A model-based cost-effectiveness analysis of pharmacogenomic panel testing in cardiovascular disease management: preemptive, reactive, or none?

Abstract

Purpose: Pharmacogenomics (PGx) studies how inherited genetic variations in individuals affect drug absorption, distribution, and metabolism. PGx panel testing can potentially help improve efficiency and accuracy in individualizing therapy. This study compared the cost-effectiveness between preemptive PGx panel testing, reactive PGx panel testing and usual care (no testing) in cardiovascular disease management.

Methods: We developed a decision analytic model from the US payer's perspective for a hypothetical cohort of 10,000 patients ≥45 years old, using a short-term decision tree and long-term Markov model. The testing panel included the following gene-drug pairs: CYP2C19-clopidogrel, CYP2C9/VKORC1-warfarin, and SLCO1B1-statins with 30 test-return days. Costs were reported in 2019 US dollars and effectiveness was measured in quality-adjusted life years (QALYs). The primary outcome was incremental cost-effectiveness ratio (ICER = ΔCost/ΔQALY), assuming 3% discount rate for costs and QALYs. Scenario and probabilistic sensitivity analyses were performed to assess the impact of demographics, risk level, and follow-up timeframe.

Results: Preemptive testing was found to be cost-effective compared with usual care (ICER $86,227/QALY) at the willingness-to-pay threshold of $100,000/QALY while reactive testing was not (ICER $148,726/QALY). Sensitivity analyses suggested that our cost-effectiveness results were sensitive to longer follow-up, and the age group 45-64 years.

Conclusion: Compared with usual care, preemptive PGx panel testing was cost-effective in cardiovascular disease management.

Keywords: cardiovascular diseases management; cost-effectiveness; genomic panel testing; pharmacogenomics.

Fig. 1. Model structure for the cost-effectiveness analysis.

(a) Decision tree structure. The simulation begins with study individuals entering the model as the target population, and proceeding into one of three treatment strategies: preemptive pharmacogenomics (PGx) panel testing (genetic testing performed before a disease is diagnosed), reactive PGx panel testing (genetic testing activated when a drug is needed), and usual care (no testing is performed). Ma Markov model a, Mb Markov model b, uc usual care. Subscripts 1 to n indicate n possibilities of genetic variations, with 0 indicating no variation. Preemptive testing and usual care share the same Markov model structure, while reactive testing is under a different model structure. The actual detailed decision tree is listed in eFig. 6. (b, c) Simplified Markov model structures. Preemptive PGx panel testing strategy and usual care share the same model structure for similar transition processes among the disease states (Fig. 1b or Ma), and reactive PGx panel testing strategy has a different model structure considering the different treatment approaches with the known genetic information (Fig. 1c or Mb). The actual disease states used in the simulation are listed in eFig. 7. CVD cardiovascular disease.

Fig. 2. The timeline for preemptive pharmacogenomics (PGx) panel testing and reactive PGx panel testing strategies vs. usual care.

For the preemptive testing strategy, patients undergo genetic testing before the disease is diagnosed (drug therapy is indicated) and treatment is initiated. With the known genetic variations, treatment can be initiated directly once the patient develops the disease and the drug is indicated. However, not everyone will develop disease and therefore PGx testing may be wasteful in these healthy individuals. For the reactive testing strategy, patients undergo genetic testing after drug therapy is indicated and standard treatment is initiated. Upon receiving the genetic information, individualized treatment is initiated. In this way, genetic testing is only given to patients who are prescribed at least one of the three medications (i.e., clopidogrel, warfarin, or statin). However, once the results are ready, further treatment could be initiated immediately after the disease is diagnosed and the drug is indicated. No genetic testing is performed under the usual care strategy.

Fig. 3. Tornado diagram for incremental cost-effectiveness ratio (ICER).

(a) Preemptive pharmacogenomics (PGx) panel testing vs. usual care. (b) Preemptive vs. reactive PGx panel testing. Each horizontal bar represents the change in ICER when the value of the corresponding parameter is varied from its lower limit to its upper limit. Red color suggests negative correlation, and blue suggests positive correlation. The top 20 parameters that impacted the ICER values the most are listed. Cost and probability values are reported on a monthly basis, while utility was reported on a yearly basis. ADE adverse drug events, AF atrial fibrillation, CHD coronary heart disease, CVD cardiovascular disease, HR hazard ratio, MI myocardial infarction, PAD peripheral artery disease, RR relative risk, STR stroke, Tx treatment, UC usual care, Var genetic variation, WTP willingness-to-pay.

Fig. 4. Estimated cost-effectiveness density for the preemptive testing vs. usual care, reactive vs. usual care, and preemptive vs. reactive testing.

Each dot represents the incremental costs and the quality-adjusted life years (QALYs) gained from each sample of the 10,000 microsimulations. The willingness-to-pay (WTP) line was at $100,000/QALY. UC usual care.