DECLARATION OF CONFLICT OF INTEREST

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Earl Briggs
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1 DECLARATION OF CONFLICT OF INTEREST

2 Probing prognosis in heart failure Daniel R. Wagner CHL/INCCI/CRP-Santé Luxembourg

3 Disclosures Grants: Cordis, Boston Scientific, Medtronic, Sorin, Hexacath, Abbott, Astra-Zeneca Advisory board: Abbott, Astra-Zeneca Stock/royalties: Cardiokine, Monitor-IT, Firalis

5 Probing prognosis in heart failure Is the prediction of prognosis important? ESC guidelines History, examination and imaging Known and recent biomarkers New data New approach based on systems biology

6 Prognosis of my patient Transplantation Devices End-of-life discussion

7 Prognosis? CHF in 2002: occluded LAD, stenting of RCA Diabetes, atrial fibrillation, depression, small CVA, ICD NYHA weight EF 18% 35% 23% 18% 20% 25% 25% 25% 24% 18% Crea Hb nl nl nl nl Pro-BNP 12,906 12,447 32,770 22,014 BNP TNT 2.0 nl nl nl nl nl

8 Prognosis in heart failure: ESC guidelines 2008 Determining prognosis in HF is complex The impact on prognosis of specific treatments is difficult to predict

10 NEJM, 2001

11 EHJ, 2006

12 Circulation, 2011

13 Peripheral muscles 2 weeks post HTX 3 months post HTX

14 Survival in CHF and peak torque index of knee flexors Hulsmann et al., Eur J Heart Fail, 2003

MR and prognosis in chronic HF 100 75 Survivants (%) 50 25 ERO < 20 ----

15 MR and prognosis in chronic HF Survivants (%) ERO < p= MONTHS Lancellotti et al. Circulation 2003

16 EHJ, 2007

17 Eur J Heart Fail, 2009

18 Circulation, 2010

19 B-type natriuretic peptide guided treatment of heart failure and all cause mortality Baseline Patient Characteristics NT-proBNP / Clinical Age Gender LVEF (%) Creatinine (µmol/l) NT-proBNP (pg/ml) (n) (mean ± SD) (M/F) (mean ± SD) (mean ± SD) (median [IQR]) Christchurch pilot 33 / ± / ± ± [ ] TIME-CHF 251 / ± / ± ± [ ] PRIMA 174 / ± / ± ± [ ] Vienna 92 / ± / ± ± [ ] BATTLESCARRED 121 / ± / ± ± [ ] SIGNAL-HF 126 / ± / ± ± [ ] STARBRITE 68 / ± / ± ± 57 BNP = 640 [ ] PROTECT 75 / ± / ± ± [ ] TOTAL 941 / ± / ± ± 48

20 All Cause Mortality Age Interaction Age < 75 years HR=0.53 [ ]; p=0.001 Age > 75 years HR = 0.99 [ ]; p=0.97 BNP-guided BNP-guided Clinically -guided Clinically -guided

21 Discharge BNP and Outcome Data from OPTIMIZE-HF 7039 patients age > 65 1-year mortality: 35% 1-year rehospitalization: 79% NRI (net reclassification index) over clinical variables for BNP 5.5% for mortality 4.2% for rehospitalization Kociol RD et al., Circ Heart Fail 2011

22 I-Preserve Trial Multivariable* Analysis of Primary Endpoint by Quartile of baseline NT-proBNP HR and 95% CI p-value Primary Endpoint NT-proBNP Quartile 1 (n=870) NT-proBNP Quartile 2 (n=867) NT-proBNP Quartile 3 (n=873) NT-proBNP Quartile 4 (n=870) p< p< p< Hazard ratio *Age, sex, NYHA class, ischemic etiology, hypertension etiology, history of A Fib, DM, COPD, hospitalization in the previous 6 months, BMI, JVD, BP, HR, Hgb, pulmonary congestion on CXR, EF, egfr, serum albumin, sodium, and neutrophil count.

23 I-Preserve Trial Multivariable Analysis* of the Effect of Irbesartan by baseline NT-proBNP median HR and 95% CI p-value Primary Endpoint All Patients (n=3480) Patients with NT-proBNP < median Patients with NT-proBNP > median Irbesartan better Hazard ratio p=0.37 p= Interaction p= p= Placebo better *Age, sex, NYHA class, ischemic etiology, hypertension etiology, history of A Fib, DM, COPD, hospitalization in the previous 6 months, BMI, JVD, BP, HR, Hgb, pulmonary congestion on CXR, EF, egfr, serum albumin, sodium, and neutrophil count.

24 Troponin and prognosis in HF Kociol et al., JACC, 2010

25 NEJM, 2008

26 Circulation, 2007

29 Circulation, 2008

30 Circ Res, 2010

31 Circ CV Genetics, 2010

32 NEJM, 2008

33 Network approach: step #1: inputs List of known HF biomarkers: 37 List of candidate genes that are functionally similar to this set: 68 Inputs: list of 105 proteins Azuaje et al., BMC Sytems Biology, 2010

34 Network approach: step # 2: PPI network Inputs and all known interacting proteins (databases BioGRID, MINT) Total: 772 proteins

35 Network approach step #3: clustering into modules

36 Network approach step #4: highways, hubs and traffic Top 20 traffic hotpots: 6 OLD: MMP2, MMP9, IL6, Chromogranin B (CHGB) 14 NEW: Fibronectin 1 (FN1) and integrin B 1 (ITB1): regulate leukocyte adhesion

37 Network approach with integration of OMICS data Bioinformatics, 2010

38 Network approach with OMICS data Funct Int Genomics, 2010

39 Candidate biomarker: VEGFB

40 Candidate biomarker: TGFBR1 Reclassification performance of TGFBR1 over a clinical model including age, gender and time to reperfusion

41 Network approach: drug-drug, drug-protein, protein-protein interactions Drugs: green and blue, drug targets: orange, other proteins: grey Azuaje et al., Nature Scientific Reports, 2011

42 Conclusions Prediction of prognosis in HF is difficult Integration of clinical and biological data may be helpful The benefit of recent biomarkers remains to be demonstrated A systems biology approach may help to find new biomarkers A systems biology approach may help to better understand the underlying mechanisms of the failing heart and to improve therapeutic interventions

43 My patient s perspective of prognosis Until 2005 Until 2010

Biomarker-guided HF: What have we learned (so far)?

Biomarker-guided HF: What have we learned (so far)? James L. Januzzi, Jr, MD, FACC, FESC Associate Professor of Medicine Harvard Medical School Director, Cardiac ICU Massachusetts General Hospital DECLARATION