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(Circulation. 2007;115:2103-2110.)
© 2007 American Heart Association, Inc.
Coronary Heart Disease |
From the University of Leicester, Department of Cardiovascular Sciences (S.Q.K., O.S.D., R.J.O., P.A.Q., I.B.S., J.E.D., L.L.N.), Leicester Royal Infirmary, Leicester, UK; and Research Department (J.S., N.G.M., A.B.), BRAHMS Aktiengesellschaft, Hennigsdorf, Germany.
Correspondence to Professor Leong L. Ng, Department of Cardiovascular Sciences, Clinical Sciences Bldg, Leicester Royal Infirmary, Leicester LE2 7LX, UK. E-mail lln1{at}le.ac.uk
Received July 28, 2006; accepted February 16, 2007.
| Abstract |
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Methods and Results In this prospective single-hospital study, we recruited 980 consecutive postacute myocardial infarction patients (718 men, median [range] age 66 [24 to 95] years), with follow-up over 342 (range 0 to 764) days. Plasma copeptin was highest on admission (n=132, P<0.001, day 1 versus days 2 to 5) and reached a plateau at days 3 to 5. In the 980 patients, copeptin (measured at days 3 to 5) was elevated in patients who died (n=101) or were readmitted with heart failure (n=49) compared with survivors (median [range] 18.5 [0.6 to 441.0] versus 6.5 [0.3 to 267.0] pmol/L, P<0.0005). With logistic regression analysis, copeptin (odds ratio, 4.14, P<0.0005) and NTproBNP (odds ratio, 2.26, P<0.003) were significant independent predictors of death or heart failure at 60 days. The area under the receiver operating characteristic curves for copeptin (0.75) and NTproBNP (0.76) were similar. The logistic model with both markers yielded a larger area under the curve (0.84) than for NTproBNP (P<0.013) or copeptin (P<0.003) alone, respectively. Cox modeling predicted death or heart failure with both biomarkers (log copeptin [hazard ratio, 2.33], log NTproBNP [hazard ratio, 2.70]). In patients stratified by NTproBNP (above the median of
900 pmol/L), copeptin above the median (
7 pmol/L) was associated with poorer outcome (P<0.0005). Findings were similar for death and heart failure as individual end points.
Conclusions The vasopressin system is activated after acute myocardial infarction. Copeptin may predict adverse outcome, especially in those with an elevated NTproBNP (more than
900 pmol/L).
Key Words: heart failure myocardial infarction natriuretic peptides peptides plasma
| Introduction |
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Clinical Perspective p 2110
The role of the AVP system as measured by its surrogate, copeptin, in the prognostication of AMI is unknown. In the present study, we investigated whether copeptin would be of benefit in determining the prognosis after AMI, particularly for death and HF. We compared this with NTproBNP, a peptide of established prognostic value benefit in this group of patients.1,11,12
| Methods |
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Healthy volunteers (n=700, 409 males, median age 60.7 [range 45.5 to 80.6] years) were derived from an HF screening study that was being performed concurrently in the community14 and were taking no medical therapy and had no history of hypertension, diabetes mellitus, or ischemic heart disease and no ECG or echocardiographic abnormalities (including segmental wall-motion abnormalities, valvular disease, and left ventricular [LV] hypertrophy). The estimated glomerular filtration rate (eGFR) of these subjects was calculated from the simplified formula derived from the Modification of Diet in Renal Disease (MDRD) study, recently validated in patients with HF.15
Plasma Samples
Blood samples were drawn at 3 to 5 days after the onset of chest pain for determination of plasma copeptin and NTproBNP levels. After 15 minutes bed rest, 20 mL of blood was collected into tubes containing EDTA and aprotinin. All plasma was stored at 70°C until assayed in a blinded fashion in a single batch. In a subgroup of 132 patients from the original 980-patient cohort, blood sampling was performed daily for 5 days from admission to discharge.
Echocardiography
Transthoracic echocardiography was performed in patients with a Sonos 5500 instrument (Philips Medical Systems, Reigate, UK). A 16-segment LV wall-motion index based on the American Society of Echocardiography mode was derived by scoring each LV segment (1=normal, 2=hypokinesis, 3=akinesis, and 4=dyskinesis; Paradoxical Motion) and dividing the total by the number of segments scored. LV ejection fraction was calculated with the biplane method of discs formula.16 Impaired LV systolic function was defined as an LV ejection fraction <40% or an LV wall-motion index >1.8.
