1. Pros and Cons in Implanting of the DAPT Score
Roxana Mehran MD, FACC, FSCAI, FAHA, FESC
Professor of Medicine (Cardiology), and
Population Health Science and Policy
The Icahn School of Medicine at Mount Sinai
CRT 2017
20th Anniversary
Washington, DC

2. Disclosure Statement of Financial Interest
Within the past 12 months, I or my spouse/partner have had a financial interest/arrangement or affiliation with the organization(s) listed below.
Affiliation/Financial Relationship
Company
The Medicines Co., BMS, Astra Zeneca, Lilly/Daiichi Sankyo
Abbott Vascular, Boston Scientific, CSL Behring, Janssen (J+J), Claret
Advisory board for Janssen (J+J), Medscape, Osprey
Grant/Research Support
Consulting Fees/Honoraria

3. Outline Reasons/Rationale for Risk Scores
Risk scores for Duration/Potency of Antiplatelet therapy – is it necessary?
Benefits/Harms of Different DAPT Durations
Correlates of Thrombosis/Bleeding
Current Algorithms
Strengths/Weaknesses
Challenges/Future Directions

4. What is a Risk Score General Definition Advantages of Risk Scores
A tool that quantifies risk and serves as a platform to evaluate the efficacy and toxicity of different therapies
Ideally intuitive, easy to calculate, readily available
Advantages of Risk Scores
Provide empiric, quantitative estimates of risk
Subjective “intuition” is imprecise
Maximize benefits from therapy – identify the “right patient”
Minimize harms – primum non nocere
More efficient allocation of scarce health-care resources

5. Basis for Risk Scores with DAPT Duration
Principle 1
More intense platelet inhibition (prolonged exposure or potent therapy) reduces thrombosis
Principle 2
More intense platelet inhibition (prolonged exposure or potent therapy) increases bleeding
Principle 3
Magnitude of benefit is counterbalanced by a roughly equal incremental harm  nullifying any mortality advantage

6. Which Event Matters More?
Baber U, Dangas G, Mehran R et al., JACC Int 2016

7. Correlates of Thrombosis and Bleeding
ACS DM
Smoking
Prior Revasc
Older Age
Triple Rx
Anemia
CKD

8. Current Algorithms

9. Treatment Algorithm for Duration of P2Y12 Inhibitor Therapy in Patients Treated With PCI – 2016 ACC/AHA Focused Update on DAPT Duration
Levine et al - J Am Coll Cardiol 2016;68:1082–115

10. DAPT Duration Decision-Making in patients with Stable CAD
How to discriminate between ischemic and bleeding risk?
Piccolo R, Giustino G, Mehran R, Windecker S – Lancet 2015

11. Elements of Clinical Prediction Score and Distribution of Score Among Randomized DAPT Study Patients (Derivation Cohort, Patients)
The DAPT Score
Yeh et al - JAMA. 2016;315(16):

12. Yeh et al – AHA 2015

13. Yeh et al – AHA 2015

14. DAPT Scores - Multivariable Prediction Models unified into a single integer score to predict clinical benefit
Yeh et al – AHA 2015

15. Continued Thienopyridine vs. Placebo High vs. Low DAPT Score
Stent Thrombosis
or MI
GUSTO Moderate
Or Severe Bleed
Net Adverse
Events
Risk Difference (Continued Thienopyridine – Placebo), 12-30M
NNT
153
NNT 34
NNH 64
NNH
272
NNH
109
NNT 37
P<0.001
P=0.02
P<0.001
P values are for comparison of risk differences across DAPT Score category (interaction).
Yeh et al – AHA 2015

16. Unclear utility in EES-treated patients
Non-significant interaction for myocardial infarction or stent thrombosis for long- versus short-DAPT according to high versus low score
Significant interaction for all-cause mortality
Yeh et al - JAMA. 2016;315(16):

17. Predicting Risks for Coronary Thrombosis and Major Bleeding After PCI with DES: Risk Scores from PARIS Registry
Integer Risk Score for Major Bleeding
Integer Risk Score for Coronary Thrombosis
Parameter Score
Age, years
< 50
50-59
60-69
70-79
>80 +1 +2 +3 +4
BMI, kg/m2
<25
> 35
Current Smoking
Yes No
Anemia
Present
Absent
CKD*
Triple Therapy on discharge
Parameter Score
Diabetes Mellitus
None
Non-Insulin
Insulin +1 +3
Acute Coronary Syndrome No
Yes, Tn (-)
Yes, Tn (+) +2
Current Smoking
Yes
CKD*
Present
Absent
Prior PCI
Prior CABG
*Defined as CrCl < 60 mL/min/1.73 m2
Baber U, Mehran R, Giustino G et al – J Am Coll Cardiol. 2016;67(19):

