Stable Warfarin Dose Research Articles

Meta-analysis of genome-wide association studies of stable warfarin dose in patients of African ancestry

AbstractWarfarin dose requirements are highly variable because of clinical and genetic factors. Although genetic variants influencing warfarin dose have been identified in European and East Asian populations, more work is needed to identify African-specific genetic variants to help optimize warfarin dosing. We performed genome-wide association studies (GWASs) in 4 African cohorts from Uganda, South Africa, and Zimbabwe, totaling 989 warfarin-treated participants who reached stable dose and had international normalized ratios within therapeutic ranges. We also included 2 African American cohorts recruited by the International Warfarin Pharmacogenetics Consortium (n = 316) and the University of Alabama at Birmingham (n = 199). After the GWAS, we performed standard error-weighted meta-analyses and then conducted stepwise conditional analyses to account for known loci in chromosomes 10 and 16. The genome-wide significance threshold was set at P < 5 × 10−8. The meta-analysis, comprising 1504 participants, identified 242 significant SNPs across 3 genomic loci, with 99.6% of these located within known loci on chromosomes 10 (top SNP: rs58800757, P = 4.27 × 10−13) and 16 (top SNP: rs9925964, P = 9.97 × 10−16). Adjustment for the VKORC1 SNP -1639G>A revealed an additional locus on chromosome 2 (top SNPs rs116057875/rs115254730/rs115240773, P = 3.64 × 10−8), implicating the MALL gene, that could indirectly influence warfarin response through interactions with caveolin-1. In conclusion, we reaffirmed the importance of CYP2C9 and VKORC1 in influencing warfarin dose requirements, and identified a new locus (MALL), that still requires direct evidence of biological plausibility.

Advances in warfarin's anticoagulation therapy in Chinese population after mechanical valve replacement

Warfarin is an irreplaceable oral anticoagulant for patients with mechanical heart valves, the stable pharmacogenetic-based warfarin dose prediction algorithms have improved the effectiveness and safety of warfarin anticoagulation therapy. Genetic factors are the main factors affecting the stable dose of warfarin. Single nucleotide polymorphisms such as VKORC1 and CYP2C9 affect the anticoagulation effect of warfarin through pharmacodynamic or pharmacokinetic pathways. Age, body surface area, combined use of drugs, and other nongenetic factors also affect the stable dose of warfarin. Previously published algorithms for warfarin dose prediction included mainly the white race, and most algorithms were constructed using traditional multiple linear regression. However, domestic studies have used machine learning methods to construct warfarin dose prediction algorithms based on the Chinese Han post-mechanical valve replacement population and have achieved better prediction efficiency. This article reviews the advances of warfarin anticoagulation influencing factors and the clinical application of stable dose prediction algorithms.

Association between CYP2C9 and VKORC1 genetic polymorphisms and efficacy and safety of warfarin in Chinese patients.

Genetic variation has been a major contributor to interindividual variability of warfarin dosage requirement. The specific genetic factors contributing to warfarin bleeding complications are largely unknown, particularly in Chinese patients. In this study, 896 Chinese patients were enrolled to explore the effect of CYP2C9 and VKORC1 genetic variations on both the efficacy and safety of warfarin therapy. Univariate analyses unveiled significant associations between two specific single nucleotide polymorphisms rs1057910 in CYP2C9 and rs9923231 in VKORC1 and stable warfarin dosage (P < 0.001). Further, employing multivariate logistic regression analysis adjusted for age, sex and height, the investigation revealed that patients harboring at least one variant allele in CYP2C9 exhibited a heightened risk of bleeding events compared to those with the wild-type genotype (odds ratio = 2.16, P = 0.04). Moreover, a meta-analysis conducted to consolidate findings confirmed the associations of both CYP2C9 (rs1057910) and VKORC1 (rs9923231) with stable warfarin dosage. Notably, CYP2C9 variant genotypes were significantly linked to an increased risk of hemorrhagic complications (P < 0.00001), VKORC1 did not demonstrate a similar association. The associations found between specific genetic variants and both stable warfarin dosage and bleeding risk might be the potential significance of gene detection in optimizing warfarin therapy for improving patient efficacy and safety.

