Transcript Reducing Heart Failure Readmissions: Case Studies

Reducing Heart Failure Readmissions:
Case Studies Utilizing Biomarkers
for Risk Stratification
HeartFailureCases.com
Reducing Heart Failure Readmissions:
Case Studies Utilizing Biomarkers for Risk Stratification
• Accredited by Educational Review Systems (ERS)
• Supported by an educational grant from:
Critical Diagnostics
• Content support provided by Medavera, Inc.
• Date of release: April 1, 2014
• Date of expiration: March 31, 2016
• Estimated time to complete this educational activity:
1 Hour
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Continuing Education Credit(s)
• Physicians: 1.0 hour
– This Enduring Material activity, Reducing Heart Failure Readmissions:
Case Studies Utilizing Biomarkers for Risk Stratification, has been reviewed
and is acceptable for up to 1.00 Prescribed credit(s) by the American Academy
of Family Physicians. AAFP certification begins 04/01/2014. Physicians should
claim only the credit commensurate with the extent of their participation in
the activity. Program expires 3/31/2016.
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programs in the clinical laboratory sciences by the ASCLS P.A.C.E. Program.
This program is approved for 1 hour of continuing education credit.
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Continuing Education Credit(s)
• Statement of Need
– There are approximately 5.8 million people in the U.S. with heart failure
resulting in 1 million annual hospitalizations. With rehospitalization rates
reaching nearly 25%, heart failure care has been targeted by the Centers
for Medicare & Medicaid Services (CMS) for improvements. Biomarkers
specific to the determinants of heart failure readmissions may play an
increasingly prominent role in risk assessment of patients, tailoring
of therapy, and possible reduction of short term hospital readmissions.
The current climate is demanding solutions and additional education
and discussion is needed.
• Intended Audience
– Health care professionals (physicians, nurses, laboratorians etc.)
involved in the care of patients with heart failure.
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Continuing Education Credit(s)
• Instructions
For CME credit, please view the slides and
1. Take the online post-test
Or
2. Download and print the CME application
and fax to 678.401.0259.
Questions? Call Medavera, Inc. at : 417.890.9722
Or email: [email protected]
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Medical Advisors for Activity
James L. Januzzi Jr, MD, FACC, FESC
Roman W. Desanctis Endowed Clinical Scholar
Director, Cardiac ICU, Massachusetts General Hospital
Associate Professor of Medicine,
Harvard Medical School
Boston, Massachusetts
Aurelia M. O’Connell, PhD, ACNP, BC, RN, PHN, FAHA
Associate Professor
Azusa Pacific University
School of Nursing
Azusa, California
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Faculty Disclosures
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Faculty
Commercial Interest
Honorarium
James L. Januzzi Jr
Roche
Critical Diagnostics
Siemens
BG Medicine
Grant
Grant, Honorarium
Grant
Grant
Aurelia M. O’Connell
None
None
Learning Objectives
1. Review current statistics on heart failure incidence, trends,
and readmission rates.
2. Identify CMS initiatives to reduce Medicare readmissions
and penalties that are and will continue to be assigned.
3. Summarize the changes in the 2013 update to the ACCF/AHA
Guideline for the Management of Heart Failure.
4. Assess various biomarkers used in heart failure
prognostication in pathophysiology, clinical trial evidence,
and clinical rationale.
5. Apply information learned in case studies to real-life care
scenarios to risk stratify heart failure patients.
6. Utilize information provided toward implementation
of strategies that will reduce heart failure readmissions
within one’s own institution.
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Heart Failure Statistics and Trends
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Heart Failure Statistics
• Heart failure (HF) is one of the most rapidly
increasing cardiovascular disorders.
• Leading cause of hospitalization in individuals
over 65 years of age1
• Third leading cause of hospitalization in the
U.S. in all age groups2
HF is the most common cause of readmission.3
Rates approach 30% within 60-90 days of discharge.4
1Krumholz
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HM, Chen YT, Wang Y et al. Am Heart J. 2000;139(1 Pt 1):72–7..
2Heart Disease and Stroke Statistics—2012 Update. Circulation. 2012;125:e2-220.
3Jencks SF, Williams MV, Coleman EA. N Engl J Med. 2009;360:1418-28.
4Gheorghiade M, Vaduganathan M, Fonarow GC et al. J Am Coll Cardiol. 2013;61:391-403.
Hospital Discharges for HF Are Increasing
1979-2009
Discharge in Thousands
700
600
500
400
Female
Male
300
200
100
0
1979 1980 1985 1990 1995 2000 2005 2009
Years
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National Hospital Discharge Survey/National Center for Health Statistics
and National Heart, Lung, and Blood Institute. 2008.
Projected Prevalence and Cost of HF
4
90
3.5
80
3
70
2.5
60
2
1.5
1
0.5
25% Increase
2010 2015 2020 2025 2030
Year
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40
30
20
0
12
Projected U.S. Direct Costs
for Heart Failure
Billions ($)
Percent (%)
Projected U.S. Prevalence
of Heart Failure
10
215% Increase
0
2010 2015 2020 2025 2030
Year
Konstam MA. Circulation.2012;125:820-7.
