Overcoming the Risk-Treatment Paradox in Non-STE ACS: It s Time! Christopher Granger, MD

Transcription

1 Overcoming the Risk-Treatment Paradox in Non-STE ACS: It s Time! Christopher Granger, MD

2 Disclosures Research contracts: AstraZeneca, Bayer, Novartis, GSK, Sanofi-Aventis, BMS, Pfizer, The Medicines Company, Roche, Daiichi, Takeda, and Boehringer Ingelheim Consulting/Honoraria: AstraZeneca, Bayer, GSK, BMS, Janssen, Lilly, Novartis, Roche, Boehringer Ingelheim, The Medicines Company, and Sanofi-Aventis For full listing see

3 76 year old woman presents with ACS History of hypertension, CABG 1990, MI and stents to LAD in 2003, asthma Increased chest pain, and continued chest pain in ED. Treated with nitrates, morphine; beta blocker; wheezing. BP 126/68, pulse 101, 80 kg, mild CHF Creatinine 1.7 mg/dl (creat clearance 35); TnT 0.27 ng/ml; probnp 2200 pg/ml. ECG showed 1mm anterolateral ST depression; echo EF 45% GRACE risk score death in-hospital 16% death/mi 6 mo 50%

4 Absolute risk reduction = Relative risk reduction X Baseline risk

5 To optimize absolute treatment benefits, higher risk patients should be consistently treated with effective therapies

6 Risk-treatment paradox: Higher risk patients are less likely to be treated (fibrinolytic therapy, statins)

7 Key concepts Using quantitative risk models to define risk of Death good Death/MI moderate Risk models fail to capture very important information (like changing risk over time) Clinical judgment is not very good (underestimates impact of age) Most people are risk averse Result is that we fail to treat patients with greatest absolute benefit from therapies

8 What is the evidence for the risktreatment paradox in ACS?

9 % of Patients Risk Treatment Paradox Cath PCI CABG Cath, p=0.0002; PCI, p=0.03; CABG, p= GRACE GRACE Risk Score (Deciles) Am Heart J Apr;153(4):

10 GRACE Intervention by GRACE risk tertile and hospital cath percentage, excluding age >75 and creatinine > 265 Hospital cath rate low medium high Fox KAA. Heart 2007;93:

11 112,848 patients with MI 279 GWTG hospitals 2000 and 2008 in-hospital mortality model (C-statistic: 0.75) Motivala AA JACC 2011;58:

12 Early Cath (<48h) Use by Risk Status Low Risk Mod Risk High Risk % % 2002Q1 2002Q2 2002Q3 2002Q4 2003Q1 2003Q2 2003Q3 2003Q4 2004Q1 2004Q2 2004Q3 2004Q4 - Tricoci et al AHA 2005

13 Arch Intern Med. 2007;167(10):

14 Arch Intern Med. 2007;167(10):

15 Independent Predictors of Early Cath Cardiology Care Age (per 10 yrs) Prior CHF Renal Insufficiency Signs of CHF Caucasian Race Female Sex Adjusted Odds Ratio Bhatt et al, JAMA 2004

16 Temporal trends in cath/pci for ACS by risk, across Canada 1999 to 2007 % cath % PCI GRACE Risk: low (<109), intermediate ( ), high (>140, or 3% in-hosp death) risk Jedrzkiewicz S. Can J Cardiol 2009;25:e370-e376

17 Risk stratification

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19 # of Patients In-hospital Mortality, % Risk Stratification in 2012: Troponin Alone is Not Enough 3500 Troponin Positivity and In-hospital Mortality as a Function of GRACE Risk Score 40% Troponin - Troponin + Mortality 35% 30% 25% 20% 15% % 500 5% 0 < >226 0% GRACE Risk Score Intervals N=27,406 Non-STE ACS Patients Steg, Fitzgerald, Fox, AJM 2009

20 GRACE Risk Model Variables Age (continuous) 76 Killip class II Blood pressure 126/68 ST deviation yes Cardiac arrest no Creatinine 1.7 Elevated CK-MB/Tn yes Heart rate In-hosp 12 mo Death 14% 45% Death/MI 30% 49% Granger et al Archives Int Med

21 Global Registry of Acute Coronary Events (GRACE) Score Fox K A A et al. BMJ Open 2014;4:e004425

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23 Estimated Event Rates (with 95% CIs) at 24 Months by Age CV Death, MI, Stroke TIMI Major Bleeding Roe M. Circulation. 2013; 128:

24 Time dimension: 1. risk changes over time 2. predictors of risk change over time 3. risk for one event may have a different pattern or change than another event

