Cleveland Heartlab 5th Annual Symposium: James Januzzi, Assessment of Heart Failure: Early Identification and Across the Spectrum

1. Assessment of Heart Failure:
Early identification and across the spectrum
James L. Januzzi, Jr, MD, FACC, FESC
Hutter Family Professor of Medicine, Harvard Medical School
DeSanctis Endowed Clinical Scholar, Massachusetts General Hospital
Cardiometabolic Trial Group, Harvard Clinical Research Institute

2. Disclosures
• I disclose the following relationships with industry that
are relevant to my talk:
– Grants: Roche Diagnostics, Critical Diagnostics, Singulex,
Thermo Fisher
– Consulting and/or CEC participation: Roche Diagnostics, Critical
Diagnostics, Amgen, Zensun, Novartis, Radiometer, Boeringer-
Ingelheim
• I will discuss off-label use of biomarkers
• I will not torture you (much) with deep discussions
regarding the biology of the various markers we have
studied (though I think they are all extremely interesting).

4. Why should we care about heart failure?
Lifetime Risk for CHF by Sex and Age
Men Women
0.25
0.2
0.15
0.1
0.05
0
40 50 60 70 80 90
Attained Age
Lloyd-Jones et al. Circulation 2002
Cumulative Risk
0.25
0.2
0.15
0.1
0.05
0
40 50 60 70 80 90
Attained Age

5. Age-adjusted hospitalization rates
for heart failure
Heart failure is the #1 DRG in patients >65 years
Heart failure is the only CV diagnosis increasing in incidence
Fang, J. et al. J Am Coll Cardiol 2008;52:428-434

6. Acute/sub-acute HF outcomes
35
30
25
20
15
10
5
0
United States Canada
% mortality
30 days
1 year
Ko, Arch Intern Med 2005

7. Improvement in Heart Failure
Assessment and Management is Needed
• Direct and indirect cost estimates for HF up
to $56 billion USD annually
• Average HF Admission costs between $7,000
- $13,000 USD/admission
• Re-hospitalization rate: 50% within 6 months
• ACA has made HF readmission a major focus
for improvement
Berkowitz et al Lippincotts Case Manag. 2005
Schlendorf et al Curr Treat Options in Cardiovasc Med 2011

8. The challenges of good heart
failure care…

9. Although HF is common…
• It is a challenging diagnosis to recognize and
manage!
– Symptoms vary
– Signs may be difficult to identify on exam
– Assessing severity of HF and treating it adequately
may be very hard

11. Diagnostic Uncertainty is Associated
with Poor Prognosis in Acute Dyspnea
31% of subjects in
PRIDE were judged
uncertainly by the
managing physician
Their prognosis was
significantly worse, with
higher rates of death and
re-hospitalization and
longer lengths of stay!
Indecision present (n=185)
Indecision absent (n=407)
0 73 146 219 292 365
Days from presentation
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
Cumulative hazard (%)
Green et al, Arch Int Medicine, 2008;168:741
Log rank P <.001

12. Assessment of Heart Failure
No gold standard for the evaluation,
prognostication and management of HF exists
History and Physical
What about diagnostic
testing?
Can tools be found to
provide objective means to
“parse the phenotypes of
HF”?
Januzzi, “Parsing the phenotypes of heart failure”; JACC, 2014

13. “Markers?”

14. van Kimmenade and Januzzi, Clin Chem 2011

15. The evolving role of HF biomarkers
Diagnosis
Prognosis
• Management

16. Braunwald, Am Jour Med, 1964

18. Indication Class LOE
Assist in the diagnostic evaluation of HF I A
Assist in the prognostic evaluation of HF I A
Biomarker guided HF care
To assist in achievement of GDMT IIa B
Biomarker guided HF care
To improve outcome in chronic HF IIb B
Yancy, et al, 2013

19. Results: NT-proBNP Levels
P<.001
1175
No prior CHF (N=355) Prior HF (N=35) Acute HF (N=209)
Not acute HF (N=390)
4435
4500
4000
3500
3000
2500
2000
1500
1000
500
0
NT-proBNP (pg/ml)
Januzzi et al, AJC 2005

20. Diagnostic Uncertainty is Associated
with Poor Prognosis in Acute Dyspnea
31% of subjects in
PRIDE were judged
uncertainly by the
managing physician
Their prognosis was
significantly worse, with
higher rates of death and
re-hospitalization and
longer lengths of stay!
Indecision present (n=185)
Indecision absent (n=407)
0 73 146 219 292 365
Days from presentation
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
Cumulative hazard (%)
Green et al, Arch Int Medicine, 2008;168:741
Log rank P <.001