Novel Gene Polymorphisms for Stable Warfarin Dose in a Korean Population: Genome-Wide Association Study.

Warfarin has a narrow therapeutic window and high intra- and inter-individual variability. Considering that many published papers on genotype-guided dosing are derived from European populations, the aim of this study was to investigate novel genetic variants associated with the variability of stable warfarin dose in the Korean population with cardiac valve replacement, using the GWAS approach. This retrospective cohort study was performed from January 1982 to December 2020 at the Severance Cardiovascular Hospital of Yonsei University College of Medicine. GWAS was performed to identify associations between genotypes and the warfarin maintenance dose, by comparing the allele frequency of genetic variants between individuals. Then, the extent of genetic and non-genetic factors on the dose variability was determined by multivariable regression analysis. The study enrolled 214 participants, and the most robust signal cluster was detected on chromosome 16 around VKORC1. Followed by VKORC1, three novel variants (NKX2-6 rs310279, FRAS1 rs4386623, and FAM201A rs1890109) showed an association with stable warfarin dose requirement in univariate analysis. The algorithm was constructed by using multivariable analysis that includes genetic and non-genetic factors, and it could explain 58.5% of the variations in stable warfarin doses. In this variability, VKORC1 rs9934438 and FRAS1 rs4386623 accounted for 33.0% and 9.9%, respectively. This GWAS analysis identified the fact that three novel variants (NKX2-6 rs310279, FRAS1 rs4386623, and FAM201A rs1890109) were associated with stable warfarin doses. Additional research is necessary to validate the results and establish personalized treatment strategies for the Korean population.

Computational Structural Validation of CYP2C9 Mutations and Evaluation of Machine Learning Algorithms in Predicting the Therapeutic Outcomes of Warfarin.

The study aimed to identify the key pharmacogenetic variable influencing the therapeutic outcomes of warfarin using machine learning algorithms and bioinformatics tools. Warfarin, a commonly used anticoagulant drug, is influenced by cytochrome P450 (CYP) enzymes, particularly CYP2C9. MLAs have been identified to have great potential in personalized therapy. The purpose of the study was to evaluate MLAs in predicting the critical outcomes of warfarin therapy and validate the key predictor genotyping variable using bioinformatics tools. An observational study was conducted on adults receiving warfarin. Allele discrimination method was used for estimating the single nucleotide polymorphisms (SNPs) in CYP2C9, VKORC1, and CYP4F2. MLAs were used for identifying the significant genetic and clinical variables in predicting the poor anticoagulation status (ACS) and stable warfarin dose. Advanced computational methods (SNPs' deleteriousness and impact on protein destabilization, molecular dockings, and 200ns molecular dynamics simulations) were employed for examining the influence of CYP2C9 SNPs on structure and function. Machine learning algorithms revealed CYP2C9 to be the most important predictor for both outcomes compared to the classical methods. Computational validation confirmed the altered structural activity, stability, and impaired functions of protein products of CYP2C9 SNPs. Molecular docking and dynamics simulations revealed significant conformational changes with mutations R144C and I359L in CYP2C9. We evaluated various MLAs in predicting the critical outcome measures associated with warfarin and observed CYP2C9 as the most critical predictor variable. The results of our study provide insight into the molecular basis of warfarin and the CYP2C9 gene. A prospective study validating the MLAs is urgently needed.

Developing Chinese race-specific warfarin dose prediction algorithms.

Numerous genotype-guided warfarin dosing algorithms have been developed to individualize warfarin doses, but they can only explain 47-52% of the variability. This study aimed to develop new warfarin algorithms suitable to predict the stable warfarin dose for the Chinese population and to compare their prediction performance with those of the most commonly used algorithms. Multiple linear regression analysis with the warfarin optimal dose (WOD), logarithm (log) WOD, 1/WOD, and [Formula: see text], respectively, as the dependent variables were performed to deduce a new warfarin algorithm (NEW-Warfarin). WOD was the stable dose that maintained the international normalized ratio (INR) within the target range (2.0-3.0). Three major genotype-guided warfarin dosing algorithms were selected and compared against NEW-Warfarin predictive performance using the mean absolute error (MAE). Furthermore, patients were divided into five groups according to warfarin indications [atrial fibrillation (AF), pulmonary embolism (PE), cardiac-related disease (CRD), deep vein thrombosis (DVT), and other diseases (OD)]. Multiple linear regression analyses were also performed for each group. The regression equation with [Formula: see text] as the dependent variable had the highest coefficient of determination (R2 = 0.489). The NEW-Warfarin had the best predictive accuracy compared to the three algorithms selected. Group analysis, according to indications, showed that the R2 of the five groups were PE (0.902) > DVT (0.608) > CRD (0.569) > OD (0.436) > AF (0.424). Dosing algorithms based on warfarin indications are more suitable for predicting warfarin doses. Our research provides a novel strategy to develop indication-specific warfarin dosing algorithms to improve the efficacy and safety of warfarin prescribing.