25 Theoretical construct of differing hazards of composite of death, re-mi, and ischaemic stroke over time Armstrong P W Eur Heart J 2012;33:

26 Change in Prognostic Factors Over Time For 30d Death in ASSENT-3: From Baseline to Day 4 Age SBP/HR ECG measures CHF Stroke Other factors 100% Other 80% ECG Stroke % ΣX 2 60% 40% SBP /HR CHF 20% AGE 0% Baseline 3 hours Day 2 Day 4 (n=6066) (n=6066) (n=5968) (n=5839) Chang EHJ 2006 Westerhout Am Heart J 2013;165: e2

27 Cumulative incidence (%) Cumulative incidence (%) CV Death, MI, Stroke Early vs. Late Risk Hospital Discharge Clopidogrel Ticagrelor Clopidogrel Ticagrelor HR 0.88 (95% CI ), p= HR 0.80 (95% CI ), p< No. at risk Days after randomisation Days after randomisation * Ticagrelor 9,333 8,942 8,827 8,763 8,673 8,543 8,397 7,028 6,480 4,822 Clopidogrel 9,291 8,875 8,763 8,688 8,688 8,437 8,286 6,945 6,379 4,751 *Excludes patients with any primary event during the first 30 days

28 Time dependent risk and potential for long-term antithrombotic therapy Trial Population Treatment PEGASUS MI, 1-3 yrs Ticagrelor COMPASS CAD/PVD Rivaroxaban DAPT stented Clopidogrel

29 Do we have examples of where risk scoring seems to identify patients who should be treated differently?

30 TIMACS Interventions and Timing Early N=1,593 Delayed N=1,438 Coronary Angiography (%) Median time (h ± iqr) 14 (3-21) 50 (41-81) PCI (%) Median time (h ± iqr) 16 (3-23) 52 (41-101) CABG (%) Median time (d ± iqr) 7.7 ( ) 10.8 ( ) Mehta SR et al. N Engl J Med Preliminary 2009;360: Results AHA Nov 7, 2008 Iqr=interquartile range 30

31 TIMACS Death, MI, Stroke at 6 Months Pre-specified Subgroups Characteristic Overall Age < 65 >=65 N Early % Delayed % HR (95% CI) Interaction 0.85 ( ) 0.98 ( ) 0.83 ( ) p-value Female Male ( ) 0.89 ( ) No ST deviation ST deviation ( ) 0.81 ( ) No elevated marker 668 Elevated Marker ( ) 0.81 ( ) GRACE GRACE >= ( ) 0.65 ( ) Mehta SR et al. N Engl J Med Preliminary 2009;360: Results AHA Nov 7, Hazard Ratio (95% CI) Early better Delayed better 31

32 Death/MI/Stroke at 6 mo. (%) TIMACS GRACE Risk Score: Primary Outcome Death, MI or Stroke at 6 mo. HR % CI P= Interaction P= HR % CI P= Delayed Early 5 0 Mehta SR N Engl J Med Preliminary 2009;360: Results AHA Nov 7, 2008 Low/Int Risk GRACE Score < 140 N=2070 High Risk GRACE Score >= 140 N=961 32

33 What is the reason for the risktreatment paradox?

34 Physicians Assessment Not Well Aligned with Quantitative Assessment GRACE Risk Score Physician assessment of risk was associated with use of aggressive treatment Physicians alone were not good at estimating risk Independent Predictors of High Risk According to Treating Physician Independent Predictors Age, y Adjusted OR (95% CI) <65 1 [Reference] P value ( ) ( ) Low (n=285) Intermediate (n=708) High (n=743) Patient Risk Category According to the Treating Physician Previous TIA / stroke 1.33 ( ).047 Previous CABG 0.77 ( ).04 Killip class I 1 [Reference] II 1.64 ( ).02 III/IV 7.93 ( ) <.001 ST-segment deviation 2.12 ( ) <.001 T-wave inversion 1.62 ( ).001 Abnormal cardiac biomarker w/in 24 hrs 5.07 ( ) <.001 Goodman, et al. Arch Int Med 2009;169:

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36 Conclusion: It Is Time to Overcome the Risk-Treatment Paradox Quantitative risk stratification is recognized to be important for optimal care of ACS Physicians gut feeling (alone) about risk is not accurate High risk patients frequently do not receive appropriate aggressive treatment The dynamic nature of patient risk is not adequately appreciated in our current tools Better electronic health system tools, measurement, and feedback is necessary to enhance ability to apply risk assessment to improve outcomes

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