What is the Problem With Readmissions?
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CMS and Heart Failure
• Medically unnecessary treatment of HF is one
of the most claimed improper payments.1
• HF is the 4th highest diagnosis in recovered payments by
the Center for Medicare & Medicaid Services (CMS).1
• Average cost per rehospitalization is $22,700
per patient.2
• Providers must differentiate themselves based on
quality of care, patient satisfaction, and transparency.
1CMS.
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2Agency
The Medicare Recovery Audit Contractor(RAC) Program: An Evaluation of the 3-year Demonstration. 2008.
for Healthcare Research and Quality, US Department of Health & Human Services (hcupnet.ahrq.gov). 2010.
HF readmissions are a key opportunity to remain
competitive in an increasingly transparent environment.
Low Readmission Rates
Kaiser Foundation Hospital
Los Angeles, CA
Mercy Hospital
Buffalo, NY
Baylor University Medical Center
Dallas, TX
High Readmission Rates
University Hospital of Brooklyn
Brooklyn, NY
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Boston Medical Center
Boston, MA
Kendall Regional Medical Center
Miami, FL
Patients can see readmission rates for any hospital at www.medicare.gov/HospitalCompare.
CMS and Medicare Readmission Penalties
• Nearly 25% of all patients hospitalized for heart failure
are readmitted within 30 days.
• CMS has labeled HF as an area of excessive readmission.
• CMS penalties will ensue to reduce readmission rates.
Penalties Will Reduce Medicare Payments
Percent of Payments
Received
101
100
99
98
97
96
95
1% Loss
FY 2012
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FY 2013
2% Loss
FY 2014
3% Loss
FY 2015
http://www.ama-assn.org/amednews/2012/08/27/gvsb0827.htm. American Medical Association.
Accessed online 12/28/2012.
Rehospitalization Diagnoses
• In a recently published study, 30-day readmissions
were analyzed in 329,308 patients from 2007 to 2009.
• Heart failure was the most common diagnosis for
rehospitalization.
Most Common Rehospitalization Diagnoses
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Heart Failure
Acute MI
Pneumonia
Heart failure (%)
35.2
19.3
8.53
Renal disorders (%)
8.11
5.28
5.27
Pneumonias (%)
4.98
4.89
22.4
Arrhythmias and conduction disorders (%)
4.04
4.95
2.68
Septicemia/shock (%)
3.55
3.96
5.95
Cardiorespiratory failure (%)
3.50
3.14
4.69
Dharmarajan K, Hsieh AF, Lin Z, et al. J Am Med Assoc. 2013;309:355-63.
Readmission and Index Hospitalization
• Also, in this analysis, heart failure was the most common
readmission diagnosis regardless of index hospitalization.
• The prior admission is referred to as the “index hospitalization.”
In the event that there is more than one discharge from an acute
care hospital within a 30-day period, the index hospitalization
is the hospitalization closest in time to the readmission.
Parameter
Heart Failure
Acute MI
Pneumonia
Readmission rate (%)
24.8
19.9
18.3
Readmissions for indication of index hospitalization (%)
35.2
10.0
22.4
12
10
12
61.0
67.6
62.6
Median time to 30-day readmission (d)
Readmissions in post discharge days 1 to 15 (%)
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Dharmarajan K, Hsieh AF, Lin Z, et al. J Am Med Assoc. 2013;309:355-63.
Conclusions of Readmissions Analysis
“The diagnoses associated with 30-day readmission are diverse and
are not associated with patient demographic characteristics or time
after discharge for older patients initially hospitalized with HF, acute
MI, or pneumonia. Although a high percentage of 30-day readmissions
occurred relatively soon after hospitalization, readmissions remained
frequent during days 16 through 30 after discharge regardless of
patient age, sex, or race.”
“This heightened vulnerability of recently hospitalized patients to
a broad spectrum of conditions throughout the post discharge period
favors a generalized approach to preventing readmissions that
is broadly applicable across potential readmission diagnoses and
effective for at least the full month after hospitalization.”
“Strategies that are specific to particular diseases or periods may
only address a fraction of patients at risk for rehospitalization.”
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Dharmarajan K, Hsieh AF, Lin Z et al. J Am Med Assoc. 2013;309:355-63.
HF Readmission Comorbidities
Heart Failure
Renal Disorders
Arrhythmias
Pneumonias
Cardiac Failure
The large number of potential
comorbidities dramatically
increases the complexity
of heart failure readmissions.
Septicemia/Shock
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Dharmarajan K, Hsieh AF, Lin Z et al. J Am Med Assoc. 2013;309:355-63.
