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European Journal of Heart Failure 2008 10(3):315-320; doi:10.1016/j.ejheart.2008.01.009
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© 2008 European Society of Cardiology

Prognostic impact of NT-proBNP and renal function in comparison to contemporary multi-marker risk scores in heart failure patients

Roman Pfistera,*, Holger Diedrichsa, Anne Schiedermaira, Stephan Rosenkranza, Martin Hellmichb, Erland Erdmanna and Christian Alfons Schneidera

a Department III of Internal Medicine, University of Cologne Germany
b Institute of Medical Statistics, Informatics and Epidemiology, University of Cologne Germany

* Corresponding author. Department III of Internal Medicine University of Cologne, Kerpener Str. 62, 50924 Cologne, Germany. Tel.: +49 221 478 5382; fax: +49 221 478 6574. E-mail address: roman.pfister{at}uk-koeln.de (R. Pfister).


   Abstract
Top
 Abstract
1. Introduction
 2. Methods
 3. Results
 4. Discussion
 References
 
Background: Multi-marker risk scores accurately predict prognosis in heart failure patients but calculation is complex.

Aims: To compare the prognostic accuracy of the Seattle Heart Failure Survival Score (SHFS) and a model derived from the CHARM programme, with laboratory parameters NT-proBNP and glomerular filtration rate (GFR).

Methods and results: In a sample of 290 heart failure patients, 39 patients died, 22 were hospitalised with acute heart failure and 4 underwent urgent cardiac transplantation during a median follow-up of 498 days. NT-proBNP, GFR, CHARM and SHFS showed an AUC for an endpoint during 1-year of 0.80, 0.72, 0.79 and 0.69, respectively. The hazard ratio for an endpoint during follow-up was 2.1, 2.6, 1.9 and 2.1 per 1 SD increase of log NT-proBNP and CHARM and per 1 SD decrease of GFR and SHFS, respectively. In multivariate analysis, log NT-proBNP and GFR added independent prognostic information to CHARM and SHFS, respectively.

Conclusion: NT-proBNP and GFR independently predicted endpoint-free survival in systolic heart failure patients, with NT-proBNP being superior and equally predictive to the SHFS and CHARM score, respectively. Assessment of both laboratory markers can simplify prognostic stratification, addition to multi-marker scores should be evaluated.

Key Words: Heart failure • Prognosis • NT-proBNP • Renal function • Seattle Heart Failure Model • CHARM model

Received August 8, 2007; Revised November 26, 2007; Accepted January 16, 2008


   1. Introduction
Top
 Abstract
 1. Introduction
2. Methods
 3. Results
 4. Discussion
 References
 
The prognosis of heart failure patients has improved considerably over the last decade. Nevertheless, heart failure still carries a high risk of mortality which can be as high as 10% per year according to recent clinical trials [1]. Early identification of high risk patients may facilitate the timely planning of advanced treatment choices (biventricular stimulation, ICD or heart transplantation). Although several independent prognostic variables have been identified [2-5], the prognostic performance of single variables is weak as heart failure is a very heterogeneous disease. The Seattle Heart Failure Score (SHFS), which has been developed recently, includes 14 continuous variables and 10 categorical values to estimate the individual risk of heart failure patients [6]. This model has an excellent accuracy for the prediction of event-free survival under modern treatment options. Nevertheless, although a computer calculator exists for the SHFS, estimation of this score is still complex and resource consuming. Twenty four different variables derived from the patient's history, medication, haemodynamics, laboratory examinations and cardiac functional parameters are necessary for calculation of the SHFS. Thus, broad application of the SHFS in routine clinical practice is a challenge to health care providers.

The aim of this study was to compare the prognostic accuracy of the cardiac functional marker NT-proBNP and the renal functional parameter glomerular filtration rate (GFR), with the SHFS and also with a recently published risk score derived from the CHARM programme, in a sample of ambulatory and hospitalised patients with systolic heart failure. Assessment of these laboratory risk markers is easy and could rigorously simplify risk calculation in heart failure patients.