Time-to-event modeling for achieving a stable warfarin dose using genetic and non-genetic covariates.

Time-to-event modeling is gaining importance in drug dosage determination, particularly using pharmacometrics approaches. To evaluate the various time-to-event models for estimating the time to achieve a stable warfarin dose in the Bahraini population. A cross-sectional study evaluating the non-genetic and genetic covariates (single nucleotide polymorphisms (SNPs) in CYP2C9, VKORC1 and CYP4F2 genotypes) was conducted in patients receiving warfarin for at least 6 months. Time to achieving a stable dose of warfarin was defined as the duration (in days) from the day of initiating warfarin until 2 consecutive prothrombin time-international normalized ratio (PT-INR) values were observed in the therapeutic range with a gap of at least 7 days. Exponential, Gompertz, log-logistics, and Weibull models were tested, and the model with the lowest objective function value (OFV) was chosen. The covariate selection was carried out using the Wald test and OFV. A hazard ratio (HR) with a 95% confidence interval (95% CI) was estimated. A total of 218 participants were included in the study. The Weibull model was observed to have the lowest OFV (1989.82). The expected time to reach a stable dose in the population was 21.35 days. The CYP2C9 genotypes were identified as the only significant covariate. The HR (95% CI) for achieving a stable warfarin dose within 6 months of initiation for individuals with CYP2C9 *1/*2 was 0.2 (0.09, 0.3), 0.2 (0.1, 0.5) for CYP2C9 *1/*3, 0.14 (0.04, 0.6) for CYP2C9 *2/*2, 0.2 (0.03, 0.9) for CYP2C9 *2/*3, and 0.8 (0.45, 0.9) for those with the C/T genotype for CYP4F2. We estimated the population time-to-event parameters for achieving a stable warfarin dose in our population and observed CYP2C9 genotypes to be the main predictor covariate followed by CYP4F2. The influence of these SNPs needs to be validated in a prospective study and an algorithm to predict a stable warfarin dose and the time to achieve it needs to be developed.

Pharmacogenomic variabilities in geo-ancestral subpopulations and their clinical implications: Results of collaborations with Hmong in the United States.

Underrepresentation of subpopulations within geo-ancestral groups engaged in research can exacerbate health disparities and impair progress toward personalized medicine. This is particularly important when implementing pharmacogenomics which uses genomic-based sources of variability to guide medication selection and dosing. This mini-review focuses on pharmacogenomic findings with Hmong in the United States and their potential clinical implications. By actively engaging Hmong community in pharmacogenomic-based research, several clinically relevant differences in allele frequencies were observed within key pharmacogenes such as CYP2C9 and CYP2C19 in Hmong compared to those in either East Asians or Europeans. Additionally, using state-of-the-art genome sequencing approaches, Hmong appear to possess novel genetic variants within CYP2D6, a critical pharmacogene affecting pharmacokinetics of a broad range of medications. The allele frequency differences and novel alleles in Hmong have translational impact and real-world clinical consequences. For example, Hmong patients exhibited a lower warfarin stable dose requirement compared to East Asian patients. This was predicted based on Hmong's unique genetic and non-genetic factors and confirmed using real-world data from clinical practice settings. By presenting evidence of the genetic uniqueness and its translational impact within subpopulations, such as the Hmong, we hope to inspire greater inclusion of other geo-ancestrally underrepresented subpopulations in pharmacogenomic-based research.

Local Ancestry-Informed Candidate Pathway Analysis of Warfarin Stable Dose in Latino Populations.

