# 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.

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. ## INTRODUCTION Pharmacogenomics (PGx) investigates the effect of inherited genetic variations on drug absorption, distribution, and metabolism.1,2 PGx can be used to predict response variability for certain drugs, which in turn can facilitate individualized disease management. The US Food and Drug Administration (FDA) has approved 385 drugs with genomic biomarker information in their labeling to assist in PGx-guided therapy, which has seen increased adoption in clinical practice in recent years.3,4,5,6 Cardiovascular diseases (CVD) are among the most prevalent and expensive chronic diseases. In the United States, approximately 630,000 people die from CVDs every year, and the economic burden is projected to increase to$1.1 trillion in 2035.7,8 Given the substantial burden of morbidity and costs associated with CVD, PGx-guided therapy can potentially play an important role in reducing this burden by preventing drug-related adverse events and optimizing treatment effectiveness.2

Having access to PGx testing results can inform a clinician of the presence or absence of specific gene variant(s) that a drug might adversely react to, and thus can help the clinician prescribe the right medication at the right dose. PGx testing can be done preemptively or reactively. Preemptive testing can be conducted before a disease is symptomatic and a drug with PGx information is prescribed. The results can then be integrated into patients’ health records, making this information available at the point of care.9,10 Reactive testing, on the other hand, is conducted before any high-risk medication is prescribed to better understand the effects of medication options and to maximize treatment effectiveness upon receiving the PGx test results.5,9 Panel testing is a method that allows for multiple genes to be analyzed in one assay. Given that patients often have multiple gene variants, panel testing may be significantly more efficient and less expensive than conducting multiple single-gene assays.11,12,13 In the cardiovascular arena, as suggested by FDA Table of Pharmacogenomic Biomarkers in Drug Labeling, the Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines, as well as a recent comprehensive literature review by Zhu et al., four of the most prominent genes and the related drug choices included in a PGx panel test are CYP2C19 (clopidogrel vs. ticagrelor), CYP2C9/VKORC1 (warfarin vs. novel oral anticoagulants [NOACs]), and SLCO1B1 (statins vs. proprotein convertase subtilisin/kexin type 9 [PCSK9] inhibitors).14,15 The recent price reduction for PCSK9 inhibitors from $14,000/year to$5850/year (Repatha and Praluent) may substantially change CVD management because while statins are by far the most well-established first-line pharmacotherapy for hyperlipidemia management, PCSK9 inhibitors have been found to have considerable clinical significance in hyperlipidemia management for patients with elevated risk of statin-related adverse events (e.g., myopathy) and/or for whom statins are ineffective.16,17,18,19 SLCO1B1 variants have been found to be associated with muscle toxicity from many statins (e.g., simvastatin, rosuvastatin), but the associations between dosage, SLCO1B1 variations, and clinical outcomes are not yet well established.15,20 The emergence of PCSK9 inhibitor as an alternative to statin reshaped lipid treatment.21 However, for many insurers, PCSK9 prescription needs a 3-month statin trial proven to be ineffective and also requires prior authorization.22 PGx testing can flag patients unlikely to benefit from statins, and initiation of PCSK9 therapy instead of statin for them would be a cost-saving and efficient strategy that can avoid statin-related adverse events and circumvent the prior authorization process. Evidence on the cost-effectiveness of implementing PGx panel testing, either preemptively or reactively, compared with usual care for any of the gene–drug combinations is scarce.14

The aim of this study was to compare cost-effectiveness of preemptive and reactive PGx panel testing strategies with usual care (no PGx testing) to guide treatment choices for a population with average CVD risk. The secondary aim of the study was to assess cost-effectiveness of the three strategies in different patient subgroups varied by age, gender, race, CVD risk level, and timeframe for whom experimental studies are either very costly, infeasible, or require significantly longer time to complete.

## MATERIALS AND METHODS

We developed a decision analytic model composed of a decision tree and long-term Markov models (up to 100 years) to evaluate the cost-effectiveness of three treatment strategies in CVD management: (1) preemptive PGx panel testing, (2) reactive PGx panel testing, and (3) usual care for treatment with no PGx testing (Fig. 1a). Health states corresponding to preemptive and reactive PGx panel testing are shown in Fig. 1b, c. Each arrow illustrates a transition pathway from one disease state to another and is assigned a corresponding probability value. All patients start at treatment-free state, which is assumed to be the same as disease-free state. The preemptive group and usual care group share the same model structure because they do not have a time period in which they must wait for the genetic testing results to come back; however, they both have distinct sets of transition probabilities. Usual care group is assigned the probabilities from outcome studies with no genetic information, while preemptive group is assigned the probabilities obtained from clinical trials. In the reactive testing arm, the patient first undergo usual care when diagnosis is established, and after one cycle (one month), patients follow a path similar to those in the preemptive cohort described above. Other disease states include treatment without adverse events, treatment with adverse events, death from CVD, and background deaths, which are adopted as non-CVD deaths. All the adverse events from corresponding medications are considered as different health states from treatments without adverse events, lasting repetitive cycles until the patients transition to another state. The details of disease states, medications, and their adverse events are listed in eFig. 1.

