Dataset Information

Personalized Anticoagulation: Optimizing Warfarin Management Using Genetics and Simulated Clinical Trials.

ABSTRACT: BACKGROUND:Clinical trials testing pharmacogenomic-guided warfarin dosing for patients with atrial fibrillation have demonstrated conflicting results. Non-vitamin K antagonist oral anticoagulants are expensive and contraindicated for several conditions. A strategy optimizing anticoagulant selection remains an unmet clinical need. METHODS AND RESULTS:Characteristics from 14?206 patients with atrial fibrillation were integrated into a validated warfarin clinical trial simulation framework using iterative Bayesian network modeling and a pharmacokinetic-pharmacodynamic model. Individual dose-response for patients was simulated for 5 warfarin protocols-a fixed-dose protocol, a clinically guided protocol, and 3 increasingly complex pharmacogenomic-guided protocols. For each protocol, a complexity score was calculated using the variables predicting warfarin dose and the number of predefined international normalized ratio (INR) thresholds for each adjusted dose. Study outcomes included optimal time in therapeutic range ?65% and clinical events. A combination of age and genotype identified different optimal protocols for various subpopulations. A fixed-dose protocol provided well-controlled INR only in normal responders ?65, whereas for normal responders <65 years old, a clinically guided protocol was necessary to achieve well-controlled INR. Sensitive responders ?65 and <65 and highly sensitive responders ?65 years old required pharmacogenomic-guided protocols to achieve well-controlled INR. However, highly sensitive responders <65 years old did not achieve well-controlled INR and had higher associated clinical events rates than other subpopulations. CONCLUSIONS:Under the assumptions of this simulation, patients with atrial fibrillation can be triaged to an optimal warfarin therapy protocol by age and genotype. Clinicians should consider alternative anticoagulation therapy for patients with suboptimal outcomes under any warfarin protocol.

SUBMITTER: Ravvaz K

PROVIDER: S-EPMC5734112 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Personalized Anticoagulation: Optimizing Warfarin Management Using Genetics and Simulated Clinical Trials.

Ravvaz Kourosh K Weissert John A JA Ruff Christian T CT Chi Chih-Lin CL Tonellato Peter J PJ

Circulation. Cardiovascular genetics 20171201 6

<h4>Background</h4>Clinical trials testing pharmacogenomic-guided warfarin dosing for patients with atrial fibrillation have demonstrated conflicting results. Non-vitamin K antagonist oral anticoagulants are expensive and contraindicated for several conditions. A strategy optimizing anticoagulant selection remains an unmet clinical need.<h4>Methods and results</h4>Characteristics from 14 206 patients with atrial fibrillation were integrated into a validated warfarin clinical trial simulation fra ...[more]

PMID: 29237680

Similar Datasets

Project description:INTRODUCTION:Warfarin is an effective anticoagulant and the only oral anticoagulant available for patients with mechanical heart valves. The prothrombin time and the associated international normalised ratio (INR) are routinely tested to monitor the response to anticoagulation therapy in patients. Patients who undergo mechanical heart valve replacement need lifelong anticoagulation therapy, and their INR is regularly measured to adjust the anticoagulation strength and the dose of anticoagulation drugs. Appropriate warfarin anticoagulation management can reduce patient complications, such as bleeding and thrombosis, and improve the long-term survival rate. We propose modern internet technology as a platform to build a warfarin anticoagulation follow-up system after valve replacement surgery. This system will provide doctors and patients with more standardised and safer follow-up methods as well as a method to further reduce the risk of warfarin anticoagulation-related complications and improve its therapeutic effects. METHODS AND ANALYSIS:A prospective, multicentre, randomised, controlled trial will be conducted. A total of 700 patients who require long-term warfarin anticoagulation monitoring after heart valve replacement will be enrolled and randomly divided at a 1:1 ratio into a traditional outpatient anticoagulation management group and a group undergoing a new method of management based on the internet technology with follow-up for 1?year. Differences in the percentage of time in the therapeutic range (TTR), drug dose adjustments, bleeding/thrombosis and other related complications will be observed. The primary endpoint is the difference in the TTR between the two groups. The purpose of this study is to explore a safer and more effective mode of doctor-patient interaction and communication in the internet era. As of 13 July 2019, 534 patients had been enrolled. ETHICS AND DISSEMINATION:This study protocol was approved by the Ethics Committee of Beijing Anzhen Hospital, Capital Medical University. The results will be published in a peer-reviewed medical journal. TRIAL REGISTRATION NUMBER:ChiCTR1800016204.

