Skip main navigation

Increased 90-Day Mortality in Patients With Acute Heart Failure With Elevated Copeptin

Secondary Results From the Biomarkers in Acute Heart Failure (BACH) Study
Originally publishedhttps://doi.org/10.1161/CIRCHEARTFAILURE.110.960096Circulation: Heart Failure. 2011;4:613–620

Abstract

Background—

In patients with heart failure (HF), increased arginine vasopressin concentrations are associated with more severe disease, making arginine vasopressin an attractive target for therapy. However, AVP is difficult to measure due to its in vitro instability and rapid clearance. Copeptin, the C-terminal segment of preprovasopressin, is a stable and reliable surrogate biomarker for serum arginine vasopressin concentrations.

Methods and Results—

The Biomarkers in Acute Heart Failure (BACH) trial was a 15-center, diagnostic and prognostic study of 1641 patients with acute dyspnea; 557 patients with acute HF were included in this analysis. Copeptin and other biomarker measurements were performed by a core laboratory at the University of Maryland. Patients were followed for up to 90 days after initial evaluation for the primary end point of all-cause mortality, HF-related readmissions, and HF-related emergency department visits. Patients with copeptin concentrations in the highest quartile had increased 90-day mortality (P<0.001; hazard ratio, 3.85). Mortality was significantly increased in patients with elevated copeptin and hyponatremia (P<0.001; hazard ratio, 7.36). Combined end points of mortality, readmissions, and emergency department visits were significantly increased in patients with elevated copeptin. There was no correlation between copeptin and sodium (r=0.047).

Conclusions—

This study showed significantly increased 90-day mortality, readmissions, and emergency department visits in patients with elevated copeptin, especially in those with hyponatremia. Copeptin was highly prognostic for 90-day adverse events in patients with acute HF, adding prognostic value to clinical predictors, ser um sodium, and natriuretic peptides.

Clinical Trial Registration—

URL: http://www.clinicaltrials.gov. Unique identifier: NCT00537628.

Introduction

Heart failure (HF) is a major and growing public health issue in the United States.1

Accurate prognostic evaluation can help to identify high-risk individuals who would benefit from closer follow-up and more intensive intervention. The Biomarkers in Acute Heart Failure (BACH) trial was a multinational study of 1641 patients who presented to the emergency department (ED) with acute dyspnea. In the primary analysis of BACH, midregion prohormone adrenomedullin was shown to be highly prognostic in patients with acute HF.2 The secondary goal of the study was to evaluate the prognostic potential of other novel biomarkers. Copeptin, the C-terminal fragment of the arginine vasopressin (AVP) precursor, preprovasopression, was one of the biomarkers evaluated in the BACH study.

Clinical Perspective on p 620

It is well known that patients with severe HF commonly present with hyponatremia, which is an indicator of poor prognosis.3,4 Hyponatremia in Patients with HF is mediated in part by AVP, which is a posterior pituitary peptide hormone with both antidiuretic and vasoconstrictive properties. AVP concentrations are commonly elevated in patients with HF.5 Despite its critical role in free water regulation, AVP is a suboptimal biomarker due its in vitro instability and rapid clearance.6 Copeptin is synthesized and secreted in equimolar amounts to AVP. Both AVP and copeptin have short half-lives in vivo. Therefore, plasma copeptin concentrations mirror plasma AVP concentrations. Unlike AVP, copeptin is very stable in vitro, making it an ideal surrogate biomarker for AVP.7 Previous studies have shown that increased copeptin concentration is a strong predictor of mortality in patients with chronic HF and HF caused by acute myocardial infarction.8,9,10 This secondary analysis of the BACH trial aims to evaluate the effectiveness of copeptin as a prognostic biomarker in patients with acute HF.

Methods

The methods of the BACH study were previously reported in detail.2 Briefly, 1641 patients presenting to the ED with acute dyspnea were prospectively enrolled in the study. To determine the gold standard diagnosis of acute HF, 2 cardiologists independently reviewed all medical records pertaining to the patients and classified the diagnosis as dyspnea caused by acute HF or other causes. For the current analysis, BACH patients were included if they had a primary diagnosis of acute HF by the reviewing cardiologists and had both copeptin and sodium concentrations measured during the initial evaluation.

