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Comparative clinical outcomes between direct oral anticoagulants and warfarin among elderly patients with non-valvular atrial fibrillation in the CMS medicare population

  • Alpesh AminEmail author
  • Allison Keshishian
  • Oluwaseyi Dina
  • Amol Dhamane
  • Anagha Nadkarni
  • Eric Carda
  • Cristina Russ
  • Lisa Rosenblatt
  • Jack Mardekian
  • Huseyin Yuce
  • Christine L. Baker
Open Access
Article
  • 545 Downloads

Abstract

Atrial fibrillation (AF) prevalence increases with age; > 80% of US adults with AF are aged ≥ 65 years. Compare the risk of stroke/systemic embolism (SE), major bleeding (MB), net clinical outcome (NCO), and major adverse cardiac events (MACE) among elderly non-valvular AF (NVAF) Medicare patients prescribed direct oral anticoagulants (DOACs) vs warfarin. NVAF patients aged ≥ 65 years who initiated DOACs (apixaban, dabigatran, and rivaroxaban) or warfarin were selected from 01JAN2013-31DEC2015 in CMS Medicare data. Propensity score matching was used to balance DOAC and warfarin cohorts. Cox proportional hazards models estimated the risk of stroke/SE, MB, NCO, and MACE. 37,525 apixaban–warfarin, 18,131 dabigatran–warfarin, and 55,359 rivaroxaban–warfarin pairs were included. Compared to warfarin, apixaban (HR: 0.69; 95% CI 0.59–0.81) and rivaroxaban (HR: 0.82; 95% CI 0.73–0.91) had lower risk of stroke/SE, and dabigatran (HR: 0.88; 95% CI 0.72–1.07) had similar risk of stroke/SE. Apixaban (MB: HR: 0.61; 95% CI 0.57–0.67; NCO: HR: 0.64; 95% CI 0.60–0.69) and dabigatran (MB: HR: 0.79; 95% CI 0.71–0.89; NCO: HR: 0.84; 95% CI 0.76–0.93) had lower risk of MB and NCO, and rivaroxaban had higher risk of MB (HR: 1.08; 95% CI 1.02–1.14) and similar risk of NCO (HR: 1.04; 95% CI 0.99–1.09). Compared to warfarin, apixaban had a lower risk for stroke/SE, MB, and NCO; dabigatran had a lower risk of MB and NCO; and rivaroxaban had a lower risk of stroke/SE but higher risk of MB. All DOACs had lower risk of MACE compared to warfarin.

Keywords

Apixaban Dabigatran Rivaroxaban Warfarin Non-valvular atrial fibrillation Medicare 

Highlights

  • The prevalence of NVAF and risk of stroke increase with age.

  • Few studies have compared DOACs to warfarin among elderly NVAF patients regarding such outcomes.

  • This study showed that compared to warfarin, all DOACs were associated with lower risk of MACE, and there were varying rates of stroke/SE, MB, and NCO between the individual DOACs and warfarin.

  • The findings warrant more studies to better understand effectiveness and safety profiles in the elderly NVAF population.

Introduction

The 2010 Global Burden of Disease Study estimated the worldwide age-adjusted prevalence of atrial fibrillation (AF) at 596 per 100,000 men and 373 per 100,000 women, equating to 33.5 million individuals (20.9 and 12.6 million men and women, respectively) [1]. In the United States, the estimated prevalence of AF is 3–5 million [2, 3]. The proportion of AF patients was found to increase sharply with age, especially in people aged ≥ 65 years, who account for three-quarters of the AF population [3].

Patients with AF diagnoses are at a nearly fivefold greater risk of stroke [4]. Moreover, the AF-attributable risk for ischemic stroke is age-dependent and increases from 4.6 to 7.9% to > 10% among patients aged 50–59, 60–69, and ≥ 70 years, respectively [4]. Hence, the stroke risk stratification schema CHA2DS2-VASc score considers older age (65–74 and ≥ 75 years) as a risk factor for stroke and thromboembolism in AF patients [5].

Oral anticoagulants (OACs) prevent stroke and systemic embolism (SE) among AF patients; they are recommended by the American College of Cardiology (ACC) and the American Heart Association (AHA) guidelines for patients with non-valvular AF (NVAF) and prior stroke, transient ischemic attack (TIA), or a CHA2DS2-VASc score ≥ 2 [6]. Warfarin, a vitamin K antagonist (VKA), has been used for stroke prevention among AF patients for decades. However, the narrow therapeutic window and increased risk of bleeding have hindered use, especially among the elderly [6].