Accuracy of warfarin dose prediction algorithms may be improved by including data from diverse populations in genetic studies of dose variability. Here, we surveyed single nucleotide polymorphisms in vitamin K-related genetic pathways for association with warfarin dose requirements in two admixed Latino populations in standard-principal component adjusted and contemporary-local ancestry adjusted regression models. A total of five variants from vitamin K-related genes/pathways were associated with warfarin dose in both cohorts (P < 0.0125) in standard models. Local ancestry-adjusted analysis unveiled 35 associated variants with absolute effects ranging from β=9.04 ( ±2.23) to 39.18 ( ±10.89) per ancestral allele in the discovery cohort and β=6.47 (± 2.02) to 17.82 (± 6.83) in the replication cohort. Importantly, we demonstrate the technical validity of the Tractor model in cohorts with admixed ancestry from three founder populations and bring attention to the technical hurdles obstructing the inclusion of diverse, especially admixed, populations in pharmacogenomic research.

A genome-wide association study of plasma concentrations of warfarin enantiomers and metabolites in sub-Saharan black-African patients.

Diversity in pharmacogenomic studies is poor, especially in relation to the inclusion of black African patients. Lack of funding and difficulties in recruitment, together with the requirement for large sample sizes because of the extensive genetic diversity in Africa, are amongst the factors which have hampered pharmacogenomic studies in Africa. Warfarin is widely used in sub-Saharan Africa, but as in other populations, dosing is highly variable due to genetic and non-genetic factors. In order to identify genetic factors determining warfarin response variability, we have conducted a genome-wide association study (GWAS) of plasma concentrations of warfarin enantiomers/metabolites in sub-Saharan black-Africans. This overcomes the issue of non-adherence and may have greater sensitivity at genome-wide level, to identify pharmacokinetic gene variants than focusing on mean weekly dose, the usual end-point used in previous studies. Participants recruited at 12 outpatient sites in Uganda and South Africa on stable warfarin dose were genotyped using the Illumina Infinium H3Africa Consortium Array v2. Imputation was conducted using the 1,000 Genomes Project phase III reference panel. Warfarin/metabolite plasma concentrations were determined by high-performance liquid chromatography with tandem mass spectrometry. Multivariable linear regression was undertaken, with adjustment made for five non-genetic covariates and ten principal components of genetic ancestry. After quality control procedures, 548 participants and 17,268,054 SNPs were retained. CYP2C9*8, CYP2C9*9, CYP2C9*11, and the CYP2C cluster SNP rs12777823 passed the Bonferroni-adjusted replication significance threshold (p < 3.21E-04) for warfarin/metabolite ratios. In an exploratory GWAS analysis, 373 unique SNPs in 13 genes, including CYP2C9*8, passed the Bonferroni-adjusted genome-wide significance threshold (p < 3.846E-9), with 325 (87%, all located on chromosome 10) SNPs being associated with the S-warfarin/R-warfarin outcome (top SNP rs11188082, CYP2C19 intron variant, p = 1.55E-17). Approximately 69% of these SNPs were in linkage disequilibrium (r 2 > 0.8) with CYP2C9*8 (n = 216) and rs12777823 (n = 8). Using a pharmacokinetic approach, we have shown that variants other than CYP2C9*2 and CYP2C9*3 are more important in sub-Saharan black-Africans, mainly due to the allele frequencies. In exploratory work, we conducted the first warfarin pharmacokinetics-related GWAS in sub-Saharan Africans and identified novel SNPs that will require external replication and functional characterization before they can be considered for inclusion in warfarin dosing algorithms.