   2. Methods
Top
 Abstract
 1. Introduction
 2. Methods
3. Results
 4. Discussion
 References
 
2.1. Study population
Two hundred and ninety patients with a diagnosis of systolic heart failure treated in the Department III of Internal Medicine of the University Hospital of Cologne, Germany, were consecutively included in this analysis between 1st April 2003 and 1st May 2004. Diagnosis of heart failure was defined according to the guidelines of the European Society of Cardiology [7]. In addition, ejection fraction (EF) had to be <50% [8]. Of these 290 patients, 102 were ambulatory patients attending our outpatient clinic which is a tertiary referral centre for heart failure patients. All of these patients had been in a stable condition for at least three months on unchanged medication. None of the patients was decompensated at the time of examination. The remaining 188 patients were hospitalised due to coronary heart disease (n=90, 47.9%), heart failure (57, 30.3%) or rhythm disturbances (36, 19.1%), and 5 patients were admitted for non-cardiac reasons (2.7%). Patients with acute coronary syndrome were excluded from the study.

All patients agreed to participation in the study as part of their routine care and the local ethics committee had no objections to the analysis.

2.2. Data collection
All SHFS parameters were routinely evaluated in all patients. The natriuretic peptide NT-proBNP was measured in serum using the Elecsys proBNP assay (Roche Diagnostics), as previously described [9]. GFR was calculated using the MDRD method [10]. Left ventricular EF was estimated by echocardiography or ventriculography, as previously described [11,12]. In addition, a second prognostic model for heart failure derived from the CHARM programme was evaluated [13]. Of the 23 clinical variables included in the CHARM model, 96.4% were available in the 290 patients included in this study. In the hospitalised patients, all parameters were assessed after recompensation on the day before discharge.

The pre-specified endpoints were all-cause mortality, hospitalisation for acute heart failure and urgent cardiac transplantation. The endpoints were checked by telephone call. If an endpoint was confirmed verbally by the patient, records and discharge letters for the corresponding hospital admission were checked. For patients who died, the cause and date were confirmed by the treating physician.

2.3. Statistics
Continuous variables appeared to be distributed non-normally and, thus, were summarized using median and interquartile range. Comparisons between groups were performed by non-parametric rank tests (Mann-Whitney U test). Qualitative variables were summarized using percentages.

The area under the receiver operator characteristics curve (AUC of ROC) analysis was calculated to examine the discriminatory value of variables for the detection of an endpoint during 1-year and to determine cut-off values. Cut-off values were chosen for high sensitivity with acceptable specificity. Significance between AUC values was tested as described by DeLong [14] for identical populations and with the z-test for the comparison of ambulatory and hospitalised populations.

The median follow-up time was calculated by inverse Kaplan-Meier curve analysis. Outcome according to NT-proBNP, GFR, CHARM score and SHFS, dichotomized by cut-off values derived from the ROC was depicted using Kaplan-Meier curves; the log-rank test was used to compare survival curves. Univariate and multivariate Cox regression was performed to calculate the relative risk (hazard ratio) of variables. For better comparability of the relative risk of variables, the hazard ratio was referred to the change of 1 standard deviation (SD) of the variables.

P values <0.05 were considered statistically significant. Statistical analysis was performed using SPSS version 14.0 for windows and R version 2.5.1.


   3. Results
Top
 Abstract
 1. Introduction
 2. Methods
 3. Results
4. Discussion
 References
 
3.1. Baseline characteristics
Table 1 shows the baseline characteristics for the 290 study patients. The participants were predominantly male and in NYHA class II. Ischaemic cardiomyopathy was the underlying aetiology in most cases. According to current therapy guidelines, most patients were on an ACE-inhibitor/angiotensin-blocker (83.7%) and β-blocker (90.7%). If only patients with EF<35% were observed, 90.3% were on an ACE-inhibitor/angiotensin-blocker, 94.7% were on a β-blocker and 67.3% were on aldosterone antagonists.


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  		Table 1 Baseline characteristics of the study patients (median and interquartiles or number and percentage)

 
3.2. Outcome prediction
During a median follow-up of 498 days (95% CI 416-580), 39 patients died, 22 were hospitalised for acute heart failure and 4 underwent urgent cardiac transplantation. None of the patients were lost to follow-up. Patients with an endpoint had significantly higher NT-proBNP values (median ± IQR: 3030±5220 vs. 809±1334 pg/ml), significantly lower GFR (57±38 vs. 73±31 ml/min/1.73 m2), significantly higher CHARM score (23.9±8.4 vs. 18.1±7.5) and significantly lower mean life expectancy calculated by SHFS (8.5±8 vs. 11.1±5.3 years, all p<0.0001).

