antithrombotic drugs: assessing the benefits and harms in
TRANSCRIPT
Antithrombotic drugs: Assessing the
benefits and harms in medically complex patients with cardiovascular
disease
Alexandra Hajduk, PhD, MPH Associate Research Scientist, Section of Geriatrics
Yale School of Medicine
Amgad Mentias, MD Cardiology Fellow, Division of Cardiovascular Medicine
University of Iowa Hospitals and Clinics
Mary Vaughan-Sarrazin, PhD Associate Professor, Department of Internal Medicine
University of Iowa
Gregory Ouellet, MD Instructor, Section of Geriatrics
Yale School of Medicine
HCSRN-OAICs AGING Initiative
October 25, 2018
Your attendee ID helps the host interact and manage your attendance in the webinar.
It can be found by clicking the small “i” circle in the top left corner
Find Your Attendee ID
When you would like to interact with the presenter Click the speech bubble icon so that it is highlighted blue
Then type your questions in the Q&A box:
Hosts will acknowledge questions in order
How to ask a question
If you are having technical issues Type your questions in the chat box:
Hosts will communicate via chat to try and fix any technical issues
Technical Issues
Today’s Speakers
Alexandra Hajduk, PhD, MPH
Amgad Mentias, MD
Mary Vaughan-Sarrazin, PhD
Gregory Ouellet, MD
For questions about the AGING Initiative or today’s webinar, please contact:
Thank you!
— 1 —
COMPARATIVE EFFECTIVENESS OF ORAL ANTICOAGULANTS IN PATIENTS WITH ATRIAL FIBRILLATION AND MULTIPLE CHRONIC CONDITIONS: A MEDICARE ANALYSIS
Aging Webinar 01/10/2019
Amgad Mentias, Mary Vaughan-Sarrazin University of Iowa
Disclosures
• No relevant financial disclosures • Dr. Sarrazin: This work is supported by a grant from the
Agency for Healthcare Research and Quality (AHRQ).
Background
Atrial fibrillation (AF) affects 3 million adults in the United States.(1)
AF increases stroke risk by 3 to 5 fold.(2)
Anticoagulation reduces risk of stroke.
Several agents are available for non valvular AF:
1. Warfarin
2. Direct oral anticoagulants (DOAC’s): Dabigatran, Apixaban,
Rivaroxaban and Edoxaban.
• Anticoagulation increases risk of dangerous bleeding.
Background
• AF prevalence increase with aging.(1)
• Elderly people usually suffer from multiple co-morbid
conditions (MCC).
• Age and age-related comorbidities are risk factors for
ischemic stroke and bleeding
Background
• In a study of ≈ 300,000 AF patients and 500,000 age-sex matched controls:
- Prevalence of any concomitant disease was 70% in AF compared to 28% in controls.
- Concomitant diseases included stroke, renal failure, COPD, hypertension, diabetes and neoplasms. (3)
Background
• In another study from UK: (4)
- AF with ≥4 co-morbidities had a 6-fold higher risk of mortality compared to participants without any long-term condition.
• Risk of mortality:
- CHF (HR 2.96, 95% CI 1.83-4.80)
- COPD (HR 3.31, 95% CI 2.14-5.11)
- Osteoporosis (HR 3.13, 95% CI 1.63-6.01)
Background
• In AF patients >75 years old, >50% are not prescribed any AC despite high stroke risk by CHA2DS2-Vasc score. (5)
• Frailty is an important factor that sway physicians from prescribing AC. (5,6)
• Cognitive impairment is specially important in warfarin and affects quality of AC by affecting time in INR therapeutic range. (6)
Background
• In two studies (one from France and one from US):
- DOAC’s were prescribed to younger, and healthier patients compared to warfarin.
- DOAC’s were less likely to be prescribed to patients with higher CHADS2Vasc, and HAS-BLED scores.
Background
• Randomized controlled trials are always limited in
generalizability and application to real world patients.
− In the ROCKET-AF (Rivaroxaban) trial, < 15% of the
study participants had CHADS2 score ≥5.(9)
− In the RELY trial (Dabigatran), less than 1/3rd of study
participants had a CHADS2 score ≥3.(10)
Aim of the study
• Compare the effectiveness of warfarin, rivaroxaban,
and dabigatran in stroke prevention in elderly patients
with AF and low, moderate, and high levels of
comorbidity.
• Compare the safety of warfarin, rivaroxaban, and
dabigatran in elderly patients with AF and low,
moderate and high levels of comorbidity.
