Racial Differences in Quality of Anticoagulation Therapy for Atrial Fibrillation (from the TREAT-AF Study)

Racial Differences in Quality of Anticoagulation Therapy for Atrial Fibrillation (from the TREAT-AF Study)

Racial Differences in Quality of Anticoagulation Therapy for Atrial Fibrillation (from the TREAT-AF Study) Celina Yong, MD, MBA, MSca,b, Farnaz Azarba...

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Racial Differences in Quality of Anticoagulation Therapy for Atrial Fibrillation (from the TREAT-AF Study) Celina Yong, MD, MBA, MSca,b, Farnaz Azarbal, MDa, Freddy Abnousi, MD, MBA, MSca,b, Paul A. Heidenreich, MD, MSa,b, Susan Schmitt, PhDb, Jun Fan, MSb, Claire T. Than, MPHb, Aditya J. Ullal, BAb, Felix Yang, MDc, Ciaran S. Phibbs, PhDb,d, Susan M. Frayne, MD, MSa,b, P. Michael Ho, MD, PhDe, Supriya Shore, MD, MSCSf, Kenneth W. Mahaffey, MDa, and Mintu P. Turakhia, MD, MASa,b,* The influence of race on quality of anticoagulation control is not well described. We examined the association between race, international normalized ratio (INR) monitoring intensity, and INR control in warfarin-treated patients with atrial fibrillation (AF). Using data from the Veterans Health Administration (VHA), we performed a retrospective cohort study of 184,161 patients with a new diagnosis of AF/flutter from 2004 to 2012 who received any VHA prescription within 90 days of diagnosis. The primary predictor was race, ascertained from multiple VHA and linked Medicare demographic files. The primary outcome was first-year and long-term time in therapeutic range (TTR) of INR 2.0 to 3.0. Secondary outcomes were INR monitoring intensity and warfarin persistence. Of the 116,021 patients who received warfarin in the cohort, INR monitoring intensity was similar across racial groups. However, TTR was lowest in blacks and highest in whites (first year 0.49 – 0.23 vs 0.57 – 0.21, p <0.001; long term 0.52 – 0.20 vs 0.59 – 0.18, p <0.001); 64% of whites and 49% of blacks had long-term TTR >55% (p <0.001). After adjusting for site and patient-level covariates, black race was associated with lower first-year and long-term TTRs (4.2% and 4.1% below the conditional mean, relative to whites; p <0.0001 for both). Oneyear warfarin persistence was slightly lower in blacks compared to whites (58% vs 60%, p <0.0001). In conclusion, in patients with AF anticoagulated with warfarin, differences in INR control are most evident among blacks, underscoring the need to determine if other types of intensive management or warfarin alternatives may be necessary to improve anticoagulation among vulnerable AF populations. Published by Elsevier Inc. (Am J Cardiol 2015;-:-e-) In the United States, more than 2 million adults have atrial fibrillation (AF), and the prevalence is anticipated to increase to 6 million by 2050.1 Stroke and death are the most significant complications of atrial fibrillation, and

a Division of Cardiovascular Medicine, Department of Medicine and Division of Neonatal and Developmental Medicine, Department of Pediatrics, Stanford University; bDepartment of Medicine, Veterans Affairs Palo Alto Health Care System, Palo Alto, California; cDepartment of Medicine, Maimonides Medical Center, Brooklyn, New York; eDepartment of Medicine, Veterans Affairs Eastern Colorado Health Care System, Denver, Colorado; and fDepartment of Medicine, Emory University, Atlanta, Georgia. Manuscript received July 7, 2015; revised manuscript received and accepted September 25, 2015. The content and opinions expressed are solely the responsibility of the authors and do not necessarily represent the views or policies of the Department of Veterans Affairs. Support for VA/CMS data is provided by the Department of Veterans Affairs, Veterans Health Administration, Office of Research and Development, Health Services Research and Development, and VA Information Resource Center, Palo Alto, CA (Project Numbers SDR 02-237 and 98004). It is also supported by the IIR 04-248 grant from Veterans Affairs (VA/CMS Project ID: Frayne-01). See page 7 for disclosure information. *Corresponding author: Tel: (650) 858-3932; fax: (866) 756-3025. E-mail address: [email protected] (M.P. Turakhia). d

