Value of Pharmacogenetic Testing Assessed with Real-World Drug Utilization and Genotype Data

Abstract

Implementation of pharmacogenetic testing in clinical care has been slow and with few exceptions is hindered by the lack of real-world evidence on how to best target testing. In this retrospective register-based study, we analyzed a nationwide cohort of 1,425,000 patients discharged from internal medicine or surgical wards and a cohort of 2,178 university hospital patients for purchases and prescriptions of pharmacogenetically actionable drugs. Pharmacogenetic variants were obtained from whole genome genotype data for a subset (n = 930) of the university hospital patients. We investigated factors associated with receiving pharmacogenetically actionable drugs and developed a literature-based cost-benefit model for pre-emptive pharmacogenetic panel testing. In a 2-year follow-up, 60.4% of the patients in the nationwide cohort purchased at least one pharmacogenetically actionable drug, most commonly ibuprofen (25.0%) and codeine (19.4%). Of the genotyped subset, 98.8% carried at least one actionable pharmacogenetic genotype and 23.3% had at least one actionable gene-drug pair. Patients suffering from musculoskeletal or cardiovascular diseases were more prone to receive pharmacogenetically actionable drugs during inpatient episode. The cost-benefit model included frequently dispensed drugs in the university hospital cohort, comprising ondansetron (19.4%), simvastatin (7.4%), clopidogrel (5.0%), warfarin (5.1%), (es)citalopram (5.3%), and azathioprine (0.5%). For untargeted pre-emptive pharmacogenetic testing of all university hospital patients, the model indicated saving €17.49 in direct healthcare system costs per patient in 2 years without accounting for the cost of the test itself. Therefore, it might be reasonable to target pre-emptive pharmacogenetic testing to patient groups most likely to receive pharmacogenetically actionable drugs.

Figure 1

Overview of the study design and registers used.

Figure 2

Number of pharmacogenetically actionable drugs per patient (a) during the initial inpatient episode and in 2‐year follow‐up in the HUS cohort (n = 2,178), and (b) in 2‐year follow‐up in the nationwide cohort (n = 1,425,263).

Figure 3

Pharmacogenetically actionable drug utilization incidences in 2‐year follow‐up. (a) Drug initiations in a nationwide cohort of 1,425,263 Finnish hospital‐treated patients, and (b) in the HUS cohort of 2,178 hospital‐treated patients in the HUS Helsinki University Hospital area. (c and d) The drug initiation incidences according to associated genes in the nationwide cohort and in the HUS cohort, respectively.

Figure 4

(a) Number of actionable genotypes per patient and (b) the distribution of pharmacogenetic actionable genotypes in the subset of the HUS cohort participants (n = 930) and in the PGx panel cohort (n = 967). CYP3A5 normal metabolizer frequency is represented by the black diagonal stripes on light blue background and ABCG2 poor function frequency (0.41%) is represented by the light blue diagonal stripes on white background. DF, decreased function; IF, increased function; IM, intermediate metabolizer; NF, normal function; NM, normal metabolizer; PF, poor function; PM, poor metabolizer; RM, rapid metabolizer; UM, ultrarapid metabolizer.