Performance Evaluation of Warfarin Dose Prediction Algorithms and Effects of Clinical Factors on Warfarin Dose in Chinese Patients

2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Weiqi Gao ◽  
Zhihong Li ◽  
Weihong Chen ◽  
Shuqiu Zhang
Keyword(s):  
Performance Evaluation ◽  
Warfarin Dose ◽  
Chinese Patients ◽  
Clinical Factors ◽  
Dose Prediction ◽  
Prediction Algorithms

Estimation of the warfarin dose with a pharmacogenetic refinement algorithm in Chinese patients mainly under low-intensity warfarin anticoagulation

2012 ◽  
Vol 108 (12) ◽  
pp. 1132-1140 ◽  
Author(s):  
Jie Yang ◽  
Lei Gao ◽  
Yan Zhang ◽  
Hongjuan Wang ◽  
Caiyi Lu ◽  
...  
Keyword(s):  
Validation Cohort ◽  
Warfarin Dose ◽  
Chinese Patients ◽  
Fixed Dose ◽  
Dose Requirement ◽  
Chinese Han ◽  
Derivation Cohort ◽  
Dose Prediction ◽  
Low Intensity ◽  
Refinement Algorithm

SummaryPharmacogenetic (PG) dosing algorithms have been confirmed to predict warfarin therapeutic dose more accurately;however, most of them are based on standard intensity of warfarin anticoagulation, and their utility outside this range is limited. This study was designed to develop and validate a PG refinement algorithm in Chinese patients mainly under low-intensity warfarin anticoagulation. Consented Chinese-Han patients (n=310) under stable warfarin treatment were randomly divided into a derivation (n=207) and a validation cohort (n=103), with 83% and 80% of the patients under low-intensity anticoagulation, respectively. In the derivation cohort, a PG algorithm was constructed on the basis of genotypes (CYP2C9*3 and VKORC1–1639A/G) and clinical data. After integrating additional covariates of international normalised ratio (INR) values (INR on day 4 of therapy and target INR) and genotype of CYP4F2 (rs2108622), a PG refinement algorithm was established and explained 54% of warfarin dose variability. In the validation cohort, warfarin dose prediction was more accurate (p <0.01) with the PG refinement algorithm than with the PG algorithm and the fixed dose approach (3 mg/day). In the entire cohort, the PG refinement algorithm could accurately identify larger proportions of patients with lower dose requirement (≤2 mg/day) and higher dose requirement (≥4 mg/day) than did the PG algorithm. In conclusion, PG refinement algorithm integrating early INR response and three genotypes CYP2C9*3, VKORC1–1639A/G, CYP4F2 rs2108622) improves the accuracy of warfarin dose prediction in Chinese patients mainly under low-intensity anticoagulation.

scholarly journals Machine Learning for Prediction of Stable Warfarin Dose in US Latinos and Latin Americans

2021 ◽  
Vol 12 ◽  
Author(s):  
Heidi E. Steiner ◽  
Jason B. Giles ◽  
Hayley Knight Patterson ◽  
Jianglin Feng ◽  
Nihal El Rouby ◽  
...  
Keyword(s):  
Machine Learning ◽  
Warfarin Dose ◽  
Multiple Linear Regression Model ◽  
Multivariate Adaptive Regression Splines ◽  
European Ancestry ◽  
Support Vector ◽  
Latin Americans ◽  
Dose Prediction ◽  
Prediction Algorithms ◽  
Linear And Nonlinear

Populations used to create warfarin dose prediction algorithms largely lacked participants reporting Hispanic or Latino ethnicity. While previous research suggests nonlinear modeling improves warfarin dose prediction, this research has mainly focused on populations with primarily European ancestry. We compare the accuracy of stable warfarin dose prediction using linear and nonlinear machine learning models in a large cohort enriched for US Latinos and Latin Americans (ULLA). Each model was tested using the same variables as published by the International Warfarin Pharmacogenetics Consortium (IWPC) and using an expanded set of variables including ethnicity and warfarin indication. We utilized a multiple linear regression model and three nonlinear regression models: Bayesian Additive Regression Trees, Multivariate Adaptive Regression Splines, and Support Vector Regression. We compared each model’s ability to predict stable warfarin dose within 20% of actual stable dose, confirming trained models in a 30% testing dataset with 100 rounds of resampling. In all patients (n = 7,030), inclusion of additional predictor variables led to a small but significant improvement in prediction of dose relative to the IWPC algorithm (47.8 versus 46.7% in IWPC, p = 1.43 × 10−15). Nonlinear models using IWPC variables did not significantly improve prediction of dose over the linear IWPC algorithm. In ULLA patients alone (n = 1,734), IWPC performed similarly to all other linear and nonlinear pharmacogenetic algorithms. Our results reinforce the validity of IWPC in a large, ethnically diverse population and suggest that additional variables that capture warfarin dose variability may improve warfarin dose prediction algorithms.

scholarly journals Accuracy assessment of pharmacogenetic algorithms for warfarin dose prediction in Chinese patients