Biomarker Measurement

All blood samples were collected in ethylenediaminetetraacetic acid (EDTA)-containing plastic tubes through venipuncture. Plasma was separated from the blood sample immediately and stored at −70°C. The plasma samples were then sent to a central laboratory at the University of Maryland School of Medicine for biomarker analysis. Copeptin was measured with a standard sandwich immunoluminometric assay (B.R.A.H.M.S. LUMItest CT-proAVP, B.R.A.H.M.S AG, Hennigsdorf/Berlin, Germany), which was described in detail in previous studies.711 This assay used a murine monoclonal antibody directed to amino acids 137 to 144 (GPAGAL) of preprovasopressin as a capture antibody. Performance of the copeptin assay included a limit of quantification of 0.4 pmol/L and within-run imprecision of <10%. Total imprecision was <10% for levels >9 pmol/L and <20% for levels >2.25 pmol/L. B-type natriuretic peptide (BNP) was measured with a Triage 2-site immunoassay (Biosite, San Diego, CA) formatted for Beckman-Coulter instrumentation (Brea, California). Performance of the BNP assay in the laboratory included a limit of quantification of 5.0 ng/L, within-run imprecision of 1.5%, and total imprecision of 3.0%. N-terminal pro–B-type natriuretic peptide (NT-proBNP) was measured by electrochemiluminescence on the ElecSys 2010 analyzer (Roche Diag-nostics, Indianapolis, IN). Performance in the laboratory included a limit of quantification of 10.0 ng/L, within-run imprecision of 1.5%, and total imprecision of 3.0%.

Statistical Analysis

Values are expressed as means and standard deviations or counts and percentages as appropriate. Diagnostic groups were compared with the use of independent-samples t tests, χ2 tests, ANOVA, Kruskal-Wallis tests, and Mann–Whitney tests as appropriate. Correlation between sodium and copeptin was tested by Spearman test. All analyses were exploratory and used a probability value of 0.05 for significance.

For the prognostic evaluation of copeptin, we first constructed Kaplan–Meier survival curves and performed log rank tests for mortality by copeptin quartiles and by subgroups divided by a predetermined sodium cut-point of 135 mEq/L and copeptin median. We then performed Cox regressions for copeptin quartiles for the mortality end point and the 4 subgroups, based on sodium and copeptin cut-points for the mortality end point, the combined end point of mortality and HF-related readmission, and the combined end point of mortality, HF related readmission, and HF-related ED visits. Copeptin quartile 1 and the elevated sodium and low copeptin subgroup were used as the comparison groups. Correlation and interaction between sodium and copeptin was also analyzed. Receiver operating characteristic (ROC) curves were generated for copeptin, BNP, sodium, and NT-proBNP. Optimal cut-points for NT-proBNP and Copeptin were selected on the basis of the points closest to the left upper corner of their respective ROC curves. We then performed univariate Cox regressions for the prediction of mortality for NT-proBNP and copeptin. A multivariate analysis including copeptin, sodium, and NT-proBNP by their respective cut-points was also performed.

To construct a multivariate model for the prediction of mortality, we performed univariate analysis for clinical and laboratory predictors of mortality (Table 1). For the statistically significant parameters with the exception of the biomarkers, we ran a stepwise multivariate analysis by Cox model both forward and backward. Significant parameters from this multivariate analysis were included in a new multivariate analysis, which also included sodium, copeptin, and NT-proBNP. To determine the incremental value of sodium and copeptin, we first constructed a multivariate model with body mass index (BMI), systolic blood pressure (BP), and NT-proBNP. We then added sodium and copeptin individually to this model to determine their incremental χ2 value. We also calculated the incremental C-statistic when both sodium and copeptin were added to this model.12 Net reclassification improvement for the mortality end point was calculated for adding copeptin to NT-proBNP, adding copeptin to sodium, and adding both sodium and copeptin added to NT-proBNP.

