Impact of Pharmacogenomic Information on Values of Care and Quality of Life Associated with Codeine and Tramadol-Related Adverse Drug Events

Abstract

Objective: To assess the potential impact of Pharmacogenomic (PGx) variation in cytochrome P450 2D6 (CYP2D6) enzyme function, using loss in quality-adjusted life years (QALYs) associated with treatment problems, and the willingness to pay to avoid treatment problems from patients' and payers' perspectives.

Patients and methods: The study included patients prescribed tramadol or codeine, or both, between January 1, 2005, and December 31, 2017. Demographic information and adverse drug events, including adverse drug events and poor pain control, were collected from the electronic health records using natural language processing techniques and review by trained abstractors. Patients' willingness to pay and QALY estimates were based on comprehensive literature review. The CYP2D6 phenotypes were divided into 4 groups: ultra-rapid metabolizers, normal metabolizers, intermediate metabolizers, and poor metabolizers.

Results: Among the 2860 identified patients, 63 (2%) were ultrarapid metabolizers, 1449 (50%) were normal metabolizers, 1155 (40%) were intermediate metabolizers, and 193 (7%) were poor metabolizers. The patients' average estimated willingness-to-pay value to avoid treatment problems was $23 per month; poor metabolizers developed problems with the highest estimated willingness-to-pay value ($32 per month). The mean QALY loss among all patients was 0.024 QALYs (8.8 healthy days); poor metabolizers had the highest loss (0.027 QALYs, 9.9 healthy days).

Conclusion: Patients with various phenotypes developed different treatment problem profiles. Poor CYP2D6 metabolizers developed problems with highest willingness to pay, and they might potentially benefit most from PGx-guided treatment and problem prevention.

Keywords: ADE, adverse drug event; CYP2D6, Cytochrome P450 2D6; PGx, pharmacogenomics; QALY, quality-adjusted life year; REP, Rochester Epidemiology Project; RIGHT, Right Drug; Right Dose, Right Time-Using Genomic Data to Individualize Treatment.

Figure 1

Patients’ willingness-to-pay values, loss of QALYs for each type of adverse drug outcome. Bubble size represents the proportion of the study population that experienced the specific treatment problem. QALY, quality-adjusted life year.

Figure 2

Differences in types of treatment problems (including drug adverse effects and poor pain control) due to codeine and tramadol prescriptions by CYP2D6 phenotype. P values were from logistic regressions of genetic groups on adverse events.

Figure 3

Estimated patient willingness to pay, QALYs, and payer willingness to pay overall and by CYP2D6 phenotype. (A) Estimated amount patients would be willing to pay to avoid treatment problems. (B) Estimated loss of QALYS owing to treatment problems. (C) Estimated willingness to pay from payer’s perspective. P values are from logistic regressions of genetic groups on adverse events. ADE, adverse drug events; QALY, quality-adjusted life-years.

Figure 4

Estimated patient willingness to pay and payer willingness to pay overall and by CYP2D6 phenotype with patients’ total values for opioid treatments, including all adverse symptoms and poor pain control. Payers’ willingness to pay was calculated from $50,000 per QALY multiplied by the QALYs lost owing to treatment problems. All the values were converted to 2018 US dollars using the gross domestic product price deflator.