Warfarin Therapy and Pharmacogenetics

The aim of this work was to review several studies investigating the effects of genetic polymorphisms on warfarin dosing in regional and Iraqi studies and to report any consistent pattern of relevant findings. Despite the growing use of the recently introduced direct oral anticoagulants, warfarin is still the mainstay agent for oral anticoagulation because of its cost-effectiveness. However, a difficulty to establish a stable warfarin dose is frequently encountered. In addition to the warfarin narrow window of efficacy and safety, the main contributor to the challenging dosing is the wide range of variability in warfarin pharmacokinetics and pharmacodynamics among different patients as well as within the single patient context. A link between nonappropriateness of warfarin doses and dramatically increased risk of thromboembolic and hemorrhagic events has been well documented. Several single nucleotide polymorphisms (SNPs) in the genes implicated in warfarin pharmacokinetic and pharmacodynamic processes have been highlighted as possible contributors to warfarin dosing instability. Vitamin K epoxide reductase complex 1 gene SNPs have consistently been found to be the predominant genetic factor contributing the dosing variations. The SNP rs9923231 was significantly associated with the greatest predicting capability of warfarin dosage. However, a range of about 30%–50% of the variances in warfarin dosing was explained by the combined contribution effect of several genetic and nongenetic (clinical) factors in the regional and Iraqi studies.

Influence of Vitamin D Status on the Maintenance Dose of Warfarin in Patients Receiving Chronic Warfarin Therapy.

IntroductionConsidering the anticoagulant actions of vitamin D, we hypothesize that vitamin D status might affect the required dose of warfarin for maintaining the therapeutic international normalized ratio (INR).MethodsIn a retrospective single-center cohort study, serum levels of 25-hydroxyvitamin D were assessed for 89 subjects receiving a stable dose of warfarin for 3 months or longer and had a stable INR between 2 and 3.5 for at least three consecutive visits. A warfarin sensitivity index (WSI), defined as the steady-state INR divided by the mean daily warfarin dose, was used for measuring the warfarin dose response. The relation between the serum level of 25-hydroxyvitamin D and WSI value and the difference in the mean WSI value between the subjects with different vitamin D status categories (sufficient, insufficient, and deficient) were assessed.ResultsTwenty-one subjects had vitamin D deficiency, 43 had vitamin D insufficiency, and only 25 had normal levels of 25-hydroxyvitamin D. Based on the multiple linear regression analysis, there was a significant but weakly positive correlation between WSI and 25-hydroxyvitamin D serum levels, as the value of WSI increases by almost 0.0027434 for every unit increase in 25-hydroxyvitamin D serum level (p value = 0.041). Using one-way ANOVA analysis, there was a trend in a significant difference between the groups with different vitamin D status categories regarding the mean WSI value (F = 2.95, p value = 0.057), as subjects with sufficient vitamin D state compared to those with vitamin D deficiency had a higher WSI value.ConclusionsAlthough the study’s limitations limit our ability to draw definite conclusions, the present data suggest that in addition to other traditional factors, vitamin D status might also affect warfarin sensitivity and maintenance dose requirement. However, to more clearly explain this link, further studies with high involvement subjects are required.

Stable warfarin dose prediction in sub-Saharan African patients: A machine-learning approach and external validation of a clinical dose-initiation algorithm.

Warfarin remains the most widely prescribed oral anticoagulant in sub‐Saharan Africa. However, because of its narrow therapeutic index, dosing can be challenging. We have therefore (a) evaluated and compared the performance of 21 machine‐learning techniques in predicting stable warfarin dose in sub‐Saharan Black‐African patients and (b) externally validated a previously developed Warfarin Anticoagulation in Patients in Sub‐Saharan Africa (War‐PATH) clinical dose–initiation algorithm. The development cohort included 364 patients recruited from eight outpatient clinics and hospital departments in Uganda and South Africa (June 2018–July 2019). Validation was conducted using an external validation cohort (270 patients recruited from August 2019 to March 2020 in 12 outpatient clinics and hospital departments). Based on the mean absolute error (MAE; mean of absolute differences between the actual and predicted doses), random forest regression (12.07 mg/week; 95% confidence interval [CI], 10.39–13.76) was the best performing machine‐learning technique in the external validation cohort, whereas the worst performing technique was model trees (17.59 mg/week; 95% CI, 15.75–19.43). By comparison, the simple, commonly used regression technique (ordinary least squares) performed similarly to more complex supervised machine‐learning techniques and achieved an MAE of 13.01 mg/week (95% CI, 11.45–14.58). In summary, we have demonstrated that simpler regression techniques perform similarly to more complex supervised machine‐learning techniques. We have also externally validated our previously developed clinical dose–initiation algorithm, which is being prospectively tested for clinical utility.

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

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.