In recent years, randomized clinical trials have demonstrated that compared to warfarin, direct OACs (DOACs)—including apixaban, dabigatran, edoxaban, and rivaroxaban—were all associated with similar to lower risk of stroke/SE and major bleeding (MB) among elderly patients [7, 8, 9]. Introduced in 2008, the Fit-fOR-The-Aged (FORTA) classification is the first system with both negative (harmful or critical drugs: D and C labels) and positive (beneficial drugs: A and B labels) labelling at the individual drug and drug group levels. Based on FORTA and the Delphi process, warfarin, dabigatran, edoxaban, and rivaroxaban were labelled B (beneficial; safely and effectively treat AF), and apixaban was labeled A (absolutely; most beneficial risk–benefit ratio) for the treatment of AF patients aged > 65 years [10].

Using the largest US claims database of elderly patients, we evaluated real-world comparative risks of stroke/SE, MB, net clinical outcomes (stroke/SE or MB [NCO]), and major adverse cardiac events (MACE) among NVAF patients who initiated either DOACs (apixaban, dabigatran, and rivaroxaban) or warfarin. This study added more recent data and additional outcome measures to our previous study, which provides comprehensive and current evidence to help prevent stroke among the elderly NVAF population [11]. The results also supplement clinical trials and add key information to real-world literature.

Methods

Data source

This retrospective observational study used the fee-for-service (FFS) US Centers for Medicare & Medicaid Services (CMS) data from 01JAN2012-31DEC2015. This dataset is composed of adults aged ≥ 65 years, certain young people with disabilities, and people with end-stage renal disease. As of 2015, > 38 million beneficiaries were enrolled in this insurance [12]. The data include institutional (inpatient, skilled nursing facility, home health, hospice, and hospital outpatient) and non-institutional (physician/supplier–carrier and durable medical equipment) claims and Part D prescription claims, coded using International Classification of Diseases, Ninth/Tenth Revision, Clinical Modification (ICD-9/10-CM) diagnosis and procedure codes, the Health Care Common Procedure Coding System, Current Procedural Terminology codes, and National Drug Codes [13].

Patient selection

AF (ICD-9-CM: 427.31 or ICD-10-CM: I48.0-I48.2, I48.91) patients aged ≥ 65 years with ≥ 1 pharmacy claim for apixaban, dabigatran, edoxaban, rivaroxaban, or warfarin between 01JAN2013-31DEC2015 (identification period) were selected. The first DOAC claim date during the identification period was designated as the index date for patients with any DOAC claim; the first warfarin prescription date was designated as the index date for those without a DOAC claim [14]. Patients were also required to have continuous health plan enrollment with both medical and pharmacy benefits for the 12-month pre-index (baseline) period.

To select OAC treatment-naïve patients, those with any OAC claim during the baseline period were excluded. Patients with evidence of valvular heart disease or transient AF during the baseline period were also excluded. To omit OAC use for the treatment or prophylaxis of venous thromboembolism (VTE), patients with VTE in the baseline period or who had hip or knee replacement surgery within 6 weeks prior to the index date were excluded. Detailed selection criteria appear in Fig. 1.

Fig. 1

Patient selection criteria

Outcome measures

The primary outcomes were the occurrence of stroke/SE and MB, identified by hospitalizations with stroke/SE or MB as the principal diagnosis. Stroke/SE was further categorized by ischemic stroke, hemorrhagic stroke, and SE; MB was categorized by gastrointestinal (GI) bleeding, intracranial hemorrhage (ICH), and MB at other key sites [15, 16].

The secondary outcomes were NCO (a composite of stroke/SE and MB) and MACE, comprised of stroke (hemorrhagic and ischemic stroke), myocardial infarction (MI), and all-cause death. Claims databases cannot evaluate cardiovascular-related death, so the MACE definition included all-cause death.

Patients were censored at the earliest of the discontinuation date of the index treatment (no evidence of a prescription for 30 days from the last day of the index medication days’ of supply), date of switch from the index drug to another OAC (a prescription for an OAC other than the index drug within 30 days before or after the discontinuation date), date of death, end of continuous enrollment, or end of study.

Statistical methods

One-to-one propensity score matching (PSM) was conducted between DOACs and warfarin (apixaban versus warfarin, dabigatran versus warfarin, and rivaroxaban versus warfarin) to control for potential confounders such as baseline demographics and clinical characteristics.

