Population diversity and the performance of warfarin dosing algorithms.
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ABSTRACT:
SUBMITTER: Suarez-Kurtz G
PROVIDER: S-EPMC3175514 | biostudies-literature | 2011 Sep
REPOSITORIES: biostudies-literature
Publications
Population diversity and the performance of warfarin dosing algorithms.
British journal of clinical pharmacology 20110901 3
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Project description:Summary backgroundGene-based warfarin dosing algorithms have largely been developed in homogeneous populations, and their generalizability has not been established.ObjectivesWe sought to assess the performance of published algorithms in a racially diverse and multiethnic sample, and determine if additional clinical variables or genetic variants associated with dose could enhance algorithm performance.Patients and methodsIn 145 compliant patients on warfarin with a goal international normalized ratio (INR) of 2-3, stable, therapeutic doses were compared with predicted doses using 12 reported algorithms that incorporated CYP2C9 and VKORC1 variants. Additional covariates tested with each model included race, concurrent medications, medications known to interact with warfarin and previously described CYP4F2, CALU and GGCX variants.ResultsThe mean patient age was 67 +/- 14 years; 90 (62%) were male. Eighty-two (57%) were Caucasian, 28 (19%) African-American, 20 (14%) Hispanic and 15 (10%) Asian. The median warfarin dose was 35 mg per week (interquartile range 23-53 mg per week). Gene-based dosing algorithms explained 37-55% of the variation in warfarin dose requirements. Neither the addition of race, number of concurrent medications nor the number of concurrent medications interacting with warfarin enhanced algorithm performance. Similarly, consideration of CYP4F2, CALU or GGCX variant genotypes did not improve algorithms.ConclusionsExisting gene-based dosing algorithms explained between approximately one-third and one-half of the variability in warfarin dose requirements in this racially and ethnically diverse cohort. Additional clinical and recently described genetic variants associated with warfarin dose did not enhance prediction in our patient population.
| S-EPMC4441275 | biostudies-literature
Project description:AimsNumerous algorithms have been developed to guide warfarin dosing and improve clinical outcomes. We reviewed the algorithms available for various populations and the covariates, performances and risk of bias of these algorithms.MethodsWe systematically searched MEDLINE up to 20 May 2020 and selected studies describing the development, external validation or clinical utility of a multivariable warfarin dosing algorithm. Two investigators conducted data extraction and quality assessment.ResultsOf 10 035 screened records, 266 articles were included in the review, describing the development of 433 dosing algorithms, 481 external validations and 52 clinical utility assessments. Most developed algorithms were for dose initiation (86%), developed by multiple linear regression (65%) and mostly applicable to Asians (49%) or Whites (43%). The most common demographic/clinical/environmental covariates were age (included in 401 algorithms), concomitant medications (270 algorithms) and weight (229 algorithms) while CYP2C9 (329 algorithms), VKORC1 (319 algorithms) and CYP4F2 (92 algorithms) variants were the most common genetic covariates. Only 26% and 7% algorithms were externally validated and evaluated for clinical utility, respectively, with <2% of algorithm developments and external validations being rated as having a low risk of bias.ConclusionMost warfarin dosing algorithms have been developed in Asians and Whites and may not be applicable to under-served populations. Few algorithms have been externally validated, assessed for clinical utility, and/or have a low risk of bias which makes them unreliable for clinical use. Algorithm development and assessment should follow current methodological recommendations to improve reliability and applicability, and under-represented populations should be prioritized.
| S-EPMC8056736 | biostudies-literature
Project description:The aim of this study was to compare the accuracy of genetic tables and formal pharmacogenetic algorithms for warfarin dosing.Pharmacogenetic algorithms based on regression equations can predict warfarin dose, but they require detailed mathematical calculations. A simpler alternative, recently added to the warfarin label by the U.S. Food and Drug Administration, is to use genotype-stratified tables to estimate warfarin dose. This table may potentially increase the use of pharmacogenetic warfarin dosing in clinical practice; however, its accuracy has not been quantified.A retrospective cohort study of 1,378 patients from 3 anticoagulation centers was conducted. Inclusion criteria were stable therapeutic warfarin dose and complete genetic and clinical data. Five dose prediction methods were compared: 2 methods using only clinical information (empiric 5 mg/day dosing and a formal clinical algorithm), 2 genetic tables (the new warfarin label table and a table based on mean dose stratified by genotype), and 1 formal pharmacogenetic algorithm, using both clinical and genetic information. For each method, the proportion of patients whose predicted doses were within 20% of their actual therapeutic doses was determined. Dosing methods were compared using McNemar's chi-square test.Warfarin dose prediction was significantly more accurate (all p < 0.001) with the pharmacogenetic algorithm (52%) than with all other methods: empiric dosing (37%; odds ratio [OR]: 2.2), clinical algorithm (39%; OR: 2.2), warfarin label (43%; OR: 1.8), and genotype mean dose table (44%; OR: 1.9).Although genetic tables predicted warfarin dose better than empiric dosing, formal pharmacogenetic algorithms were the most accurate.