0002-9149/15/$ - see front matter Published by Elsevier Inc. http://dx.doi.org/10.1016/j.amjcard.2015.09.047

therapeutic international normalized ratio (INR) levels from warfarin anticoagulation reduce the relative risk of stroke by approximately 60%.2 Therefore, time in therapeutic range (TTR) has been widely adopted as a process and quality measure by multiple stakeholders (National Quality Foundation). Nonetheless, variation in INR control between clinical centers and between countries has been observed in epidemiologic studies and clinical trials.3,4 Although this could indicate site-level variation in care structures, given geographic variation in race it could also indicate racial differences in TTR. As such, questions regarding the influence of race on quality of anticoagulation control remain unanswered. We examined the association between race and TTR in a large national cohort with AF. We also assessed whether INR monitoring intensity and warfarin persistence vary by race. Methods The Retrospective Evaluation and Assessment of Therapies in AF cohort is a retrospective cohort of patients with newly diagnosed AF from the Veterans Administration (VHA) health care system.5 The VHA is the largest integrated health system in the United States. We used the following centralized, comprehensive VHA data sets of care: (1) the VHA National Patient Care Database, (2) the www.ajconline.org

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VHA Managerial Cost Accounting national pharmacy data set, (3) the VHA Fee Basis inpatient and outpatient data sets, (4) Medicare inpatient and outpatient institutional claims data (part A, part B, and carrier files), and (5) the VHA Vital Status File. The study was approved by the local institutional review board. Methods regarding cohort construction have been previously described in detail.5,6 Our cohort included patients with newly diagnosed AF (International Classification of Diseases, Ninth Revision 427) associated with an inpatient or outpatient VHA encounter (“index AF diagnosis”). The initial Retrospective Evaluation and Assessment of Therapies in AF cohort included patients with newly diagnosed AF from October 1, 2003 to September 30, 2008 (VHA fiscal years 2004 to 2008) but was subsequently expanded to also include VHA fiscal years 2009 to 2012. Patients were required to have a second confirmatory AF diagnosis within 30 and 365 days of the index AF diagnosis. Patients had to have at least one primary care, cardiology, women’s health, nephrology, geriatric, or anticoagulation clinic VHA outpatient visit in the continental US and receipt of a VHA outpatient prescription within 90 days of the index AF diagnosis. To account for Medicare-eligible veterans who could be receiving part of their anticoagulation care outside of the VHA, we used a previously validated method to link patients with their Medicare inpatient and outpatient claims data for the corresponding time period.7 The primary predictor was race (white, black, Asian, native Hawaiian/Pacific Islander, American-Indian/Alaska Native, or multiracial). VHA medical record race information was augmented with self-reported race data from the VHA Vital Status Master File and the Centers for Medicare and Medicaid Services. We used a previously validated method of calculating race,8 in which race was calculated multiple times, each time relying on different race data to verify high agreement regardless of whether the source data were self-reported, proxy-reported, or observer-reported, as well as whether the source year differed. If multiple races were coded across different claims, we then tabulated all race values and assigned the most frequent value. In the event of a tie, we assigned the most recent value. This “most frequent/most recent” algorithm has also been previously validated at the VHA.9 We retained patients with missing race data to evaluate for informative missingness. Of the 184,161 patients in our study, only 0.8% had race unclassified. The primary dependent variables were first-year and long-term TTR. Secondary dependent variables were (1) first-year and long-term INR monitoring rate (INRMR) and (2) one-year warfarin persistence, defined as the proportion of patients on warfarin at 1 year from their index AF diagnosis. Our approach has been validated10e12 and used previously with VHA data.13 We calculated the TTR using a modification of the Rosendaal method.14 We used INR values from outpatient and inpatient VHA files. We excluded periods of VHA and Medicare inpatient hospitalizations from the days eligible for TTR analyses because warfarin is frequently not administered during hospitalizations and because inpatient warfarin management may not reflect outpatient care

Figure 1. INRMR was defined as the percentage of nonhospitalized, 30-day blocks that an outpatient INR was obtained while on warfarin. To avoid strict calendar-based monitoring, we allowed a 6-days buffer at the beginning and end of each 30-day block, comprising a 42-day window for acceptable INR monitoring by VHA quality metrics. For example, if the INR was monitored on the date indicated by the arrow for June (labeled “INR” in red), and then, it was monitored on the date indicated by the arrow for July (labeled “INR” in green), and then, on the date indicated by the arrow for August (labeled “INR” in blue), then, all these dates would be considered within the acceptable INR monitoring window of 42 days although they do not decrease strictly on a month-to-month basis.