Genotype-Guided vs Clinically-Guided Stable Warfarin Dose Prediction and Stable Dose Establishment In A Predominantly Non-European Ancestry Population

ABSTRACT Background Warfarin dosing varies due to individual genetic and clinical factors. The utility of genotype‐guided warfarin dosing to improve stable warfarin dose is not extensively studied in non-European populations. Research design and methods Retrospective cohort study of patients initiating warfarin receiving genotype (PGx)‐guided or clinically guided dosing. Primary outcomes included dose discordance between estimated dose at discharge and eventual stable dose. Results No significant difference in warfarin dose discordance was observed (PGx: 9.1 ± 8.8 mg/week difference vs. Clinical: 7.9 ± 8.9 mg/week difference; P = 0.446). PGx‐guided dosing did not reduce time to achieve stable dose (1.8 ± 2.5 months vs. 2.1 ± 2.6 months; P = 0.508). Vitamin K intake level did not alter prediction accuracy or time to stable dose (PGx‐predicted: dose difference P = 0.493, time to stable dose P = 0.336; Clinically predicted: dose difference P = 0.145, time to dose INR P = 0.095). Conclusions PGx‐guided warfarin dosing did not improve eventual stable dose discordance or reduce the time to achieve stable dose in this predominantly non‐European cohort. Dietary vitamin K intake level did not impact warfarin dose discordance or time to achieve stable warfarin dose. Additional study is needed to identify populations that best benefit from PGx‐guided warfarin dosing.

Effect of NPC1L1 polymorphism on warfarin stable dose in Chinese patients under heart valve replacement surgery.

Warfarin is the most often anticoagulant choice for preventable thromboembolism. Notably, vitamin K plays a vital role in the process of warfarin's anticoagulant effect. Therefore, we presume NPC1L1, a key transporter of vitamin K (VK) intestinal absorption, may modulate the anticoagulant effect of warfarin. Studies have shown that NPC1L1(-762T>C, rs2073548) and p53 (P72R, rs1042522) variations are implicated in influencing NPC1L1 expression. This study aimed to assess the association between these two variants and warfarin stable dose (WSD). A two-stage extreme phenotype design was used to explore the influence of these two variants (rs2073548, rs1042522) on WSD variance in 655 Chinese patients undergoing heart valve replacement surgery. NPC1L1 rs2073548, p53 rs1042522, VKORC1 rs9923231 and CYP2C9*1/*3 polymorphisms were genotyped by polymerase chain reaction-restriction fragment polymorphism (PCR-RFLP) or Sanger sequencing, respectively. WSD was identified when target monitoring international normalized ratio (INR) value at 2.0-3.0. In the discovery phase, NPC1L1 rs2073548 A allele carriers occupied a significantly higher rate in the low dose group (P=.019). However, in the validation group, warfarin dosage in patients with the rs2073548 AA, AG and GG genotypes were 2.91±0.97mg/day, 3.02±1.00mg/day and 3.00±1.06mg/day, respectively. Multiple linear regression analysis results suggested that CYP2C9*3 and VKORC1 rs9923231, but not NPC1L1 rs2073548, were independent predictors of WSD in Chinese heart valve replacement (HVR) surgical patients.

Effect of gene polymorphism on bleeding complications in Chinese Han patients taking warfarin

The purpose of this study was to analyse the effects of demographic factors, clinical factors, and genetic polymorphisms of related gene loci on warfarin bleeding-related complications in the Han population. Retrospective medical record review. The study cases were patients treated at the Fujian Medical University Union Hospital from March 2016 to February 2020, and all received regular warfarin anticoagulation treatment for at least 3months, and were provided the initial standard dose and stable dose of warfarin. Data were collected from 451 qualifying patients (47% male, 53% female). The average age of patients was 53.8 ± 12.2years, and the average body surface area was 1.6 ± 0.18 m2. There were nine major bleeding events and 141 minor bleeding events. In the univariate logistic analysis, the p-value of the four factors body weight, body surface area (BSA), amiodarone, and rs429358 was < 0.10. However, the final p-values for amiodarone and rs429358 were < 0.05 in the multifactorial logistic analysis. The ApoE (rs429358) gene polymorphism influences bleeding complications in Chinese Han patients treated with warfarin. The sample size of this study was relatively small; hence an international study with a larger sample size is needed in the future.