Table 1. Demographic Information by Alive Versus Mortality

ParameterAlive After 90 DaysMortality Within 90 DaysP Value
Age70.7±13.975.3±12.80.012
Heart rate89.4±25.189.7±23.30.93
Temperature36.7±0.736.8±0.70.418
Systolic blood pressure145.2±31.7129.1±30.2<0.001
Diastolic blood pressure83.6±18.978.5±16.80.041
Respiratory rate21.7±6.322.6±7.10.278
Body mass index29±8.324.8±5.2<0.001
Black race22.6%9.5%0.017
Male62.9%57.8%0.431
Smoking78.0%71.2%0.241
Supplemental oxygen41.7%46.9%0.431
Wheezing20.6%24.6%0.49
Weight gain27.5%32.0%0.503
Paroxysmal nocturnal dyspnea47.8%41.0%0.341
Orthopnea63.8%62.9%0.889
Dyspnea at rest49.5%58.7%0.167
S36.6%8.1%0.665
Heart murmur26.6%41.3%0.015
Edema60.7%68.7%0.216
Rales53.6%64.1%0.114
Jugular venous distension36.4%50.0%0.042
Arrhythmia42.5%50.8%0.215
Asthma6.1%1.6%0.145
Chronic renal insufficiency30.1%41.0%0.085
Congestive heart failure65.2%64.5%0.92
Coronary artery disease49.3%52.5%0.638
Chronic obstructive pulmonary disease23.5%27.4%0.494
Diabetes38.3%38.1%0.976
Hyperlipidemia46.5%37.9%0.215
Hypertension79.1%74.2%0.38
Myocardial infarction31.4%32.8%0.831
Pneumonia15.8%13.8%0.696
Pulmonary embolism6.5%6.4%0.964
Coronary artery bypass graft16.5%14.3%0.65
Angioplasty/stent19.9%18.0%0.733
Stroke12.1%25.0%0.005
Pacemaker/defibrillator19.6%12.5%0.172
Prosthetic valve5.0%6.4%0.643
Blood urea nitrogen, mg/dL23.0 (16.0–34.7)31.0 (17.4–53.4)0.009
Creatinine, mg/dL1.2 (0.91–1.6)1.4 (0.98–2.1)0.032
B-type natriuretic peptide, ng/L718.0 (371.0–1343.0)1040.5 (501.3–2337.3)0.005
N-terminal pro–B-type natriuretic peptide, ng/L4717.5 (2055.0–9430.8)9227.0 (4097.8–17 261.8)<0.001
Sodium, mEq/L139 (137.0–141.0)137 (133–139.8)<0.001
Copeptin, pmol/L24.2 (10.1–51.6)55.2 (20.0–114.1)<0.001

Values are mean±SD unless otherwise shown.

Median values are shown with 25th to 75th percentile ranges in parentheses.

Results

Baseline Characteristics

In the BACH trial, 1641 subjects were originally enrolled, of which 568 were diagnosed with acute HF. From the acute HF cohort, 557 patients with valid measurements of sodium and copeptin were included in the current analysis. Of this group, there were 64 deaths, 149 death- or HF-related readmission events, and 172 death- or HF-related readmission or HF related ED visit events. Patient demographic information by mortality within 90 days versus alive after 90 days is summarized in Table 1. Demographic information of the 4 subgroups by sodium and copeptin medians is summarized in Table 2.