Using established methodology, propensity scores were generated by logistic regression. Age, sex, US geographic region, Charlson comorbidity index (CCI) [17], CHA2DS2-VASc, and HAS-BLED scores, prior bleeding and stroke, comorbidities, baseline co-medications, and baseline inpatient visits were included in the models as covariates. The nearest neighbor without replacement method and a caliper of 0.01 were implemented in the PSM [18]. After PSM, the balance of covariates was checked based on standardized differences, with a threshold of 10% [19].

For post-PSM cohorts, the incidence of primary and secondary outcomes was calculated as the number of events per 100 person-years.

Cox proportional hazards models with robust sandwich estimates were used to evaluate the hazard ratios (HRs) of stroke/SE, MB, NCO, and MACE in each matched cohort [18]. After ensuring all the matched baseline covariates were balanced post-PSM, OAC treatment was included in the Cox models as the only independent variable.

Sensitivity analysis was conducted wherein patients were censored at 6 months of follow-up, creating more balance between cohorts.

Statistical analyses were performed using the Statistical Analysis System (SAS) Version 9.3 (Cary, NC).

Results

The study included eligible 198,171 patients; 81,410 (41.1%) were prescribed warfarin, 38,466 (19.4%) apixaban, 18,162 (9.2%) dabigatran, and 60,133 (30.3%) rivaroxaban (Fig. 1). Edoxaban was excluded due to small sample size (N = 150). Before PSM, patients who initiated warfarin were older with a mean age of 79 years, followed by those who initiated apixaban (78 years), rivaroxaban (78 years), and dabigatran (77 years). In addition, warfarin patients also had higher CCI and CHA2DS2-VASc scores than DOAC patients (Table 1).

Table 1

Baseline descriptive table before PSM

 

Warfarin (N = 81,410)

Apixaban (N = 38,466)

Dabigatran (N = 18,162)

Rivaroxaban (N = 60,133)

N/mean

%/SD

N/mean

%/SD

STDa

N/mean

%/SD

STDa

N/mean

%/SD

STDa

Age

78.9

7.5

78.3

7.5

6.9

77.0

7.0

25.0

77.6

7.3

16.8

65–74

26,091

32.0%

13,627

35.4%

7.1

7479

41.2%

19.0

23,255

38.7%

13.9

75–84

35,012

43.0%

15,916

41.4%

3.3

7607

41.9%

2.3

25,119

41.8%

2.5

≥ 85

20,307

24.9%

8923

23.2%

4.1

3076

16.9%

19.8

11,759

19.6%

13.0

Sex

Male

41,002

50.4%

18,581

48.3%

4.1

9338

51.4%

2.1

29,894

49.7%

1.3

Female

40,408

49.6%

19,885

51.7%

4.1

8824

48.6%

2.1

30,239

50.3%

1.3

Race

White

73,714

90.5%

35,311

91.8%

4.4

16,309

89.8%

2.5

54,642

90.9%

1.1

Black

4246

5.2%

1432

3.7%

7.2

785

4.3%

4.2

2336

3.9%

6.4

Hispanic

1037

1.3%

417

1.1%

1.8

290

1.6%

2.7

931

1.5%

2.3

Other

2413

3.0%

1306

3.4%

2.5

778

4.3%

7.1

2224

3.7%

4.1

Geographic region

Northeast

16,018

19.7%

6514

16.9%

7.1

3606

19.9%

0.4

10,596

17.6%

5.3

North Central

25,076

30.8%

7911

20.6%

23.6

4184

23.0%

17.6

13,341

22.2%

19.6

South

26,486

32.5%

17,229

44.8%

25.4

6953

38.3%

12.0

25,007

41.6%

18.8

West

13,745

16.9%

6791

17.7%

2.0

3387

18.6%

4.6

11,080

18.4%

4.0

Other

85

0.1%

21

0.1%

1.8

32

0.2%

1.9

109

0.2%

2.0

Medicaid dual-eligibility

18,908

23.2%

7488

19.5%

9.2

4268

23.5%

0.6

13,100

21.8%

3.5

Part D low-income subsidy

21,374

26.3%

8560

22.3%

9.3

4814

26.51%

0.6

14,734

24.5%

4.0

Baseline comorbidity

Deyo-Charlson comorbidity index

3.1

2.8

2.9

2.6

9.4

2.5

2.4

24.7

2.7

2.5

17.9

CHADS2 score

2.9

1.4

2.8

1.5

6.9

2.6

1.4

19.1

2.7

1.4

15.3

CHA2DS2-VASc score

4.7

1.7

4.6

1.8

5.5

4.4

1.7

20.2

4.5

1.7

14.3

HAS-BLED scoreb

3.3

1.3

3.4

1.3

4.3

3.2

1.2

14.1

3.3

1.2

5.2

Baseline prior bleed

24,780

30.4%

11,807

30.7%

0.6

4731

26.0%

9.8

17,374

28.9%

3.4

Baseline prior stroke

12,496

15.3%

5280

13.7%

4.6

2159

11.9%

10.1

7385

12.3%

8.9

Congestive heart failure

29,326

36.0%

12,064

31.4%

9.9

5118

28.2%

16.9

17,287

28.7%

15.6

Diabetes

32,705

40.2%

13,602

35.4%

9.9

6737

37.1%

6.3

21,456

35.7%

9.3

Hypertension

71,416

87.7%

34,649

90.1%

7.5

15,964

87.9%

0.5

53,191

88.5%

2.3

Renal disease

21,021

25.8%

8599

22.4%

8.1

2892

15.9%

24.5

10,465

17.4%

20.6

Myocardial infarction

12,024

14.8%

5040

13.1%

4.8

1940

10.7%

12.3

7224

12.0%

8.1

Dyspepsia or stomach discomfort

17,317

21.3%

8699

22.6%

3.2

3607

19.9%

3.5

13,060

21.7%

1.1

Peripheral vascular disease

46,697

57.4%

22,742

59.1%

3.6

9689

53.3%

8.1

33,670

56.0%

2.8

Peripheral artery disease

20,131

24.7%

8932

23.2%

3.5

3635

20.0%

11.3

13,237

22.0%

6.4

Transient ischemic attack

6411

7.9%

3528

9.2%

4.6

1342

7.4%

1.8

4751

7.9%

0.1

Coronary artery disease

40,079

49.2%

19,962

51.9%

5.3

8367

46.1%

6.3

29,066

48.3%

1.8

Baseline medication use

Angiotensin converting enzyme inhibitor

30,102

37.0%

13,194

34.3%

5.6

6875

37.9%

1.8

21,463

35.7%

2.7

Amiodarone

5612

6.9%

4300

11.2%

15.0

1636

9.0%

7.8

5308

8.8%

7.2

Angiotensin receptor blocker

17,030

20.9%

10,056

26.1%

12.3

4498

24.8%

9.2

15,149

25.2%

10.2

Beta blockers

42,053

51.7%

22,070

57.4%

11.5

9756

53.7%

4.1

32,812

54.6%

5.8

H2-receptor antagonist

5699

7.0%

2828

7.4%

1.4

1214

6.7%

1.3

4181

7.0%

0.2

Proton pump inhibitor

24,020

29.5%

13,008

33.8%

9.3

5358

29.5%

0.0

19,152

31.8%

5.1

Anti-platelets

15,589

19.1%

9235

24.0%

11.8

3450

19.0%

0.4

13,101

21.8%

6.5

Statins

45,149

55.5%

23,492

61.1%

11.4

10,476

57.7%

4.5

34,956

58.1%

5.4

Inpatient admission

36,572

44.9%

15,168

39.4%

11.1

6830

37.6%

14.9

24,807

41.3%

7.4

Std Difference greater than 10 is considered significant is given in bolditalic

CHA2DS2-VASc: congestive heart failure, hypertension, age ≥ 75 years, diabetes mellitus, prior stroke or transient ischemic attack or thromboembolism, vascular disease, age 65–74 years, sex category; HAS-BLED: hypertension, abnormal renal and liver function, stroke, bleeding, labile INRs (international normalized ratio), elderly, drugs, and alcohol; PSM: propensity score matching; SD: standard deviation

aStd Difference = 100*|actual std diff|

bAs the INR value was not available in the data, a modified HAS-BLED score was calculated using a range of 0 to 8

Through PSM, 37,525 apixaban, 18,131 dabigatran, and 55,359 rivaroxaban patients were separately matched to warfarin patients. Baseline characteristics were balanced after matching with mean standardized differences < 10%. For the matched cohorts, the means were: age: 77–78 years, CHA2DS2-VASc scores: 4.4–4.6, and HAS-BLED scores: 3.2–3.4 (Table 2). Patient data were assessed for a mean duration of 8–10 months. 71%, 80%, and 66% of patients were prescribed the standard dose of DOAC (apixaban 5 mg, dabigatran 150 mg, and rivaroxaban 20 mg), respectively.

