Prediction of the warfarin maintenance dose after completion of the 10 mg initiation nomogram: do we really need genotyping?

Evaluation of Stable Doses of Warfarin in a Patient Cohort

BACKGROUND

Definitions for stable dose of warfarin varies in the reported studies. International warfarin pharmacogenetic consortium (IWPC) algorithm was generated from the data based on these definitions.

OBJECTIVE

In the present study, we primarily evaluated whether any significant differences exist between the definitions for stable warfarin dose.

METHODS

A prospective cross-sectional study in adults receiving warfarin for at least 3 months was carried out. Stable doses of warfarin as defined in previous studies were compared with the standard definition. Bland-Altman plots, Pearson's correlation and intra-class coefficients (ICC) were used to assess the correlation, reliability and agreements between the doses.

RESULTS

Sixty-four patients were recruited. Twenty definitions were obtained from the previous studies. We observed that all but one showed very high or high positive correlations; and either excellent or good ICC. No significant differences between the doses initiated and predicted by IWPC algorithm.

CONCLUSION

We observed similar stable doses between the definitions except for one. Hence, IWPC algorithm may not have any bias associated with inclusion of any studies with variable definitions for stable warfarin dose.

A Simple Formula for Predicting the Maintenance Dose of Warfarin with Reference to the Initial Response to Low Dosing at an Outpatient Clinic

Objective The pharmacodynamic effect of warfarin varies among individuals, and its maintenance dose is widely distributed. Although many formulae for predicting the maintenance dose of warfarin have been developed, most of them are complex and not in practical use. Methods and Materials Among 12,738 new patients visiting the Cardiovascular Institute between 2004 and 2009, we identified 127 patients (66.6±8.8 years, 89 men) with atrial fibrillation for whom warfarin was newly started with an initial dose of 2 mg/day and the international normalized ratio (INR) at 1 year after warfarin was started was within the therapeutic range. The prediction models for the maintenance dose were developed by an exponential equation and a first-order equation. Results The initial response of the INR to the dose of 2 mg/day (initial INR) ranged from 1.00-3.24 (mean 1.43), while the maintenance dose of warfarin ranged from 0.5-14 mg (mean 3.8 mg). The maintenance dose showed an exponential correlation to the initial INR: (predicted maintenance dose) =5.522× (initial INR) ^-1.556 (R²=0.795, p<0.001). Excluding the patients with a poor response to the initial dose (initial INR <1.1, n=32) permitted a simple correlation with a first-order approximation: (predicted maintenance dose) =-2.009× (initial INR) +6.172 (R²=0.706, p<0.001). Conclusion We developed a simple formula for predicting the maintenance dose of warfarin using the initial response of the INR to low-dose warfarin.

A factor VII-based method for the prediction of anticoagulant response to warfarin

Warfarin dosing methods based on existing models for warfarin and the international normalised ratio (INR) give biased maintenance dose predictions at the upper and lower quantiles of dose requirements. The aim of this work is to propose a conceptually different approach to predict INR after warfarin dosing. Factor VII concentration was proposed as the principal driving force for the INR. The time to steady-state INR (t_SS,INR) was determined based on the INR response to changes in factor VII concentrations following warfarin initiation, and from this the steady-state INR (INR_SS) was derived. The proposed method requires timed, paired blood samples of INR and factor VII. At different simulated warfarin dose rates, the prediction error associated with the proposed method was shown to be within clinically acceptable limits for both the t_SS,INR (±2 days) and INR_SS (±0.2). The use of the method was demonstrated in two patients who were initiated with 5 mg of warfarin daily. The difference in predicted versus actual steady-state INR were 0.0 and −0.4. The proposed method represents a unique approach to predict the INR. It considers factor VII as the main driver for INR and provides valuable information about the time to steady state INR.

Warfarin Dosing Algorithms Underpredict Dose Requirements in Patients Requiring ≥7 mg Daily: A Systematic Review and Meta-analysis

There is preliminary evidence to suggest that some published warfarin dosing algorithms produce biased maintenance dose predictions in patients who require higher than average doses. We conducted a meta-analysis of warfarin dosing algorithms to determine if there exists a systematic under- or overprediction of dose requirements for patients requiring ≥7 mg/day across published algorithms. Medline and Embase databases were searched up to September 2015. We quantified the proportion of over- and underpredicted doses in patients whose observed maintenance dose was ≥7 mg/day. The meta-analysis included 47 evaluations of 22 different warfarin dosing algorithms from 16 studies. The meta-analysis included data from 1,492 patients who required warfarin doses of ≥7 mg/day. All 22 algorithms were found to underpredict warfarin dosing requirements in patients who required ≥7 mg/day by an average of 2.3 mg/day with a pooled estimate of underpredicted doses of 92.3% (95% confidence interval 90.3-94.1, I² = 24%).