CMS Readmission Adjustment Scenario
HF Admissions
200
Average Payment
$20,000
Actual Readmissions Expected Readmissions
50
51
Hospital-specific readmissions
adjustment factor
51
/
50
= 1.02 – 1 = 0.02
Current readmission
penalty formula:
200
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$20,000
x 0.02 = $80,000 Loss
http://cms.gov/Medicare/Medicare-Fee-for-Service-Payment/AcuteInpatientPPS/Readmissions-Reduction-Program.html.
Accessed 01/01/13.
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CMS Readmission Penalties in 2014
• 2,225 hospitals will loose up to 2% of Medicare
reimbursements for a total of $227 million in fines
–
–
–
–
Decreased fines for 1,371 hospitals
Increased fines for 1,074 hospitals
Fine 283 hospitals that were not fined in FY2013
18 hospitals will receive the maximum 2% penalty
• FY2014 will include a modification that allows
for planned readmissions
– Would have reduced FY2013 penalties by 1.5% for HF
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http://www.advisory.com/daily-briefing/2013/08/05/cms-2225-hospitals-will-pay-readmissions-penalties-next-year.
http://www.advisory.com/Daily-Briefing/2013/05/01/CMS-wants-to-exclude-more-readmissions-from-penalty-program.
Accessed online 2/1/2014.
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What is Next in 2015?
• Maximum penalty will be raised to 3%
• The number of conditions eligible for penalties
will be expanded.
– Chronic lung disease
– Elective hip and knee replacements
• CMS may include a rate for ALL of a hospital’s
readmissions as part of its penalty calculations.
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http://www.advisory.com/daily-briefing/2013/08/05/cms-2225-hospitals-will-pay-readmissions-penalties-next-year.
Accessed online 2/1/2014.
What is Working to Help
Reduce Heart Failure Readmissions?
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What is NOT Working?
• Decreased length of stay (LOS)1
– Decreased LOS is correlated with increased
readmissions and post-discharge mortality.
• Patient compliance2
– Despite a higher risk profile, non-adherent patients
have a shorter LOS and mortality risk.
• Outdated guidelines
– Previous 2009 HF guidelines did not include
new biomarker data.
– 2013 guidelines have been updated to include
information on new biomarkers.3
1Bueno
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H, Ross JS, Wang Y et al. J Am Med Assoc. 2010;303:2141-7.
AV, Fonarow GC, Hernandez AF et al. Am Heart J. 2009;158:644-52.
3Yancy CW, Jessup M, Bozkurt B et al. J Am Coll Cardiol. 2013;62(16):e147-239. Epub 2013 Jun 5.
2Ambardekar
What is Working?
• Basoor’s Heart Failure
Checklist©
– 27-question discharge checklist
– Cut 30-day readmissions
from 19% to 6%
– Readmission rates continued
to be lower after six months.
•
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http://www.cardiosource.org/News-Media/Media-Center/News-Releases/2012/03/HF-Checklist.aspx. Accessed 2/3/14.
Basoor A, Doshi NC, Cotant JF et al. Cong Heart Fail. 2013;19:200-6.
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What is Working?
• Brigham and Women’s Hospital
– 10,731 discharges
– 2,398 readmissions
• Computerized algorithm for 30-day readmissions
– Based on past administrative discharge data
• Prediction score identified independent factors
which can be used to calculate risk.
– 879 readmissions were identified as potentially avoidable
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Donze J, Aujesky D, Williams D, Schnipper JL. J Am Med Assoc Intern Med. 2013;173(8):632-8.
What is Working?
• Seven risk factors were identified
as part of the 30-day
readmissions algorithm.
• HOSPITAL Risk Prediction Model
1. Hemoglobin at discharge
2. Discharge from Oncology
3. Sodium at discharge
4. Procedure during index admissions
5. Index Type
6. Admissions in the last 12 months
7. Length of stay
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Donze J, Aujesky D, Williams D, Schnipper JL. J Am Med Assoc Intern Med. 2013;173(8):632-8.
What is Working?
• For each of the seven factors,
risk is calculated with 1-2 points
per factor based on severity.
• Patients with 7 or more points have an 18% risk
of potentially avoidable readmission within 30 days.
• Model can be used before discharge to assess
the risk of readmissions.
– May be used to identify patients who need more
intensive transitional care
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Donze J, Aujesky D, Williams D, Schnipper JL. J Am Med Assoc Intern Med. 2013;173(8):632-8.
What is Working?
• Two rural South Dakota hospitals
– Avera Tri-State Affiliates Hospitals in Souix Falls, SD
– Patients were from a general hospital or a cardiac
specialty hospital
• Intensive transitions of care program
• Self-management training of patients
• Appropriate outpatient follow-up and monitoring
of the patient by the health care system
A 42% relative reduction in 30-day readmission rates
was documented in patients participating in the program.
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Huntington MK, Guzman AI, Roemen A et al. S D Med. 2013;66(9):370-3.
What is Working?
• The following six strategies have been associated
with lower 30-day HF readmission rates:
1.
2.
3.
4.
5.
