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Original Research |

Cost-Effectiveness of Genotype-Guided and Dual Antiplatelet Therapies in Acute Coronary Syndrome

Dhruv S. Kazi, MD, MSc, MS; Alan M. Garber, MD, PhD; Rashmee U. Shah, MD, MS; R. Adams Dudley, MD, MBA; Matthew W. Mell, MD; Ceron Rhee, MBA; Solomon Moshkevich, MBA; Derek B. Boothroyd, PhD; Douglas K. Owens, MD; and Mark A. Hlatky, MD
[+] Article and Author Information

From San Francisco General Hospital and Philip R. Lee Institute of Health Policy Studies, University of California, San Francisco, San Francisco, California; Harvard University, Cambridge, Massachusetts; Harvard Medical School, Boston, Massachusetts; University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania; and Graduate School of Business, Center for Primary Care Outcomes Research, and Center for Health Policy, and Stanford University School of Medicine, Stanford, California; and Veterans Affairs Palo Alto Health Care System, Palo Alto, California.

Disclaimer: The findings and conclusions in this article are those of the authors and do not necessarily represent the views of the U.S. Department of Veterans Affairs.

Acknowledgments: The authors thank Phil Lavori, PhD, Department of Health Research and Policy, and Jay Bhattacharya, MD, PhD, and Matthew Franzen, Centers for Health Policy and Primary Care and Outcomes Research, Stanford University, for their help and guidance with the analysis. They also thank Kristin Sainani, PhD, Department of Health Research and Policy, for her comments on a previous version of the manuscript and Elaine Steel, Beth Thew, L. Marie Dach, and Antonella Vassallo for their administrative support.

Grant Support: Funded in part by the American Heart Association Pharmaceutical Roundtable–Spina Outcomes Research Center (0875162N), U.S. Department of Veterans Affairs, Stanford University, and the University of California San Francisco.

Potential Conflicts of Interest: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M13-1999.

Reproducible Research Statement: Statistical protocol: Available from Dr Kazi (kazi@alumni.stanford.edu). Statistical code and data set: Not available.

Requests for Single Reprints: Dhruv S. Kazi, MD, MSc, MS, Division of Cardiology, San Francisco General Hospital, 1001 Potrero Avenue, Room 5G1, San Francisco, CA 94110; e-mail, kazi@alumni.stanford.edu.

Current Author Addresses: Dr. Kazi: Division of Cardiology, San Francisco General Hospital, 1001 Potrero Avenue, Room 5G1, San Francisco, CA 94110.

Dr. Garber: Office of the President and Provost, Harvard University, Massachusetts Hall, Harvard Yard, Cambridge, MA 02138.

Dr. Shah: University of Pittsburgh Medical Center, 200 Lothrop Street, B-571.3 Scaife Hall, Pittsburgh, PA 15213.

Dr. Dudley: Philip R. Lee Institute for Health Policy Studies, University of California, San Francisco, 3333 California Street, Suite 265, San Francisco, CA 94118.

Dr. Mell: Stanford Medical Center Vascular Surgery Clinic, 300 Pasteur Drive H3600, MC5642 Stanford, CA 94305.

Mr. Rhee: 13078 Via Escuela Court, Saratoga, CA 95070.

Mr. Moshkevich: 544 Guerrero Street, #3, San Francisco, CA 94110.

Dr. Boothroyd: Stanford University School of Medicine, Department of Health Research and Policy, HRP Redwood Building, Room T150, 259 Campus Drive, Stanford, CA 94305-5405.

Dr. Owens: Stanford University, Center for Health Policy and Center for Primary Care and Outcomes Research, 117 Encina Commons, Room 201, Stanford, CA 94305-6019.

Dr. Hlatky: Stanford University School of Medicine, Department of Health Research and Policy, HRP Redwood Building, Room T150, 259 Campus Drive, Stanford, CA 94305-5405.

Author Contributions: Conception and design: D.S. Kazi, M.W. Mell, C. Rhee, D.K. Owens, M.A. Hlatky.

Analysis and interpretation of the data: D.S. Kazi, A.M. Garber, M.W. Mell, S. Moshkevich, D.K. Owens, M.A. Hlatky.

Drafting of the article: D.S. Kazi.

Critical revision of the article for important intellectual content: D.S. Kazi, A.M. Garber, R.U. Shah, R.A. Dudley, M.W. Mell, S. Moshkevich, D.B. Boothroyd, D.K. Owens, M.A. Hlatky.

Final approval of the article: D.S. Kazi, A.M. Garber, R.U. Shah, R.A. Dudley, M.W. Mell, D.K. Owens.

Provision of study materials or patients: D.S. Kazi.

Statistical expertise: D.S. Kazi, A.M. Garber, R.U. Shah, D.B. Boothroyd, D.K. Owens, M.A. Hlatky.

Obtaining of funding: M.A. Hlatky.

Administrative, technical, or logistic support: D.S. Kazi, A.M. Garber, R.A. Dudley, M.A. Hlatky.

Collection and assembly of data: D.S. Kazi, R.U. Shah, M.W. Mell, C. Rhee, S. Moshkevich, D.B. Boothroyd


Ann Intern Med. 2014;160(4):221-232. doi:10.7326/M13-1999
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Background: The choice of antiplatelet therapy after acute coronary syndrome (ACS) is complicated: Ticagrelor and prasugrel are novel alternatives to clopidogrel, patients with some genotypes may not respond to clopidogrel, and low-cost generic formulations of clopidogrel are available.