Cost-effectiveness of apixaban versus low molecular weight heparin/vitamin k antagonist for the treatment of venous thromboembolism and the prevention of recurrences

BACKGROUND

Prior analyses beyond clinical trials are yet to evaluate the projected lifetime benefit of apixaban treatment compared to low-molecular-weight heparin (LMWH)/vitamin K antagonist (VKA) for treatment of venous thromboembolism (VTE) and prevention of recurrences. The objective of this study is to assess the cost-effectiveness of initial plus extended treatment with apixaban versus LMWH/VKA for either initial treatment only or initial plus extended treatment.

METHODS

A Markov cohort model was developed to evaluate the lifetime clinical and economic impact of treatment of VTE and prevention of recurrences with apixaban (starting at 10 mg BID for 1 week, then 5 mg BID for 6 months, then 2.5 mg BID for an additional 12 months) versus LMWH/VKA for 6 months and either no further treatment or extended treatment with VKA for an additional 12 months. Clinical event rates to inform the model were taken from the AMPLIFY and AMPLIFY-EXT trials and a network meta-analysis. Background mortality rates, costs, and utilities were obtained from published sources. The analysis was conducted from the perspective of the United Kingdom National Health Service. The evaluated outcomes included the number of events avoided in a 1000-patient cohort, total costs, life-years, quality-adjusted life-years (QALYs), and cost per QALY gained.

RESULTS

Initial plus extended treatment with apixaban was superior to both treatment durations of LMWH/VKA in reducing the number of bleeding events, and was superior to initial LMWH/VKA for 6 months followed by no therapy, in reducing VTE recurrences. Apixaban treatment was cost-effective compared to 6-month treatment with LMWH/VKA at an incremental cost-effectiveness ratio (ICER) of £6692 per QALY. When initial LMWH/VKA was followed by further VKA therapy for an additional 12 months (i.e., total treatment duration of 18 months), apixaban was cost-effective at an ICER of £8528 per QALY gained. Sensitivity analysis suggested these findings were robust over a wide range of inputs and scenarios for the model.

CONCLUSIONS

In the UK, initial plus extended treatment with apixaban for treatment of VTE and prevention of recurrences appears to be economical and a clinically effective alternative to LMWH/VKA, whether used for initial or initial plus extended treatment.

A review of a priori regression models for warfarin maintenance dose prediction

A number of a priori warfarin dosing algorithms, derived using linear regression methods, have been proposed. Although these dosing algorithms may have been validated using patients derived from the same centre, rarely have they been validated using a patient cohort recruited from another centre. In order to undertake external validation, two cohorts were utilised. One cohort formed by patients from a prospective trial and the second formed by patients in the control arm of the EU-PACT trial. Of these, 641 patients were identified as having attained stable dosing and formed the dataset used for validation. Predicted maintenance doses from six criterion fulfilling regression models were then compared to individual patient stable warfarin dose. Predictive ability was assessed with reference to several statistics including the R-square and mean absolute error. The six regression models explained different amounts of variability in the stable maintenance warfarin dose requirements of the patients in the two validation cohorts; adjusted R-squared values ranged from 24.2% to 68.6%. An overview of the summary statistics demonstrated that no one dosing algorithm could be considered optimal. The larger validation cohort from the prospective trial produced more consistent statistics across the six dosing algorithms. The study found that all the regression models performed worse in the validation cohort when compared to the derivation cohort. Further, there was little difference between regression models that contained pharmacogenetic coefficients and algorithms containing just non-pharmacogenetic coefficients. The inconsistency of results between the validation cohorts suggests that unaccounted population specific factors cause variability in dosing algorithm performance. Better methods for dosing that take into account inter- and intra-individual variability, at the initiation and maintenance phases of warfarin treatment, are needed.

Predicting warfarin dosage from clinical data: a supervised learning approach

OBJECTIVE

Safety of anticoagulant administration has been a primary concern of the Joint Commission on Accreditation of Healthcare Organizations. Among all anticoagulants, warfarin has long been listed among the top ten drugs causing adverse drug events. Due to narrow therapeutic range and significant side effects, warfarin dosage determination becomes a challenging task in clinical practice. For superior clinical decision making, this study attempts to build a warfarin dosage prediction model utilizing a number of supervised learning techniques.