Project description:BackgroundGuidelines recommend warfarin use in patients with atrial fibrillation solely on the basis of risk for ischemic stroke without antithrombotic therapy. These guidelines rely on ischemic stroke rates observed in older trials and do not explicitly account for increased risk for hemorrhage.ObjectiveTo quantify the net clinical benefit of warfarin therapy in a cohort of patients with atrial fibrillation.DesignMixed retrospective and prospective cohort study of patients with atrial fibrillation between 1996 and 2003.SettingAn integrated health care delivery system.Patients13 559 adults with nonvalvular atrial fibrillation.MeasurementsWarfarin exposure, patient characteristics, CHADS(2) score (1 point for each of congestive heart failure, hypertension, age, and diabetes and 2 points for stroke), and outcome events were ascertained from health plan records and databases. Net clinical benefit was defined as the annual rate of ischemic strokes and systemic emboli prevented by warfarin minus intracranial hemorrhages attributable to warfarin, multiplied by an impact weight. The base-case impact weight was 1.5, reflecting the greater clinical impact of intracranial hemorrhage versus thromboembolism.ResultsPatients accumulated more than 66 000 person-years of follow-up. The adjusted net clinical benefit of warfarin for the cohort overall was 0.68% per year (95% CI, 0.34% to 0.87%). Adjusted net clinical benefit was greatest for patients with a history of ischemic stroke (2.48% per year [CI, 0.75% to 4.22%]) and for those 85 years or older (2.34% per year [CI, 1.29% to 3.30%]). The net clinical benefit of warfarin increased from essentially zero in CHADS(2) stroke risk categories 0 and 1 to 2.22% per year (CI, 0.58% to 3.75%) in CHADS(2) categories 4 to 6. The patterns of results were preserved when weighting factors for intracranial hemorrhage of 1.0 and 2.0 were used.LimitationsResidual confounding is a possibility. Some outcome events were probably missed by the screening algorithm or when medical records were unavailable.ConclusionExpected net clinical benefit of warfarin therapy is highest among patients with the highest untreated risk for stroke, which includes the oldest age category. Risk assessment that incorporates both risk for thromboembolism and risk for intracranial hemorrhage provides a more quantitatively informed basis for the decision on antithrombotic therapy in patients with atrial fibrillation.Primary funding sourceNational Institute on Aging; National Heart, Lung, and Blood Institute; and Massachusetts General Hospital.

Project description:BackgroundThere is currently much interest in pharmacogenetics: determining variation in genes that regulate drug effects, with a particular emphasis on improving drug safety and efficacy. The ability to determine such variation motivates the application of personalized drug therapies that utilize a patient's genetic makeup to determine a safe and effective drug at the correct dose. To ascertain whether a genotype-guided drug therapy improves patient care, a personalized medicine intervention may be evaluated within the framework of a randomized controlled trial. The statistical design of this type of personalized medicine intervention requires special considerations: the distribution of relevant allelic variants in the study population; and whether the pharmacogenetic intervention is equally effective across subpopulations defined by allelic variants.MethodsThe statistical design of the Clarification of Optimal Anticoagulation through Genetics (COAG) trial serves as an illustrative example of a personalized medicine intervention that uses each subject's genotype information. The COAG trial is a multicenter, double blind, randomized clinical trial that will compare two approaches to initiation of warfarin therapy: genotype-guided dosing, the initiation of warfarin therapy based on algorithms using clinical information and genotypes for polymorphisms in CYP2C9 and VKORC1; and clinical-guided dosing, the initiation of warfarin therapy based on algorithms using only clinical information.ResultsWe determine an absolute minimum detectable difference of 5.49% based on an assumed 60% population prevalence of zero or multiple genetic variants in either CYP2C9 or VKORC1 and an assumed 15% relative effectiveness of genotype-guided warfarin initiation for those with zero or multiple genetic variants. Thus we calculate a sample size of 1238 to achieve a power level of 80% for the primary outcome. We show that reasonable departures from these assumptions may decrease statistical power to 65%.ConclusionsIn a personalized medicine intervention, the minimum detectable difference used in sample size calculations is not a known quantity, but rather an unknown quantity that depends on the genetic makeup of the subjects enrolled. Given the possible sensitivity of sample size and power calculations to these key assumptions, we recommend that they be monitored during the conduct of a personalized medicine intervention.Trial registrationclinicaltrials.gov: NCT00839657.