Typically, odds ratios (OR), relative ratios, or hazard ratios are reported comparing disease development between patients with and without genetic variation, and are calculated using the following equations:

$$P_{\rm{var}} = {\rm{OR}}/\left( 1 + {\rm{OR}} \right)$$
(1a)
$$P_{{\rm{non}}\_{\rm{var}}} = 1/\left( {1 + {\rm{OR}}} \right),$$
(1b)

where pvar is the probability with genetic variation, Pnon_var is the probability with no genetic variation, and OR is the odds ratio of the genetic variation compared with the nonvariation group. Hazard ratios and relative ratios are estimated as OR. Cycle length for the model is one month. We adopted the US health-care payer’s perspective primarily because health insurers will play a major role in the adoption of preemptive versus reactive PGx panel testing in the United States.

### Study population

The study population include a hypothetical cohort of 10,000 individuals resembling the US population aged 45 years and older (median: 60 years old, distribution listed in eFig. 2), sex (49% male), and race (80% White, 14% African American, 6% Asian) distribution as per the National Vital Statistics System, Centers for Disease Control and Prevention, and Census Bureau, and is assumed to have average risk of developing a first CVD event (approximately 4% in 10 years).23,24,25 The model tracks individuals in the cohort through each health state and estimates costs and quality-adjusted life years (QALYs) over 50 years. The specific CVD-related conditions and/or outcomes considered are identified in the systematic review14 as well as from a comprehensive review of guidelines from the American Heart Association, American College of Cardiology, and other cardiology-related societies or organizations (eTable 1) are as follows: myocardial infarction (MI), coronary heart disease (CHD), atrial fibrillation (AF), valvular heart disease (VHD), hyperlipidemia (HLP), peripheral artery disease (PAD), and ischemic stroke (stroke).

### Testing strategies

As indicated previously, the PGx panel test for the study include the following genes: CYP2C19, CYP2C9/VKORC1, and SLCO1B1.14,15 The genetic polymorphisms included in the simulation model are listed in eTable 2. Alternative drugs as suggested by extant evidence are used when PGx testing suggests genetic variations associated with potential harm (eTable 2):26,27,28 ticagrelor is used as an alternative drug to clopidogrel, NOACs are used as alternatives to warfarin, and PCSK9 inhibitors are used as alternative to statins. We model the alternative treatments that are not known to have a drug–gene relationship with the indicated genes (NOAC and PCSK9 inhibitor), or have a trace of drug–gene relationship but are found to be cost-effective under genotype-guided strategy (ticagrelor).15,20,28,29

The timelines of the three test strategies are illustrated in Fig. 2. Patients undergo preemptive PGx panel testing before disease develops and drug therapy is indicated. Reactive PGx panel testing refers to tests administered after the first disease is diagnosed and drug therapy is indicated, but before the treatment is initiated. There is a 30-day test turnaround time for reactive testing. In this study, the reactive testing is panel-based instead of single gene–based, which means that all four genes would be tested once one of the index drugs is indicated. The genetic information would be kept in the patient’s health records and would inform appropriate treatment for other diseases developed in the future. In that sense, the reactive testing strategy is “partially preemptive.” Patients with no genetic testing at any point in time are defined as the usual care group, and no alternative drugs are given. The major differences between preemptive and reactive testing are (1) preemptive strategy tests everyone, while reactive strategy only tests patients who develop disease; (2) patients in the reactive testing arm undergo usual care for one month, when all disease probabilities follow those of the usual care arm.

### Model inputs

The range and distribution of parameters were obtained using currently available results from published studies, including randomized controlled clinical trials, meta-analyses, cohort or cross-sectional studies, and cost-effectiveness analyses (eTable 3). Overall non-CVD death rates were captured by age and race from the CDC’s National Vital Statistics System (eFig. 3).23 Model input values for the parameters were converted to monthly rates and then converted into monthly probabilities to conform to the monthly model cycles (Eq. 2).

$$p = 1 - e^{\left( { - r \times t} \right)},$$
(2)

where p is the probability for in a time period (e.g., 1 month), r is the disease developing rate, and t is the time period (e.g., 1 month).