processes or quality. However, we included inpatient INR values on the date of inpatient admission if the patient was on warfarin within the previous 30 days leading up to the hospitalization to increase the sensitivity of identifying supratherapeutic or subtherapeutic INR values because such out-of-range values could have precipitated the admission. Two TTR values were calculated for each patient: (1) for the first 12 months after the index AF diagnosis (first year) and (2) from the index AF diagnosis until warfarin discontinuation or administrative censoring (long term). We calculated these separately because INR lability is often greater in the first year after warfarin prescription compared to long term or steady state care.15 Outpatient and inpatient INR VHA laboratory data, as well as VHA outpatient encounters and events data and outpatient fee basis data, were used to calculate first-year and long-term INRMRs. Guidelines recommend that patients have INR monitoring once every 4 weeks, although there may be more frequent monitoring after initiation of warfarin.16 As in previous reports, we extended the acceptable period of monitoring to a 42-day interval,17 consisting of a 30-day block with 6 additional days on the beginning and end of the block (Figure 1). The rationale is to allow for situations in which adequately monitored patients do not have INR monitoring strictly by calendar month. INR monitoring at a minimum of every 42 days has been recommended as a quality measure for the minimum acceptable monitoring frequency, including in the VHA health care system.18 Although outpatient INR tests provided most data for INR monitoring episodes, we also used inpatient INR tests that were performed on the date of discharge. To avoid potential underestimation of INR monitoring intensity, we also included INR testing claims identified from VHA Fee Basis outpatient claims. We did not count INRs performed on the day of admission because they typically would be performed as part of admission laboratories (e.g., in the emergency department) rather than as part of routine

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Figure 2. Flowchart of patient cohort selection. A total of 703,559 patients were initially screened. After exclusion of patients with AF in previous 4 years, those not seen in clinic, or residing outside the continental US, our final analysis included 184,161 patients.

monitoring. We restricted our measurement of INRMR to include only INRs drawn during periods of warfarin exposure. INR eligibility was determined by evaluating outpatient warfarin dispensation from the VHA Decision Support System (DSS) files. Active treatment with warfarin was determined by filling of warfarin prescriptions. The period of active use is determined on the basis of the prescribed days of supply. A grace period of 30 days between prescriptions was allowed to prevent underestimation of the length of active treatment. Inpatient days were excluded from analysis. Because our cohort was comprised patients newly diagnosed with AF, we excluded patients who had been on warfarin for <90 days for a given fiscal year to obtain a clinically meaningful INRMR. Baseline co-morbidities were identified up to 2 years before the date of first AF diagnosis using National Patient Care Database, fee basis outpatient care, and inpatient

encounter files. We also calculated measurements of stroke risk (CHADS2 score), warfarin bleeding risk (Anticoagulation and Risk Factors in Atrial Fibrillation [ATRIA] score), and co-morbidity burden (Selim and Charlson indexes) as previously described.5 Because distance to care could affect likelihood of warfarin prescription and INR monitoring,10 we calculated the distance (in miles) from the center of the patient’s home zip code to the nearest VHA medical center (to approximate distance to cardiology care) and to the nearest VHA outpatient care facility (to approximate distance to primary care). We also examined the difference between these 2 distances as we have previously shown that this explains variation in the use of primary care versus cardiology specialty care for newly diagnosed AF.5 We compared differences in baseline characteristics between patients of different races using chi-square tests for

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Table 1 Patient baseline characteristics

n[184,161 Age (mean) Gender, female (%) CHADS2 score Charlson comorbidity index Selim comorbidity index ATRIA Bleeding Risk Score Hypertension CHF Diabetes Prior stroke/tia Dialysis Renal failure (eGFR 40), (n) Cardiovascular Medications Warfarin Any Anticoagulant ASA Clopidogrel Any anti-platelet ACE inhibitor or ARB Diuretic Niacin or fibrate Statin Class I antiarrhythmic Class III antiarrhythmic Alpha-blocker Beta-blocker Calcium channel blocker Digoxin Amiodarone Distance (miles) to closest VHA medical center Distance (miles) to closest VHA community clinic D of two distances (miles) Seen by cardiology within 90 days of AF (%)

White

Black

Asian

Native Hawaiian/ Pacific islander

Native American

Multi-Racial

Missing

P-Value

163,601 7110 2,622 (2%) 1.61.2 1.71.6 4.02.6 3.02.2 103,285(63%) 24,263 (15%) 50,562 (31%) 11,943 (7%) 2,326 (1%) 13,943 (9%) (n¼149,850)

15,372 6612 295 (2%) 1.81.3 2.11.7 4.52.7 3.52.4 11,482 (75%) 4,066 (26%) 5,768 (38%) 1,344 (9%) 776 (5%) 1,696 (12%) (n¼14,038)

612 7013 12 (2%) 1.61.2 1.61.5 3.72.4 3.22.5 380 (62%) 107 (17%) 186 (30%) 45 (7%) 27 (4%) 75 (13%) (n¼584)

1,296 7010 29 (2%) 1.71.2 1.81.6 4.12.5 3.02.3 886 (68%) 219 (17%) 488 (38%) 92 (7%) 32 (2%) 145 (12%) (n¼1,197)

762 6710 19 (2%) 1.51.3 1.81.6 4.02.6 2.72.3 444 (58%) 106 (14%) 284 (37%) 63 (8%) 27 (4%) 68 (10%) (n¼696)