Partnering with community physicians and groups
Partnering with local hospitals
Having nurses responsible for medication reconciliation
Arranging for follow-up visits before discharge
Having a process in place to send discharge or electronic
summaries directly to the patient’s primary care physician
6. Assigning staff to follow up on test results
after the patient is discharged
The more strategies are used by a single institution,
the lower the readmission rates.
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Bradley EH, Curry L, Horwitz LI et al. Circ Cardiovasc Qual Outcomes. 2013;6:444-50.
What Assessments Are Working?
• LVF assessment1
– Readmitted patients are twice as likely to not have
left ventricular function (LVF) assessments.
• Blood glucose at presentation2
– Independent predictor of 30-day mortality
– Easily modifiable, potential therapeutic target
• Biomarker assessments3-4
– Independent predictors of mortality and readmission
– Single and multimarker assessments
1Mazimba
2Mebazaa
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S, Grant N, Parikh A et al. Am J Med Qual. 2012 Oct 30. [Epub ahead of print].
A, Gayat E, Lassus J et al. J Am Coll Cardiol. 2012 Jan 16. [Epub ahead of print].
3Noveanu M, Breidthardt T, Potocki M et al. Crit Care. 2011;15:R1.
4Zaya M, Phan A, Schwarz ER. World J Cardiol. 2012;4:23–30.
The 2013 ACCF/AHA Guideline
for Heart Failure Management
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The 2013 ACCF/AHA Guideline for Heart
Failure Management: Emphasis on Care
• Validated multivariable risk scores can be useful to estimate
subsequent risk in HF patients.
• Use of additional biomarkers for diagnosis, risk stratification,
and prognosis is recommended.
• Use of non-pharmacological interventions are encouraged.
– Patient education, exercise, diet
• New pharmacological recommendations:
– Aldosterone antagonists and digoxin could be beneficial
– Combined use of angiotensin-converting-enzyme inhibitor,
angiotensin receptor blocker, and aldosterone antagonist
is considered potentially harmful
• Device therapy recommendations now include
NYHA Class I and II HF.
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Yancy CW, Jessup M, Bozkurt B et al. J Am Coll Cardiol. 2013;62(16):e147-239.
Highlights for the 2013 ACCF/AHA Guideline
for Heart Failure Management
• Participation in performance improvement processes
based on professionally developed clinical practice
guidelines
• Care coordination and transitions of care from primary
care physicians to cardiologists, to palliative care
and hospice
• Shared decision making between patients
and family members
• Improvement in quality of life as well as survival
and performance metrics
• Importance of education, informed decisions
for next steps and advanced directives
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Yancy CW, Jessup M, Bozkurt B et al. J Am Coll Cardiol. 2013;62(16):e147-239.
2013 Heart Failure Guidelines Recommend
Additional Biomarkers for Hospitalized and Acute Patients
2013 ACCF/AHA Guideline for the Management of Heart Failure
• Markers of myocardial fibrosis are predictive of hospitalization
and death in patients with HF.
• These markers are also additive to natriuretic peptide levels
in their prognostic value.
• ST2 and Gal-3 are recognized markers for myocardial fibrosis.
Class IIb
1. The usefulness of BNP- or NT-proBNP−guided therapy for acutely
decompensated HF is not well established (259, 260). (Level of Evidence: C)
2. Measurement of other clinically available tests such as biomarkers of
myocardial injury or fibrosis may be considered for additive risk stratification
in patients with acutely decompensated HF (248, 253, 256, 257, 261-267).
(Level of Evidence: A)
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Yancy CW, Jessup M, Bozkurt B et al. J Am Coll Cardiol. 2013;62(16):e147-239
Recommendations for Biomarkers in HF
Setting
COR
LOE
Diagnosis or exclusion of HF
Ambulatory, Acute
I
A
Prognosis of HF
Ambulatory, Acute
I
A
Ambulatory
IIa
B
Acute
IIb
C
Ambulatory
IIb
B
Natriuretic peptides
Achieve GDMT
Guidance for acutely
decompensated HF therapy
Biomarkers of myocardial fibrosis
(Gal-3, ST2)
Additive risk stratification
COR, class of recommendations; GDMT, guidance directed medical therapy; LOE, level of evidence
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Adapted from Yancy CW, Jessup M, Bozkurt B et al. J Am Coll Cardiol. 2013;62(16):e147-239
Which Biomarkers Are Being Used
for Patient Risk Stratification?
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Heart Failure Biomarkers Have
Distinct Mechanisms of Action
Myocardial Insult
Myocyte Stretch
BNP, NT-proBNP
Mycardial Injury
Troponins
Oxidative Stress
Myeloperoxidase, oxLDL
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Maladaptive Remodeling
Neurohormonal Activation
Inflammation
C-reactive protein, tumor
necrosis factor-α, Fas,
interleukins, osteoprotegerin,
adiponectin
Hypertrophy/fibrosis
Matrix metalloproteinases,
collagen propeptides,
galectin-3, soluble ST2
Apoptosis
Renin Angiotensin System
Renin, angiotensin II,
aldosterone
Sympathetic Nervous System
Norepinephrine, chromogranin A
Arginine Vasopressin System
Arginine vasopressin
Kim HN, Januzzi JL. Curr Treat Options Cardiovasc Med. 2010;12:519-31.