Methods: Data and Patients Source of data: • Medicare inpatient, carrier, and pharmacy claims (2010-2013). Inclusion criteria: • Age 66 older • ICD-9 code 427.31 as primary diagnosis. • New AF diagnosis (One inpatient or two outpatient claims within 90
days and no AF in prior 12 months) • Started on dabigatran (150 mg) twice daily, rivaroxaban (20 mg) once
daily, or warfarin within 90 days after AF diagnosis. Exclusion criteria: • Enrolled in a Medicare managed care during the observation period, • Not enrolled in a Part D drug prescription plan at the time of AF
diagnosis.
Anticoagulant Type defined by first AC received after AF diagnosis.
Methods: Outcomes
• Primary outcome:
Acute admission for acute ischemic stroke
Acute admission for major bleeding event
• Secondary outcomes:
− Gastrointestinal Bleeding (GIB)
− Non-GI major hemorrhage (including intracranial hemorrhage)
− Acute myocardial infarction (MI)
− All-cause mortality death
Methods: Comorbidity Assessment Three scoring systems to assess MCC burden:
1. CHA2DS2-Vasc Stroke Risk score:
Scored as: 1 point: congestive heart failure hypertension age 65-74 years diabetes vascular disease female gender.
2 points: history of stroke age ≥75 years.
- Ranges from 0 to 9 (Minimum score in our study was 1).
- Categorized as: 1-3 Low, (4-5) Moderate, (≥6) High
Methods: Comorbidity Assessment
2) HAS-BLED Bleeding Risk score: - Score as: 1 point: hypertension
prior stroke history or predisposition towards bleeding age ≥65 years alcohol/drug use that increases bleeding risk Liver and renal diseases. History of labile international normalized ratio (INR) (not relevant for our study)
- Ranges from 0 to 9 (minimum score = 1 in our study)
- Categorized as: (=1) Low, (=2) moderate, (≥3) high.
Methods: Comorbidity Assessment
3) Gagne Comorbidity Score: - All-cause mortality risk
- Weighted Scoring representing: unexplained weight loss, hemiplegia, alcohol abuse, tumor, renal disease, metastatic cancer, dementia, arrhythmia, pulmonary disease, coagulopathy, complicated diabetes, anemia, electrolyte imbalance, liver disease, peripheral vascular disease, psychosis, pulmonary circulatory disorder, HIV/AIDS, and heart failure.
- Categorized as: (0-2) Low, (3-4) Moderate, (≥5) High.
Methods: Statistical analysis
- Patients divided into low, moderate and high MCC using alternative comorbidity scores
- Analysis used three-way propensity score matching
3-way match
rivaroxaban dabigatran
warfarin
Methods: Statistical analysis
• Matching conducted separately by MCC level, by three scores
9 matching algorithms.
• Analysis of matched samples used Cox Proportional Hazards Regression for time to key outcomes.
• Censoring events included:
1) End of observation (December 31, 2013)
2) Cessation of the initial AC (last fill date + days supplied)
3) Death.
Results: Demographics
Results: Comorbid Conditions
Results: MCC Level by drug
Results: Ischemic Stroke
Results: Major bleeding
Results: Gastrointestinal bleeding
Results: All-cause mortality
Conclusion
• Rivaroxaban, Dabigatran and warfarin are similarly effective for stroke prevention in AF patients with MCC.
• Dabigatran use is associated with a lower bleeding risk whereas rivaroxaban use is associated with a higher bleeding risk compared to warfarin use in high-risk patients
• Both DOAC’s were associated with lower all-cause mortality compared to warfarin, regardless of MCC level.
Limitations
1. Possible residual confounding from unmeasured factors even after propensity matching.
2. All of our patients were older than 65 years, which might limit generalizability to younger patients.
3. Lack of information on type of AF (paroxysmal vs. permanent), INR levels, and time in therapeutic range in warfarin users.
References
1. Naccarelli G V, Varker H, Lin J, Schulman KL. Increasing Prevalence of Atrial Fibrillation and Flutter in the United States. Am J Cardiol. 2009;104(11):1534-1539. doi:10.1016/j.amjcard.2009.07.022.
2. Wolf PA, Abbott RD, Kannel WB. Atrial fibrillation as an independent risk factor for stroke: the Framingham Study. Stroke. 1991;22(8):983-988. doi:10.1161/01.STR.22.8.983.
3. Andersson T, Magnuson A, Bryngelsson I-L, et al. All-cause mortality in 272 186 patients hospitalized with incident atrial fibrillation 1995–2008: a Swedish nationwide long-term case–control study. Eur Heart J. 2013;34(14):1061-1067. doi:10.1093/eurheartj/ehs469.