Table 2

Baseline descriptive and mean follow-up time table after PSM between warfarin and DOACs

 

Apixaban–warfarin cohort

Dabigatran–warfarin cohort

Rivaroxaban–warfarin cohort

Apixaban

Warfarin

Dabigatran

Warfarin

Rivaroxaban

Warfarin

(N = 37,525)

(N = 37,525)

(N = 18,131)

(N = 18,131)

(N = 55,359)

(N = 55,359)

N/mean

%/SD

N/mean

%/SD

N/mean

%/SD

N/mean

%/SD

N/mean

%/SD

N/mean

%/SD

Age

78.4

7.5

78.4

7.4

77.1

7.0

77.3

7.1

77.9

7.3

78.0

7.3

65–74

13,136

35.0%

13,204

35.2%

7449

41.1%

7472

41.2%

20,220

36.5%

20,202

36.5%

75–84

15,614

41.6%

15,698

41.8%

7606

42.0%

7602

41.9%

23,651

42.7%

23,685

42.8%

≥ 85

8775

23.4%

8623

23.0%

3076

17.0%

3057

16.9%

11,488

20.8%

11,472

20.7%

Sex

Male

18,176

48.4%

18,112

48.3%

9313

51.4%

9268

51.1%

27,463

49.6%

27,494

49.7%

Female

19,349

51.6%

19,413

51.7%

8818

48.6%

8863

48.9%

27,896

50.4%

27,865

50.3%

Race

White

34,436

91.8%

34,369

91.6%

16,288

89.8%

16,308

89.9%

50,418

91.1%

50,373

91.0%

Black

1424

3.8%

1451

3.9%

785

4.3%

816

4.5%

2282

4.1%

2309

4.2%

Hispanic

412

1.1%

427

1.1%

288

1.6%

269

1.5%

788

1.4%

797

1.4%

Other

1253

3.3%

1278

3.4%

770

4.2%

738

4.1%

1871

3.4%

1880

3.4%

Geographic region

Northeast

6486

17.3%

6530

17.4%

3606

19.9%

3559

19.6%

10,234

18.5%

10,215

18.5%

North central

7906

21.1%

7897

21.0%

4184

23.1%

4135

22.8%

13,233

23.9%

13,260

24.0%

South

16,433

43.8%

16,467

43.9%

6932

38.2%

7161

39.5%

21,568

39.0%

21,515

38.9%

West

6679

17.8%

6615

17.6%

3379

18.6%

3245

17.9%

10,241

18.5%

10,292

18.6%

Other

21

0.1%

16

0.0%

30

0.2%

31

0.2%

83

0.1%

77

0.1%

Medicaid dual-eligibility

7399

19.7%

7509

20.0%

4257

23.5%

4230

23.3%

12,157

22.0%

12,053

21.8%

Part D low-income subsidy

8454

22.5%

8584

22.9%

4801

26.5%

4782

26.4%

13,697

24.7%

13,620

24.6%

Baseline comorbidity

Deyo-Charlson comorbidity index

2.9

2.6

2.9

2.7

2.5

2.4

2.5

2.4

2.7

2.5

2.7

2.6

CHADS2 score

2.8

1.5

2.8

1.4

2.6

1.4

2.6

1.4

2.7

1.4

2.7

1.4

CHA2DS2-VASc score

4.6

1.8

4.7

1.7

4.4

1.7

4.4

1.7

4.5

1.7

4.5

1.7

HAS-BLED scorea

3.4

1.3

3.4

1.3

3.2

1.2

3.2

1.2

3.3

1.3

3.3

1.3

Baseline prior bleed

11,495

30.6%

11,455

30.5%

4726

26.1%

4748

26.2%

16,013

28.9%

16,128

29.1%

Baseline prior stroke

5202

13.9%

5221

13.9%

2159

11.9%

2226

12.3%

7131

12.9%

7146

12.9%

Congestive heart failure

11,897

31.7%

12,028

32.1%

5114

28.2%

5177

28.6%

16,729

30.2%

16,615

30.0%

Diabetes

13,442

35.8%

13,565

36.1%

6731

37.1%

6753

37.2%

20,370

36.8%

20,298

36.7%

Hypertension

33,730

89.9%

33,816

90.1%

15,934

87.9%

15,991

88.2%

48,716

88.0%

48,780

88.1%

Renal disease

8479

22.6%

8508

22.7%

2892

16.0%

2984

16.5%

10,376

18.7%

10,392

18.8%

Myocardial infarction

4941

13.2%

4990

13.3%

1940

10.7%

2040

11.3%

6890

12.4%

6877

12.4%

Dyspepsia or stomach discomfort

8427

22.5%

8411

22.4%

3597

19.8%