18. PARIS Score - Methods
Separate prediction models were generated using Cox proportional hazards regression, with time to first occurrence of CTE or MB serving as the dependent outcome in each respective model.
Using the fully adjusted regression coefficients from each respective model, we generated userfriendly integer risk scores for each outcome
Patients were then grouped into levels of low, intermediate, and high risk, with thresholds reflecting clinically meaningful (at least 2-fold) gradients in risk from 1 group to the next.
External validation of each score was performed in the ADAPT-DES (Assessment of Dual Antiplatelet Therapy With Drug-Eluting Stents) registry
Baber U, Mehran R, Giustino G et al – J Am Coll Cardiol. 2016;67(19):

19. PARIS Registry
PARIS RISK SCORES
Percentage of patients
Predicted risk of Coronary Thrombosis
Coronary thrombosis risk score distribution and predicted risk
Baber U, Mehran R, Giustino G et al – J Am Coll Cardiol. 2016;67(19):

20. PARIS Registry
PARIS RISK SCORES
Percentage of patients
Predicted risk for Major Bleeding
Major bleeding risk score distribution and predicted risk
Baber U, Mehran R, Giustino G et al – J Am Coll Cardiol. 2016;67(19):

21. Cross- classification between thrombotic risk and bleeding Risk
Baber U, Mehran R, Giustino G et al – J Am Coll Cardiol. 2016;67(19):

22. Risk/Benefit Trade-off with Prolonged DAPT as a Function of Thrombotic and Bleeding Risk Using the PARIS Score
The x-axis displays bleeding risk score. The y-axis displays absolute risk difference in coronary thrombosis and major bleeding at 2 years using the adjusted probability estimates from each respective outcome model. Positive risk differences indicate that a patient’s risk for thrombosis exceeds bleeding whereas negative risk differences indicate the opposite. Each line is fitted to the mean risk difference according to bleeding risk score and ischemic risk category (low, intermediate, or high).
Baber U, Mehran R, Giustino G et al – J Am Coll Cardiol. 2016;67(19):

23. Metrics to Evaluate Risk Scores
Discrimination
Does the model assign higher risk estimates to those experiencing versus not experiencing the event?
Calibration
How well do predicted estimates match observed?
Validation
Performance in a separate cohort
Other considerations
Generalizability, ease of use, data quality

24. DAPT Score Vs. PARIS Score
Derivation Cohort
C-statistic for ischemia = 0.70
C-statistic for bleeding = 0.68
Validation Cohort
C-statistic for ischemia = 0.64
C-statistic for bleeding = 0.64
Derivation Cohort
C-statistic for ischemia = 0.70
C-statistic for bleeding = 0.72
Validation Cohort
C-statistic for ischemia = 0.65
C-statistic for bleeding = 0.64
PARIS Registry
Integer risk score to predict “net clinical benefit” with long- versus short-DAPT beyond 12 months in patients not experiencing ischemic or bleeding events in the first 12 months after stenting
Pros
Developed in a high-quality and well-conducted RCTs, therefore allowing for direct estimation of treatment effects according to the randomized treatment
Simple to use (common clinical variables)
App and online calculator available
Con
Unclear if useful in tailoring “upfront” duration of DAPT post-PCI
Most of the included risk factors predict ischemia but not bleeding – may underestimate bleeding risk
Unclear if applicable to new-generation DES
Modest discrimination
Two separate integer risk scores to predict ischemic and bleeding events in real-world patients undergoing PCI.
Pros
Developed in a real-world population
Allows separate estimation of ischemic and bleeding risk
Can be applied to tailor “upfront” DAPT
Simple to use (common clinical variables)
May allow better stratification for bleeding risk
Developed in mostly new-generation DES patients
Con
Does not allow direct estimation of “net clinical benefit”
Does not incorporate any anatomical or procedural variable

25. Strengths/Weakness DAPT Score PARIS Scores Derivation cohort RCT
Registry
Discrimination for bleeding
Moderate
Discrimination for thrombosis
Calibration
Adequate
External Validation
Yes

26. Strengths/Weaknesses con’t
Estimate treatment effect across levels of risk
DAPT >> PARIS
Rigorous follow-up, data quality and control
Generalizability
PARIS >> DAPT
Bleeding parameters included

27. Challenges/
Future Directions

28. Does One Score Fit All? Anticoagulation in AF CHADS HAS-BLED, ATRIA
Estimate stroke risk
Originally derived in RCT cohorts
HAS-BLED, ATRIA
Estimate bleeding risk
Based on observational or administrative data
Both are complementary to inform decision-making

29. Need for Implementation
Barriers to Implementation
Too time consuming
Do not help
Unaware
Not Considered
Sposito A et al., 2009

30. Conclusions
Risk scores are useful to reliably quantify risk and identify patients most likely to benefit from a given therapy
In absence of any mortality advantage, decision-making surrounding DAPT duration may be enhanced by using prediction tools
Differences in the DAPT and PARIS scores largely reflect the underlying derivation cohorts
Both may serve complementary roles in decision-making, analogous to current approaches for AF
In absence of implementation, however, score development and validation will serve little clinical purpose