Table 2. Demographic Information by Sodium and Copeptin Median

ParameterElevated Sodium, Low Copeptin (n=118)Low Sodium, Low Copeptin (n=160)Elevated Sodium, Elevated Copeptin (n=135)Low Sodium, Elevated Copeptin (n=144)P Value
Age68.6±13.571.2±14.473±12.771.8±14.40.078
Heart rate88.4±2289.9±26.988.5±25.690.5±24.20.87
Temperature36.6±0.636.8±0.736.7±0.636.7±0.80.063
Systolic blood pressure151.3±30.5139.6±29.1148.3±29.6136.2±36.2<0.001
Diastolic blood pressure87.8±19.581.3±17.485.3±17.978.9±19.4<0.001
Respiratory rate21±5.921.1±5.722.8±6.822.2±6.90.073
Body mass index30.6±8.429.1±9.327.7±5.827.2±8.20.006
Black20.3%17.6%25.4%21.7%0.44
Male55.9%60.6%68.9%63.2%0.189
Smoking25.6%21.8%24.8%19.7%0.642
Supplemental oxygen6.0%9.6%10.0%9.1%0.678
Wheezing15.2%17.6%29.3%22.3%0.036
Weight gain29.2%26.4%31.4%25.2%0.669
Paroxysmal nocturnal dyspnea53.1%45.1%52.5%45.8%0.594
Orthopnea57.9%57.0%72.7%67.6%0.019
Dyspnea at rest45.3%46.5%56.8%53.6%0.178
S37.4%4.3%10.9%6.5%0.241
Murmur25.7%27.0%23.0%39.3%0.045
Edema53.9%61.1%66.3%70.1%0.042
Rales53.2%49.0%58.5%64.2%0.063
Jugular venous distension34.7%33.0%34.9%54.5%0.003
Arrhythmia41.4%41.6%46.1%44.6%0.838
Asthma5.2%6.3%6.3%4.2%0.834
Chronic renal insufficiency14.8%18.7%42.3%48.2%<0.001
Congestive heart failure63.2%56.3%70.5%71.3%0.02
Coronary artery disease55.0%44.9%45.7%54.2%0.198
Chronic obstructive pulmonary disease16.5%21.4%22.5%34.0%0.007
Diabetes36.4%35.8%46.6%34.7%0.154
Hyperlipid44.2%49.0%47.1%41.7%0.634
Hypertension77.1%74.2%84.1%79.2%0.229
Myocardial infarction38.3%27.8%30.2%31.4%0.324
Pneumonia16.8%15.8%12.1%17.4%0.654
Pulmonary embolus6.1%7.6%5.6%6.3%0.906
Coronary artery bypass graft15.5%17.6%10.9%20.3%0.193
Angioplasty19.8%21.0%18.4%19.1%0.953
Stroke13.9%14.6%11.7%13.9%0.912
Pacemaker/defibrillator14.5%17.7%21.5%20.8%0.466
Prosthetic valve1.7%5.7%3.9%8.4%0.096
Blood urea nitrogen, mg/dL18 (14.0–23.0)19 (13.4–26.7)29.9 (22.7–44.3)33.3 (22.7–44.3)<0.001
Creatinine, mg/dL1.0 (0.82–1.24)1.04 (0.84–1.33)1.40 (1.10–1.99)1.50 (1.20–2.20)<0.001
B-type natriuretic peptide, ng/L523 (265.0–892.3)564 (285.5–1010.8)1033 (573.0–1916.0)1105 (508.0–2051.5)<0.001
N-terminal pro–B-type natriuretic peptide, ng/L2863 (1460–5056)3879 (1542–6403)7703 (3666–14 278)8243 (3778–18 648)<0.001

Values are mean±SD unless otherwise shown.

Median values are shown with 25th to 75th percentile ranges in parentheses.

Sodium and Copeptin in HF

There was no correlation between copeptin and sodium concentrations by Spearman test (r=0.047). Copeptin concentrations by quartiles of sodium were very similar without significant differences between the 4 groups (P=0.438) (Figure 1). The prognostic effects of sodium and copeptin were additive (P<0.001) but not synergistic (P=0.376).

Figure 1.

Figure 1. Copeptin concentration by sodium quartiles.

Prognostic Value of Copeptin in HF

When copeptin was divided into deciles, most of the mortalities occurred in deciles 8 to 10 (Figure 2). When analyzed as quartiles, patients in the highest copeptin quartile had significantly increased 90-day mortality when compared with patients in the lowest copeptin quartile (P<0.001, hazard ratio [HR], 3.85), whereas patients in the second and third quartiles did not have significantly increased mortality (Figure 3 and Table 3).

Figure 2.

Figure 2. Mortality by deciles of copeptin.

Figure 3.

Figure 3. Survival by copeptin quartiles.

Table 3. Cox Regression for All-Cause Mortality by Copeptin Quartiles

Copeptin QuartilesHazard Ratio95% CI Lower95% CI UpperP Value
Intergroup variability<0.001
    Copeptin quartile 2, cutoff: 11 pmol/L1.3510.5693.2070.495
    Copeptin quartile 3, cutoff: 27 pmol/L1.3470.5673.1960.500
    Copeptin quartile 4, cutoff: 57 pmol/L3.851.8338.087<0.001

CI indicates confidence interval.

All curves are compared with copeptin quartile 1.

The ROC curve for morality according to BNP had an area under the curve (AUC) of 0.608 and standard error (SE) of 0.04. The ROC curve for morality according to NT-proBNP had an AUC of 0.668 and SE of 0.038. The ROC curve for morality according to copeptin had an AUC of 0.662 and SE of 0.036. The ROC curve for morality according to sodium had an AUC of 0.673 and SE of 0.036. Because NT-proBNP had higher AUC than BNP, NT-proBNP was used in our multivariate model. ROC for the mortality end point for NT-proBNP, BNP, copeptin, and sodium are shown in Figure 4. Optimal cut-points by ROC for copeptin and NT-proBNP were 38.5 pmol/L and 6305 ng/L, respectively. A predetermined sodium cut-point of 135mEq/L was used in our analyses. Both NT-proBNP and copeptin were statistically significant predictors of mortality by univariate analysis (NT-proBNP: P<0.001, hazard ratio [HR], 3.466; Copeptin: P<0.001, HR, 2.872). Copeptin, sodium, and NT-proBNP by their cut-points were statistically significant predictors of mortality in a multivariate model (copeptin: P=0.007, HR, 2.115; sodium: P<0.001, HR, 2.739; NT-proBNP: P=0.003, HR, 2.383).

Figure 4.

Figure 4. Receiver operating characteristic for N-terminal pro-B-type natriuretic peptide (NT-proBNP), copeptin, sodium, and B-type natriuretic peptide (BNP).

Statistically significant clinical and laboratory predictors of mortality in our patient population were age (P=0.01), systolic BP (P<0.001), diastolic BP (P=0.04), BMI (P<0.001), black race (P=0.022), cardiac murmur on examination (P=0.019), jugular venous distention (P=0.038), wheezing on examination (P=0.013), history of stroke (P=0.005), log creatinine (P=0.020), log blood urea nitrogen (P=0.018), NT-proBNP (P<0.001), copeptin (P<0.001), and sodium (P<0.001). When these parameters with the exception of sodium, copeptin, and NT-proBNP were entered into a stepwise multivariate analysis by Cox model, log creatinine (P=0.006), systolic BP (P=0.010), and BMI (P<0.001) were statistically significant. In a subsequent multivariate analysis with log creatinine, systolic BP, BMI, NT-proBNP, copeptin, and sodium, statistically significant parameters were systolic BP, BMI, copeptin, and sodium. Log creatinine and NT-proBNP did not reach statistical significance in this model (Table 4).

Table 4. Multivariate Analysis by Cox Model for the End Point of Mortality With Parameters Including Significant Clinical and Laboratory Predictors of Mortality, With Sodium, Copeptin, and NT pro-BNP by Cut-Point

ParameterHazard Ratio95% CI Lower95% CI LowerP Value
Systolic blood pressure, mm Hg0.9870.9780.9960.005
Body mass index, kg/m20.9350.8860.9860.014
Sodium by cut-point, 135 mEq/L2.1331.1803.8550.012
Copeptin by cut-point, 38.5 pmol/L2.0141.0653.8100.031
NT-pro-BNP cut-point, 6305 ng/L1.6950.8873.2400.111
Log creatinine, mg/dL1.4230.3965.1100.589

CI indicates confidence interval.

When patients were divided into subgroups on the basis of copeptin median (26.99 pmol/L) and the predetermined sodium cut-point of 135mEq/L, mortality was significantly increased in the elevated sodium and elevated copeptin subgroup and the low sodium and elevated copeptin subgroup when compared with patients in the elevated sodium and low copeptin subgroup (P=0.0495, HR, 1.875 and P<0.001, HR, 7.364, respectively) (Figure 5). For the composite end point of 90-day mortality and HF-related readmission (149 events), compared with the elevated sodium and low copeptin subgroup, the elevated sodium and elevated copeptin subgroup and the low sodium and elevated copeptin subgroup both had increased event rates (P=0.027, HR, 1.520 and P<0.001, HR, 4.264, respectively). For the composite end point of mortality, HF-related readmission and HF-related ED visit (172 events), significantly increased event rates were again seen in the elevated sodium and elevated copeptin subgroup and the low sodium and elevated copeptin subgroup (P=0.0499, HR, 1.405 and P<0.001, HR, 3.871, respectively) (Table 5).

Figure 5.

Figure 5. Survival by copeptin median and sodium cut-point of 135 mEq/L.

Table 5. Cox Regression for Mortality, the Composite End Point of Mortality and Heart Failure–Related Readmission, and the Composite End Point of Mortality, Heart Failure–Related Readmission, and Heart Failure–Related Emergency Department Visit

Hazard Ratio95% CI Lower95% CI UpperP Value
End point: Mortality
    Intergroup variability<0.001
    High sodium, high copeptin1.8751.0013.510.049
    Low sodium, low copeptin2.0730.8045.3420.131
    Low sodium, high copeptin7.3643.59815.073<0.001
End point: Mortality and readmission
    Intergroup variability<0.001
    High sodium, high copeptin1.521.0492.2030.027
    Low sodium, low copeptin0.9870.4832.0130.970
    Low sodium, high copeptin4.2642.6056.981<0.001
End point: Mortality, readmission, and emergency department visit
    Intergroup variability<0.001
    High sodium, high copeptin1.4051.0001.9740.049
    Low sodium, low copeptin0.09730.5111.8540.935
    Low sodium, high copeptin3.8712.4186.196<0.001

CI indicates confidence interval.

Patients were divided into 4 subgroups by copeptin median (26.99 pmol/L) and sodium cut-point 135 mEq/L. All subgroups were compared with the subgroup with elevated sodium and low copeptin.

When sodium was added to a model including BMI, systolic BP, and NT-proBNP, the incremental χ2 was 6.260 (P=0.012). When copeptin was added to the same model, the incremental χ2 was 4.13 (P=0.042). The C-statistic for the model including BMI, systolic BP, and NT-proBNP was 0.720 (95% confidence interval [CI], 0.651 to 0.789). Adding both sodium and copeptin to the model raised the C-statistic to 0.751 (95% CI, 0.684 to 0.817). Furthermore, when copeptin was added to NT-proBNP for the mortality end point, the net reclassification improvement (NRI) was 11.2% (P=0.096). When copeptin was added to sodium for the mortality end point, the NRI was 27.4% (P<0.001). When both sodium and copeptin was added to NT-proBNP for the mortality end point, the NRI was 32.7% (P<0.001).

Discussion

This current study was the first large-scale investigation to evaluate the prognostic potential of copeptin in patients with acute HF. This study demonstrated that elevated copeptin concentrations were associated with increased 90-day mortality, HF-related readmissions, and HF-related ED visits in patients with acute HF, highlighting the prognostic utility of copeptin. For the prediction of 90-day mortality in patients with acute HF, copeptin was comparable to NT-proBNP and added incremental predictive value to clinical predictors of mortality. Although there were large variations in the copeptin and sodium concentrations in this study, we found that sodium and copeptin were independent predictors of mortality with additive prognostic value. Patients with elevated copeptin concentrations had significantly increased risk for 90-day mortality and adverse outcomes, especially in those with concurrent hyponatremia.

Prior studies have shown that AVP was a major contributor to hyponatremia and that hyponatremia was associated with poor prognosis in Patients with HF.5,6 Although AVP was a major contributor to hyponatremia, there was no correlation between sodium and copeptin concentrations in our acute HF population. This observation was noted in previous studies as well.5 The lack of correlation between sodium and copeptin concentrations in our study might be due to the complex interactions of various pathophysiological processes and medications involved in sodium and free water regulation in patients with acute HF. Because copeptin concentrations were directly related to AVP concentrations, it could be postulated that elevated AVP concentrations is a potent predictor for mortality, rehospitalization, and ED visits in acute Patients with HF, especially in those with hyponatremia. We must also point out that the correlation between elevated copeptin concentrations and mortality did not necessarily establish a causal relationship.

Lately, the association between hyponatremia and increased mortality prompted great interest in using AVP antagonists to treat Patients with HF with hyponatremia. Although AVP antagonists have been used to treat hyponatremia in the general population with encouraging results, efforts to use them in Patients with HF were not as successful.13 The findings from our study might suggest why AVP antagonists were not effective in all Patients with HF. Future studies using AVP antagonists should be considered in patients with elevated copeptin concentrations and low sodium concentrations because these patients are most likely to have mortality benefit from AVP antagonist therapy.

Our study was limited by the modest size of the acute HF cohort and short follow-up period, resulting in a relatively small number of mortality end points, which limited the number of covariates that could be analyzed in the multivariate models. Future studies focusing primarily on a larger acute HF cohort with longer follow-up are needed to confirm the findings of our study and to further examine the incremental prognostic value copeptin adds to traditional clinical predictors and current biomarkers.