Project description:BACKGROUND:Warfarin dosage regimens using CYP2C9 and VKORC1 polymorphisms have been extensively studied in adults and is included in US Food and Drug Administration-approved warfarin labeling. However, no dosage algorithm is available for pediatric patients. OBJECTIVE:To derive a genetics-based pediatric dosge regimen for warfarin, including starting dose and titration scheme. METHODS:A model-based approach was developed based on a previously validated warfarin dosage model in adults, with subsequent comparison to pediatric data from pediatric warfarin dose, genotyping, and international normalized ratio (INR) results. The adult model was based on a previously established model from the CROWN (CReating an Optimal Warfarin dosing Nomogram) trial. Pediatric warfarin data were obtained from a study conducted at the Children's Hospital of Los Angeles with 26 subjects. Variant alleles of CYP2C9 (rs1799853 or *2, and rs1057910 or *3) and the VKORC1 single nucleotide polymorphism (SNP) rs9923231 (-1639 G>A) were assessed, where the rs numbers are reference SNP identification tags assigned by the National Center for Biotechnology Information. RESULTS:A pediatric warfarin model was derived using the previously validated model and clinical pharmacology considerations. The model was validated, and clinical trial simulation and stochastic modeling were used to optimize pediatric dosage and titration. The final dosage regimen was optimized based on simulations targeting a high (?60%) proportion of INRs within the therapeutic range by week 2 of warfarin therapy while minimizing INRs >3.5 or <2. CONCLUSIONS:The proposed pediatric warfarin dosage scheme based on individual CYP2C9 (alleles *1,*2,*3) and VKORC1 rs9923231 (-1639 G>A) genotypes may offer improved dosage compared to current treatment strategies, especially in patients with variant CYP2C9 and VKORC1 alleles. This pilot study provides the foundation for a larger prospective evaluation of genetics-based warfarin dosage in pediatric patients.

Project description:Treatment response variability across patients is a common phenomenon in clinical practice. For many drugs this inter-individual variability does not require much (if any) individualisation of dosing strategies. However, for some drugs, including chemotherapies and some monoclonal antibody treatments, individualisation of dosages are needed to avoid harmful adverse events. Model-informed precision dosing (MIPD) is an emerging approach to guide the individualisation of dosing regimens of otherwise difficult-to-administer drugs. Several MIPD approaches have been suggested to predict dosing strategies, including regression, reinforcement learning (RL) and pharmacokinetic and pharmacodynamic (PKPD) modelling. A unified framework to study the strengths and limitations of these approaches is missing. We develop a framework to simulate clinical MIPD trials, providing a cost and time efficient way to test different MIPD approaches. Central for our framework is a clinical trial model that emulates the complexities in clinical practice that challenge successful treatment individualisation. We demonstrate this framework using warfarin treatment as a use case and investigate three popular MIPD methods: 1. Neural network regression; 2. Deep RL; and 3. PKPD modelling. We find that the PKPD model individualises warfarin dosing regimens with the highest success rate and the highest efficiency: 75.1% of the individuals display INRs inside the therapeutic range at the end of the simulated trial; and the median time in the therapeutic range (TTR) is 74%. In comparison, the regression model and the deep RL model have success rates of 47.0% and 65.8%, and median TTRs of 45% and 68%. We also find that the MIPD models can attain different degrees of individualisation: the Regression model individualises dosing regimens up to variability explained by covariates; the Deep RL model and the PKPD model individualise dosing regimens accounting also for additional variation using monitoring data. However, the Deep RL model focusses on control of the treatment response, while the PKPD model uses the data also to further the individualisation of predictions.

Dataset Information

Personalized Anticoagulation: Optimizing Warfarin Management Using Genetics and Simulated Clinical Trials.

Publications

Personalized Anticoagulation: Optimizing Warfarin Management Using Genetics and Simulated Clinical Trials.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets