Key result
The Bayesian dosing tool produced positively biased warfarin maintenance dose predictions with a mean prediction error of /day, primarily over-predicting doses for patients requiring ≥/day.
Why the study?
Does a Bayesian dosing tool accurately predict warfarin maintenance doses, and is its performance influenced by CYP2C9 and VKORC1 genotypes in patients requiring warfarin?
Observational (n=140)
Yes
Does a Bayesian dosing tool accurately predict warfarin maintenance doses, and is its performance influenced by CYP2C9 and VKORC1 genotypes in patients requiring warfarin?
A Bayesian dosing tool overpredicted warfarin maintenance doses in patients requiring ≥7 mg/d, and this bias was not explained by CYP2C9/VKORC1 genotypes or prior population differences.
Answers are grounded in this paper and cite their sources.
Bayesian warfarin dosing overpredicts maintenance doses in high-requirement patients; leaves open whether recalibration or added covariates improve accuracy before clinical use.
BACKGROUND: A previously established Bayesian dosing tool for warfarin was found to produce biased maintenance dose predictions. In this study, we aimed (1) to determine whether the biased warfarin dose predictions previously observed could be replicated in a new cohort of patients from 2 different clinical settings, (2) to explore the influence of CYP2C9 and VKORC1 genotype on predictive performance of the Bayesian dosing tool, and (3) to determine whether the previous population used to develop the kinetic-pharmacodynamic model underpinning the Bayesian dosing tool was sufficiently different from the test (posterior) population to account for the biased dose predictions. METHODS: The warfarin maintenance doses for 140 patients were predicted using the dosing tool and compared with the observed maintenance dose. The impact of genotype was assessed by predicting maintenance doses with prior parameter values known to be altered by genetic variability (eg, EC50 for VKORC1 genotype). The prior population was evaluated by fitting the published kinetic-pharmacodynamic model, which underpins the Bayesian tool, to the observed data using NONMEM and comparing the model parameter estimates with published values. RESULTS: The Bayesian tool produced positively biased dose predictions in the new cohort of patients (mean prediction error [95% confidence interval]; 0.32 mg/d [0.14-0.5]). The bias was only observed in patients requiring ≥7 mg/d. The direction and magnitude of the observed bias was not influenced by genotype. The prior model provided a good fit to our data, which suggests that the bias was not caused by different prior and posterior populations. CONCLUSIONS: Maintenance doses for patients requiring ≥7 mg/d were overpredicted. The bias was not due to the influence of genotype nor was it related to differences between the prior and posterior populations. There is a need for a more mechanistic model that captures warfarin dose-response relationship at higher warfarin doses.
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Saffian et al. (2016) conducted an observational in Patients initiating warfarin therapy (n=140). Bayesian dose individualization tool (TCIWorks) vs. Observed maintenance dose was evaluated on Mean prediction error (MPE) of predicted versus observed warfarin maintenance dose (MPE 0.32 mg/day, 95% CI 0.14-0.50). The Bayesian dosing tool produced positively biased warfarin maintenance dose predictions with a mean prediction error of /day, primarily over-predicting doses for patients requiring ≥/day.
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