In conclusion, elevated copeptin concentrations were associated with increased 90-day mortality, HF-related readmissions, and HF-related ED visits in patients with acute HF. In addition, mortality was significantly increased in the subgroup of patients with elevated copeptin and hyponatremia. Copeptin was an excellent predictor of 90-day mortality in multivariate models and provided additional prognostic information over sodium and clinical predictors. When combined, sodium and copeptin provided significant incremental prognostic value to clinical predictors of mortality and NT-proBNP. These findings from the BACH trial demonstrated the utility of using the novel biomarker copeptin to risk stratify patients with acute HF.

Sources of Funding

This study was supported by BRAHMS GmbH Biotechnology Centre, Hennigsdorf, Berlin, Germany.

Disclosures

Dr Mueller received research grants from BRAHMS, Roche, and Abbott; was on the speaker's bureau from BRAHMS, Roche, Abbott, and Alere; and was a consultant from BRAHMS and Roche. Dr Peacock was on the scientific advisory board for Abbott, Alere, Beckman-Coulter, Electrocore, Nanosphere, and The Medicines Co; received research grants from Abbott, Alere, BAS, BRAHMS, EKR, Nanosphere, and The Medicines Co; was on the speaker's bureau for Abbott and Alere; and had Ownership Interest in Comprehensive Research Associates and Vital Sensors. Dr Ponikowski was part of a speaker's bureau for Merck-Serono, Pfizer, and Sanofi-Aventis and was a consultant for Vifor and Athera. Dr Richards received research support from Roche and Inverness Medical. Dr Filippatos received research support from Biosite, BRAHMS, and Roche. Dr Ng was a consultant for Inverness and BRAHMS. Dr Daniels received research support from Biosite and Roche and was a consultant for Roche. Dr Neath received honoraria from BRAHMS and was a consultant for TMO USA. Dr Christenson received research support from Siemens, BG-Medicine, BRAHMS, Roche, Inverness, and Nanosphere and was a consultant for Siemens, BG-Medicine, Critical Care Diagnostics, Inverness, and Abbott. Dr McCord received research support from BRAHMS and honoraria from Emory University. Dr von Haehling received honoraria from BRAHMS. Dr Morgenthaler was employed by BRAHMS. Dr Anker received a research grant from BRAHMS; received honoraria from Abbott and Biosite; and was a consultant for BRAHMS.

Footnotes

Correspondence to Yang Xue, MD,
University of California San Diego, 200 W Arbor Dr, MC 8411, San Diego, CA 92103
. E-mail