To establish a model for the prediction of initial standard and maintenance doses of warfarin for the Han Chinese population based on gene polymorphism: a multicenter study.

The purpose of this paper is to study the correlation between demographic and clinical factors and warfarin dose of patients in Chinese Han population taking warfarin and study gene polymorphisms impact of related gene loci (CYP2C9*3, VKORC1-1639G > A) on warfarin doses, to establish a model to predict initial standard dose and maintenance dose based on CYP2C9*3, VKORC1-1639G > A genotype. The study collects the data of patients in our hospital and other subcenters which incorporates 2160 patients to establish the initial dose model and 1698 patients for the stable dose model, and sequences 26 multigene sites in 451 patients. Based on the patient's dosage, clinical data, and demographic characteristics, the genetic and non-genetic effects on the initial dose and stable dose of warfarin are calculated by using statistical methods, and the prediction model of initial standard dose and maintenance dose can be established via multiple linear regression. The initial dose of warfarin (mg/day) was calculated as (1.346 + 0.350 × (VKORC1-1639G > A)-0.273 × (CYP2C9*3) + 0.245 × (body surface area)-0.003 × (age)-0.036 × (amine-iodine) + 0.021 × (sex))2. This model incorporated seven factors and explained 55.3% of the individualization differences of the warfarin drug dose. The maintenance dose of warfarin (mg/day) was calculated as (1.336 + 0.299 × (VKORC1-1639G > A) + 0.480 × (body surface area)-0.214 × (CYP2C9*3)-0.074 × (amine-iodine)-0.003 × (age)-0.077 × (statins)-0.002 × (height))2. This model incorporated six factors and explained 42.4% of the individualization differences in the warfarin drug dose. The genetic and non-genetic factors affecting warfarin dose in Chinese Han population were studied systematically in this study. The pharmacogenomic dose prediction model constructed in this study can predict anticoagulant efficacy of warfarin and has potential application value in clinical practice.

THE EFFECTS OF GENETIC AND NON GENETIC FACTORS ON WARFARIN DOSE RESPONSE IN VENOUS THROMBOEMBOLISM

BACKGROUND Warfarin is the most commonly used oral anticoagulant for the treatment and prevention of thromboembolic disorders. Pharmacogenomics studies have shown that variants in CYP2C9 and VKORC1 genes are strongly and consistently associated with warfarin dose variability. METHODOLOGY In this review, we included patients on stable warfarin dose and had the genetics and non-genetics factors associated with mean warfarin dose. We searched PubMed, Medline, Scopus, Google scholar and reference lists of relevant reviews. CONCLUSION Genetic and non-genetic factors affects the dose of warfarin. Genetic factors and Non genetic factors plays a signicant role and that may affects the dose of warfarin

Comparison of multivariate linear regression and a machine learning algorithm developed for prediction of precision warfarin dosing in a Korean population

BackgroundPersonalized warfarin dosing is influenced by various factors including genetic and non‐genetic factors. Multiple linear regression (LR) is known as a conventional method to develop predictive models. Recently, machine learning approaches have been extensively implemented for warfarin dosing due to the hypothesis of non‐linear association between covariates and stable warfarin dose. ObjectiveTo extend the multiple linear regression algorithm for personalized warfarin dosing in a Korean population and compare with a machine learning‐‐based algorithm. MethodFrom this cohort study, we collected information on 650 patients taking warfarin who achieved steady state including demographic information, indications, comorbidities, comedications, habits, and genetic factors. The dataset was randomly split into training set (90%) and test set (10%). The LR and machine learning (gradient boosting machine [GBM]) models were developed on the training set and were evaluated on the test set. ResultLR and GBM models were comparable in terms of accuracy of ideal dose (75.38% and 73.85%), correlation (0.77 and 0.73), mean absolute error (0.58 mg/day and 0.64 mg/day), and root mean square error (0.82 mg/day and 0.9 mg/day), respectively. VKORC1 genotype, CYP2C9 genotype, age, and weight were the highest contributors and could obtain 80% of maximum performance in both models. ConclusionThis study shows that our LR and GMB models are satisfactory to predict warfarin dose in our dataset. Both models showed similar performance and feature contribution characteristics. LR may be the appropriate model due to its simplicity and interpretability.