The base-case value for each of the variables was derived from the population that closely resembled the modeling population in specific health status. For example, the base-case value for monthly medication cost of NOACs was obtained from the cost-effectiveness study on NOAC in atrial fibrillation patients.27 The base-case analysis was calculated using the base-case values to report a most expected estimation that could happen in a real-world scenario. The ranges of the parameters were derived from multiple studies that exist in the current literature, with the same payer perspective as ours and that focused on US populations. Two major sources were used to identify references: (1) the database of our previous work, a systematic review of cost-effectiveness in CVD patient management (12 references);2 and (2) clinical guidelines and the studies cited by these disease-specific guidelines (21 references) (eTable 3). Assumptions were made on parameters for which pertinent data were not available (eTable 4).

### Costs

We included costs associated with medications (e.g., warfarin), genetic panel testing, disease treatment (e.g., monthly costs for post-MI), disease costs by event (i.e., hospitalization for MI and stroke), treatment of drug side effects (e.g., bleeding from warfarin use), and costs of CVD- and non-CVD-related deaths. When available, we captured costs from published literature with the same perspective and converted them to 2019 US dollars using the US gross domestic product deflator (Supplementary Material II).30

### Utility

QALYs were used to calculate the effectiveness of the three strategies, including disutility of diseases, their treatments and adverse drug effects from the treatments. Utility values were captured from literature on the US population with the same disease diagnosis (eTable 3). Each disease state was assigned a value that reflected the overall quality of life when living with that disease state. There are two types of death modeled in this study: CVD death and non-CVD death. Both deaths were absorbing states, while the utility value of 0.2355 was used for the final cycle when a patient dies of CVD rather than the health state the patient was in just prior to CVD death.27,31 No other death transition resulted in a lower utility than that of the health state just prior to death.

The study reported incremental cost-effectiveness ratios (ICERs) as the primary outcome for the base-case and scenario analyses. We assessed cost-effectiveness of the three treatment strategies under different scenarios to illustrate the hypothetical outcomes that could be achieved for a specific patient population. In each of the scenarios, one parameter was set at a fixed value or range of values while the other parameters still followed the assumed model distributions. Parameters included in the scenario analyses were patient’s age, sex, risk level, race, and study timeframe (Table 1), which were known to be associated with health outcomes, disease rates, and death rates along with the effects of the genetic variants.32 One-way sensitivity analyses were conducted for all the parameters. The ranges of ICERs calculated from the minimum value and maximum value of each parameter were compared and the top 20 parameters with the largest ICER ranges were reported. Probabilistic sensitivity analysis was conducted and cost-effectiveness acceptability curve was reported. Cost and utility values followed gamma distribution and probability values followed beta distribution.33 Tables were generated for parameters (e.g., non-CVD death, probabilities from disease-free to disease development) changing with race, gender, and age (eTable 5). The analysis used a 3% annual discount rate for both costs and QALYs. Cost-effectiveness was examined using the willingness-to-pay (WTP) threshold set at $100,000 per QALY. The model was constructed and analyzed using TreeAge Pro 2019 software (TreeAge Software, Williamstown, MA). ## RESULTS ### Base-case analysis The results of the base-case analysis are presented in Table 1. Preemptive PGx panel testing compared with usual care (no PGx testing) resulted in an ICER of$86,227/QALY. Reactive PGx panel testing compared with usual care yielded an ICER of $148,726/QALY. These results suggested that preemptive panel testing was cost-effective compared with usual care at a WTP of$100,000/QALY, while reactive panel testing was not cost-effective. Compared with usual care, preemptive testing resulted in an average of 0.35 additional QALYs for each person, while reactive testing resulted in an average increase of 0.09 QALYs; preemptive testing increased the average costs by $30,431 per person, while reactive testing increased costs by$13,344 per person. Compared with reactive testing, preemptive testing was found to be cost-effective with an ICER of $64,921/QALY. The key outcomes modeled in the analysis are listed in eTable 5, including proportion of patients with each CVD disease, proportion of death and CVD death, and all adverse events. ### Scenario analyses The changes in age, sex, race, and risk level did not change the study conclusions about cost-effectiveness of preemptive testing compared with usual care (column “Conclusion,” Table 1). Preemptive testing changed from ineffective to effective beginning at 26 years, implying that the cost-effectiveness of preemptive testing was sensitive to changes in the study timeframe. On the other hand, reactive testing was found to be cost-effective in certain scenarios. Compared with usual care, the cost-effectiveness of preemptive PGx testing varied by age (Table 1): the 45–55 and 55–65 year age groups had ICERs of$84,398/QALY and $97,717/QALY, respectively, and were cost-effective. The groups with age ≥65 years old (65–74 years, and 75 years and above) had ICERs higher than WTP and were not cost-effective. Reactive PGx testing was cost-effective for 45–54 years old (ICER of$95,480) and was not cost-effective for age ≥55 years old.