Objective: To determine the most cost-effective strategy for dual antiplatelet therapy after percutaneous coronary intervention for ACS.

Design: Decision-analytic model.

Data Sources: Published literature, Medicare claims, and life tables.

Target Population: Patients having percutaneous coronary intervention for ACS.

Time Horizon: Lifetime.

Perspective: Societal.

Intervention: Five strategies were examined: generic clopidogrel, prasugrel, ticagrelor, and genotyping for polymorphisms of CYP2C19 with carriers of loss-of-function alleles receiving either ticagrelor (genotyping with ticagrelor) or prasugrel (genotyping with prasugrel) and noncarriers receiving clopidogrel.

Outcome Measures: Direct medical costs, quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratios (ICERs).

Results of Base-Case Analysis: The clopidogrel strategy produced $179 301 in costs and 9.428 QALYs. Genotyping with prasugrel was superior to prasugrel alone, with an ICER of $35 800 per QALY relative to clopidogrel. Genotyping with ticagrelor was more effective than genotyping with prasugrel ($30 200 per QALY relative to clopidogrel). Ticagrelor was the most effective strategy ($52 600 per QALY relative to genotyping with ticagrelor).

Results of Sensitivity Analysis: Stronger associations between genotype and thrombotic outcomes rendered ticagrelor substantially less cost-effective ($104 800 per QALY). Genotyping with prasugrel was the preferred therapy among patients who could not tolerate ticagrelor.

Limitation: No randomized trials have directly compared genotyping strategies or prasugrel with ticagrelor.

Conclusion: Genotype-guided personalization may improve the cost-effectiveness of prasugrel and ticagrelor after percutaneous coronary intervention for ACS, but ticagrelor for all patients may be an economically reasonable alternative in some settings.

Primary Funding Sources: American Heart Association, U.S. Department of Veterans Affairs, Stanford University, and University of California San Francisco.

Figures

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Figure 1.

Cost-effectiveness plane.

Results of the base-case analysis are presented on the cost-effectiveness plane, with clopidogrel at the origin. The lines indicate the cost-effectiveness frontier, and the slope of the frontier that connects 2 strategies is the incremental cost-effectiveness ratio (in 2011 U.S. dollars per QALY). Both low- (solid line) and high-discrimination scenarios (dashed line) are shown; strategies that are inside the corresponding frontier (hollow markers) are eliminated by strict or extended dominance.

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Appendix Figure 1.

Sensitivity analysis on the association between genotype and clinical outcomes.

The value of genotyping depends on its ability to discriminate between patients at high and low risk for thrombotic events. In this analysis, the base case assumes a low-discrimination scenario: that carriers of loss-of-function alleles are at modestly greater risk for thrombotic events than noncarriers. The ICER of genotyping with ticagrelor is measured relative to clopidogrel, and the ICER for ticagrelor is measured relative to genotyping with ticagrelor. As the discrimination of the test is dialed up (moving rightward on the x-axis), carriers have more thrombotic events and fewer bleeding events relative to noncarriers. This results in improved outcomes associated with genotyping, making genotyping with ticagrelor more cost-effective and treating all patients with ticagrelor independent of genotype less cost-effective. As a point of reference, the rate ratio for cardiovascular death (carriers to noncarriers) was 35% greater in the high-discrimination scenario than in the base case. ICER = incremental cost-effectiveness ratio; QALY = quality-adjusted life-year.

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Appendix Figure 2.

Tradeoff between bleeding and thrombosis.

In 2-way sensitivity analyses, we simultaneously varied the rate of cardiovascular death and fatal bleeding among patients receiving ticagrelor (relative to patients receiving clopidogrel), holding constant the event rates among patients receiving prasugrel. In the low-discrimination scenario and at a willingness-to-pay threshold of $50 000/quality-adjusted life-year, genotyping with ticagrelor was the most cost-effective strategy at baseline (dotted lines), but relatively small improvements in the efficacy or safety of ticagrelor (e.g., 1.3% decrease in cardiovascular mortality rates) made treating all patients with ticagrelor the most cost-effective option. In the high-discrimination scenario, genotyping with ticagrelor was robust to large changes in the efficacy and safety of ticagrelor. CV = cardiovascular; HR = hazard ratio.

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Appendix Figure 3.

Effect of population frequency of CYP2C19 loss-of-function polymorphisms.

As the population frequency of CYP2C19 loss-of-function polymorphisms increases, treating all patients receiving a percutaneous coronary intervention for acute coronary syndrome with ticagrelor (independent of genotyping) becomes more cost-effective. At a threshold of $50 000/QALY, ticagrelor is the most cost-effective strategy when carriers constitute 52.7% or more of the population. ICER = incremental cost-effectiveness ratio; QALY = quality-adjusted life-year.

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Figure 2.

Probabilistic sensitivity analysis.

Results of the probabilistic sensitivity analysis are illustrated as acceptability curves, which plot the proportion of simulations in which a certain strategy is “optimal” (or most cost-effective) against the amount one is willing to pay per QALY gained. In the low-discrimination scenario, genotyping with ticagrelor is the preferred strategy in 42.3% of the simulations at a willingness-to-pay threshold of $50 000/QALY (green vertical line) and ticagrelor is the preferred strategy in 32% of the simulations, reflecting the underlying uncertainty. Greater thresholds make ticagrelor more economically attractive. In the high-discrimination scenario, which assumes stronger associations between loss-of-function genotype and the rate of thrombotic events, genotyping with ticagrelor is the optimal strategy in 63.4% of the simulations at a threshold of $50 000/QALY. QALY = quality-adjusted life-year.

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