2012 ◽  
Vol 87 (5) ◽  
pp. 541-544 ◽  
Author(s):  
Xiaoying Lei ◽  
Yanhai Guo ◽  
Jianbin Sun ◽  
Heping Zhou ◽  
Yonglan Liu ◽  
...  
Keyword(s):  
Accuracy Assessment ◽  
Warfarin Dose ◽  
Chinese Patients ◽  
Dose Prediction

An Ensemble Model With Clustering Assumption for Warfarin Dose Prediction in Chinese Patients

2019 ◽  
Vol 23 (6) ◽  
pp. 2642-2654 ◽  
Author(s):  
Yanyun Tao ◽  
Yenming J. Chen ◽  
Ling Xue ◽  
Cheng Xie ◽  
Bin Jiang ◽  
...  
Keyword(s):  
Warfarin Dose ◽  
Chinese Patients ◽  
Ensemble Model ◽  
Dose Prediction

The Negative Impact of Biological Variation in the Effect and Clearance of Warfarin on Methods for Prediction of Dose Requirements

1986 ◽  
Vol 56 (03) ◽  
pp. 371-375 ◽  
Author(s):  
Peretz Weiss ◽  
Hillel Halkin ◽  
Shlomo Almog
Keyword(s):  
Individual Variation ◽  
Negative Impact ◽  
Warfarin Dose ◽  
Empirical Therapy ◽  
Maintenance Phase ◽  
Individual Variability ◽  
Dose Prediction ◽  
Group I ◽  
Over Time ◽  
Variance Explained

SummaryWithin-individual variation over time in the clearance (Cl) and effect (PT%) of warfarin, was measured in 25 inpatients (group I) studied after standard single or individualized split loading doses and 1-3 times (n = 16) 8-16 weeks later during maintenance. Mean Cl (2.5 α 0.9 ml/min) was similar in both phases but significant changes occurred in 6/16 patients, exceeding those expected from within-individual variation alone (defined by its 95% tolerance limits -24% to +62%). Initial PT% (21 α 5) was unaffected by dosing schedule, total or free plasma warfarin, varying between patients by only 18-24%. Mean initial and maintenance dose-PT% ratios (8.2 mg/d: 21% and 4.1 mg/d: 40%) were similar but significant changes in sensitivity to warfarin occurred in 4/16 patients. In group I and 64 other outpatients on maintenance therapy, between-individual variability was 36-52% for Cl and 49-56% for effect. PT% correlated best (r = 0.56) with free and total plasma warfarin but poorly with dose (r = 0.29), with only 30% of PT% variance explained at best, due to high between patient variability.Warfarin dose prediction whether based on extrapolation from initial effects to the maintenance phase, or on iterative methods not allowing for between- or within-patient variation in warfarin clearance or effect which may occur independently over time, have not improved on empirical therapy. This, due to the elements of biological variability as well as the intricacy of the warfarin - prothrombin complex interaction not captured by any kinetic-dynamic model used for prediction to date.

Pharmacogenetic algorithm for predicting daily dose of warfarin in Caucasian patients of Czech origin

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Aleš Tomek ◽  
Tereza Růžičková ◽  
Vojtěch Kaplan ◽  
Zuzana Lacinová ◽  
Simona Kumstýřová ◽  
...  
Keyword(s):  
Validation Cohort ◽  
Warfarin Dose ◽  
Therapeutic Index ◽  
Coefficient Of Determination ◽  
Dose Prediction ◽  
Pharmacogenetic Algorithm ◽  
Daily Dose ◽  
Caucasian Patients ◽  
Variant Alleles ◽  
Initiation Of Therapy

Abstract Objectives Warfarin use is limited by a low therapeutic index and significant interindividual variability of the daily dose. The most important factor predicting daily warfarin dose is individual genotype, polymorphisms of genes CYP2C9 (warfarin metabolism) and VKORC1 (sensitivity for warfarin). Algorithms using clinical and genetic variables could predict the daily dose before the initiation of therapy. The aim of this study was to develop and validate an algorithm for the prediction of warfarin daily dose in Czech patients. Methods Detailed clinical data of patients with known and stable warfarin daily dose were collected. All patients were genotyped for polymorphisms in genes CYP2C9 and VKORC1. Results Included patients were divided into derivation (n=175) and validation (n=223) cohorts. The final algorithm includes the following variables: Age, height, weight, treatment with amiodarone and presence of variant alleles of genes CYP2C9 and VKORC1. The adjusted coefficient of determination is 72.4% in the derivation and 62.3% in the validation cohort (p<0.001). Conclusions Our validated algorithm for warfarin daily dose prediction in our Czech cohort had higher precision than other currently published algorithms. Pharmacogenetics of warfarin has the potential in the clinical practice in specialized centers.

scholarly journals Erratum to: Warfarin dose prediction in children using pharmacometric bridging – comparison with published pharmacogenetic dosing algorithms

2013 ◽  
Vol 69 (9) ◽  
pp. 1737-1737
Author(s):  
Anna-Karin Hamberg ◽  
Lena E. Friberg ◽  
Katarina Hanséus ◽  
Britt-Marie Ekman-Joelsson ◽  
Jan Sunnegårdh ◽  
...  
Keyword(s):  
Warfarin Dose ◽  
Dose Prediction
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