886 6911 14 (2%) 1.71.3 1.91.6 4.42.6 3.22.3 620 (70%) 171 (19%) 316 (36%) 82 (9%) 28 (3%) 88 (10%) (n¼825)

1,632 5911 45 (3%) 0.91.0 1.01.2 2.52.0 708 (43%) 137 (8%) 348 (21%) 50 (3%) 11 (1%) -

<0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001

101,817 (62%) 102,742(63%) 40.901 (25%) 16,845 (10%) 53,271 (33%) 98,348 (60%) 87,236 (53%) 13,589 (8%) 100,309(61%) 3,288 (2%) 5,990 (4%) 3,851 (2%) 110,679 (68%) 59,515 (36%) 34,127 (21%) 15,184 (9%) 40.544.2

10,291 (67%) 10.376 (67%) 6,719 (44%) 1,139 (7%) 7,349 (48%) 10,451 (68%) 10,139 (66%) 674 (4%) 8,946 (58%) 193 (1%) 346 (2%) 943 (6%) 11,311 (74%) 7,146 (46%) 3.039 (20%) 1,550 (10%) 23.227.9

368 (60%) 372 (61%) 238 (39%) 46 (8%) 261 (43%) 436 (59%) 306 (50%) 40 (7%) 375 (61%) 15 (2%) 14 (2%) 20 (3%) 409 (67%) 234 (38%) 111 (18%) 56 (9%) 20.528.8

836 (65%) 843 (65%) 379 (29%) 133 (10%) 472 (36%) 800 (61%) 734 (57%) 82 (6%) 814 (63%) 25 (2%) 34 (3%) 37 (3%) 907 (70%) 475 (37%) 264 (20%) 125 (10%) 39.442.8

465 (61%) 467 (61%) 234 (31%) 76 (10%) 288 (38%) 417 (60%) 406 (53%) 61 (8%) 438 (57%) 12 (2%) 27 (4%) 29 (4%) 512 (67%) 242 (32%) 167 (22%) 67 (9%) 49.548.6

572 (65%) 578 (65%) 294 (33%) 85 (10%) 355 (40%) 481 (63%) 523 (59%) 83 (9%) 541 (61%) 20 (2%) 28 (3%) 35 (4%) 621 (70%) 364 (41%) 176 (20%) 90 (10%) 35.858.1

882 (54%) 902 (55%) 408 (25%) 73 (4%) 454 (28%) 1,160 (53%) 693 (42%) 120 (7%) 784 (48%) 55 (3%) 44 (3%) 31 (2%) 1,104 (68%) 503 (31%) 322 (20%) 137 (8%) 47.952.0

<0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 0.0209 0.0351 <0.0001

13.512.9

8.59.2

7.27.6

11.411.3

15.415.4

12.412.5

12.512.8

<0.0001

27.041.4 61,082 (37%)

14.725.2 6,736 (44%)

13.326.2 287 (47%)

28.140.7 495 (38%)

34.144.1 278 (36%)

23.456.4 363 (41%)

35.450.0 660 (40%)

<0.0001 <0.0001

ASA ¼ acetylsalicylic acid (aspirin); ATRIA bleeding risk score ¼ anticoagulation and risk factors in atrial fibrillation bleeding risk score based on risk factors of anemia, severe renal disease, age 75 years, previous hemorrhage, and diagnosed hypertension; CHADS2 ¼ stroke risk score based on risk factors of congestive heart failure, hypertension, age 75 years, diabetes mellitus, previous stroke or Transient Ischemic Attack; CHF ¼ congestive heart failure; eGFR ¼ estimated glomerular filtration rate in mL/min/1.73 m2.

categorical variables and t tests for continuous variables. For the primary outcome of TTR, we kept the outcome variable as continuous because the relation between TTR and stroke or hemorrhage would be expected to be continuous. Additionally, although 60% TTR has been demonstrated to correlate with improved efficacy in stroke reduction,2 there is still no consensus on what threshold constitutes an “acceptable” TTR from quality, effectiveness, and safety standpoints. We determined the univariate effect of each variable on TTR separately and then completed a fully adjusted multivariate model. We used linear regression for our adjusted models using a generalized linear and latent mixed regression model adjusting for patient co-morbidities, INRMR, VHA parent station, and differential distance to quantify the

independent effect of race on the conditional mean of TTR. We performed all analyses with SAS, version 9.3, (SAS Institute Inc; Cary, North Carolina) and STATA, version 11.0, (StataCorp; College Station, Texas). Results Of 523,675 patients identified with AF in FY04-FY12, 184,161 had newly diagnosed AF and met inclusion criteria (Figure 2). Analysis of baseline characteristics (Table 1) demonstrated that black patients had higher CHADS2 risk scores, ATRIA bleeding risk scores, Charlson, and Selim co-morbidity scores compared to all other races (p <0.0001). In particular, congestive heart failure, hypertension, and diabetes were significantly higher among blacks (p <0.0001).