NTproBNP Assay
The NTproBNP assay used in the present study was based on a noncompetitive assay as published previously.2 Sheep antibodies were raised to the N-terminal of human NTproBNP, and monoclonal mouse antibodies were raised to the C-terminal. Samples or NTproBNP standards were incubated in C-terminal IgG-coated wells with the biotinylated N-terminal antibody for 24 hours at 4°C. Detection was with methyl-acridinium esterlabeled streptavidin on an MLX plate luminometer (Dynex Technologies Ltd, Worthing, UK). The lower limit of detection was 0.3 pmol/L. There was no cross-reactivity with atrial natriuretic peptide, B-type natriuretic peptide, or C-type natriuretic peptide.
Copeptin Assay
Copeptin was detected with a novel commercial assay in the chemiluminescence/coated-tube format (BRAHMS AG, Hennigsdorf, Germany), as described previously.10 Briefly, tubes were coated with a purified sheep polyclonal antibody raised against a peptide that represented amino acids 132 to 147 of preproAVP. A purified sheep polyclonal antibody raised against a peptide representing amino acids 149 to 164 of preproAVP was labeled with methyl acridinium N-hydroxysuccinimide ester (InVent GmbH, Bielefeld, Germany) and used as tracer. Dilutions of a peptide representing amino acids 132 to 164 of preproAVP in normal horse serum served as standards. The immunoassay was performed by incubating 50 µL of samples/standards and 200 µL of tracer in coated tubes for 2 hours at room temperature. Tubes were washed 4 times with 1 mL of wash solution (BRAHMS AG), and bound chemiluminescence was measured with an LB952T luminometer (Berthold, Bad Wildbad, Germany).
End Points
We assessed the value of both copeptin and NTproBNP for the prediction of the primary end point (death or HF). We also investigated death, HF, and recurrent AMI as individual secondary end points. Hospitalization for HF was defined as a hospital readmission for which HF was the primary reason. Myocardial infarction was diagnosed on the basis of established criteria as described above.13 End points were obtained by reviewing the Office of National Statistics Registry and by contacting each patient. There was a minimum 60-day follow-up of all surviving patients.
Statistical Analysis
Statistical analyses were performed on SPSS version 14 (SPSS Inc, Chicago, Ill). The continuous variables in the 2 independent groups were compared with the Mann-Whitney U test. Comparisons in the daily sampling study were performed with the general linear model with repeated measures, with correction for multiple comparisons by the Bonferroni method. Spearman correlations were performed. To test the independent predictive power for death or HF of peptide levels, binary logistic regression analyses and survival analyses with Cox proportional hazards modeling and Kaplan-Meier models were conducted. Levels of NTproBNP and copeptin were normalized by log transformation. Thus, odds ratios and hazard ratios refer to a 10-fold rise in the levels of these markers. Logistic regression and Cox models were always constructed with the same variables entered simultaneously (which included variables statistically significant in univariate analyses and those variables that may have an effect on the end point on the basis of previous studies). These included age; gender; past medical history of AMI, HF, diabetes mellitus, hypertension, or hypercholesterolemia; smoking history; anterior site of AMI; STEMI; Killip class; eGFR; use of thrombolysis; therapy with angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, and ß-blockers; NTproBNP; and copeptin (with addition of echocardiographic data in a substudy; see below). To compare the accuracy of NTproBNP and copeptin, receiver operating characteristic curves were generated, and the area under the curves was calculated. Comparisons between receiver operating characteristic curves were by the method of Hanley and McNeil.17 A 2-tailed probability value of less than 0.05 was deemed to be statistically significant.
All authors had full access to and take full responsibility for the integrity of the data. All authors have read and agree to the manuscript as written.
| Results |
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Patient Characteristics
The demographic features of the patient population are shown in Table 1. Median length of follow-up was 342 days, with a range of 0 to 764 days. No patient was lost to follow-up, and the minimum length of follow-up for survivors was 60 days, which enabled a censored primary end point of death or HF to be determined at this time point for logistic regression analysis. During follow-up, 101 patients (10.3%) died, and 49 (5.0%) were readmitted with HF. There were 780 STEMI patients, 67.8% of whom received thrombolytic therapy.
Plasma Profile of Copeptin and NTproBNP
Daily blood samples were obtained for 5 days after admission in a subgroup of 132 patients (102 males, median age [range] 64 [32 to 90] years), 16 of whom subsequently experienced the primary end point of death or HF. Figure 1 illustrates the time course of plasma NTproBNP, showing significant changes with day of sampling (P<0.001), with peak levels on day 2 (P<0.001 and 0.02 compared with day 1 and day 3, respectively, using the Bonferroni correction). In contrast, the plasma copeptin peak was most evident on day 1 (significantly elevated compared with days 2, 3, 4, and 5, P<0.001 with the Bonferroni correction), falling to a plateau by days 3 to 5.