What Is a Useful HF Biomarker?
Criteria
Able to be used for serial testing
Changes only reflect disease progression
Not affected by changes during acute phase of HF
No differences with regards to age, gender,
body mass index, or other medical conditions
Levels decrease in response to successful therapy
Compliments or exceeds effectiveness of existing tests
Low reference change value (RCV)
(Value attributed to normal biological variation)
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Wu A. eJIFCC. 2012;23:1-5.
Biomarkers in HF Classification
NYHA
Class1
I
II
III
IV
Symptoms1
Predictive
Biomarkers2
Cardiac disease, but no symptoms and no limitation
in ordinary physical activity, e.g. shortness of breath
when walking, climbing stairs etc.
Mild symptoms (mild shortness of breath and/or angina)
and slight limitation during ordinary activity.
Yes
Marked limitation in activity due to symptoms,
even during less-than-ordinary activity, e.g. walking
short distances (20–100 m). Comfortable only at rest.
Yes
Severe limitations. Experiences symptoms even while
at rest. Bedbound patients.
Yes
Biomarkers can be predictive of NYHA classes > II.2
1The
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Criteria Committee of the New York Heart Association. Nomenclature and Criteria for Diagnosis of Diseases
of the Heart and Great Vessels. 9th ed. Boston, Mass: Little, Brown & Co; 1994:253-6.
2Silva Marques J, Luz-Rodrigues H, David C et al. Rev Port Cardiol. 2012;31:701-10.
Role of Biomarkers in HF Readmissions
• Biomarkers may predict which patients are at
increased risk for readmission.
– Original presentation of HF or other cardiac event
• Early intervention
– Serial monitoring may allow for interventions at an
earlier stage, thereby reducing readmissions.
Which biomarkers would be useful
for reducing readmission rates?
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Dunlay SM & Jaffe AS. Clin Chem. 2013;59:737-39.
Heart Failure Biomarker Capabilities
Diagnosis
Prognosis
Therapy
Guidance
++++
++++
++
GDF-15
−
+++
unknown
Highly sensitive troponins
+
++++
unknown
CRP
−
++
unknown
TNF-α
−
++
unknown
IL-6
−
++
unknown
MPO
−
++
unknown
NGAL
−
++++
unknown
Biomarker
NT-proBNP and BNP
NT-proBNP, N-terminal prohormone of brain natriuretic peptide;
BNP, brain natriuretic peptide; GDF-15, growth differentiation factor-15;
CRP, C-reactive protein; TNF-α, tumor necrosis factor-α; IL-6, interleukin-6;
MPO, myeloperoxidase; NGAL, neutrophil gelatinase-associated lipocalin.
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Adapted from van Kimmenade, Januzzi JL. Clin Chem. 2012;58:127-38.
Galectin-3 Mechanism
Cardiac Stress
Increases Galectin-3
Cardiac Remodeling
Left Ventricular Dysfunction
Myocardial Fibrosis
Gal-3
Gal-3
Gal-3
Cardiac Fibrosis/Remodeling
with Collagen Crosslinking
Collagen
Myofibroblast
Differentiation
Myofibroblast
Activation
Conversion of
Procollagen to Collagen
Spontaneous
Aggregation of Collagen
Galectin-3 Increases Cardiac Fibrosis and Remodeling
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Galectin-3 Is a Marker of NYHA Class
Galectin-3 (ng/mL)
100
P < 0.001
80
60
40
20
0
Healthy
Control
II
III
IV
NYHA Class
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Chen K, Jiang RJ, Wang CQ et al. Eur Rev Med Pharmacol Sci. 2013;23:1005-11.
Galectin-3 Vs. NT-proBNP
1.0
1.0
0.8
0.8
0.6
0.4
0.4
0.2
0.0
0.0
0.6
0.8 1.0
1-Specificity
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0.2
0.0 0.2 0.4
46
NT-proBNP ROC Curve
Sensitivity
Sensitivity
Gal-3 ROC Curve
0.0 0.2 0.4 0.6 0.8 1.0
1-Specificity
Chen K, Jiang RJ, Wang CQ et al. Eur Rev Med Pharmacol Sci. 2013;23:1005-11.
Cumulative Incidence of HF (%)
Cumulative Incidence of HF Increases
with Higher Galectin-3 Quartiles
10
Quartile 1
Quartile 2
Quartile 3
Quartile 4
8
6
4
2
0
0
2
4
835
842
835
834
811
808
801
789
760
762
755
712
No. at Risk
Quartile 1
Quartile 2
Quartile 3
Quartile 4
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6
8
10
747
736
726
662
702
661
647
591
278
235
233
228
Years
Ho JE, Liu C, Lyass A et al. J Am Coll Cardiol. 2012;60(14):1249–56.
