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The predictive value of stable precursor fragments of vasoactive peptides in patients with chronic heart failure: data from the GISSI-heart failure (GISSI-HF) trial
Ist Teil von
European journal of heart failure, 2010-04, Vol.12 (4), p.338-347
Ort / Verlag
England: Blackwell Publishing Ltd
Erscheinungsjahr
2010
Quelle
MEDLINE
Beschreibungen/Notizen
Aims
Though various neurohormonal systems are concurrently activated during heart failure (HF), their biological effectors are not always easy to measure due to their short life in vivo, instability in biological samples, or very low concentrations. We measured the plasma concentrations of four stable precursor fragments of neurohormonal systems in patients with chronic HF and evaluated their relationship with outcome.
Methods and results
This study was performed in 1237 patients with chronic and stable HF enrolled in the GISSI‐heart failure trial (GISSI‐HF). The following four precursor fragments, mid‐regional pro‐atrial natriuretic peptide (MR‐proANP), mid‐regional pro‐adrenomedullin (MR‐proADM), C‐terminal pro‐endothelin‐1 (CT‐proET‐1) and C‐terminal pro‐vasopressin (CT‐proAVP or copeptin), were measured at randomization and after 3 months. Baseline concentrations were independent predictors of clinical outcome (median follow‐up 3.9 years). The addition of MR‐proANP improved net reclassification for mortality when added to multivariable models based on clinical risk factors alone [net reclassification improvement (NRI) = 0.12, P = 0.0007] or together with NT‐proBNP (NRI = 0.06, P = 0.01). Changes in MR‐proANP concentrations were related to mortality [HR (95% CI) 1.38 (0.99–1.93), P = 0.0614 and 1.58 (1.13–2.21), P = 0.0078 in the middle and highest vs. lowest tertiles], while changes in the other markers were not.
Conclusion
In patients with chronic and stable HF enrolled in a multicentre, randomized, clinical trial, measurement of stable precursor fragments of vasoactive peptides provided prognostic information independent of natriuretic peptides which are currently the best biomarkers for risk stratification.