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Copeptin Levels (Univariate Analysis)
Plasma levels of copeptin obtained in the plateau phase (days 3 to 5) in 980 patients with AMI were elevated compared with the normal range (Table 1). Copeptin was raised in patients with death or HF compared with event-free survivors (Table 3). There were no significant differences in copeptin levels between males and females, anterior or other site of AMI, patients with a past history of AMI or hypertension, and whether thrombolysis was administered or not; however, there was a significantly higher level in patients who had a past history of HF or diabetes mellitus (Table 3). Copeptin levels were higher in STEMI versus non-STEMI patients and in those with Killip class above 1 (Table 3). Plasma copeptin levels were correlated with age, eGFR, Killip class (Table 3), and NTproBNP (r=0.36, P<0.0005).
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NTproBNP Levels (Univariate Analysis)
Plasma NTproBNP obtained in the plateau phase (days 3 to 5) was significantly higher in patients who died or were readmitted with HF than in event-free survivors (Table 3). Significant differences in NTproBNP levels were noted between males and females, in those with a Killip class above 1, and in patients with a past medical history of HF, hypertension, AMI, or diabetes mellitus (Table 3). Plasma NTproBNP levels were also higher in STEMI than in non-STEMI patients and in those with anterior site of AMI. Plasma NTproBNP was correlated with age, eGFR, and Killip class (Table 3).
Primary End Points: Copeptin and NTproBNP as Predictors of Death and HF
Univariate predictors of death or HF are reported in Table 4. When clinical characteristics (as listed under Statistical Analysis above) were entered into a multivariate binary logistic model (Table 2), copeptin (odds ratio, 4.14) and NTproBNP (odds ratio, 2.26), together with gender, use of angiotensin-converting enzyme inhibitors or angiotensin receptor blockers, and presence of STEMI, independently predicted the primary end point at a censored time of 60 days after AMI, when 86 events had accrued. Past history of AMI, hypertension, HF, or diabetes mellitus, Killip class, and eGFR were not predictors.
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The areas under the receiver operating characteristic curve for copeptin (0.75; 95% CI, 0.69 to 0.81) and NTproBNP (0.76; 95% CI, 0.71 to 0.82) were similar. The logistic model combining these 2 markers yielded an area under the curve of 0.84 (95% CI, 0.79 to 0.89; P<0.001), which exceeded that of copeptin (P<0.003) or NTproBNP alone (P<0.013; comparison of areas under the receiver operating characteristic curves by the method of Hanley and McNeil17).
For prediction of 60-day mortality and HF, stratification by NTproBNP (less than or greater than the median) correctly identified 68 end points, with an additional 10 identified by stratification by copeptin (less than or greater than the median). When copeptin levels were used for risk stratification, 69 end points were identified correctly, with an additional 9 identified with stratification by NTproBNP. Thus, only 8 end points were identified incorrectly when both markers were used.
Cox proportional hazards modeling using the same predictors revealed NTproBNP, copeptin, age, gender, use of ß-blockers, and past history of AMI as independent predictors of death or HF (Table 5). Neither Killip class nor eGFR was an independent predictor.
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Kaplan-Meier survival curves that plot quartiles of copeptin or NTproBNP (Figure 2) provide visual confirmation of the findings from the Cox models, that both copeptin and NTproBNP are useful predictors of death or HF after AMI. In patients stratified by NTproBNP (median 914 pmol/L), copeptin gave additional information on death or HF in those patients who had an NTproBNP level above the median (P<0.0005; Figure 3). Thus, patients can be classified into low (both markers less than the median), intermediate (either marker greater than the median), or high risk (both markers greater than the median) groups (log rank for trend, P<0.0005).
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Relationship of Primary End Points With Copeptin, NTproBNP, and Echocardiographic Parameters
Echocardiographic parameters were available for 628 subjects (64%) for the index admission. Plasma copeptin and NTproBNP levels were elevated in patients with impaired LV systolic function (Table 3). In this echocardiography subgroup, there were 96 deaths or HF readmissions. Multivariate logistic regression analysis of clinical and biomarker variables listed under Statistical Analysis with echocardiographic presence of impaired LV systolic function revealed copeptin, gender, and presence of STEMI as significant independent predictors of death or HF at 60 days (Table 4). Cox modeling of predictors of death or HF revealed copeptin, NTproBNP, echocardiographic evidence of HF, age, gender, and Killip class >1 as predictors (Table 5). eGFR was not an independent predictor in either logistic or Cox models.
Secondary End Points: Copeptin and NTproBNP as Predictors of Death
Copeptin and NTproBNP were significantly higher in patients who died than in event-free survivors (Table 3). Cox proportional hazards modeling suggested that the same variables (copeptin, NTproBNP, age, gender, use of ß-blockers, and past history of AMI) were independent predictors of death. Kaplan-Meier analysis confirmed lower mortality in patients with copeptin levels below the median and the highest mortality in those with both biomarkers elevated above the median (P<0.0005).