3691

20.4%

11,843

21.4%

11,852

21.4%

Peripheral vascular disease

22,042

58.7%

22,245

59.3%

9669

53.3%

9867

54.4%

30,815

55.7%

30,831

55.7%

Peripheral artery disease

8717

23.2%

9076

24.2%

3633

20.0%

3707

20.4%

12,412

22.4%

12,567

22.7%

Transient ischemic attack

3384

9.0%

3395

9.0%

1338

7.4%

1344

7.4%

4342

7.8%

4373

7.9%

Coronary artery disease

19,294

51.4%

19,501

52.0%

8347

46.0%

8582

47.3%

26,481

47.8%

26,523

47.9%

Baseline medication use

Angiotensin converting enzyme inhibitor

12,998

34.6%

13,084

34.9%

6859

37.8%

6841

37.7%

19,972

36.1%

20,044

36.2%

Amiodarone

3867

10.3%

3801

10.1%

1614

8.9%

1637

9.0%

4355

7.9%

4360

7.9%

Angiotensin receptor blocker

9532

25.4%

9538

25.4%

4478

24.7%

4603

25.4%

13,103

23.7%

13,042

23.6%

Beta blockers

21,347

56.9%

21,379

57.0%

9731

53.7%

9777

53.9%

29,724

53.7%

29,670

53.6%

H2-receptor antagonist

2728

7.3%

2797

7.5%

1208

6.7%

1232

6.8%

3800

6.9%

3822

6.9%

Proton pump inhibitor

12,520

33.4%

12,521

33.4%

5347

29.5%

5553

30.6%

17,089

30.9%

17,116

30.9%

Anti-platelets

8722

23.2%

8814

23.5%

3436

19.0%

3510

19.4%

11,334

20.5%

11,404

20.6%

Statins

22,711

60.5%

22,960

61.2%

10,449

57.6%

10,589

58.4%

31,640

57.2%

31,568

57.0%

Inpatient admission

14,935

39.8%

15,081

40.2%

6819

37.6%

6986

38.5%

23,133

41.8%

23,214

41.9%

Patients on standard dose DOAC

26,628

71.0%

  

14,496

80.0%

  

36,656

66.2%

  

Mean follow-up time (in days)

230.3

211.3

281.3

260.0

257.0

265.9

285.6

264.7

275.8

265.7

284.0

262.7

CHA2DS2-VASc: congestive heart failure, hypertension, age ≥ 75 years, diabetes mellitus, prior stroke or transient ischemic attack or thromboembolism, vascular disease, age 65–74 years, sex category; HAS-BLED: hypertension, abnormal renal and liver function, stroke, bleeding, labile INRs (international normalized ratio), elderly, drugs, and alcohol; PSM: propensity score matching; SD: standard deviation

aAs the INR value was not available in the data, a modified HAS-BLED score was calculated using a range of 0 to 8

Stroke/SE and MB

Compared to warfarin, apixaban (HR: 0.69; 95% confidence interval [CI] 0.59–0.81, p < 0.001) and rivaroxaban (HR: 0.82; 95% CI 0.73–0.91, p < 0.001) were associated with a significantly lower risk of stroke/SE ; dabigatran (HR: 0.88; 95% CI 0.72–1.07, p = 0.206) was associated with a non-significantly lower risk of stroke/SE (Fig. 2). All DOACs were associated with a lower risk of hemorrhagic stroke versus warfarin.

Fig. 2

Incidence rate and hazard ratio of stroke/SE and major bleeding for propensity score-matched patients

Compared to warfarin, apixaban (HR: 0.61; 95% CI 0.57–0.67, p < 0.001), and dabigatran (HR: 0.79; 95% CI 0.71–0.89, p < 0.001) were associated with a significantly lower risk of MB, and rivaroxaban (HR: 1.08; 95% CI 1.02–1.14, p = 0.006) was associated with a higher risk of MB, mainly due to GI bleeding (Fig. 2). All DOACs were associated with a lower risk of ICH versus warfarin.

