Brian F. Gage

Estimation of the warfarin dose with clinical and pharmacogenetic data.

BACKGROUND Genetic variability among patients plays an important role in determining the dose of warfarin that should be used when oral anticoagulation is initiated, but practical methods of using genetic information have not been evaluated in a diverse and large population. We developed and used an algorithm for estimating the appropriate warfarin dose that is based on both clinical and genetic data from a broad population base. METHODS Clinical and genetic data from 4043 patients were used to create a dose algorithm that was based on clinical variables only and an algorithm in which genetic information was added to the clinical variables. In a validation cohort of 1009 subjects, we evaluated the potential clinical value of each algorithm by calculating the percentage of patients whose predicted dose of warfarin was within 20% of the actual stable therapeutic dose; we also evaluated other clinically relevant indicators. RESULTS In the validation cohort, the pharmacogenetic algorithm accurately identified larger proportions of patients who required 21 mg of warfarin or less per week and of those who required 49 mg or more per week to achieve the target international normalized ratio than did the clinical algorithm (49.4% vs. 33.3%, P<0.001, among patients requiring < or = 21 mg per week; and 24.8% vs. 7.2%, P<0.001, among those requiring > or = 49 mg per week). CONCLUSIONS The use of a pharmacogenetic algorithm for estimating the appropriate initial dose of warfarin produces recommendations that are significantly closer to the required stable therapeutic dose than those derived from a clinical algorithm or a fixed-dose approach. The greatest benefits were observed in the 46.2% of the population that required 21 mg or less of warfarin per week or 49 mg or more per week for therapeutic anticoagulation.

Selecting patients with atrial fibrillation for anticoagulation: stroke risk stratification in patients taking aspirin

Background—The rate of stroke in atrial fibrillation (AF) depends on the presence of comorbid conditions and the use of antithrombotic therapy. Although adjusted-dose warfarin is superior to aspirin for reducing stroke in AF, the absolute risk reduction of warfarin depends on the stroke rate with aspirin. This prospective cohort study tested the predictive accuracy of 5 stroke risk stratification schemes. Methods and Results—The study pooled individual data from 2580 participants with nonvalvular AF who were prescribed aspirin in a multicenter trial (Atrial Fibrillation, Aspirin, Anticoagulation I study [AFASAK-1], AFASAK-2, European Atrial Fibrillation Trial, Primary Prevention of Arterial Thromboembolism in patients with nonrheumatic Atrial Fibrillation in primary care study, and Stroke Prevention and Atrial Fibrillation [SPAF]-III high risk or SPAF-III low risk). There were 207 ischemic strokes during 4887 patient-years of aspirin therapy. All schemes predicted stroke better than chance, but the number of patients categorized as low and high risk varied substantially. AF patients with prior cerebral ischemia were classified as high risk by all 5 schemes and had 10.8 strokes per 100 patient-years. The CHADS2 scheme (an acronym for Congestive heart failure, Hypertension, Age >75, Diabetes mellitus, and prior Stroke or transient ischemic attack) successfully identified primary prevention patients who were at high risk of stroke (5.3 strokes per 100 patient-years). In contrast, patients identified as high risk by other schemes had 3.0 to 4.2 strokes per 100 patient-years. Low-risk patients identified by all schemes had 0.5 to 1.4 strokes per 100 patient-years of therapy. Conclusions—Patients with AF who have high and low rates of stroke when given aspirin can be reliably identified, allowing selection of antithrombotic prophylaxis to be individualized.

Use of Pharmacogenetic and Clinical Factors to Predict the Therapeutic Dose of Warfarin

Initiation of warfarin therapy using trial‐and‐error dosing is problematic. Our goal was to develop and validate a pharmacogenetic algorithm. In the derivation cohort of 1,015 participants, the independent predictors of therapeutic dose were: VKORC1 polymorphism −1639/3673 G>A (−28% per allele), body surface area (BSA) (+11% per 0.25 m2), CYP2C9*3 (−33% per allele), CYP2C9*2 (−19% per allele), age (−7% per decade), target international normalized ratio (INR) (+11% per 0.5 unit increase), amiodarone use (−22%), smoker status (+10%), race (−9%), and current thrombosis (+7%). This pharmacogenetic equation explained 53–54% of the variability in the warfarin dose in the derivation and validation (N= 292) cohorts. For comparison, a clinical equation explained only 17–22% of the dose variability (P < 0.001). In the validation cohort, we prospectively used the pharmacogenetic‐dosing algorithm in patients initiating warfarin therapy, two of whom had a major hemorrhage. To facilitate use of these pharmacogenetic and clinical algorithms, we developed a nonprofit website, http://www.WarfarinDosing.org.