21. REDHOT Study: BNP Values
& Patient Disposition
976
767
1200
1000
800
600
400
200
0
Discharged Admitted
• BNP values blinded to
physicians judging severity
of HF
• BNP median values ~22%
higher in patients discharged
home from E.D.
BNP (pg/ml)
Maisel, et. al, JACC, 2004

22. REDHOT Study:
Baseline BNP Values and Mortality
Alive
Deceased
764 727
1224
2096
2500
2000
1500
1000
500
0
30 Day 90 Day
BNP (pg/ml)
Maisel, et. al, JACC, 2004

23. NT-proBNP improves accuracy of
ADHF diagnosis
All subjects had an NT-proBNP
> age-adjusted URL
Un-blinded NT-proBNP
results led to considerable
increase in the correct
diagnosis of ADHF
Meisel, et al, EHJ Acute Cardiac Care 2012
p < 0.007
75
59
25
41
100
80
60
40
20
0
Un-blinded Blinded
% patients
AHF-AHF+

24. Effect of Selective NT-proBNP Testing On
Costs/Outcomes:
Results of the Randomized IMPROVE-CHF Trial
Effect of Selective NT-proBNP
Testing on Utilization/Costs
Effect of Selective NT-proBNP
Testing on Outcomes
Moe, et al 2007, Circulation

25. Optimizing Natriuretic Peptide
Use in Acute Diagnosis:
The importance of logical
interpretation

26. Correlations of Natriuretic Peptides with
Cardiac Structure and Function
• Left ventricle
– Size
– Systolic function
– Diastolic function
• Right ventricle
– Size
– Systolic function
• Atrial size and pressure
• Valve disease
– Aortic
– Mitral
– Tricuspid
• Heart rhythm
• Ischemic heart disease
• Pericardial disease

27. Correlations of Natriuretic Peptides with
Cardiac Structure and Function
• Left ventricle
– Size
– Systolic function
– Diastolic function
• Right ventricle
– Size
– Systolic function
• Atrial size and pressure
• Valve disease
– Aortic
– Mitral
– Tricuspid
• Heart rhythm
• Ischemic heart disease
• Pericardial disease

28. Clinical correlates of elevated NPs
• Heart failure
• ACS
• Heart muscle disease
• Pericardial disease
• Valvular heart disease
• Atrial fibrillation
• Pulmonary hypertension
• Myocarditis
• Cardiac surgery
• Congenital heart disease
• Cardioversion
• Advancing age
• Anemia
• Pulmonary embolism
• Sleep apnea
• Critical illness
• Sepsis
• Burns
• Toxic-metabolic insults
• Renal failure

29. Clinical correlates of elevated NPs
• Heart failure
• ACS
• Heart muscle disease
• Pericardial disease
• Valvular heart disease
• Atrial fibrillation
• Pulmonary hypertension
• Myocarditis
• Cardiac surgery
• Congenital heart disease
• Cardioversion
• Advancing age
• Anemia
• Pulmonary embolism
• Sleep apnea
• Critical illness
• Sepsis
• Burns
• Toxic-metabolic insults
• Renal failure

30. The evolving role of HF biomarkers
Diagnosis
Prognosis
• Management

31. Indication Class LOE
Assist in the diagnostic evaluation of HF I A
Assist in the prognostic evaluation of HF I A
Biomarker guided HF care
To assist in achievement of GDMT IIa B
Biomarker guided HF care
To improve outcome in chronic HF IIb B
Yancy, et al, 2013

32. Cumulative Hazard: NT-proBNP
0 365 730 1095 1460
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
NT-proBNP ≥300 ng/L
NT-proBNP <300 ng/L
Days from enrollment
Cumulative hazard
P <.0001
Januzzi, et al., Clin Chem 2012

33. A bad day in the heart failure clinic
Mrs. Smith, I measured
your NT-proBNP and it
is 5524 pg/mL. This
tells me that you’re
likely to be hospitalized
soon. Isn’t that
interesting?
What the…?

34. Why might we need biomarkers
for prognostication?
• To supplement clinical judgment
– Grading severity of HF
• To provide information on processes not entirely
obvious at the bedside
• To offer unique information regarding therapeutic
intervention
– To monitor risk
– To guide therapy?
• To prevent onset of disease?

35. Why might we need biomarkers
for prognostication?
• To supplement clinical judgment
– Grading severity of HF
• To provide information on processes not entirely
obvious at the bedside
• To offer unique information regarding therapeutic
intervention
– To monitor risk
– To guide therapy?
• To prevent onset of disease?

