if the test has efficacy, quality, and reproducibility, Lurvey said that a particular concern is that these individual pharmacogenomic tests are going to add a large cost to the system.
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Representing the FDA view, Mansfield said that the test must also be safe and effective. Data Collection and Interpretation Williams noted that it is difficult to translate randomized controlled trial findings into real world clinical practice.
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As an example, Williams noted that home INR monitoring is not available in the United States, but it is done in Europe, and outcomes are much better there. Veenstra responded that there is a great potential benefit for genetic testing in populations that are rural or underserved, but those are populations that are challenging to study.
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As a result, the amount of time spent in range increased from 50 percent to 75 percent for the patients in that population. OPEN DISCUSSION Electronic Health Records and Data Capture A participant noted that it is currently very challenging to try to compare data across electronic health record systems because there are no data element standards.
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Currently each plan does its own evidence development and technology assessment, she said, so bringing plans together to develop a unified approach is complicated. With the tremendous pressure on private sector plans to cover genetic tests, this type of wide-ranging coverage decision could produce the evidence required to determine clinical utility.
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Given that cost is always a concern in this kind of situation, are there ways to collect quality evidence more efficiently? Burke said that one model might be provided by the development of the Oncotype DX test, a prospective-retrospective approach in which a hypothesis was developed and then addressed using existing specimens from prior randomized controlled trials.
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Tissue from colorectal cancer patients follows a standardized pathway, and ordering Lynch screening is not dependant on any individual care provider. Veenstra added that ideally there would be a generalizable model that can be modular and adaptable because it is not acceptable to wait an average of 17 years from the point when enough evidence is collected to demonstrate clinical utility until the time that a test is fully implemented.
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