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Table 2 Quality of anticoagulation by race (unadjusted, n ¼ 116,021)

First Year INRMR eligible days INRMR (%) TTR eligible days TTR (%) % with TTR > 55% Long Term INRMR eligible days INRMR (%) TTR eligible days TTR (%) % with TTR > 55% Warfarin Persistence (% on warfarin at one year from index AF diagnosis)

White

Black

Asian

Native Hawaiian/ Pacific islander

Native American

Multi-Racial

Missing

P-Value

28180 8923 (n¼99,222) 25383 5721 (n¼78,966) 43,858 (56%)

27481 9217 (n¼9,793) 24883 4923 (n¼8,083) 3,425 (42%)

27283 9316 (n¼358) 24486 5521 (n¼311) 164 (53%)

28279 9021 (n¼816) 25781 5622 (n¼670) 371 (55%)

27382 8824 (n¼454) 25083 5522 (n¼356) 188 (53%)

28078 9119 (n¼555) 25680 5522 (n¼463) 238 (51%)

27481 9120 (n¼860) 24882 5622 (n¼706) 377 (53%)

<0.0001 <0.0001

940748 8822 (n¼102,137) 829652 5918 (n¼90,083) 57,791 (64%) 60%

869704 9116 (n¼10,115) 772606 5220 (n¼9,134) 4,463 (49%) 58%

925741 9116 (n¼368) 806663 5918 (n¼341) 217 (64%) 56%

982761 9020 (n¼834) 880658 5919 (n¼743) 464 (62%) 62%

788632 8722 (n¼472) 685548 5720 (n¼411) 234 (57%) 58%

943747 9018 (n¼570) 850656 5818 (n¼513) 302 (59%) 62%

736635 9118 (n¼879) 640532 5820 (n¼779) 472 (61%) 55%

<0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 <0.001

Figure 3. Independent effect of race on mean first-year and long-term TTR (relative to whites). These data express the independent effect of race on mean TTR, using the white subjects for comparison. The horizontal line reflects the mean TTR seen in white subjects. The plots for each race category demonstrate the mean and 95% confidence interval for first-year and long-term TTR, controlling for age, gender, co-morbidities, medications, distance to medical care, site of medical and INRMR. Black race was associated with a significantly lower first-year and long-term TTR after multivariate adjustment. Am ¼ American; Pac Isl ¼ Pacific Islander.

In this cohort, 62.2% of patients received warfarin (Table 1). Compared to whites, blacks had a significantly higher rate of VHA warfarin, aspirin, diuretic, angiotensin blocker, calcium channel blocker, and amiodarone use

(p <0.0001). The mean distance to the clinic and medical center was substantially closer for Asians and blacks compared to other groups, reflecting that Asians and blacks were more likely live in urban areas. After Asians, blacks also had

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the highest percentage of use of VHA cardiology care within 90 days of AF diagnosis. First-year and long-term fraction of TTR for black patients were lower than for all other racial groups (Table 2, p <0.0001), although the long-term differences were numerically smaller. Blacks also had the lowest proportion of patients with TTR >55% compared to all other races (p <0.0001). After multivariate adjustment for site and patient covariates, mean first-year and long-term TTR for blacks were 4.2 points and 4.1 points lower, respectively, below the conditional mean of TTR relative to whites (p <0.0001 for both). Compared to whites, the adjusted conditional mean first-year TTR was 2.2 points lower in Asians, 0.4 points lower in native Hawaiian/Pacific Islanders, and 0.05 points lower in native Americans (p <0.0001 for all, Figure 3). During this period, first-year and long-term INRMRs were consistently high across races, with 87% to 93% of the days eligible for outpatient INR monitoring covered with INR testing. One-year warfarin persistence was similar across races. Although our p value suggests statistically significant differences, given the relatively small absolute differences, the clinical significance of this is minimal (Table 2). Discussion The principal finding of this study is that despite similar INR monitoring intensity among warfarin-treated patients with AF, anticoagulation control was substantially lower in blacks than for all other racial groups and was highest in whites, even after accounting for clinical, sociodemographic, and site-level factors. Moreover, blacks lived closer to medical care than other racial groups and had greater utilization of cardiology care after new diagnosis of AF. There are several potential explanations for these observed differences. First, there may be pharmacogenetic differences in warfarin metabolism and dose response by race. Warfarin dose requirements have previously been demonstrated to be higher for blacks and lower for Asians and whites,19 due in part to racial differences in genotype frequencies.20 However, the Clarification of Optimal Anticoagulation through Genetics trial recently showed that genotype-guided dosing of warfarin did not improve anticoagulation control compared to a clinically guided strategy alone. Among black patients, the mean TTR was actually less in the genotype-guided group than the clinically guided group (p ¼ 0.0003).21 Factors downstream of INR testing may further explain racial differences, including patient, provider, and systembased complexities. Previous studies have shown that blacks have a lower awareness of their diagnosis of AF compared to other races.22 Geographic differences, which may be a proxy for cultural differences, could be driving differences even more so than race itself.23 A VHA analysis of older data (2006 to 2008) using a subset of sites prescribing warfarin across all indications (not only AF) found that predictors of poor TTR included hospitalizations, pill burden, alcohol abuse, and bipolar disorder.24 These and other nonmodifiable factors (e.g., age, gender, region, socioeconomic status, co-morbidities) accounted for 69% of the variation between blacks and whites.25 Therefore, in