Cumulative Incidence of Death (%)
Cumulative Incidence of All-Cause Mortality
Increases with Higher Galectin-3 Quartiles
25
Quartile 1
Quartile 2
Quartile 3
Quartile 4
20
15
10
5
0
0
2
4
835
841
842
831
811
809
807
785
772
764
763
714
No. at Risk
Quartile 1
Quartile 2
Quartile 3
Quartile 4
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6
8
10
751
743
736
674
707
672
661
609
281
232
238
238
Years
Ho JE, Liu C, Lyass A et al. J Am Coll Cardiol. 2012;60(14):1249–56.
Association of Galectin-3 with Clinical Characteristics
95% CI
≤ 75th
Percentilea
>75th
Percentileb
Difference
Lower
Upper
P-value
Age
66
27–99
67
31–94
−1.0
−5.1
7.1
0.856
African–American
Male
History of HF
History of Renal Disease
Heart Rate
Respiratory Rate
Systolic BP
BNP
BUN
Sodium
Creatinine
Five Day Event
Thirty Day Event
59
88
111
27
85
20
147
731
17
139
1.2
3
22
39.10%
58.30%
73.50%
17.90%
49–165
12–90
89–262
9.0–4850.0
4.0–83.0
100.0–146.0
0.5–6.8
2.00%
14.60%
12
30
44
28
86
20
139
1191
35
138
2.4
0
14
24.00%
60.00%
88.00%
56.00%
50–140
12–32
96–221
26.0–5050.0
10.0–105.0
128.0–147.0
0.9–9.6
0.00%
28.00%
15.10%
1.70%
14.50%
38.10%
0.5
0
−8.5
459.5
18
−1.0
1.2
−2.0%
13.40%
−0.3
−14.0
1.1
22.8
−5.3
1.8
−19.2
−68.3
8.7
−2.0
0.6
−5.3
1.1
27.6
16.5
24.4
52
6.3
−1.8
2.2
987.3
27.3
0
1.8
5.7
27.9
0.053
0.83
0.024
<0.001
0.533
0.946
0.043
0.025
<0.001
0.135
<0.001
0.422
0.032
a7.3–29.7
ng/ml.
ng/ml.
BNP, B-type natriuretic peptide; BUN, blood urea nitrogen.
b29.9–82.9
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Fermann GJ, Lindsell CJ, Storrow AB et al. Biomarkers. 2012;17:706–13.
Potential Uses of Gal-3 as a Biomarker of HF
• Quantification of absolute risk in heart failure
• There is a strong association of Gal-3 level
with renal dysfunction
– Different prognostic cut points of Gal-3 should
be used for patients with renal disease.
• Gal-3 may play an important role in delineating
patients who may benefit from therapies versus
those who may not
– Low levels of Gal-3 may identify a subgroup
that benefits from rosuvastatin
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Ahmad T, Felker GM. J Am Heart Assoc. 2012;1:e004374.
Myocardial Fibrosis Markers in All-Cause
Mortality and Cardiovascular Mortality
• Another marker of cardiac fibrosis, ST2 is independently
associated with all-cause and cardiovascular mortality.
• Incorporation of ST2 into a full-adjusted model
for all-cause mortality improved discrimination
and calibration, and reclassified significantly better.
• Incorporation of another myocardial fibrosis marker,
Gal-3, showed no significant increase in discrimination
or reclassification and worse calibration metrics.
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Bayes-Genis A, de Antonio M, Vila J et al. J Am Coll Cardiol. 2014;63:158-66.
ST2 Mechanism
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Patients with Elevated sST2 Get More Benefit
from Intensive Therapy
P < 0.001
Mean Cardiovascular Events
2.5
2
P < 0.001 for trend
1.5
P = 0.09
OR 6.0
1
OR 2.5
0.5
OR 1.7
Ref
0
Low sST2/
High sST2/
High sST2/
Low sST2/
High-dose BB Low-dose BB High-dose BB Low-dose BB
*BB, beta blocker
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Gaggin HK, Motiwala S, Bhardwaj A et al. Circ Heart Fail. 2013;6:1206-13.
ST2 is Associated With Long-Term Outcomes
• In univariate analysis, ST2 was significantly
associated with:
– Death or hospitalization (hazard ratio, 1.48; P < 0.0001)
– Cardiovascular death or HF hospitalization
(hazard ratio, 2.14; P < 0.0001)
– All-cause mortality (hazard ratio, 2.33; P < 0.0001)
• In multivariate models, ST2 was independently
associated with outcomes after adjustments for clinical
variables and amino-terminal NT-proBNP.
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Felker GM, Fiuzat M, Thompson V et al. Circ Heart Fail. 2013;6:1172-9.
Survival in HF with Reduced EF According to sST2
Survival Probability (%)
100
90
80
sST2 < 43.8 ng/mL
70
60
50
sST2 > 43.8 ng/mL
40
30
Log-Rank test: P = 0.0021
20
0
500
1000
1500
2000
2500
3000
Time (Days)
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Gruson D, Lepoutre T, Ahn SA, Rousseau MF. Int J Cardiol. 2014. Epub ahead of print.