Baseline Risk of Major Bleeding in Non–ST-Segment–Elevation Myocardial Infarction The CRUSADE (Can Rapid risk stratification of Unstable angina patients Suppress ADverse outcomes with Early implementation of the ACC/AHA guidelines) Bleeding Score

Treatment of non–ST-segment elevation myocardial infarction (NSTEMI) has traditionally focused on preventing or minimizing ischemic complications with potent antithrombotic medications and catheter-based interventions.1–3 Yet these reductions in recurrent ischemic events have come at the cost of increased major bleeding,4–7 which is itself associated with worse clinical outcomes.7–13 Bleeding complications have received attention recently, in part because newer antithrombotic agents for NSTEMI have unique ischemia and bleeding profiles. Some agents demonstrate low rates of major bleeding with similar efficacy,5,14 while others demonstrate higher rates of major bleeding with superior efficacy.15 Given the importance of safety and efficacy,12 the recent American College of Cardiology (ACC)/American Heart Association (AHA) practice guidelines placed renewed emphasis on risk stratification to guide treatment for NSTEMI.3 While tools for ischemic risk stratification are well described (i.e., TIMI, PURSUIT, and GRACE risk scores),16–18 bleeding risk stratification is more limited. The few bleeding risk stratification models in existence include treatments known to influence bleeding or are derived from subgroups or trial populations not representative of those at greatest risk.10,13,19 Consequently, better estimation of baseline risk of bleeding in NSTEMI patients is needed to facilitate optimal treatment selection. Using data from the Can Rapid risk stratification of Unstable angina patients Suppress ADverse outcomes with Early implementation of the ACC/AHA guidelines (CRUSADE) Quality Improvement Initiative, we developed and validated a scoring system to estimate baseline risk of in-hospital major bleeding in patients with NSTEMI. The CRUSADE bleeding score provides a tool that equips clinicians with the means to consider safety outcomes when making treatment decisions for patients with NSTEMI.Background— Treatments for non–ST-segment–elevation myocardial infarction (NSTEMI) reduce ischemic events but increase bleeding. Baseline prediction of bleeding risk can complement ischemic risk prediction for optimization of NSTEMI care; however, existing models are not well suited for this purpose. Methods and Results— We developed (n=71 277) and validated (n=17 857) a model that identifies 8 independent baseline predictors of in-hospital major bleeding among community-treated NSTEMI patients enrolled in the Can Rapid risk stratification of Unstable angina patients Suppress ADverse outcomes with Early implementation of the ACC/AHA guidelines (CRUSADE) Quality Improvement Initiative. Model performance was tested by c statistics in the derivation and validation cohorts and according to postadmission treatment (ie, invasive and antithrombotic therapy). The CRUSADE bleeding score (range 1 to 100 points) was created by assignment of weighted integers that corresponded to the coefficient of each variable. The rate of major bleeding increased by bleeding risk score quintiles: 3.1% for those at very low risk (score ≤20); 5.5% for those at low risk (score 21–30); 8.6% for those at moderate risk (score 31–40); 11.9% for those at high risk (score 41–50); and 19.5% for those at very high risk (score >50; Ptrend <0.001). The c statistics for the major bleeding model (derivation=0.72 and validation=0.71) and risk score (derivation=0.71 and validation=0.70) were similar. The c statistics for the model among treatment subgroups were as follows: ≥2 antithrombotics=0.72; <2 antithrombotics=0.73; invasive approach=0.73; conservative approach=0.68. Conclusions— The CRUSADE bleeding score quantifies risk for in-hospital major bleeding across all postadmission treatments, which enhances baseline risk assessment for NSTEMI care.

Accuracy of ICD-9-CM codes for identifying cardiovascular and stroke risk factors.

Objectives:We sought to determine which ICD-9-CM codes in Medicare Part A data identify cardiovascular and stroke risk factors. Design and Participants:This was a cross-sectional study comparing ICD-9-CM data to structured medical record review from 23,657 Medicare beneficiaries aged 20 to 105 years who had atrial fibrillation. Measurements:Quality improvement organizations used standardized abstraction instruments to determine the presence of 9 cardiovascular and stroke risk factors. Using the chart abstractions as the gold standard, we assessed the accuracy of ICD-9-CM codes to identify these risk factors. Main Results:ICD-9-CM codes for all risk factors had high specificity (>0.95) and low sensitivity (≤0.76). The positive predictive values were greater than 0.95 for 5 common, chronic risk factors—coronary artery disease, stroke/transient ischemic attack, heart failure, diabetes, and hypertension. The sixth common risk factor, valvular heart disease, had a positive predictive value of 0.93. For all 6 common risk factors, negative predictive values ranged from 0.52 to 0.91. The rare risk factors—arterial peripheral embolus, intracranial hemorrhage, and deep venous thrombosis—had high negative predictive value (≥0.98) but moderate positive predictive values (range, 0.54–0.77) in this population. Conclusions:Using ICD-9-CM codes alone, heart failure, coronary artery disease, diabetes, hypertension, and stroke can be ruled in but not necessarily ruled out. Where feasible, review of additional data (eg, physician notes or imaging studies) should be used to confirm the diagnosis of valvular disease, arterial peripheral embolus, intracranial hemorrhage, and deep venous thrombosis.