context, our findings indicate that the comparable quality of INR monitoring across races is not an adequate proxy for expected quality of anticoagulation (TTR). Rather, there is a need for more nuanced warfarin management and patient engagement in vulnerable patients and populations. The finding of shorter distance to VHA clinics and hospitals in blacks and Asians is also noteworthy. This may reflect more urban living, which could be confounded by poorer socioeconomic status. However, Asians had similar short distances from their homes to the nearest medical center and this did not necessarily translate to low TTRs. Furthermore, a greater proportion of blacks were evaluated in cardiology clinics after AF diagnosis. Novel oral anticoagulants (NOACs) may have a role in patients with poor TTR or even centers with poor average TTRs because INR monitoring and redosing is not required. The SAMe-TT2R2 score, which incorporates race, has been shown to be predictive for an increased risk of stroke, bleeding, and death in patients with AF taking vitamin K antagonists. This kind of tool may aid providers in identifying patients who may do better on NOACs.26 Comparison of data from the Rivaroxaban Once Daily Oral Direct Factor Xa Inhibition Compared with Vitamin K Antagonism for Prevention of Stroke and Embolism Trial in Atrial Fibrillation (ROCKET AF) trial show that the treatment effect of rivaroxaban compared with warfarin for the prevention of stroke and systemic embolism is consistent regardless of center TTR.27 In the Randomised Evaluation of Long-term Anticoagulation Therapy (RE-LY) trial, advantages of dabigatran compared to warfarin were greater at sites with poor INR control in terms of all vascular events, nonhemorrhagic events, and mortality.4 Data from the Apixaban for Reduction in Stroke and Other Thromboembolic Events in Atrial Fibrillation (ARISTOTLE) trial showed that rates of stroke or systemic embolism, major bleeding, and mortality were consistently lower with apixaban than with warfarin across center average TTR and individual TTR quartiles.28 However, a recent study of Veterans Affairs patients with AF showed that white patients were more likely to be switched from warfarin to dabigatran compared to other races,29 suggesting that the patients who may be most likely to benefit from NOACs may also be the least likely to receive them. Further studies are required to determine the potential benefit of NOACs in such patients; if poor adherence is still a problem in this population, then simply replacing vitamin K antagonists with NOACs may not be the solution. Our study has a number of limitations. Although we adjusted for a wide range of clinical, site, and geographic covariates, unidentified confounders such as socioeconomic differences could affect results. Second, although we identified INR testing in VHA and Medicare records, we were unable to identify care in other health care systems, which could affect results if there is differential dual or split care utilization by race in the private sector. However, this is unlikely because our patient cohort and censoring criteria were conditioned on receipt of warfarin and anticoagulation services in the VHA. Furthermore, patients with VHA and Medicare benefits tend to use VHA services for prescription fills because copayments are lower in the VHA than in