NCO and MACE

As a composite of stroke/SE and MB, the risk of NCO was significantly lower than warfarin for apixaban (HR: 0.64; 95% CI 0.60–0.69, p < 0.001) and dabigatran, (HR: 0.84; 95% CI 0.76–0.93, p = 0.001) but similar for rivaroxaban (HR: 1.04; 95% CI 0.99–1.09, p = 0.169) (Fig. 3).

Fig. 3

Incidence rates and hazard ratios of net clinical outcome and MACE for propensity score-matched patients

Compared to warfarin, all DOACs were associated with a lower risk of MACE (apixaban: HR: 0.70; 95% CI 0.67–0.74, p < 0.001; dabigatran: HR: 0.76; 95% CI 0.71–0.82, p < 0.001; rivaroxaban: HR: 0.83; 95% CI 0.80–0.86, p < 0.001; Fig. 3).

Sensitivity analysis

In the sensitivity analysis wherein the follow-up period was censored at 6 months, the results were consistent with the main analysis (Supplemental Table 1).

Discussion

Using Medicare FFS data from 2012 to 2015, this study showed that compared to warfarin among elderly patients with NVAF, apixaban was associated with significant lower risks of stroke/SE, MB, NCO, and MACE. Dabigatran was associated with significantly lower risks of MB, NCO, and MACE as well as a numerically lower risk of stroke/SE. Rivaroxaban was associated with lower risks of stroke/SE and MACE, but higher MB and numerically higher NCO risks compared to warfarin.

The study results supplement RCT findings for apixaban, dabigatran, and rivaroxaban compared to warfarin and their corresponding age subgroup analyses [20, 21, 22, 23, 24, 25]. In the RE-LY trial, patients (overall and ≥ 75 years) with 150 mg dabigatran had lower rates of stroke/SE and similar rates of MB compared to warfarin [20, 23]. In this real-world study among NVAF patients aged ≥ 65 years, 150 mg and 75 mg dabigatran showed numerically lower stroke/SE and significantly lower MB risks versus warfarin. Although NCO was not studied in the RE-LY trial’s elderly group, overall dabigatran and warfarin patient analysis demonstrated that compared to warfarin, 150 mg twice-daily dabigatran was associated with a non-significantly lower risk of net clinical benefit (a composite of stroke/SE, pulmonary embolism, MI, death, and MB) [20]. In this study, elderly dabigatran patients were associated with significantly lower NCO and MACE risks than warfarin patients.

In the ARISTOTLE trial, apixaban was associated with lower rates of stroke/SE, MB, and net clinical events (stroke/SE, MB, and all-cause death) compared to warfarin among all patients and patients aged ≥ 65 years [22, 25]. This study found consistent trends. In the ROCKET AF trial, rivaroxaban was associated with a non-inferior rate of stroke/SE and similar rate of MB compared to warfarin [21]. Among patients aged ≥ 75 years, 20 and 15 mg daily rivaroxaban showed a numerically lower risk of stroke/SE but a higher risk of MB compared to warfarin [24]. This study found similar trends between rivaroxaban and warfarin among patients aged ≥ 65 years. To the best of our knowledge, no previous studies have compared net clinical benefits between rivaroxaban and warfarin.

Several real-world studies have focused on effectiveness and safety comparisons between DOACs and warfarin in an elderly NVAF population [11, 26, 27, 28, 29]. Our previous study of the elderly Medicare population from 2012 to 2014 consistent results of stroke/SE and major bleeding were found for the comparisons between DOACs and warfarin [11]. This study provides more recent and comprehensive analysis with updated data and added NCO and MACE outcomes. Using Medicare data from 2010 to 2012, Graham et al. [26] demonstrated that compared to warfarin, elderly NVAF dabigatran initiators (aged ≥ 65 years) were associated with lower risks of ischemic stroke, ICH, and death; similar risk of acute MI and MB; and a higher major GI bleeding risk. Our results (over an updated time-frame) showed consistent trends for ICH and GI bleeding. However, the ischemic stroke risk was similar, and the MB risk was lower for dabigatran versus warfarin patients in our study. Using Humana data, Deitelzweig et al. [27] found that NVAF patients aged ≥ 65 years with Medicare Advantage coverage who were treated with apixaban were associated with significantly lower risks of stroke/SE and MB compared to warfarin. This study noted consistent trends.

