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Establishment of a Han Chinese-specific pharmacogenetic-guided warfarin dosing algorithm.

ABSTRACT: Warfarin is the most common oral anticoagulant. Because of a narrow therapeutic range, interindividual differences in drug responses, and the risk of bleeding, there are many challenges in using warfarin. We need to predict the warfarin maintenance dose. However, ethnic-specific algorithms may be required, and some Chinese algorithms do not perform adequately. Therefore, we aimed to establish a Han Chinese appropriate algorithm.We recruited a study group consisting of 361 Han Chinese patients receiving warfarin treatment who had heart valve replacements. Genotyping of 38 single nucleotide polymorphisms (SNPs) in 13 candidate genes was carried out using the MassARRAY. In the derivation cohort, a multiple linear regression model was constructed to predict the warfarin dosage. We evaluated the accuracy of our algorithm in the validation cohort and compared it with the other 5 algorithms based on Han Chinese and other races.We established a Han Chinese-specific pharmacogenetic-guided warfarin dosing algorithm. Warfarin maintenance dosage (mg/day) = 1.787 - 0.023 × (Age) + 1.151 × (BSA [m]) + 0.917 × (VKORC1 AG) + 4.619 × (VKORC1 GG) + 0.595 × (CYP4F2 TT) + 0.707 × (CYP2C19 CC). It explained 58.3% of the variance in warfarin doses in Han Chinese patients and was superior to the other 5 algorithms. The ability of the 6 algorithms which estimate the required dose correctly was tested. Our model had a mean absolute error of 0.74?mg/day, the other 5 models have mean absolute error of 0.81?mg/day,1.05?mg/day, 1.24?mg/day, 1.18?mg/day, and 0.85?mg/day, respectively. Our model had a mean percentage error of 26.9%, the other 5 models have the mean percentage error of 27.7%, 27.2%, 52.3%, 45.7%, and 29.3%, respectively.Physicians can not adopt algorithm from other race directly to predict warfarin dose in patients with heart valve replacements, they should establish a new algorithm or adjust another algorithm to fit their patients. The algorithm established in this study has the potential to assist physicians in determining warfarin doses that are close to the appropriate doses.

SUBMITTER: Pei L

PROVIDER: S-EPMC6133597 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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Establishment of a Han Chinese-specific pharmacogenetic-guided warfarin dosing algorithm.

Pei Lin L Tian Xiaoyi X Long Yan Y Nan Wenhui W Jia Mei M Qiao Rui R Zhang Jie J

Medicine 20180901 36

Warfarin is the most common oral anticoagulant. Because of a narrow therapeutic range, interindividual differences in drug responses, and the risk of bleeding, there are many challenges in using warfarin. We need to predict the warfarin maintenance dose. However, ethnic-specific algorithms may be required, and some Chinese algorithms do not perform adequately. Therefore, we aimed to establish a Han Chinese appropriate algorithm.We recruited a study group consisting of 361 Han Chinese patients re ...[more]

PMID: 30200121

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Project description:ObjectivesTo verify the accuracy of the International Warfarin Pharmacogenetics Consortium (IWPC) algorithm, identify the effects of genetic and clinical factors on warfarin stable dose, and to establish a new warfarin stable dose prediction algorithm for the elderly Han-Chinese population under the guidance of pharmacogenetics.MethodsAccording to the inclusion criteria, 544 non-valvular atrial fibrillation patients taking warfarin for anticoagulation treatment were enrolled. Data information of three groups including the whole population, people under 65 years old and over 65 years old were substituted into the IWPC algorithm respectively to verify its accuracy. The basic data and clinical information of 360 elderly people were collected for statistical analysis and the genotypes of VKORC1-G1639A and CYP2C9 were detected by Sanger sequencing. The new algorithm of the elder pharmacogenetics warfarin dosing was obtained by stepwise multiple regression. The determination coefficient (R2), root mean squared error (RMSE), and the proportion of the predicted value within the true value range of ±20%(20%-p) were used to evaluate the accuracy of the IWPC algorithm and the new algorithm.ResultsAmong the three different age groups, the warfarin stable dose predictive accuracy of IWPC algorithm was the lowest in the elderly patients above 65-year-old. In this study, the important factors influencing the stable dose of warfarin in the elderly Han-Chinese were height, weight, body surface area, serum creatinine level, amiodarone usage, CYP2C9 (*1*2, *1*3), and VKORC1 (GG/GA) genotypes. By means of stepwise multiple regression analysis, we established a new elder warfarin dosing algorithm (R2=0.3714) containing height, creatinine, amiodarone usage, CYP2C9 (*1*2 or *1*3), and VKORC1 (GA or GG) genotypes. The prediction accuracy and clinical availability of the Elderly algorithm was significantly better than that of IWPC algorithm verified by RMSE, R2, and (20%-p) methods.ConclusionsThe IWPC model may not be suitable for the elder Han-Chinese population. Polymorphism of CYP2C9 and VKORC1 obviously affected warfarin stable dose of the elder Han-Chinese. Combination of genetic data with demographic and clinical factors could help to better improve warfarin doses in the elder Han-Chinese population.

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.

Project description:ObjectiveTo study the performance of pharmacogenetics-based warfarin dosing algorithms in the initial and the stable warfarin treatment phases in a cohort of Han-Chinese patients undertaking mechanic heart valve replacement.MethodsWe searched PubMed, Chinese National Knowledge Infrastructure and Wanfang databases for selecting pharmacogenetics-based warfarin dosing models. Patients with mechanic heart valve replacement were consecutively recruited between March 2012 and July 2012. The predicted warfarin dose of each patient was calculated and compared with the observed initial and stable warfarin doses. The percentage of patients whose predicted dose fell within 20% of their actual therapeutic dose (percentage within 20%), and the mean absolute error (MAE) were utilized to evaluate the predictive accuracy of all the selected algorithms.ResultsA total of 8 algorithms including Du, Huang, Miao, Wei, Zhang, Lou, Gage, and International Warfarin Pharmacogenetics Consortium (IWPC) model, were tested in 181 patients. The MAE of the Gage, IWPC and 6 Han-Chinese pharmacogenetics-based warfarin dosing algorithms was less than 0.6 mg/day in accuracy and the percentage within 20% exceeded 45% in all of the selected models in both the initial and the stable treatment stages. When patients were stratified according to the warfarin dose range, all of the equations demonstrated better performance in the ideal-dose range (1.88-4.38 mg/day) than the low-dose range (<1.88 mg/day). Among the 8 algorithms compared, the algorithms of Wei, Huang, and Miao showed a lower MAE and higher percentage within 20% in both the initial and the stable warfarin dose prediction and in the low-dose and the ideal-dose ranges.ConclusionsAll of the selected pharmacogenetics-based warfarin dosing regimens performed similarly in our cohort. However, the algorithms of Wei, Huang, and Miao showed a better potential for warfarin prediction in the initial and the stable treatment phases in Han-Chinese patients undertaking mechanic heart valve replacement.

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Establishment of a Han Chinese-specific pharmacogenetic-guided warfarin dosing algorithm.

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Establishment of a Han Chinese-specific pharmacogenetic-guided warfarin dosing algorithm.

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