4. Jani BD, Nicholl BI, McQueenie R, et al. Multimorbidity and co-morbidity in atrial fibrillation and effects on survival: findings from UK Biobank cohort. EP Eur. November 2017. doi:10.1093/europace/eux322.
5. Induruwa I, Evans NR, Aziz A, Reddy S, Khadjooi K, Romero-Ortuno R. Clinical frailty is independently associated with non-prescription of anticoagulants in older patients with atrial fibrillation. Geriatr Gerontol Int. April 2017. doi:10.1111/ggi.13058.
6. Gorzelak-Pabiś P, Zyzak S, Krewko Ł, Broncel M. Assessment of the mean time in the therapeutic INR range and the SAME-TT2R2 score in patients with atrial fibrillation and cognitive impairment. Polish Arch Intern Med. 2016;126(7-8):494-501. doi:10.20452/pamw.3475.
Reference (continued)
7. Huiart L, Ferdynus C, Renoux C, et al. Trends in initiation of direct oral anticoagulant therapies for atrial fibrillation in a national population-based cross-sectional study in the French health insurance databases. BMJ Open. 2018;8(3):e018180. doi:10.1136/bmjopen-2017-018180.
8. Desai NR, Krumme AA, Schneeweiss S, et al. Patterns of Initiation of Oral Anticoagulants in Patients with Atrial Fibrillation— Quality and Cost Implications. Am J Med. 2014;127(11):1075-1082.e1. doi:10.1016/j.amjmed.2014.05.013.
9. Patel MR, Mahaffey KW, Garg J, et al. Rivaroxaban versus Warfarin in Nonvalvular Atrial Fibrillation. N Engl J Med. 2011;365(10):883-891. doi:10.1056/NEJMoa1009638.
10. Connolly SJ, Ezekowitz MD, Yusuf S, et al. Dabigatran versus Warfarin in Patients with Atrial Fibrillation. N Engl J Med. 2009;361(12):1139-1151. doi:10.1056/NEJMoa0905561.
Thank you
Haddad F, et al. Circulation. 2008;117:1436-1448.
Evaluating Benefits & Harms of Triple Antithrombotic Therapy in Medically Complex
Older Adults with AMI and AF GIG Initiative Pilot
AGING Initiative Pilot Plus+ Webinar January 10, 2019
1
Atrial Fibrillation (AF) in Acute Myocardial Infarction (AMI)
AF affects ~20% of AMI survivors Predicts worse outcomes: Readmission Stroke Death
Rathore, et al., Circ 2000; Jabre, et al., Circ 2011; Kundu, et al., Int J Cardiol 2016 2
Guideline-recommended treatments for AMI and AF
Aspirin (ASA)
Oral anticoagulant
Antiplatelet (P2Y12 I)
AMI: Aspirin & P2Y12 Inhibitor Minimum one year
AF: Oral anticoagulant indefinitely
Triple therapy 3
THROMBOSIS
4
What do guidelines say about AF+AMI?
(Levine 2016)
“In patients with long-standing AF or a moderate-to-high CHA2DS2-VASc score, efforts should be directed to minimize duration of triple therapy, and decisions about stent insertion should consider the potential requirement for long-term anticoagulant therapy.”
(January 2014)
2016 ACC/AHA Update on dual antiplatelet therapy in coronary artery disease
2014 ACC/AHA Atrial fibrillation guideline
5
Triple Therapy and Bleeding Risk
Hess, et al., JACC 2015 6
Triple Therapy and Stroke Risk
Hess et al., JACC 2015 7
Triple Therapy and Cardiac Events
D’Ascenzo 2015 Zhao 2011
8
Geriatric Conditions, AMI, & AF
• Medically complex older patients: • Less included in drug clinical trials • Benefit less from treatments/interventions • ↑ risk of drug-drug or drug-disease interactions
• Geriatric conditions affect outcomes in AMI & AF • Prevalence in older adults with AMI+AF? • Influence on outcomes? • Moderating influence on treatments?