Compared with usual care, preemptive tests had higher ICER ($91,451/QALY) among male patients than female patients ($80,921/QALY) and both groups were cost-effective. Reactive tests were not cost-effective for male patients ($180,139/QALY) but were cost-effective for female patients ($83,271/QALY).

The ICER of PGx testing generally changed slightly with race (Table 1). Compared with usual care, preemptive testing resulted in a lower ICER for African Americans ($99,814/QALY), while the ICERs for Whites ($84,740/QALY) and Asians ($85,616/QALY) were similar. Reactive testing had higher ICERs for all three racial groups and was not cost-effective. If the risk level was increased by half (to 6%), preemptive testing yielded an ICER of$83,986/QALY. Doubling the risk level from 4% to 8% led to a slightly higher ICER for preemptive testing ($86,040/QALY) compared with usual care. When the risk level increased, the costs for all three strategies changed slightly, while the QALYs declined. Cost-effectiveness of preemptive PGx testing improved when patients were observed for a longer timeframe. For example, preemptive testing for a 5-year time horizon was not cost-effective (ICER =$202,950/QALY) compared with usual care; however, for a 20-year horizon, preemptive testing became cost-effective (ICER = $99,248/QALY), which went down to$94,260/QALY at 30 years. The key outcomes of CVD disease, death, and adverse events are listed in eTable 5.

### Sensitivity analyses: one-way sensitivity analyses

One-way sensitivity analysis was performed on the parameters associated with costs, utilities, and transition probabilities (Fig. 3). We examined how the ICER range changed while varying each of the model parameters in its range. We ascertained that a parameter changed the conclusion of the model’s cost-effectiveness if the ICER increased from below $100,000/QALY to above$100,000/QALY, which suggested that the outcome changed from cost-effective to not cost-effective. We found three parameters that changed the conclusion of cost-effectiveness: hazard ratio of CVD death from MI with clopidogrel treatment for patients with genetic variants versus wild type, probability of CVD death from MI with clopidogrel treatment in the usual care group, and disutility of MI event; other factors did not contribute to the ICERs of preemptive PGx testing versus usual care (Fig. 3a). None of the parameters could change the cost-effectiveness conclusion of preemptive versus reactive PGx test (Fig. 3b) and reactive versus usual care (eFig. 4).

### Sensitivity analyses: probabilistic sensitivity analysis

We performed 1,000 samples of 10,000 population where the input parameters were varied simultaneously along with the prespecified distributions for the underlying parameters (eTable 3). This analysis simulated the real-world situation in which each individual could have a set of parameters that varies from one person to another. Figure 4 illustrated the ICER plane from these samples, with each dot representing each of the samples. At a WTP of $100,000/QALY and compared with usual care, preemptive testing was cost-effective 98.1% of the time and reactive testing was cost-effective only 0.10% of the time. Compared with reactive testing, preemptive testing was cost-effective 100% of the time. Examined through cost-effectiveness acceptability curves (CEACs), preemptive PGx testing became cost-effective compared with usual care at a WTP of$90,000/QALY and above. Reactive PGx testing was not cost-effective for the WTP range $0–$200,000/QALY (eFig. 5).

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## Acknowledgements

This research was partially supported by a research grant from Mayo Clinic Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery and Mayo Clinic Center for Individualized Medicine. The views expressed in the article are those of the authors and do not necessarily reflect the views of Mayo Clinic.

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### Corresponding author

Correspondence to Bijan J. Borah PhD.

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### Disclosure

R.W. and L.W. are cofounders of and stockholders in OneOme LLC, a pharmacogenomic decision support company. The other authors declare no conflicts of interest.

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Zhu, Y., Moriarty, J.P., Swanson, K.M. et al. A model-based cost-effectiveness analysis of pharmacogenomic panel testing in cardiovascular disease management: preemptive, reactive, or none?. Genet Med (2020). https://doi.org/10.1038/s41436-020-00995-w

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### Key words

• pharmacogenomics
• cost-effectiveness
• genomic panel testing
• cardiovascular diseases management