A Pharmacogenetic versus a Clinical Algorithm for Warfarin Dosing

BACKGROUND The clinical utility of genotype-guided (pharmacogenetically based) dosing of warfarin has been tested only in small clinical trials or observational studies, with equivocal results. METHODS We randomly assigned 1015 patients to receive doses of warfarin during the first 5 days of therapy that were determined according to a dosing algorithm that included both clinical variables and genotype data or to one that included clinical variables only. All patients and clinicians were unaware of the dose of warfarin during the first 4 weeks of therapy. The primary outcome was the percentage of time that the international normalized ratio (INR) was in the therapeutic range from day 4 or 5 through day 28 of therapy. RESULTS At 4 weeks, the mean percentage of time in the therapeutic range was 45.2% in the genotype-guided group and 45.4% in the clinically guided group (adjusted mean difference, [genotype-guided group minus clinically guided group], -0.2; 95% confidence interval, -3.4 to 3.1; P=0.91). There also was no significant between-group difference among patients with a predicted dose difference between the two algorithms of 1 mg per day or more. There was, however, a significant interaction between dosing strategy and race (P=0.003). Among black patients, the mean percentage of time in the therapeutic range was less in the genotype-guided group than in the clinically guided group. The rates of the combined outcome of any INR of 4 or more, major bleeding, or thromboembolism did not differ significantly according to dosing strategy. CONCLUSIONS Genotype-guided dosing of warfarin did not improve anticoagulation control during the first 4 weeks of therapy. (Funded by the National Heart, Lung, and Blood Institute and others; COAG ClinicalTrials.gov number, NCT00839657.).

Clinical Pharmacogenetics Implementation Consortium Guidelines for CYP2C9 and VKORC1 Genotypes and Warfarin Dosing

Warfarin is a widely used anticoagulant with a narrow therapeutic index and large interpatient variability in the dose required to achieve target anticoagulation. Common genetic variants in the cytochrome P450–2C9 (CYP2C9) and vitamin K–epoxide reductase complex (VKORC1) enzymes, in addition to known nongenetic factors, account for ~50% of warfarin dose variability. The purpose of this article is to assist in the interpretation and use of CYP2C9 and VKORC1 genotype data for estimating therapeutic warfarin dose to achieve an INR of 2–3, should genotype results be available to the clinician. The Clinical Pharmacogenetics Implementation Consortium (CPIC) of the National Institutes of Health Pharmacogenomics Research Network develops peer–reviewed gene–drug guidelines that are published and updated periodically on http://www.pharmgkb.org based on new developments in the field. 1

CYP4F2 genetic variant alters required warfarin dose.

Warfarin is an effective, commonly prescribed anticoagulant used to treat and prevent thrombotic events. Because of historically high rates of drug-associated adverse events, warfarin remains underprescribed. Further, interindividual variability in therapeutic dose mandates frequent monitoring until target anticoagulation is achieved. Genetic polymorphisms involved in warfarin metabolism and sensitivity have been implicated in variability of dose. Here, we describe a novel variant that influences warfarin requirements. To identify additional genetic variants that contribute to warfarin requirements, screening of DNA variants in additional genes that code for drug-metabolizing enzymes and drug transport proteins was undertaken using the Affymetrix drug-metabolizing enzymes and transporters panel. A DNA variant (rs2108622; V433M) in cytochrome P450 4F2 (CYP4F2) was associated with warfarin dose in 3 independent white cohorts of patients stabilized on warfarin representing diverse geographic regions in the United States and accounted for a difference in warfarin dose of approximately 1 mg/day between CC and TT subjects. Genetic variation of CYP4F2 was associated with a clinically relevant effect on warfarin requirement.

Cost-Effectiveness of Dabigatran for Stroke Prophylaxis in Atrial Fibrillation

Background— Recent studies have investigated alternatives to warfarin for stroke prophylaxis in patients with atrial fibrillation (AF), but whether these alternatives are cost-effective is unknown. Methods and Results— On the basis of the results from Randomized Evaluation of Long Term Anticoagulation Therapy (RE-LY) and other trials, we developed a decision-analysis model to compare the cost and quality-adjusted survival of various antithrombotic therapies. We ran our Markov model in a hypothetical cohort of 70-year-old patients with AF using a cost-effectiveness threshold of

Warfarin Genotyping Reduces Hospitalization Rates Results From the MM-WES (Medco-Mayo Warfarin Effectiveness Study)

50 000/quality-adjusted life-year. We estimated the cost of dabigatran as US