Lorgunpai, et al., PLoS One 2014, Fried, et al., JAGS 2014 9
Project Aims
1. Characterize prevalence of and factors associated with receipt of triple therapy
2. Examine association of antithrombotic therap with MACE and bleeding
3. Examine MCCs and frailty as moderators of antithrombotic therapy and outcomes
10
Clinical
Psychosocial
Demographic
Geriatric 3000 AMI patients ≥75 years old
Dataset and Analytic Sample
831 patients with comorbid AF 11
Aim 1- Factors Associated with Receipt of Triple Therapy
Outcome: Receipt of triple therapy vs. alternatives at hospital discharge after AMI Factors: Multimorbidity (Charlson) Frailty (Fried) Baseline stroke (CHA2DS2-VASC) and bleeding risk (HAS-BLED) Demographics In-hospital procedures/events Other geriatric vulnerabilities (mobility, falls) Lifestyle & Psychosocial Analysis: multivariable-adjusted logistic regression of all factors associated with antithrombotic therapy at p<.05
12
Sample Composition
Mean age= 82.4 (±5.3) years
58% male
8% non-White
80% underwent cardiac cath
66% pre-existing Afib (34% diagnosed during index admission)
Median Charlson score: 3 (IQR 2-5), 49% ≥4
Frailty (Fried Criteria): 15% non-frail, 54% pre-frail, 31% frail
13
20% of sample was on triple therapy at discharge.
[CATEGORY NAME], 33%
[CATEGORY NAME], [PERCENTAGE]
[CATEGORY NAME], [PERCENTAGE]
[CATEGORY NAME], [PERCENTAGE]
Antplat + Anticoag 4%
Anticoag only 3%
None 2%
[CATEGORY NAME] 1%
14
We retained the most common 4 groups for analysis (91% of cohort)
[CATEGORY NAME], 33%
[CATEGORY NAME], [PERCENTAGE]
[CATEGORY NAME], [PERCENTAGE]
[CATEGORY NAME], [PERCENTAGE]
Antplat + Anticoag 4%
Anticoag only 3%
None 2%
[CATEGORY NAME] 1%
15
Factors associated with antithrombotic treatment (ref: triple therapy)
Relative Risk Ratio (95% Confidence Interval)
Charlson score
Frailty
CHA2DS2-VASC score (per pt)
HAS-BLED score (per pt)
Education (≤12 years)
Admission Year (2013-16)
Pre-existing AF (vs. new onset)
Percutaneous Intervention
GRACE (higher cardiac risk)
Mobility Impairment Included in model but not associated with any outcome at p<.05: age, sex, marital status, acute kidney injury, GRACE score, cognition, fall history, social support
ASA + Anticoag 0.84 (0.73-0.96)
0.61 (0.29-1.28)
0.85 (0.60-1.20)
1.25 (0.80-1.94)
1.35 (0.72-0.96)
0.78 (0.59-1.04)
0.79 (0.38-1.64)
0.013 (0.00-0.039)
1.02 (1.01-1.04)
1.44 (1.03-2.02)
DAPT 1.04 (0.94-1.15)
0.69 (0.40-1.18)
0.77 (0.61-0.99)
1.41 (1.03-1.91)
1.41 (0.90-2.19)
0.77 (0.63-0.95)
0.47 (0.28-0.78)
0.50 (0.21-1.21)
1.00 (0.99-1.01)
1.25 (0.99-1.59)
ASA only 0.96 (0.83-1.10)
1.15 (0.52-2.54)
0.58 (0.39-0.85)
1.90 (1.16-3.11)
2.02 (1.02-3.97)
0.76 (0.56-1.04)
0.58 (0.26-1.27)
0.004 (0.001-0.017)
1.00 (0.98-1.01)
1.16 (0.80-1.68) 16
Drivers of antithrombotic therapy choice:
Baseline risk of thrombosis and bleeding Percutaneous coronary intervention (i.e., stents) If you got a stent, you’re on antiplatelet
Multimorbidity associated with ↓ use of ASA+anticoagulant Frailty not predictive of therapy choice
17
Project Aims
1. Characterize prevalence of and factors associated with receipt of triple therapy
2. Examine association of antithrombotic therapy with MACE and bleeding
18
Methods
Analytic sample AMI+AF patients on 4 most common antithrombotic regimens
Outcomes Major adverse cardiovascular event (MACE) at 6 months CV death, MI, stroke, revascularization Clinically significant bleeding at 6 months Bleeding event necessitating ED visit or hospitalization
Analysis inverse probability of treatment-weighted logistic regression
19
More intensive antithrombotic therapy associated with ↓MACE, ↑ bleeds
02468
101214161820
MACE Bleed
Perc
ent e
xper
ienc
ing
outc
ome
by 6
mon
ths
ASA only DAPT ASA + Anticoagulant Triple Therapy
MACE (CV death, MI, stroke, revascularization) at 6 months: 8%
Clinically significant bleeding at 6 months: 12%
P=.036
P=.034
20
Increased risk of MACE associated with non-triple therapy regimens attenuated after adjustment/weighting*
