title: Combined use of high-sensitivity ST2 and NTproBNP to improve the prediction of death in heart failure.
abstract: OBJECTIVE
  To address the incremental usefulness of biomarkers from different disease pathways for predicting risk of death in heart failure (HF).
  
  
  RESULTS
  We used data from consecutive patients treated at a structured multidisciplinary HF unit to investigate whether a combination of biomarkers reflecting ventricular fibrosis, remodelling, and stretch [ST2 and N-terminal pro brain natriuretic peptide (NTproBNP)] improved the risk stratification of a HF patient beyond an assessment based on established mortality risk factors (age, sex, ischaemic aetiology, left ventricular ejection fraction, New York Heart Association functional class, diabetes, glomerular filtration rate, sodium, haemoglobin, and beta-blocker and angiotensin-converting enzyme inhibitor/angiotensin II receptor blocker treatments). ST2 was measured with a novel high-sensitivity immunoassay. During a median follow-up time of 33.4 months, 244 of the 891 participants in the study (mean age 70.2 years at baseline) died. In the multivariable Cox proportional hazards model, both ST2 and NTproBNP significantly predicted the risk of death. The individual inclusion of ST2 and NTproBNP in the model with established mortality risk factors significantly improved the C statistic for predicting death [0.79 (0.76-0.81); P < 0.001]. The net improvement in reclassification after the separate addition of ST2 to the model with established risk factors and NTproBNP was estimated at 9.90% [95% confidence interval (CI) 4.34-15.46; P < 0.001] and the integrated discrimination improvement at 1.54 (95% CI 0.29-2.78); P = 0.015).
  
  
  CONCLUSIONS
  Our data suggest that in a real-life cohort of HF patients, the addition of ST2 and NTproBNP substantially improves the risk stratification for death beyond that of a model that is based only on established mortality risk factors.
authors.affiliation: Heart Failure Unit, Hospital Universitari Germans Trias i Pujol, Badalona, Spain. abayesgenis@gmail.com
authors.forename: Antoni
authors.initials: A
authors.lastname: Bayes-Genis
authors.forename: Marta
authors.initials: M
authors.lastname: de Antonio
authors.forename: Amparo
authors.initials: A
authors.lastname: Galán
authors.forename: Héctor
authors.initials: H
authors.lastname: Sanz
authors.forename: Agustin
authors.initials: A
authors.lastname: Urrutia
authors.forename: Roser
authors.initials: R
authors.lastname: Cabanes
authors.forename: Lucía
authors.initials: L
authors.lastname: Cano
authors.forename: Beatriz
authors.initials: B
authors.lastname: González
authors.forename: Cristanto
authors.initials: C
authors.lastname: Díez
authors.forename: Teresa
authors.initials: T
authors.lastname: Pascual
authors.forename: Roberto
authors.initials: R
authors.lastname: Elosúa
authors.forename: Josep
authors.initials: J
authors.lastname: Lupón
mesh_terms: D000368:Aged; D015415:Biomarkers; D016001:Confidence Intervals; D003643:Death; D005260:Female; D005500:Follow-Up Studies; D006333:Heart Failure / Q000175:diagnosis / Q000378:metabolism / Q000401:mortality / Q000503:physiopathology; D006801:Humans; D007118:Immunoassay / Q000379:methods; D000072179:Interleukin-1 Receptor-Like 1 Protein; D008297:Male; D020097:Natriuretic Peptide, Brain / Q000032:analysis / Q000378:metabolism*; D010446:Peptide Fragments / Q000032:analysis / Q000378:metabolism*; D011237:Predictive Value of Tests; D011379:Prognosis; D016016:Proportional Hazards Models; D011956:Receptors, Cell Surface / Q000032:analysis / Q000378:metabolism*; D018570:Risk Assessment / Q000379:methods; D012307:Risk Factors; D020257:Ventricular Remodeling
chemical_list: D015415:Biomarkers; C079241:IL1RL1 protein, human; D000072179:Interleukin-1 Receptor-Like 1 Protein; D010446:Peptide Fragments; D011956:Receptors, Cell Surface; C109794:pro-brain natriuretic peptide (1-76); D020097:Natriuretic Peptide, Brain