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.
OBJECTIVES: To compare the effectiveness of anticoagulant therapies for the treatment of deep vein thrombosis in pregnancy. The anticoagulant drugs included are UFH, low molecular weight heparin (LMWH) and warfarin.
SEARCH STRATEGY: We searched the Cochrane Pregnancy and Childbirth Group's Trials Register (March 2010) and reference lists of retrieved studies.
SELECTION CRITERIA: Randomised controlled trials comparing any combination of warfarin, UFH, LMWH and placebo in pregnant women.
DATA COLLECTION AND ANALYSIS: We used methods described in the Cochrane Handbooks for Systemic Reviews of Interventions for assessing the eligibility of studies identified by the search strategy. A minimum of two review authors independently assessed each study.
MAIN RESULTS: We did not identify any eligible studies for inclusion in the review.We identified three potential studies; after assessing eligibility, we excluded all three as they did not meet the prespecified inclusion criteria. One study compared LMWH and UFH in pregnant women with previous thromboembolic events and, for most of these women, anticoagulants were used as thromboprophylaxis. There were only three women who had a thromboembolic event during the current pregnancy and it was unclear whether the anticoagulant was used as therapy or prophylaxis. We excluded one study because it included only women undergoing caesarean birth. The third study was not a randomised trial.
AUTHORS' CONCLUSIONS: There is no evidence from randomised controlled trials on the effectiveness of anticoagulation for deep vein thrombosis in pregnancy. Further studies are required.
METHODS: A retrospective cohort study of 200 patients on dabigatran and warfarin from January 2009 till September 2016 was carried out. Data were collected for 100 patients on dabigatran and 100 patients on warfarin.
RESULTS: The mean follow-up period was 340.7±322.3 days for dabigatran group and 410.5±321.2 days for warfarin group. The mean time in therapeutic range (TTR) was 52±18.7%. The mean CHA2DS2 -VASc score for dabigatran group was 4.4±1.1 while 5.0±1.5 for warfarin group. None in dabigatran group experienced ischemic stroke compared to one patient in warfarin group (p=0.316). There was one patient in dabigatran group suffered from ICH compared to none in warfarin group (p=0.316). Four patients in warfarin group experienced minor bleeding, while none from dabigatran group (p=0.043).
CONCLUSION: Overall bleeding events were significantly lower in dabigatran group compared to warfarin group. In the presence of suboptimal TTR rates and inconveniences with warfarin therapy, non-vitamin-K antagonist oral anticoagulants (NOAC) are the preferred agents for stroke prevention in elderly Asian patients for nonvalvular AF.
PURPOSE: The aim was to determine the metabolic fingerprint that predicts warfarin response based on the international normalized ratio (INR) in patients who are already receiving warfarin (phase I: identification) and to ascertain the metabolic fingerprint that discriminates stable from unstable INR in patients starting treatment with warfarin (phase II: validation).
EXPERIMENTAL APPROACH: A total of 94 blood samples were collected for phase I: 44 patients with stable INR and 50 with unstable INR. Meanwhile, 23 samples were collected for phase II: nine patients with stable INR and 14 with unstable INR. Data analysis was performed using multivariate analysis including principal component analysis and partial least square-discriminate analysis (PLS-DA), followed by univariate and multivariate logistic regression (MVLR) to develop a model to identify unstable INR biomarkers.
KEY RESULTS: For phase I, the PLS-DA model showed the following results: sensitivity 93.18%, specificity 91.49% and accuracy 92.31%. In the MVLR analysis of phase I, ten regions were associated with unstable INR. For phase II, the PLS-DA model showed the following results: sensitivity 66.67%, specificity 61.54% and accuracy 63.64%.
CONCLUSIONS AND IMPLICATIONS: We have shown that the pharmacometabonomics technique was able to differentiate between unstable and stable INR with good accuracy. NMR-based pharmacometabonomics has the potential to identify novel biomarkers in plasma, which can be useful in individualizing treatment and controlling warfarin side effects, thus, minimizing undesirable effects in the future.