Arrhythmias and Conduction Disturbances/Race and Quality of Anticoagulation

Medicare. In an exploratory analysis, we found that <0.3% of patients with AF prescribed warfarin in the VHA had claims of warfarin management in Medicare during the same period. Therefore, because the VHA manages its own anticoagulation clinic network, we have further evidence to conclude that differential dual or split care utilization is unlikely. Disclosures Dr. Turakhia is supported by a Veterans Health Services Research and Development Career Development Award (CDA09027-1), an American Heart Association National Scientist Development Grant (09SDG2250647), and a VHA Health Services and Development MERIT Award (IIR 09-092). Dr. Yong was supported by an ACCF/Merck Cardiovascular Research Fellowship. The other authors have no conflicts of interest to disclose. 1. Go AS, Hylek EM, Phillips KA, Chang Y, Henault LE, Selby JV, Singer DE. Heart disease and stroke statisticse2014 update: a report from the American Heart Association. Circulation 2014;129: e28ee292. 2. Hart RG, Pearce LA, Aguilar MI. Meta-analysis: antithrombotic therapy to prevent stroke in patients who have nonvalvular atrial fibrillation. Ann Intern Med 2007;146:857e867. 3. Connolly SJ, Pogue J, Eikelboom J, Flaker G, Commerford P, Franzosi MG, Healey JS, Yusuf S. Benefit of oral anticoagulant over antiplatelet therapy in atrial fibrillation depends on the quality of international normalized ratio control achieved by centers and countries as measured by time in therapeutic range. Circulation 2008;118: 2029e2037. 4. Wallentin L, Yusuf S, Ezekowitz MD, Alings M, Flather M, Franzosi MG, Pais P, Dans A, Eikelboom J, Oldgren J, Pogue J, Reilly PA, Yang S, Connolly SJ; RE-LY investigators. Efficacy and safety of dabigatran compared with warfarin at different levels of international normalized ratio control for stroke prevention in atrial fibrillation: an analysis of the RE-LY trial. Lancet 2010;376:975e983. 5. Turakhia MP, Hoang DD, Xu X, Frayne S, Schmitt S, Yang F, Phibbs CS, Than CT, Wang PJ, Heidenreich PA. Differences and trends in stroke prevention anticoagulation in primary care vs cardiology specialty management of new atrial fibrillation: the Retrospective Evaluation and Assessment of Therapies in AF (TREAT-AF) study. Am Heart J 2013;165:93e101. 6. Turakhia MP, Santangeli P, Winkelmayer WC, Xu X, Ullal AJ, Than CT, Schmitt S, Holmes TH, Frayne SM, Phibbs CS, Yang F, Hoang DD, Ho PM, Heidenreich PA. Increased mortality associated with digoxin in contemporary patients with atrial fibrillation: findings from the TREAT-AF study. J Am Coll Cardiol 2014;64:660e668. 7. Hynes DM, Koelling K, Stroupe K, Arnold N, Mallin K, Sohn MW, Weaver FM, Manheim L, Kok L. Veterans’ access to and use of Medicare and Veterans Affairs health care. Med Care 2007;45: 214e223. 8. Mor M. Assessing Race and Ethnicity. 2013 VIReC Database and Methods Cyber Seminar Series. VA Information Resource Center, 2013. 9. Halanych JH, Wang F, Miller DR, Pogach LM, Lin H, Berlowitz DR, Frayne SM. Racial/ethnic differences in diabetes care for older veterans: accounting for dual health system use changes conclusions. Med Care 2006;44:439e445. 10. Hess LM, Raebel MA, Conner DA, Malone DC. Measurement of persistence in pharmacy administrative databases: a proposal for standard definitions and preferred measures. Ann Pharmacother 2006;40:1280e1288. 11. Andrade SE, Kahler KH, Frech F, Chan KA. Methods for evaluation of medication persistence and persistence using automated databases. Pharmacoepidemiol Drug Saf 2006;15:565e574; Discussion 575e567. 12. Steiner JF, Koepsell TD, Fihn SD, Inui TS. A general method of compliance assessment using centralized pharmacy records. Description and validation. Med Care 1988;26:814e823.