36. Remodeling in ACS and HF
• Remodeling is not felt
• Remodeling is not detectable with physical exam (until it’s too late)
• Imaging can see remodeling…but then it’s too late
• Predicting remodeling with imaging is imperfect

37. Achieved NT-proBNP and risk for
remodeling
Remodeling index Hazard ratio* 95% CI P value
Increase in LVEDVi 1.56 1.15-2.12 0.004
Increase in LVESVi 1.64 1.16-2.31 0.005
Fall in LVEF 1.72 1.17-2.41 0.01
*Hazard ratio refers to risk for remodeling per log-unit of NT-proBNP at
the end of the study.
NT-proBNP entered as log-transformed due to non-normality. Model
adjusted for baseline log-transformed NT-proBNP, age, diabetes,
ischemic heart disease and New York Heart Association Symptom
Severity.
Weiner et al, Eur Jour Heart Fail 2013

38. Causes of Troponin Release in
HF
Selected causes of reduced oxygen supply:
• Anaemia
• Hypotension
Selected causes of increased myocardial oxygen demand:
• Increased transmural wall stress
• Dilated left ventricular chanber size
• Elevated pressures in cardiac chambers
• Left ventricular hypertrophy
• Diastolic stiffening of the myocardium
1. Januzzi JL Jr, et al. Eur Heart J. 2012;33:2265-2271.
2. Felker GM, et al. Eur J Heart Fail. 2012;14:1257-1264.
cTnI by Etiology
P = 0.77
0.05
0.04
0.03
0.02
0.01
0
Ischemic
Non-Ischemic
Myocardial
apoptosis or
autophagy
Coronary ischaemia due
to epicardial CAD or
endotheial dysfunction
Proteolysis or turnover
of myocardial
contractile proteins
Direct toxicity
of circulating
neurchormones,
inflammation,
infitrative processes,
etc.
Supply demand
mismatch with
subendocardial
ischaemia

39. 99th Percentile for Troponin T
300
250
200
150
100
50
0
Old URL = 0.03 ng/mL
(imprecision 10%)
0 2 4 6 8 10 12 14 16 18 20 22
TnT [pg/mL]
Frequency abs.
Blood donors (n=1251)
Apparently healthy individuals (n=500)
99th percentile= 13 pg/mL
(imprecision 10%)
50

40. High sensitivity troponin in ambulatory stage
C/D heart failure: Single measure
Mortality
Hospitalization for HF
Latini R, et al. Circulation. 2007;116:1242-1249.
The vast majority of
patients with chronic HF
in Val-Heft had
measurable hsTnT
Outcomes linear with
value of troponin
Prognostic value of
hsTnT was additive to
natriuretic peptides
Hazard ratio (95% CI)
10
1
0 1 2 3 4 5 6 7 8 9 10
Deciles
40

41. 100
90
80
70
60
50
40
30
20
10
0
hsTnI and remodeling
P <0.001
Never (N=35) Sometimes (N=33) Always (N=31)
% with any rise in LVEF
78.3%
70.0%
59.3%
% time with cTnI 10.9 pg/mL
hsTnI patterns over a
year’s time predicted
reverse remodeling…

42. The importance of galectin-3 in
fibrosis formation

43. Galectin-3 and long term
outcomes in ADHF
P < .001
1
0 . 9
0 . 8
0 . 7
0 . 6
0 . 5
0 . 4
0 . 3
0 . 2
0 . 1
0
Gal-3 elevated/NT-proBNP elevated
0 2 0 0 4 0 0 6 0 0 8 0 0 1 0 0 0 1 2 0 0 1 4 0 0
Days from enrollment
Event free survival
Gal-3 elevated/NT-proBNP low
Gal-3 low/NT-proBNP elevated
Gal-3 low/NT-proBNP low
Shah, et al, 2011

44. Galectin-3 and heart failure onset in the
General Population
Galectin predicts HF onset in the general population
• FHS: galectin-3 measured in a
general population of 3,353
patients
• Increasing galectin-3 levels were
associated with incident HF as well
as increased mortality rates
(p<0.001)
Kim et al, ACC Meeting 2012 44

45. ST2 plays a role in reducing
cardiomyocyte hypertrophy and fibrosis
Abnormalities in ST2 experimentally result in severe
cardiac remodeling and heart failure
Intact sST2 sST2 knock out