Secondary End Points: Copeptin and NTproBNP as Predictors of HF
Copeptin and NTproBNP levels were significantly higher in patients who were readmitted with HF than in event-free survivors. Cox modeling revealed the following independent significant predictors; copeptin, NTproBNP, past history of diabetes mellitus, and Killip class >1. Kaplan-Meier analysis revealed a lower readmission rate for HF in those with submedian copeptin levels (P<0.0005) and the highest HF readmission rates in those with both biomarkers elevated above the median (P<0.0005).
Secondary End Points: Copeptin and NTproBNP as Predictors of Recurrent Myocardial Infarction
Compared with survivors with no end points, patients who had recurrent AMI had similar NTproBNP and copeptin levels (Table 3).
| Discussion |
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Risk stratification at an early stage after AMI remains important and may be useful in helping to select treatment regimens in the future. A multimarker strategy for outcome after AMI using independent biomarkers has benefits in that it integrates the different pathways involved, in the hope that complementary information can be gained.18 Receiver operating characteristic curve analysis indicated that NTproBNP and copeptin were of similar accuracy in prediction of death or HF, but the combination of copeptin and NTproBNP in a multimarker risk stratification approach provided greater predictive accuracy. Kaplan-Meier analysis revealed that copeptin was particularly useful in the group of patients who had a raised NTproBNP (above
900 pmol/L). In this group, levels of copeptin above
7 pmol/L were predictive of poor outcome, thus defining a high-risk group. These levels are within 1 SD of the normal range and are in contrast to the grossly elevated levels seen in septic patients.10 Thus, even minor perturbation of the AVP system may be relevant to adverse prognosis after AMI. Even though water intake may affect copeptin levels,10 blood samples in the present study were obtained with no restriction on water or food intake.
The complementary information provided by copeptin to NTproBNP may suggest that the stimuli to the secretion of both markers are different, and plasma levels are likely to reflect different aspects of cardiovascular homeostasis. In support of this is the clear difference in secretion profile of both markers after AMI, with the copeptin peak on day 1 compared with the NTproBNP peak by day 2. There is also no gender difference in copeptin levels after AMI, in contrast to NTproBNP. In the subset with echocardiography data, both biomarkers remained independent predictors of poor outcome, but assessment of ventricular function by scanning was retained in the Cox model, which provides further evidence that these biomarkers may be providing information beyond an assessment of LV function.
The benefit of measuring both prohormones over their bioactive peptides include the lack of receptor binding or protein interactions and the longer half-lives, which result in higher plasma levels. The prohormones are also more stable in blood ex vivo, and this makes them generally more applicable in clinical practice.10
Vasopressin may have a number of deleterious effects in the post-AMI period. Acting through the V1a receptor, it has the following actions: (1) peripheral vasoconstrictor activity19 that increases afterload and ventricular stress; (2) increasing protein synthesis in myocytes,20 which leads to hypertrophy; and (3) vasoconstriction of coronary arteries. Through the V2 receptor, it retains water in the kidney tubules and contributes to an increased preload. The present findings suggest that the AVP system is another candidate neurohormonal pathway, in addition to the renin-angiotensin and sympathetic nervous systems, that may be associated with poor outcome after AMI. With the introduction of nonspecific (V1 and V2) and specific AVP V2 receptor antagonists, or "vaptans,"21,22 it may be possible to tailor therapy to those at highest risk after AMI, depending on their neurohumoral profile.
Study Limitations
The present study was a single-center study, and the results need to be replicated in larger multicenter studies. There was a preponderance of STEMI, and cut points for non-STEMI may need to be established independently. The present study used blood samples in the recovery phase of AMI, and the utility of initial triage blood samples should be investigated. Finally, although both markers predicted 78 of the 86 events at 60 days, 8 remaining events eluded prediction.
Conclusions
The present study is the first report showing copeptin to be a new prognostic marker of death or HF in patients with AMI, independent of established conventional risk factors. A multimarker approach with copeptin and NTproBNP is more informative than either marker alone and may be useful for risk stratification in AMI patients.
| Acknowledgments |
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Dr Khan was supported by a junior fellowship from the British Heart Foundation (FS/03/028/15486).
Disclosures
Dr Ng has submitted patents on behalf of the University of Leicester on biomarkers of cardiac disease. Dr Bergmann holds ownership in BRAHMS AG, owns patent rights to the markers of the study, and is a member of the board of directors of BRAHMS AG. Dr Struck holds patent rights to the markers and is an employee of BRAHMS AG. Dr Morgenthaler is an employee of BRAHMS AG, a mid-sized company, based in Hennigsdorf, Germany, that commercializes immunoassays and has developed the copeptin assay, for which it owns patent rights. The present study was not financed by BRAHMS AG. The remaining authors report no conflicts.
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