Percentage of CV Death in HF with Reduced EF
According to sST2 and BNP
Percent Cardiovascular Death
90
80
70
60
50
84%
40
77%
34%
30
sST2 < 43.8
and
BNP < 380
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sST2 > 43.8
or
BNP > 380
sST2 < 43.8
and
BNP < 380
Gruson D, Lepoutre T, Ahn SA, Rousseau MF. Int J Cardiol. 2014. Epub ahead of print.
ST2 is Predictive of HF Severity and Risk
• Elevated concentrations of sST2 are strongly
associated with HF severity.
• Predict increased risks of complications
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Januzzi JL. J Cardiovasc Trans Res. 2013;6:493–500.
The Impact of ST2 in Patient Management
• Change in ST2 shows a stronger relationship with outcome
than baseline or change in natriuretic peptides (NPs).1
• Increased ST2 = worse short term outcomes1
– Independent of NPs and serial measurements
– Adds independent prognostic information in HF
• ST2 is a potent marker of risk in HF when used with NT-proBNP.2
– Reclassifies 14.9% into more appropriate risk categories
• Among candidate biomarkers, ST2 is among the minority that
has data supporting its potential to meaningfully guide therapy.3
14.9%
Increase
1Boisot
S, Beede J, Isakson S. J Cardiac Fail. 2008;14:732-8.
B, French B, McCloskey K et al. Circ Heart Fail. 2011;4;180-7.
3Daniels LB, Clopton P, Iqbal N et al. Am Heart J. 2010;160:721-8.
2Ky
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Regression Model Predicting Total CV Events with Three Biomarkers
Variable
P-Value
Traditional clinical and biochemical variables
Age
0.98
Male
0.96
Any prior CV events
0.04
Diabetes
0.86
Smoker
0.91
NYHA grade 3 or 4
0.02
NT-proBNP*
0.06
Traditional variables + sST2*
< 0.001
Traditional variables + GDF-15*
0.004
Traditional variables + hsTnT*
0.04
Traditional variables +
hsTnT+
0.20
GDF-15
0.02
Traditional variables +
hsTnT +
0.35
GDF-15 +
0.05
sST2*
< 0.001
*NT-proBNP was scaled by 0.01; sST2 and hsTnT were scaled by 0.1; hsTnT was scaled by 0.001.
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Adapted from Gaggin HK, Szymonifka J, Bhardwaj A et al. J Am Coll Cardiol. 2014;2:65-72.
Heart Failure Biomarker Attributes
BNP1
NTproBNP1
cTnT2,3
Gal-31
ST21
Changes only reflect disease
progression
No
No
No
Yes
Yes
No differences with regards to age,
gender, body mass index, or other
medical conditions
No
No
No
Yes
Yes
Levels decrease in response
to successful therapy
Yes
Yes
No
Yes
Yes
Current
Standard
Current
Standard
Yes
Yes
Yes
No
(113%)
No
(98%)
No
(86%)
No
(63%)
Yes
(29.8%)
Criteria
Compliments/exceeds effectiveness
of existing tests
Low RCV
RCV, reference change value.
1Wu
2Frankenstein L,
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A. eJIFCC. 2012;23:1-5.
Wu AH, Hallermayer K et al. Clin Chem. 2011;57(7):1068-71.
Sato Y, Kita T, Takatsu Y, Kimura T. Heart. 2004;90:1110-3.
Case Studies Utilizing Biomarkers
for Risk Stratification
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Case Disclosure
The following cases are taken from actual patient cases
but to protect confidentiality, identities are not revealed
and some information may contain composite information
to illustrate medical teaching points.
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Case 1: Late to Seek Treatment
• 64 year-old obese Hispanic female
• Severe dyspnea on exertion, which had progressively
worsened over the previous week.
• Hypertension, self-treated with "herbal drops,"
and atrial fibrillation for the previous 10 years
– Treated with homeopathic remedies and chiropractic care.
• Post-carotid endarterectomy in 1988
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Case 1: Late to Seek Treatment
• First-time diagnosis of heart failure
–
–
–
–
–
–
–
–
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Bibasilar crackles, without wheezes or rhonchi
Blood pressure 160/90 mmHg
Heart rate 70 and irregular
Respirations 18/minute
Temperature 97ºF
Oxygen saturation 98% on room air
Heart sounds were normal
EKG showed atrial fibrillation without ectopy
Case 1: Findings
• Treated on admission with digitalis, diltiazem, diuretics,
and anticoagulation.
• BNP = 150 pg/mL
• Echocardiogram revealed left ventricular hypertrophy,
mild anterior lateral hypokinesis, mild left atrial enlargement,
and aortic sclerosis without stenosis.
• Adenosine cardiolyte stress test was negative for ischemia
but showed globally decreased left ventricular function.