46. Value of various markers for
prognosis in acute dyspnea
AUC
0.80
0.76 0.75 0.73 0.70 0.69 0.67 0.65
0.54 0.52
0.78

47. Ramification of change in ST2
in PROTECT: 3 months
60
50
40
30
20
10
0
Low/Low High/High High/Low Low/High
Event rate (%)
HRadj for events for rise >35 pg/mL during study was
3.64 (p =.009)
Model adjusted for clinical variables, ejection fraction,
renal function, NT-proBNP and GDF-15
sST2 predicted change in LV size/function
Gaggin, et al; JACC Heart Failure, 2014

48. sST2 and β blockade
2.5
2
1.5
1
0.5
0
Ref
Low
ST2/high
BB
HR
1.7
Low
ST2/low
BB
HR
2.5
High
ST2/high
BB
HR
6.0
High
ST2/low
BB
Mean CV event rate
Gaggin, et al, Circ Heart Failure, 2013
Therapies interacting with sST2:
• β blockers (PROTECT)
• Angiotensin receptor blockers (Val-
HeFT)
• Mineralocorticoid receptor antagonists
(EPHESUS, COACH)

49. ST2 Predicts HF
in Community Cohort
3
2.5
2
1.5
1
0.5
0
*Adjusted for : age, gender, BMI, systolic blood pressure, hypertension
therapy, total cholesterol, HDL cholesterol, regular cigarette smoking,
presence of diabetes, BNP, CRP, eGFR, prevalent atrial fibrillation,
prevalent cardiovascular disease, electrocardiographic left ventricular
hypertrophy, and heart murmur
Q1 Q2 Q3 Q4
sST2 quartile
Hazard ratio
Wang et al, Circulation, 2012

50. aModel adjusted for age, sex, SBP,
DBP, diabetes, smoking and BMI
Quartile 1 <16.0 ng/mL
Quartile 2 16.0-19.8 ng/mL
Quartile 3 19.8-24.8 ng/mL
Quartile 4 >24.8 ng/mL
Ho, et al, 2013, Journal of Hypertension

51. Why might we need biomarkers
for prognostication?
• To supplement clinical judgment
– Grading severity of HF
• To provide information on processes not entirely
obvious at the bedside
• To offer unique information regarding therapeutic
intervention
– To monitor risk
– To guide therapy?
• To prevent onset of disease?

52. The evolving role of HF biomarkers
Diagnosis
Prognosis
• Management

53. The Importance of Serial NP
Measurements for Prognostication
in Chronic HF
Masson, et al, J Am Coll Cardiol, 2008

54. Therapies with Effects on
B-Type Natriuretic Peptide Levels
Therapy Effect on BNP/NT-proBNP
Diuresis 
ACE-I/ARB 
-blockers 
Aldosterone antagonists 
BiV pacing 
Exercise 
Rate control of AF 
NP infusions 
Serelaxin 
LCZ696 NT-proBNP, BNP
Neuregulin 

55. Indication Class LOE
Assist in the diagnostic evaluation of HF I A
Assist in the prognostic evaluation of HF I A
Biomarker guided HF care
To assist in achievement of GDMT IIa B
Biomarker guided HF care
To improve outcome in chronic HF IIb B
Yancy, et al, 2013
*GDMT=Guideline directed medical therapy

56. Characteristics of outpatient
‘guided therapy’ trials
• Well tolerated
• More often up-titration of therapies in
biomarker guided arm
• When a low target was selected and
natriuretic peptide lowering was achieved,
better outcomes were observed
Januzzi, Journal of Cardiac Failure, 2011

57. Guided Therapy
Combined Analysis
Meta analysis of
publication data
1
1.00 (0.23, 4.43) 2.05
Savarese G, et al. PLoS ONE. 2013;8:e58287.
Troughton EW, et al. Eur Heart J. 2013.
Pooled patient data
from all available trials
.015 66.6
Better Treatment Better Control
Study ID
BNP-guided therapy
Anguita
Beck de Silva
STARBRITE
STARS-BNP
UPSTEP
Subtotal (i-squared = 0.0% p = 0.823)
NT-proBNP-guided therapy
BATTLESCARRED
Berger
PRIMA
PROTECT
SINGAL-HF
TIME-CHF
Troughton
Subtotal (i-squared = 0.0%, p = 0.692)
Overall (i-squared = 0.0%, p = 0.896)
NOTE: Weights are from random effects analysis
OR (95% CI) % Weight
0.45 (0.04, 5.39) 0.74
0.32 (0.03, 3.19) 0.87
0.61 (0.23, 1.64) 4.68
0.95 (0.54, 1.68) 13.94
0.81 (0.52, 1.28) 22.27
0.95 (0.53, 1.70) 13.27
0.64 (0.26, 1.16) 13.25
0.72 (0.45, 1.14) 21.22
0.66 (0.18, 2.43) 2.66
0.98 (0.33, 2.89) 3.92
0.67 (0.42, 1.05) 22.32
0.13 (0.62, 1.12) 0.96
0.72 (0.56, 0.91) 77.73
0.74 (0.60, 0.91) 100.00
1.00
0.95
0.90
0.85
0.80
0.75
0.70
HR=0.59 [0.41-0.84], p<0.001
BNP-guided
0.0 0.5 1.0 1.5 2.0
Years
Proportion Surviving
Clinically-guided

