Biomarker-based risk prediction in the community

Omar Abou Ezzeddine, Paul McKie, Christopher G. Scott, Richard J. Rodeheffer, Horng Haur Chen, G. Michael Felker, Allan S Jaffe, John C Jr. Burnett, Margaret May Redfield

Research output: Contribution to journalArticle

13 Citations (Scopus)

Abstract

Aims: Guided by predictive characteristics of cardiovascular biomarkers, we explored the clinical implications of a simulated biomarker-guided heart failure (HF) and major adverse cardiovascular events (MACE) prevention strategy in the community. Methods and results: In a community cohort (n = 1824), the predictive characteristics for HF and MACE of galectin-3 (Gal-3), ST2, high-sensitivity cardiac troponin I (hscTnI), high-sensitivity C-reactive protein (hsCRP), N-terminal pro-brain natriuretic peptide (NT-proBNP) and B-type natriuretic peptide (BNP) were established. We performed number needed to screen (NNS) and treat (NNT) with the intervention analyses according to biomarker screening strategy and intervention efficacy in persons with at least one cardiovascular risk factor. In the entire cohort, for both HF and MACE, the predictive characteristics of NT-proBNP and hscTnI were superior to other biomarkers; alone, in a multimarker model, and adjusting for clinical risk factors. An NT-proBNP-guided preventative intervention with an intervention effect size (4-year hazard ratio for intervention in biomarker positive cohort) of ≤0.7 would reduce the global burden of HF by ≥20% and MACE by ≥15%. From this simulation, the NNS to prevent one HF event or MACE in 4 years would be ≤100 with a NNT to prevent one HF event of ≤20 and one MACE of ≤10. Conclusions: The predictive characteristics of NT-proBNP and hscTnI for HF or MACE in the community are superior to other biomarkers. Biomarker-guided preventative interventions with reasonable efficacy would compare favourably to established preventative interventions. This data provides a framework for biomarker selection which may inform design of biomarker-guided preventative intervention trials.

Original languageEnglish (US)
Pages (from-to)1342-1350
Number of pages9
JournalEuropean Journal of Heart Failure
Volume18
Issue number11
DOIs
StatePublished - Nov 1 2016

Fingerprint

Biomarkers
Brain Natriuretic Peptide
Heart Failure
Troponin I
Galectin 3
Numbers Needed To Treat
C-Reactive Protein

Keywords

  • Biomarkers
  • Heart failure
  • Prevention

ASJC Scopus subject areas

  • Cardiology and Cardiovascular Medicine

Cite this

Biomarker-based risk prediction in the community. / Abou Ezzeddine, Omar; McKie, Paul; Scott, Christopher G.; Rodeheffer, Richard J.; Chen, Horng Haur; Michael Felker, G.; Jaffe, Allan S; Burnett, John C Jr.; Redfield, Margaret May.

In: European Journal of Heart Failure, Vol. 18, No. 11, 01.11.2016, p. 1342-1350.

Research output: Contribution to journalArticle

@article{b2628f1601e34d87af714519cdf64cbe,
title = "Biomarker-based risk prediction in the community",
abstract = "Aims: Guided by predictive characteristics of cardiovascular biomarkers, we explored the clinical implications of a simulated biomarker-guided heart failure (HF) and major adverse cardiovascular events (MACE) prevention strategy in the community. Methods and results: In a community cohort (n = 1824), the predictive characteristics for HF and MACE of galectin-3 (Gal-3), ST2, high-sensitivity cardiac troponin I (hscTnI), high-sensitivity C-reactive protein (hsCRP), N-terminal pro-brain natriuretic peptide (NT-proBNP) and B-type natriuretic peptide (BNP) were established. We performed number needed to screen (NNS) and treat (NNT) with the intervention analyses according to biomarker screening strategy and intervention efficacy in persons with at least one cardiovascular risk factor. In the entire cohort, for both HF and MACE, the predictive characteristics of NT-proBNP and hscTnI were superior to other biomarkers; alone, in a multimarker model, and adjusting for clinical risk factors. An NT-proBNP-guided preventative intervention with an intervention effect size (4-year hazard ratio for intervention in biomarker positive cohort) of ≤0.7 would reduce the global burden of HF by ≥20{\%} and MACE by ≥15{\%}. From this simulation, the NNS to prevent one HF event or MACE in 4 years would be ≤100 with a NNT to prevent one HF event of ≤20 and one MACE of ≤10. Conclusions: The predictive characteristics of NT-proBNP and hscTnI for HF or MACE in the community are superior to other biomarkers. Biomarker-guided preventative interventions with reasonable efficacy would compare favourably to established preventative interventions. This data provides a framework for biomarker selection which may inform design of biomarker-guided preventative intervention trials.",
keywords = "Biomarkers, Heart failure, Prevention",
author = "{Abou Ezzeddine}, Omar and Paul McKie and Scott, {Christopher G.} and Rodeheffer, {Richard J.} and Chen, {Horng Haur} and {Michael Felker}, G. and Jaffe, {Allan S} and Burnett, {John C Jr.} and Redfield, {Margaret May}",
year = "2016",
month = "11",
day = "1",
doi = "10.1002/ejhf.663",
language = "English (US)",
volume = "18",
pages = "1342--1350",
journal = "European Journal of Heart Failure",
issn = "1388-9842",
publisher = "Oxford University Press",
number = "11",