A few other real-world studies among DOACs and warfarin have provided comparative effectiveness and safety information by conducting subgroup analyses for age subgroups [30, 31, 32]. Using MarketScan and Optum data from 2010 to 2012, Seeger et al. showed that among patients aged 65–74 years, compared to warfarin, dabigatran was associated with similar risk for stroke and lower risk for MB; among those aged ≥ 75 years, dabigatran was associated with lower risk for stroke and similar risk for MB [30]. Using the MarketScan data from 2010 to 2014, Norby et al. [31] found that among patients aged ≥ 75 years, rivaroxaban was associated with a similar risk for ischemic stroke and MI, a lower ICH risk, and a higher GI bleeding risk compared to warfarin. Using a pooled dataset, Li et al. [32] demonstrated that among elderly patients, apixaban was associated with similar (65–74) to lower (≥ 75) stroke/SE risk and a lower (65–74 and ≥ 75) MB risk compared to warfarin. The comparisons between DOACs and warfarin in our study showed trends generally consistent with previous literature. However, more studies are needed to better understand effectiveness and safety profiles in elderly populations. Moreover, as DOAC use increases, further research will be necessary to assist in decision-making for such populations [33].

Despite growing evidence of improved safety with DOACs, warfarin is still widely used in high-risk NVAF populations [34]. Our study provides a current and comprehensive analysis comparing DOACs and warfarin regarding the risk of stroke/SE, MB, NCOs, and MACE among elderly US Medicare NVAF patients. Given the distinct clinical characteristics of the elderly NVAF population, the study results may add useful information to the literature to assist in disease management decision making.

This study has several limitations. Given its observational nature, confounding factors may have impacted the results. To control for potential confounders, a comprehensive list of baseline covariates was included in the PSM, including patient demographics and clinical characteristics. However, variables such as over-the-counter use of aspirin, serum creatinine/creatinine clearance, and laboratory test result values are not captured in the Medicare data. As claims data analysis, the study may also be subject to coding errors and inaccurate or incomplete clinical information. For example, treatments recorded based on prescription claims include no evidence of drug adherence. Moreover, since international normalized ratio values were not obtained, the quality of warfarin treatment could not be evaluated and the calculation for HAS-BLED score was modified. Moreover, proper dosage for DOACs based on age, renal function, and weight could not be assessed.

In summary, in the elderly Medicare population with NVAF, compared to warfarin, the DOACs were associated with a lower to similar risk of stroke/SE and MACE, but with varying comparative risks for MB and NCO.

Notes

Funding

This work was funded by Pfizer Inc. and Bristol-Myers Squibb.

Compliance with ethical standards

Conflict of interest

Amin is an employee of the University of California, Irvine and was a paid consultant to Bristol-Myers Squibb in connection with this study and the development of this manuscript. Keshishian is an employee of STATinMED Research, a paid consultant to Pfizer and Bristol-Myers Squibb in connection with this study and the development of this manuscript. Dina, Carda, Russ, Mardekian, and Baker are employees of Pfizer Inc., with ownership of stocks in Pfizer Inc. Dhamane, Nadkarni, and Rosenblatt are employees of Bristol-Myers Squibb Company, with ownership of stocks in Bristol-Myers Squibb Company. Yuce has no conflicts of interest.

Supplementary material

11239_2019_1838_MOESM1_ESM.docx (12 kb)
Supplementary material 1 (DOCX 11 KB)

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Copyright information

© The Author(s) 2019

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  • Alpesh Amin
    • 1
    Email author
  • Allison Keshishian
    • 2
  • Oluwaseyi Dina
    • 3
  • Amol Dhamane
    • 4
  • Anagha Nadkarni
    • 4
  • Eric Carda
    • 3
  • Cristina Russ
    • 3
  • Lisa Rosenblatt
    • 4
  • Jack Mardekian
    • 3
  • Huseyin Yuce
    • 5
  • Christine L. Baker
    • 3
  1. 1.Department of MedicineUniversity of CaliforniaOrangeUSA
  2. 2.STATinMEDAnn ArborUSA
  3. 3.Pfizer Inc.New YorkUSA
  4. 4.Bristol-Myers Squibb CompanyLawrencevilleUSA
  5. 5.New York City College of TechnologyCity University of New YorkNew YorkUSA

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