| S-EPMC3107000 | biostudies-literature
Project description:ObjectiveWarfarin has a narrow therapeutic window and large variability in dosing that are affected by clinical and genetic factors. To help guide the dosing of warfarin, the Clinical Pharmacogenetics Implementation Consortium has recommended the use of pharmacogenetic algorithms, such as the ones developed by the International Warfarin Pharmacogenetics Consortium (IWPC) and by Gage et al. when genotype information is available.MethodsIn this study, simulations were performed in Chinese cohorts to explore how dosing differences between Western (by IWPC and Gage et al.) and Chinese algorithms (by Miao et al.) would mean in terms of anticoagulation effect in clinical trials. We first tried to replicate a published clinical trial comparing genotype-guided dosing to routine clinical dosing in Chinese patients. We then made simulations where Chinese cohorts received daily doses recommended by Gage, IWPC, and Miao algorithms.ResultsWe found that in simulation conditions where dosing specifications were strictly followed, genotype-guided dosing by IWPC and Lenzini formulae was more likely to overshoot the upper limit of the therapeutic window by day 15, and thus may have a lower % time in therapeutic range (%TTR) than that of clinical dosing group. Also, in comparing Gage, IWPC, and Miao algorithms, we found that the Miao dosing cohort has the highest %TTR and the lowest risk of over-anticoagulation by day 28.ConclusionIn summary, our results confirmed that algorithms developed based on data from local patients may be more suitable for achieving therapeutic international normalized ratio window in Chinese population.
| S-EPMC11424055 | biostudies-literature
Project description:PurposeThe genes for cytochrome P450 2C9 (CYP2C9) and vitamin K epoxide reductase complex subunit 1 (VKORC1) have been identified as important genetic determinants of warfarin dosing and have been studied. We developed warfarin algorithm for Korean patients with stroke and compared the accuracy of warfarin dose prediction algorithms based on the pharmacogenetics.Materials and methodsA total of 101 patients on stable maintenance dose of warfarin were enrolled. Warfarin dosing algorithm was developed using multiple linear regression analysis. The performance of all the algorithms was characterized with coefficient of determination, determined by linear regression, and the mean of percent deviation was used to predict doses from the actual dose. In addition, we compared the performance of the algorithms using percentage of predicted dose falling within ±20% of clinically observed doses and dividing the patients into a low-dose group (≤3 mg/day), an intermediate-dose group (3-7 mg/day), and high-dose group (≥7 mg/day).ResultsA new developed algorithms including the variables of age, body weight, and CYP2C9 and VKORC1 genotype. Our algorithm accounted for 51% of variation in the warfarin stable dose, and performed best in predicting dose within 20% of actual dose and intermediate-dose group.ConclusionOur warfarin dosing algorithm may be useful for Korean patients with stroke. Further studies to elucidate clinical utility of genotype-guided dosing and find the additional genetic association are necessary.
| S-EPMC4800352 | biostudies-literature
Project description:The objective of this study was to determine whether warfarin dosing algorithms developed for Caucasians and African Americans on the basis of clinical, environmental, and genetic factors will perform better than an empirical starting dose of 5 mg/day. From April 2002 through December 2005, 259 subjects (Caucasians and African Americans) who started using warfarin were prospectively followed until they reached maintenance dose. The Caucasian algorithm included 11 variables (R(2) = 0.43). This model (which predicted 51% of the doses to within 1 mg of the observed dose) performed better than 5 mg/day (which predicted 29% of the doses to within 5 +/- 1 mg). The African-American algorithm included 10 variables (R(2) = 0.28). This model predicted 37% of the doses to within 1 mg of the observed dose, representing a small improvement compared with 5 mg/day (which predicted 34% of the doses to within 1 mg of 5 mg/day). These results were similar to the results we obtained from testing other published algorithms. The dosing algorithms explained <45% of the observed variability in Caucasians, and the algorithms performed only marginally better for African Americans when compared with giving 5 mg empirically.
| S-EPMC2538606 | biostudies-literature
Project description:Responses to warfarin (Coumadin) anticoagulation therapy are affected by genetic variability in both the CYP2C9 and VKORC1 genes. Validation of pharmacogenetic testing for warfarin responses includes demonstration of analytical validity of testing platforms and of the clinical validity of testing. We compared four platforms for determining the relevant single nucleotide polymorphisms (SNPs) in both CYP2C9 and VKORC1 that are associated with warfarin sensitivity (Third Wave Invader Plus, ParagonDx/Cepheid Smart Cycler, Idaho Technology LightCycler, and AutoGenomics Infiniti). Each method was examined for accuracy, cost, and turnaround time. All genotyping methods demonstrated greater than 95% accuracy for identifying the relevant SNPs (CYP2C9 *2 and *3; VKORC1 -1639 or 1173). The ParagonDx and Idaho Technology assays had the shortest turnaround and hands-on times. The Third Wave assay was readily scalable to higher test volumes but had the longest hands-on time. The AutoGenomics assay interrogated the largest number of SNPs but had the longest turnaround time. Four published warfarin-dosing algorithms (Washington University, UCSF, Louisville, and Newcastle) were compared for accuracy for predicting warfarin dose in a retrospective analysis of a local patient population on long-term, stable warfarin therapy. The predicted doses from both the Washington University and UCSF algorithms demonstrated the best correlation with actual warfarin doses.