}

TY - JOUR

T1 - Biomarker-based risk prediction in the community

AU - Abou Ezzeddine, Omar

AU - McKie, Paul

AU - Scott, Christopher G.

AU - Rodeheffer, Richard J.

AU - Chen, Horng Haur

AU - Michael Felker, G.

AU - Jaffe, Allan S

AU - Burnett, John C Jr.

AU - Redfield, Margaret May

PY - 2016/11/1

Y1 - 2016/11/1

N2 - Aims: Guided by predictive characteristics of cardiovascular biomarkers, we explored the clinical implications of a simulated biomarker-guided heart failure (HF) and major adverse cardiovascular events (MACE) prevention strategy in the community. Methods and results: In a community cohort (n = 1824), the predictive characteristics for HF and MACE of galectin-3 (Gal-3), ST2, high-sensitivity cardiac troponin I (hscTnI), high-sensitivity C-reactive protein (hsCRP), N-terminal pro-brain natriuretic peptide (NT-proBNP) and B-type natriuretic peptide (BNP) were established. We performed number needed to screen (NNS) and treat (NNT) with the intervention analyses according to biomarker screening strategy and intervention efficacy in persons with at least one cardiovascular risk factor. In the entire cohort, for both HF and MACE, the predictive characteristics of NT-proBNP and hscTnI were superior to other biomarkers; alone, in a multimarker model, and adjusting for clinical risk factors. An NT-proBNP-guided preventative intervention with an intervention effect size (4-year hazard ratio for intervention in biomarker positive cohort) of ≤0.7 would reduce the global burden of HF by ≥20% and MACE by ≥15%. From this simulation, the NNS to prevent one HF event or MACE in 4 years would be ≤100 with a NNT to prevent one HF event of ≤20 and one MACE of ≤10. Conclusions: The predictive characteristics of NT-proBNP and hscTnI for HF or MACE in the community are superior to other biomarkers. Biomarker-guided preventative interventions with reasonable efficacy would compare favourably to established preventative interventions. This data provides a framework for biomarker selection which may inform design of biomarker-guided preventative intervention trials.

AB - Aims: Guided by predictive characteristics of cardiovascular biomarkers, we explored the clinical implications of a simulated biomarker-guided heart failure (HF) and major adverse cardiovascular events (MACE) prevention strategy in the community. Methods and results: In a community cohort (n = 1824), the predictive characteristics for HF and MACE of galectin-3 (Gal-3), ST2, high-sensitivity cardiac troponin I (hscTnI), high-sensitivity C-reactive protein (hsCRP), N-terminal pro-brain natriuretic peptide (NT-proBNP) and B-type natriuretic peptide (BNP) were established. We performed number needed to screen (NNS) and treat (NNT) with the intervention analyses according to biomarker screening strategy and intervention efficacy in persons with at least one cardiovascular risk factor. In the entire cohort, for both HF and MACE, the predictive characteristics of NT-proBNP and hscTnI were superior to other biomarkers; alone, in a multimarker model, and adjusting for clinical risk factors. An NT-proBNP-guided preventative intervention with an intervention effect size (4-year hazard ratio for intervention in biomarker positive cohort) of ≤0.7 would reduce the global burden of HF by ≥20% and MACE by ≥15%. From this simulation, the NNS to prevent one HF event or MACE in 4 years would be ≤100 with a NNT to prevent one HF event of ≤20 and one MACE of ≤10. Conclusions: The predictive characteristics of NT-proBNP and hscTnI for HF or MACE in the community are superior to other biomarkers. Biomarker-guided preventative interventions with reasonable efficacy would compare favourably to established preventative interventions. This data provides a framework for biomarker selection which may inform design of biomarker-guided preventative intervention trials.

KW - Biomarkers

KW - Heart failure

KW - Prevention

UR - http://www.scopus.com/inward/record.url?scp=84994168916&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84994168916&partnerID=8YFLogxK

U2 - 10.1002/ejhf.663

DO - 10.1002/ejhf.663

M3 - Article

C2 - 27813304

AN - SCOPUS:84994168916

VL - 18

SP - 1342

EP - 1350

JO - European Journal of Heart Failure

JF - European Journal of Heart Failure

SN - 1388-9842

IS - 11

ER -