References

  • 1. American Heart Association, Heart Disease and Stroke Statistics: 2005 Update, American Heart Association, Dallas TX. 2005.Google Scholar
  • 2. Maisel A, Mueller C, Nowak R, Peacock WF, Landsberg JW, Ponikowski P, Mockel M, Hogan C, Wu AH, Richards M, Clopton P, Filippatos GS, Di Somma S, Anand I, Ng L, Daniels LB, Neath SX, Christenson R, Potocki M, McCord J, Terracciano G, Kremastinos D, Hartmann O, von Haehling S, Bergmann A, Morgenthaler NG, Anker SD. Mid-region pro-hormone markers for diagnosis and prognosis in acute dyspnea: results from the BACH (Biomarkers in Acute Heart Failure) trial. J Am Coll Cardiol. 2010; 55:2062–2076.CrossrefMedlineGoogle Scholar
  • 3. Klein L, O'Connor CM, Leimberger JD, Gattis-Stough W, Piña IL, Felker GM, Adams KF, Califf RM, Gheorghiade M; OPTIME-CHF Investigators. Lower serum sodium is associated with increased short-term mortality in hospitalized patients with worsening heart failure: results from the Outcomes of a Prospective Trial of Intravenous Milrinone for Exacerbations of Chronic Heart Failure (OPTIME-CHF) study. Circulation. 2005; 111:2454–2460.LinkGoogle Scholar
  • 4. Gheorghiade M, Rossi JS, Cotts W, Shin DD, Hellkamp AS, Piña IL, Fonarow GC, DeMarco T, Pauly DF, Rogers J, DiSalvo TG, Butler J, Hare JM, Francis GS, Stough WG, O'Connor CM. Characterization and prognostic value of persistent hyponatremia in patients with severe heart failure in the ESCAPE Trial. Arch Intern Med. 2007; 167:1998–2005.CrossrefMedlineGoogle Scholar
  • 5. Goldsmith SR, Francis GS, Cowley AW, Levine TB, Cohn JN. Increased plasma arginine vasopressin levels in patients with congestive heart failure. J Am Coll Cardiol. 1983; 1:1385–1390.CrossrefMedlineGoogle Scholar
  • 6. Fabian M, Forsling ML, Jones JJ, Pryor JS. The clearance and antidiuretic potency of neurohypophysical hormones in man, and their plasma binding and stability. J Physiol. 1969; 204:653–668.CrossrefMedlineGoogle Scholar
  • 7. Morgenthaler NG, Struck J, Alonso C, Bergmann A. Assay for the measurement of copeptin, a stable peptide derived from the precursor of vasopressin. Clin Chem. 2006; 52:112–119.CrossrefMedlineGoogle Scholar
  • 8. Voors AA, von Haehling S, Anker SD, Hillege HL, Struck J, Hartmann O, Bergmann A, Squire I, van Veldhuisen DJ, Dickstein K; OPTIMAAL Investigators. C-terminal provasopressin (copeptin) is a strong prognostic marker in patients with heart failure after an acute myocardial infarction: results from the OPTIMAAL study. Eur Heart J. 2009; 30:1187–1194.CrossrefMedlineGoogle Scholar
  • 9. Khan SQ, Dhillon OS, O'Brien RJ, Struck J, Quinn PA, Morgenthaler NG, Squire IB, Davies JE, Bergmann A, Ng LL. C-terminal provasopressin (copeptin) as a novel and prognostic marker in acute myocardial infarction: Leicester Acute Myocardial Infarction Peptide (LAMP) study. Circulation. 2007; 115:2103–2110.LinkGoogle Scholar
  • 10. Neuhold S, Huelsmann M, Strunk G, Stoiser B, Struck J, Morgenthaler NG, Bergmann A, Moertl D, Berger R, Pacher R. Comparison of copeptin, B-type natriuretic peptide, and amino-terminal pro-B-type natriuretic peptide in patients with chronic heart failure: prediction of death at different stages of the diseaseJ Am Coll Cardiol2008; 52:266–272.CrossrefMedlineGoogle Scholar
  • 11. Struck J, Morgenthaler NG, Bergmann A. Copeptin, a stable peptide derived from the vasopressin precursor, is elevated in serum of sepsis patients. Peptides. 2005; 26:2500–2504.CrossrefMedlineGoogle Scholar
  • 12. Kremers WK. Concordance for survival time data: fixed and time-dependent covariates and possible ties in predictor and time. Technical Report Series No. 80, Rochester, MN: Mayo Clinic; April2007.Google Scholar
  • 13. Konstam MA, Gheorghiade M, Burnett JC, Grinfeld L, Maggioni AP, Swedberg K, Udelson JE, Zannad F, Cook T, Ouyang J. Effects of oral tolvaptan in patients hospitalized for worsening heart failure: the EVEREST Outcome Trial. JAMA. 2007; 297:1319–1331.CrossrefMedlineGoogle Scholar

Clinical Perspective

This study demonstrated the potential use of the novel biomarker copeptin for the risk stratification of acute heart failure patients. Copeptin is a stable and easily measurable surrogate biomarker for arginine vasopressin. Because copeptin concentrations mirror that of arginine vasopressin, the ability to reliably measure copeptin concentrations will give clinicians insight into an important pathophysiological process in acute heart failure patients. When used by itself, copeptin is a strong independent prognostic biomarker in acute heart failure patients. Copeptin concentrations do not correlate with serum sodium. The prognostic utility of copeptin is additive to the prognostic utility of serum sodium. When used in combination with other biomarkers such as natriuretic peptides, midregion proadrenomedullin, and troponin, copeptin can help to provide the clinician with better insight into the pathophysiology and a more complete risk profile of patients with acute heart failure. In addition, copeptin concentrations can help clinicians identify high-risk patients who may benefit the most from arginine vasopressin antagonist therapy.