| Literature DB >> 28033366 |
Elizabeth J J Berm1, Judith J Gout-Zwart1, Jos Luttjeboer1, Bob Wilffert1,2, Maarten J Postma1,3,4.
Abstract
OBJECTIVE: Genotyping for CYP2D6 has the potential to predict differences in metabolism of nortriptyline. This information could optimize pharmacotherapy. We determined the costs and effects of routine genotyping for old aged Dutch depressed inpatients.Entities:
Mesh:
Substances:
Year: 2016 PMID: 28033366 PMCID: PMC5199075 DOI: 10.1371/journal.pone.0169065
Source DB: PubMed Journal: PLoS One ISSN: 1932-6203 Impact factor: 3.240
Fig 1Model structure for the treatment of major depressive disorder with nortriptyline during the first 12 weeks.
Patients who receive a dosage which led to therapeutic plasma concentrations (i.e. “correct”) did not receive a next dose evaluation. Patients who quit nortriptyline pharmacotherapy entered a wash out period with a disutility of 0.20 for 14 days. After this period, pharmacotherapy with 6 tranylcypromine was initiated for the remaining time in the model. PM = poor metabolizer, IM = intermediate metabolizer, EM = extensive metabolizer, UM = ultrarapid metabolizer.
Main model inputs and variables of deterministic and probabilistic sensitivity analysis.
| Variable | Base case | Range deterministic sensitivity analysis(min- max) | PSA distribution (parameters) | Source | Old age specific? |
|---|---|---|---|---|---|
| 35 | 31–38 | β (253, 470) | [ | No | |
| 76 | 73–78 | β (735, 239) | [ | No | |
| 56 | 53–59 | β (572, 443) | |||
| 12 | n.a. | n.a. | |||
| 25 | n.a. | n.a. | |||
| 57 | 54–60 | β (593, 455) | |||
| 43 | 34–53 | β (40, 53) | Yes | ||
| 12 | 5–19 | γ (11, 1.06) | [ | Yes | |
| 31 | Dependents on evaluation 1 | Dependent on evaluation 1 | Yes | ||
| 38 | Fixed | Fixed | Yes | ||
| 8 | 6–10 | Uniform | [ | No | |
| 11 | 9–13 | Uniform | |||
| 79 | Dependents on other genotypes | n.a. | |||
| 2 | 1–2 | Uniform | |||
| 28.6 | +/- 25% | γ (61, 0.47) | [ | Yes | |
| 13 | 0–26 | β (40, 53) | [ | No | |
| 22 | 12–33 | β (11, 39) | [ | No | |
| 8.5 | 2–17 | β (3, 37) | |||
| 0.04 | 0.01–0.12 | PERT (0, 0.04, 0.13) | [ | No | |
| 0.20 | 0.12–0.28 | β (19, 76) | [ | ||
| 0.20 | 0.12–0.28 | β (19, 76) | assumed | ||
Drug costs included in the model.
| Type of costs | Costs in 2014 |
|---|---|
| € 188.20 | |
| € 23.11 | |
| € 255.62 | |
| € 190.62 | |
| € 0.08 | |
| € 0.15 | |
| € 0.29 | |
| € 0.96 |
Outcomes of the model for 1000 patients per cohort during 12 weeks of pharmacotherapy in the base case scenario.
| Care as usual cohort | Genotyping cohort | Difference | ICER (€) | Max. test costs for €50 000/QALY (€) | |
|---|---|---|---|---|---|
| Patients with 24.9 days of inpatient care (correctly dosed)(n) | 483 | 447 | 36 | ||
| Days of inpatient care (mean) | 28.60 | 28.47 | - 0.13 | ||
| Patients who stopped therapy (n) | 248 | 247 | -1 | ||
| Costs of care (€) | 7 374 826 | 7 340 091 | - 34 734 | ||
| Costs | 0 | 188 200 | 188 200 | ||
| Total costs (€) | 7 374 826 | 7 528 292 | 153 466 | ||
| QALY loss | 4.57 | 4.46 | 0.12 | ||
| 1 333 148 | 40 | ||||
| Total costs (€) | 7 375 299 | 7 528 218 | 152 918 | ||
| QALY loss | 4.50 | 4.34 | 0.15 | ||
| 999 622 | 43 | ||||
| Total costs (€) | 7 374 826 | 7 497 172 | 122 346 | ||
| QALY loss | 4.57 | 4.52 | 0.05 | ||
| 2 380 626 | 68 | ||||
* Suboptimal dosed patients were hospitalized for 31.5 days.
Fig 2Results of deterministic sensitivity analyses for genotyping test costs to reach the €50 000/QALY cut-off (A) or to break even with costs savings (B).
Fig 3Probability on cost-effectiveness at €50 000 per QALY (solid line) or probability of test costs to break even with cost-savings (broken line) shown at different CYP2D6 genotyping test costs.