| S-EPMC2671339 | biostudies-literature
Project description:AimThis study attempted to identify predictors of S-warfarin clearance (CL[S]) and to make a pharmacokinetic evaluation of genotype-based dosing algorithms in African-Americans.MethodsUsing plasma S-warfarin concentration (Cp[S]) at a steady state and eight SNPs previously shown to influence warfarin dose in African-Americans, CL(S) and its predictors were estimated by population pharmacokinetic analysis in 60 African-Americans. The time courses of Cp(S) following either the loading dose or maintenance dose were simulated using the population pharmacokinetic estimates.ResultsCYP2C9*8 and body surface area or body weight were predictors of CL(S) (-30 and -5% per -0.1 m(2)/-10 kg reduction in CL[S], respectively) in African-Americans. Simulations of Cp(S) showed that Cp(S) at steady state was 1.4-times higher in patients with CYP2C9*8 than in those with CYP2C9*1/*1, irrespective of the algorithm for loading dose or maintenance dose.ConclusionAfrican-Americans possess independent predictors of CL(S), possibly leading to a prediction error of any dosing algorithm that excludes African-specific variant(s). Original submitted 3 September 2014; Revision submitted 3 November 2014.
| S-EPMC7347085 | biostudies-literature
Project description:PurposeNumerous studies have investigated causes of warfarin dose variability in adults, whereas studies in children are limited both in numbers and size. Mechanism-based population modelling provides an opportunity to condense and propagate prior knowledge from one population to another. The main objectives with this study were to evaluate the predictive performance of a theoretically bridged adult warfarin model in children, and to compare accuracy in dose prediction relative to published warfarin algorithms for children.MethodAn adult population pharmacokinetic/pharmacodynamic (PK/PD) model for warfarin, with CYP2C9 and VKORC1 genotype, age and target international normalized ratio (INR) as dose predictors, was bridged to children using allometric scaling methods. Its predictive properties were evaluated in an external data set of children 0-18 years old, including comparison of dose prediction accuracy with three pharmacogenetics-based algorithms for children.ResultsOverall, the bridged model predicted INR response well in 64 warfarin-treated Swedish children (median age 4.3 years), but with a tendency to overpredict INR in children ≤2 years old. The bridged model predicted 20 of 49 children (41 %) within ± 20 % of actual maintenance dose (median age 7.2 years). In comparison, the published dosing algorithms predicted 33-41 % of the children within ±20 % of actual dose. Dose optimization with the bridged model based on up to three individual INR observations increased the proportion within ±20 % of actual dose to 70 %.ConclusionA mechanism-based population model developed on adult data provides a promising first step towards more individualized warfarin therapy in children.
| S-EPMC3651819 | biostudies-literature
Project description:ObjectiveMultiple linear regression (MLR) and machine learning techniques in pharmacogenetic algorithm-based warfarin dosing have been reported. However, performances of these algorithms in racially diverse group have never been objectively evaluated and compared. In this literature-based study, we compared the performances of eight machine learning techniques with those of MLR in a large, racially-diverse cohort.MethodsMLR, artificial neural network (ANN), regression tree (RT), multivariate adaptive regression splines (MARS), boosted regression tree (BRT), support vector regression (SVR), random forest regression (RFR), lasso regression (LAR) and Bayesian additive regression trees (BART) were applied in warfarin dose algorithms in a cohort from the International Warfarin Pharmacogenetics Consortium database. Covariates obtained by stepwise regression from 80% of randomly selected patients were used to develop algorithms. To compare the performances of these algorithms, the mean percentage of patients whose predicted dose fell within 20% of the actual dose (mean percentage within 20%) and the mean absolute error (MAE) were calculated in the remaining 20% of patients. The performances of these techniques in different races, as well as the dose ranges of therapeutic warfarin were compared. Robust results were obtained after 100 rounds of resampling.ResultsBART, MARS and SVR were statistically indistinguishable and significantly out performed all the other approaches in the whole cohort (MAE: 8.84-8.96 mg/week, mean percentage within 20%: 45.88%-46.35%). In the White population, MARS and BART showed higher mean percentage within 20% and lower mean MAE than those of MLR (all p values < 0.05). In the Asian population, SVR, BART, MARS and LAR performed the same as MLR. MLR and LAR optimally performed among the Black population. When patients were grouped in terms of warfarin dose range, all machine learning techniques except ANN and LAR showed significantly higher mean percentage within 20%, and lower MAE (all p values < 0.05) than MLR in the low- and high- dose ranges.ConclusionOverall, machine learning-based techniques, BART, MARS and SVR performed superior than MLR in warfarin pharmacogenetic dosing. Differences of algorithms' performances exist among the races. Moreover, machine learning-based algorithms tended to perform better in the low- and high- dose ranges than MLR.
| S-EPMC4549222 | biostudies-literature