• Resting ejection fraction was estimated at 35%.
• Chest x-ray revealed cardiomegaly
and mild interstitial edema.
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Case 1: Diagnosis and Follow-up
• Discharged with a diagnosis of heart failure
of idiopathic cause.
• Placed on anticoagulation, digitalis, and ACE inhibitor.
• Referred to the outpatient heart failure clinic
for further follow up and repeat biomarkers
to assist in further risk stratification.
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Case 1: Questions Raised
• Why was the patient so late to receive the diagnosis
of heart failure?
• Were the biomarkers level reflective of the patient’s
course of disease?
• Why was the patient discharged?
• What might be done in the heart failure outpatient
clinic to lower the chance of readmission?
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Case 1: Answers
Why was the patient so late to receive the diagnosis
of heart failure?
Many patients do not have access to healthcare
and/or do not have health insurance. They do not seek care
until they are highly symptomatic. Many do not have primary
care providers and their cardiovascular risk factors are not
treated early. Thus, they often develop heart failure and are
seen and diagnosed during their first admission.
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Case 1: Answers
Was the natriuretic peptide level reflective of the
patient’s course of disease?
Natriuretic peptide (NP) levels are lower in people with
obesity, in those patients with and without heart failure.1 It
has been shown that increases in NP levels from less than to
more than the cutpoint were associated with increased risk
of events but further increases did not add to risk and only
substantial natriuretic peptide decreases (> 80%) reduced
mortality risk.2
1Daniels,
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LB, Clopton P, Bhalla V et al. Am Heart J. 2006 May;151(5):999-1005.
WL, Hartman KA, Grill DE et al. Clin Chem. 2009 Jan;55(1):78-84.
2Miller
Case 1: Answers
Why was the patient discharged?
Once the patient is stabilized, patients are
often discharged and have outpatient follow-up
at a heart failure clinic where they will receive
education, a weight scale, and optimization of
outpatient medications.
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Case 1: Answers
What might be done in the heart failure outpatient
clinic to lower the chance of readmission?
In addition to what is being done at the outpatient clinic,
ST2 levels may be obtained in conjunction with NP levels
which may allow better risk stratification and may help monitor
progress with treatment. Soluble ST2 values identify those
patients with a more remodeled ventricle and decompensated
hemodynamic profile1 and may identify HF patients at higher
risk of sudden cardiac death.2
1Shah
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2Pascual-Figal
RV, Chen-Tournoux AA, Picard MH et al. Circ Heart Fail. 2009 Jul;2(4):311-9.
DA, Ordoñez-Llanos J, Tornel PL et al. J Am Coll Cardiol. 2009 Dec 1;54(23):2174-9.
Case 2: Multiple Infarcts New HF
• 53 year-old male with new HF diagnosis of Stage C
NYHA Class III
– Ischemic heart disease and multiple prior MIs
• LVEF is 25% with severe mitral regurgitation (MR)
and atrial fibrillation
• First office visit
– 20 mg QD of lisinopril
– 6.25 mg BID of carvedilol
– 40 mg QD of furosemide
ST2 levels were obtained when a rise
in NT-proBNP was noted during an office visit.
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Case 2: Multiple Infarcts New HF
Biomarker Levels and Therapy in Subsequent Visits
β blocker
increased
70
Asymptomatic decompensation
(increase in loop diuretic)
8000
7000
60
ST2 (ng/mL)
5000
40
4000
30
Spironolactone
added
20
3000
2000
ST2
NT-proBNP
10
1000
0
0
1
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NT-proBNP (pg/mL)
6000
50
2
3
4
5
ST2 cutoff (35 ng/mL)
NT-proBNP HF target (1000 pg/mL)
NT-proBNP cutoff (400 pg/mL)
6
7
Visits
8
9
10
11
12
ST2 is intended to be used with other clinical
observations, clinician should not select treatment
solely based on ST2 values.
Case 2: Multiple Infarcts New HF
Therapies and Findings
• End of titration
– 25 mg BID of carvedilol
– 20 mg of lisinopril
– 25 mg of spironolactone
– 40 mg BID of furosemide
• Rise in ST2 was recognized when NT-proBNP
was elevated
– Paralleled the increase in NT-proBNP
– Indicated higher risk than NT-proBNP alone
ST2 values were elevated in conjunction with elevated
NT-proBNP and dropped in response to HF therapies.
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Case 2: Findings
NT-proBNP levels rose with asymptomatic
decompensation but ST2 levels indicated a
higher risk than NT-proBNP alone.
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Conclusions
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Conclusions
• Heart failure is a rapidly increasing and costly
cardiovascular disease.
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Conclusions
• There is both need and urgency to reduce
readmissions from heart failure.
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Conclusions
• Heart failure presents with many other
comorbidities and reducing readmissions
has proven challenging.
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Conclusions
• Novel biomarkers for heart failure play an
important role in the prognosis and treatment
of patients and may have potential to reduce
readmission rates.
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