METHODS AND MATERIALS

The data consists of complete historical records of 587 Taiwan clinical cases who received warfarin treatment as well as warfarin dose adjustment. A number of supervised learning techniques were investigated, including multilayer perceptron, model tree, k nearest neighbors, and support vector regression (SVR). To achieve higher prediction accuracy, we further consider both homogeneous and heterogeneous ensembles (i.e., bagging and voting). For performance evaluation, the initial dose of warfarin prescribed by clinicians is established as the baseline. The mean absolute error (MAE) and standard deviation of errors (σ(E)) are considered as evaluation indicators.

RESULTS

The overall evaluation results show that all of the learning based systems are significantly more accurate than the baseline (MAE=0.394, σ(E)=0.558). Among all prediction models, both Bagged Voting (MAE=0.210, σ(E)=0.357) with four classifiers and Bagged SVR (MAE=0.210, σ(E)=0.366) are suggested as the two most effective prediction models due to their lower MAE and σ(E).

CONCLUSION

The investigated models can not only facilitate clinicians in dosage decision-making, but also help reduce patient risk from adverse drug events.

Pharmacogenomics in neurology: current state and future steps

In neurology, as in any other clinical specialty, there is a need to develop treatment strategies that allow stratification of therapies to optimize efficacy and minimize toxicity. Pharmacogenomics is one such method for therapy optimization: it aims to elucidate the relationship between human genome sequence variation and differential drug responses. Approaches have focused on candidate approaches investigating absorption-, distribution-, metabolism, and elimination (ADME)-related genes (pharmacokinetic pathways), and potential drug targets (pharmacodynamic pathways). To date, however, only few genetic variants have been incorporated into clinical algorithms. Unfortunately, a large number of studies have thrown up contradictory results due to a number of deficiencies, including small sample sizes, inadequate phenotyping, and genotyping strategies. Thus, there still exists an urgent need to establish biomarkers that could help to select for patients with an optimal benefit to risk relationship. Here we review recent advances, and limitations, in pharmacogenomics for agents used in neuroimmunology, neurodegenerative diseases, ischemic stroke, epilepsy, and primary headaches. Further work is still required in all of these areas, which really needs to progress on several fronts, including better standardized phenotyping, appropriate sample sizes through multicenter collaborations and judicious use of new technological advances such as genome-wide approaches, next generation sequencing and systems biology. In time, this is likely to lead to improvements in the benefit-harm balance of neurological therapies, cost efficiency, and identification of new drugs.

Predicting the warfarin maintenance dose in elderly inpatients at treatment initiation: accuracy of dosing algorithms incorporating or not VKORC1/CYP2C9 genotypes

BACKGROUND

Initiating warfarin is challenging in frail elderly patients because of low-dose requirements and interindividual variability.

OBJECTIVES

We investigated whether incorporating VKORC1 and CYP2C9 genotype information in different models helped to predict the warfarin maintenance dose when added to clinical data and INR values at baseline (Day 0), and during warfarin induction.

PATIENTS

We prospectively enrolled 187 elderly inpatients (mean age, 85.6 years), all starting on warfarin using the same 'geriatric dosing-algorithm' based on the INR value measured on the day after three 4-mg warfarin doses (INR(3)) and on INR(6 ± 1).

RESULTS

On Day 0, the clinical model failed to accurately predict the maintenance dose (R(2) < 0.10). Adding the VKORC1 and CYP2C9 genotypes to the model increased R(2) to 0.31. On Day 3, the INR(3) value was the strongest predictor, completely embedding the VKORC1 genotype, whereas the CYP2C9 genotype remained a significant predictor (model- R(2) 0.55). On Day 6 ± 1, none of the genotypes predicted the maintenance dose. Finally, the simple 'geriatric dosing-algorithm' was the most accurate algorithm on Day 3 (R(2) 0.77) and Day 6 (R(2) 0.81), under-estimating (≥ 1 mg) and over-estimating the dose (≥ 1 mg) in fewer than 10% and 2% of patients, respectively. Clinical models and the 'geriatric dosing-algorithm' were validated on an independent sample.

CONCLUSIONS

Before starting warfarin therapy, the VKORC1 genotype is the best predictor of the maintenance dose. Once treatment is started using induction doses tailored for elderly patients, the contribution of VKORC1 and CYP2C9 genotypes in dose refinement is negligible compared with two INR values measured during the first week of treatment.