*adjusted and/or weighted for age, sex, education, marital status, Charlson score, frailty, cognition, fall risk, hospitalization year, atrial fibrillation type, revascularization, acute kidney injury, cardiac risk, thrombosis risk, bleeding risk, mobility
21
Project Aims
1. Characterize prevalence of and factors associated with receipt of triple therapy
3. Explore MCCs and frailty as moderators of antithrombotic therapy and outcomes
22
Methods
Analytic sample AMI+AF patients with 4 most common antithrombotic regimens
Outcomes Major adverse cardiovascular event (MACE) at 6 months Clinically significant bleeding at 6 months Moderators Frailty status (Fried score ≥3) MCC status (Charlson score ≥4) Analysis Exploratory 23
Frailty, MCCs, and Antithrombotic Therapy Type
0102030405060708090
100
Frail Charlson >/=4
Perc
ent w
ith C
ondi
tion
ASA Only DAPT ASA + Anticoagulant Triple Therapy
NS
NS
24
Frailty, MCCs, and 6-month Outcomes
0
2
4
6
8
10
12
14
16
18
20
MACE Bleed
Perc
ent
Frailty and Outcomes
Not frail Frail
0
2
4
6
8
10
12
14
16
18
20
MACE Bleed
Perc
ent
Multimorbidity and Outcomes
Charlson <4 Charlson ≥4
P= .20
P= .08
P= .07
P<.001
25
Frailty, antithrombotic therapy, and MACE
02468
1012141618
Not Frail FrailPerc
ent e
xper
ienc
ing
MAC
E w
ithin
6 m
onth
s
Frailty Status ASA only DAPT ASA + Anticoagulant Triple Therapy
Interaction of Antithrombotic therapy type x Frailty status= NS 26
MCC status, antithrombotic therapy, and MACE
02468
1012141618
Charlson <4 Charlson >/=4
Perc
ent e
xper
ienc
ing
MAC
E w
ithin
6 m
onth
s
MCC Status
ASA only DAPT ASA + Anticoagulant Triple Therapy
Interaction of Antithrombotic therapy type x MCC status= NS 27
Frailty, antithrombotic therapy, and bleeding
0
5
10
15
20
25
Not Frail FrailPerc
ent e
xper
ienc
ing
blee
ding
with
in 6
mon
ths
Frailty Status
ASA only DAPT ASA + Anticoagulant Triple Therapy
Interaction of Antithrombotic therapy type x frailty status= NS 28
MCC status, antithrombotic therapy, and bleeding
0
5
10
15
20
25
Charlson <4 Charlson >/=4
Perc
ent e
xper
ienc
ing
blee
d w
ithin
6 m
onth
s
MCC Status
ASA only DAPT ASA + Anticoagulant Triple Therapy
Interaction of Antithrombotic therapy type x frailty status= NS 29
Takeaways
Current antithrombotic treatment decisions are driven largely by thrombosis and bleeding risk Geriatric and functional characteristics not influential
More intensive antithrombotic treatment ↑ risk for bleeding Questionable benefit for thrombosis prevention
30
Takeaways, continued
Frailty and multimorbidity are associated with worse outcomes Both exacerbate risk of MACE More so with less intensive antithrombotic therapy
Both exacerbate risk of bleeding More so with more intensive antithrombotic therapy
31
Strengths and Limitations
Contemporary dataset Rigorous operationalization of variables
Limited power to explore MCCs and frailty as moderators Unable to investigate anticoagulant subclasses (i.e, warfarin vs. novel)
32
Implications and Future Directions
Providers should consider geriatric conditions when deliberating antithrombotic treatment choices among older adults Expand analyses to larger datasets Explore “dual antithrombotic therapy” subgroup Investigate effects of use of direct oral anticoagulants vs. warfarin
Mixed methods inquiry into clinical decision making about antithrombotic prescribing in medically complex older adults
33
Thank you!
Our Study Team Sarwat Chaudhry, MD (Yale) Jerry Gurwitz (UMass) Mary Tinetti, MD (Yale) Mayra Tisminetzky, MD, PhD (UMass)
34
Additional Slides
35
0
2
4
6
8
10
12
14
16
18
20
ASA Only DAPT ASA + Antiplatelet Triple Therapy
Perc
ent
Death within 180 Days of AMI, by Antithrombotic Therapy Type
36
CHA2DS2-VASC
37
38
To submit questions to today’s speakers: Click the speech bubble icon so that it is highlighted blue
Then type your questions in the Q&A box:
Questions
For a recording of today’s webinar or to learn more about the AGING Initiative, go to:
https://theaginginitiative.wordpress.com
For questions about the AGING Initiative or today’s webinar, please contact:
As you leave, please fill out the brief survey with your thoughts and opinions on today’s webinar!
Thank you!