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13. Watanabe JH, Bounthavong M, Chen T. Revisiting the medication possession ratio threshold for persistence in lipid management. Curr Med Res Opin 2013;29:175e180. 14. Rosendaal FR, Cannegieter SC, van der Meer FJ, Briet E. A method to determine the optimal intensity of oral anticoagulant therapy. Thromb Haemost 1993;69:236e239. 15. Fang MC, Go AS, Chang Y, Borowsky LH, Pomernacki NK, Udaltsova N, Singer DE. Warfarin discontinuation after starting warfarin for atrial fibrillation. Circ Cardiovasc Qual Outcomes 2010;3: 624e631. 16. Estes NA 3rd, Halperin JL, Calkins H, Ezekowitz MD, Gitman P, Go AS, McNamara RL, Messer JV, Ritchie JL, Romeo SJ, Waldo AL, Wyse DG, Bonow RO, DeLong E, Goff DC Jr, Grady K, Green LA, Hiniker A, Linderbaum JA, Masoudi FA, Pina IL, Pressler S, Radford MJ, Rumsfeld JS. ACC/AHA/Physician Consortium 2008 clinical performance measures for adults with nonvalvular atrial fibrillation or atrial flutter: a report of the American College of Cardiology/American Heart Association Task Force on performance measures and the Physician Consortium for performance improvement (writing committee to develop clinical performance measures for atrial fibrillation) developed in collaboration with the Heart Rhythm Society. J Am Coll Cardiol 2008;51:865e884. 17. Go AS, Hylek EM, Chang Y, Phillips KA, Henault LE, Capra AM, Jensvold NG, Selby JV, Singer DE. Anticoagulation therapy for stroke prevention in atrial fibrillation: how well do randomized trials translate into clinical practice? JAMA 2003;290:2685e2692. 18. US Department of Veterans Affairs. VHA Directive 2009-003. Anticoagulation Therapy Management 2009. 19. Limdi NA, Wadelius M, Cavallari L, Eriksson N, Crawford DC, Lee MT, Chen CH, Motsinger-Reif A, Sagreiya H, Liu N, Wu AH, Gage BF, Jorgensen A, Pirmohamed M, Shin JG, Suarez-Kurtz G, Kimmel SE, Johnson JA, Klein TE, Wagner MJ. Warfarin pharmacogenetics: a single VKORC1 polymorphism is predictive of dose across 3 racial groups. Blood 2010;115:3827e3834. 20. Perera MA, Gamazon E, Cavallari LH, Patel SR, Poindexter S, Kittles RA, Nicolae D, Cox NJ. The missing association: sequencingbased discovery of novel SNPs in VKORC1 and CYP2C9 that affect warfarin dose in African Americans. Clin Pharmacol Ther 2011;89: 408e415. 21. Kimmel SE, French B, Kasner SE, Johnson JA, Anderson JL, Gage BF, Rosenberg YD, Eby CS, Madigan RA, McBane RB, Abdel-Rahman SZ, Stevens SM, Yale S, Mohler ER 3rd, Fang MC, Shah V, Horenstein RB, Limdi NA, Muldowney JA 3rd, Gujral J, Delafontaine P, Desnick RJ, Ortel TL, Billett HH, Pendleton RC, Geller NL, Halperin JL, Goldhaber SZ, Caldwell MD, Califf RM, Ellenberg JH; COAG Investigators. A pharmacogenetic versus a clinical algorithm for warfarin dosing. N Engl J Med 2013;369: 2283e2293. 22. Srivastava A, Hudson M, Hamoud I, Cavalcante J, Pai C, Kaatz S. Examining warfarin underutilization rates in patients with atrial fibrillation: detailed chart review essential to capture contraindications to warfarin therapy. Thromb J 2008;6:6. 23. Meschia JF, Merrill P, Soliman EZ, Howard VJ, Barrett KM, Zakai NA, Kleindorfer D, Safford M, Howard G. Racial disparities in awareness and treatment of atrial fibrillation: the REasons for Geographic and Racial Differences in Stroke (REGARDS) study. Stroke 2010;41:581e587. 24. Rose AJ, Hylek EM, Ozonoff A, Ash AS, Reisman JI, Berlowitz DR. Patient characteristics associated with oral anticoagulation control: results of the Veterans AffaiRs Study to Improve Anticoagulation (VARIA). J Thromb Haemost 2010;8:2182e2191. 25. Rao SR, Reisman JI, Kressin NR, Berlowitz DR, Ash AS, Ozonoff A, Miller DR, Hylek EM, Zhao S, Rose AJ. Explaining racial disparities in anticoagulation control: results from a study of patients at the Veterans Administration. Am J Med Qual 2015;30:214e222. 26. Apostolakis S, Sullivan RM, Olshansky B, Lip GY. Factors affecting quality of anticoagulation control among patients with atrial fibrillation on warfarin: the SAMe-TT₂R₂ score. Chest 2013;144:1555e1563. 27. Piccini JP, Hellkamp AS, Lokhnygina Y, Patel MR, Harrell FE, Singer DE, Becker RC, Breithardt G, Halperin JL, Hankey GJ, Berkowitz SD, Nessel CC, Mahaffey KW, Fox KA, Califf RM; ROCKET AF Investigators. Relationship between time in therapeutic range and comparative treatment effect of rivaroxaban and warfarin: results form the ROCKET AF trial. J Am Heart Assoc 2014;3:e000521.

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28. Wallentin L, Lopes RD, Hanna M, Thomas L, Hellkamp A, Nepal S, Hylek EM, Al-Khatib SM, Alexander JH, Alings M, Amerena J, Ansell J, Aylward P, Bartunek J, Commerford P, De Caterina R, Erol C, Harjola VP, Held C, Horowitz JD, Huber K, Husted S, Keltai M, Lanas F, Lisheng L, McMurray JJ, Oh BH, Rosenqvist M, Ruzyllo W, Steg PG, Vinereanu D, Xavier D, Granger CB. Apixaban for Reduction in Stroke and Other Thromboembolic Events in Atrial

Fibrillation (ARISTOTLE) Investigators. Efficacy and safety of apixaban compared with warfarin at different levels of predicted internatinoal normalized ratio control for stroke prevention in atrial fibrillation. Circulation 2013;127:2166e2176. 29. Vaughan MS, Jones M, Mazur A, Chrischilles E, Cram P. Bleeding rates in Veterans Affairs patients with atrial fibrillation who switch from warfarin to dabigatran. Am J Med 2014;127:1179e1185.