59. Why might we need biomarkers
for prognostication?
• To supplement clinical judgment
– Grading severity of HF
• To provide information on processes not entirely
obvious at the bedside
• To offer unique information regarding therapeutic
intervention
– To monitor risk
– To guide therapy?
• To prevent onset of disease?

60. Stages of CHF — ACC/AHA Guidelines 2013
D
Refractory
C
0.2%
The majority Prior, current of symptoms
patients within
the ACC/AHA HF schema are
B
Stage A/B
Structural heart disease
11.8%
LVH, MI, low LVEF, dilatation, valvular disease
A
High-risk patients
22%
34%
Hypertension, diabetes, coronary disease, family history, cardiotoxic drugs

61. Panel of “cardiovascular stress” markers to
predict CV events in ambulatory well patients:
BNP, hsTnI, ST2, and GDF-15
Wang et al, Circulation 2012
Heart Failure
Adjusted RR
(top quartile):
6.3 (p<0.001)
*Adjusted for : age, gender, BMI, systolic blood pressure, hypertension therapy,
total cholesterol, HDL cholesterol, regular cigarette smoking, presence of
diabetes, BNP, CRP, eGFR, prevalent atrial fibrillation, prevalent cardiovascular
disease, electrocardiographic left ventricular hypertrophy, and heart murmur

62. Can biomarker testing prevent the
onset of heart failure?

63. STOP-HF Hypothesis
 BNP-driven screening and
targeted collaborative
care in the general at-risk
population will decrease
the prevalence of LVD
and HF
 39 collaborating primary
care practices,
intervention provided in a
single referral center

64. STOP HF Primary Endpoint
Routine PCP
care
•Annual BNP not
available to clinicians
• At least annual review
by PCP
• Cardiology review only
if requested by PCP
BNP-directed
care
In addition to routine PCP
care, annual BNP in all
If BNP >50 pg/ml at any time
•Shared-care
 Cardiology review
 Echo-Doppler
 Other CV investigations
 CV nurse coaching
 Cardiology follow-up

65. STOP HF Primary Endpoint
4.0%
3.5%
3.0%
2.5%
2.0%
1.5%
1.0%
0.5%
0.0%
HR = 0.59 (0.38-0.90), P =.01
LVDD LVSD HF
Control
BNP
Incident diastolic
dysfunction
Incident systolic
dysfunction
Incident
heart failure
McDonald, JAMA, 2013

66. What is the role of HF biomarkers?
 Assist in diagnosis of heart failure
 Assist in prognostication of heart failure
↑ Assist in heart failure therapy decision-making
? Predict important processes in heart failure
– Prevent onset of heart failure?

67. Conclusions
• HF is a common and growing diagnosis in modern cardiology.
• The diagnosis is associated with a high rate of morbidity and
mortality, and is extremely costly.
• Improvements in HF therapy include both better agents to treat
the diagnosis as well as newer approaches to evaluate patients.
• BNP and NT-proBNP are now Class I guideline supported for
diagnosis and prognosis in HF, and increasingly used to make
therapy decisions.
• A good understanding of the role played by biomarkers in HF is
crucial to better understand the needs of the practicing
clinician.

68. Molecular diagnostics and cancer treatment
Patient blood DNA analysis
Germline sequence data allows drug classes and
dosing to be tailored to that patient
“Tailored” therapy
Tumor genotype identifies specific “Achilles’ heel”
for that tumor
Cancer patient
Blood test
Clinical information
Surgical resection
Molecular
pathology
Standard pathology
Slide courtesy of David Louis, MD

69. Assessment of Heart Failure:
Early identification and across the spectrum
James L. Januzzi, Jr, MD, FACC, FESC
Hutter Family Professor of Medicine, Harvard Medical School
DeSanctis Endowed Clinical Scholar, Massachusetts General Hospital
Cardiometabolic Trial Group, Harvard Clinical Research Institute