Fig. 1 shows the ROC analyses of NT-proBNP, GFR, CHARM score and SHFS for the detection of patients with an endpoint during 1-year (no censored observations). The AUC values were 0.80 (95% CI 0.74-0.86) for NT-proBNP, 0.79 (95% CI 0.72-0.86) for the CHARM score, 0.72 (95% CI 0.64-0.80) for GFR and 0.69 (95% CI 0.60-0.77, all p<0.001) for SHFS. AUC of NT-proBNP was significantly higher than AUC of GFR (p=0.041) and SHFS (p=0.013) but was not significantly different from CHARM (p=0.81). AUC of GFR was not significantly different from AUC of CHARM or SHFS (p=0.077 and 0.49, respectively). Table 2 summarizes the discriminatory characteristics of the four variables with defined cut-off values.


Figure 01
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  		Fig. 1 Receiver operator curves for NT-proBNP, GFR, CHARM score and SHFS to detect patients with an endpoint during 1-year.

 


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  		Table 2 Discriminatory performance for the detection of patients with endpoint at 1-year

 
The corresponding AUC values in the ambulatory group were 0.82 (95% CI 0.73-0.90, p<0.0001) for NT-proBNP, 0.66 (95% CI 0.52-0.80, p=0.048) for GFR, 0.75 (95% CI 0.62-0.87, p=0.002) for CHARM and 0.67 (95% CI 0.53-0.82, p=0.028) for SHFS. In the hospitalised group AUC values were 0.78 (95% CI 0.70-0.87, p<0.0001) for NT-proBNP, 0.74 (95% CI 0.65-0.83, p<0.0001) for GFR, 0.81 (95% CI 0.73-0.88, p<0.0001) for CHARM and 0.69 (95% CI 0.58-0.80, p=0.001) for SHFS, respectively. In ambulatory patients, AUC of NT-proBNP was significantly higher than AUC of GFR (p=0.008) and SHFS (0.034) but was not significantly different from CHARM (p=0.21). AUC of GFR was not significantly different from AUC of CHARM and SHFS (p=0.20 and 0.83, respectively). In hospitalised patients, AUC did not differ significantly between the variables. The differences in AUC values between ambulatory and hospitalised populations were also not significant for the 4 risk parameters.

3.3. Relative risk
Fig. 2 shows the Kaplan-Meier curves for event-free survival according to NT-proBNP, GFR, CHARM and the SHFS. Patients with NT-proBNP values >814 pg/ml (p<0.0001), GFR <82.4 ml/min/1.73 m2 (p=0.001), CHARM score >16 (p<0.0001) and SHFS <13.8 years (p=0.002) had a significantly increased risk for an endpoint compared to the respective counterpart patient group. The relative risk for an endpoint during follow-up was 2.1 (95% CI 1.7-2.7, p<0.0001) per increase of 1 SD of log NT-proBNP, 1.9 (95% CI 1.5-2.5, p<0.0001) per decrease of 1 SD of GFR, 2.6 (95% CI 2.0-3.3, p<0.0001) per increase of 1 SD of the CHARM score and 2.1 (95% CI 1.5-2.8, p<0.0001) per decrease of 1 SD of the mean life expectancy calculated by SHFS, respectively. After including NT-proBNP and GFR with each risk score into a multivariate model, only log NT-proBNP (HR 1.9, 95% CI 1.5-2.4 per increase of 1 SD, p<0.0001) and SHFS (HR 1.4, 95% CI 1.02-2.0 per decrease of 1 SD, p=0.037), respectively, and log NT-proBNP (HR 1.7, 95% CI 1.3-2.3 per increase of 1 SD, p<0.0001) and the CHARM score (HR 2.1, 95% CI 1.6-2.8 per increase of 1 SD, p<0.0001) remained independent predictors. If NT-proBNP was not included to the model, GFR independently predicted outcome (HR 1.5, 95% CI 1.1-2.0 per increase of 1 SD, p<0.007) compared to CHARM and (HR 1.7, 95% CI 1.2-2.2 per increase of 1 SD, p<0.0001) compared to SHFS.


Figure 02
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  		Fig. 2 Kaplan-Meier curves for event-free survival stratified by NT-proBNP, GFR, CHARM score and SHFS.

 

   4. Discussion
Top
 Abstract
 1. Introduction
 2. Methods
 3. Results
 4. Discussion
References
 
In the present study we show that the laboratory risk markers NT-proBNP and GFR were strong predictors of event-free survival in heart failure patients, with NT-proBNP being superior and equally predictive compared to the multi-marker SHFS and CHARM scores, respectively. Both NT-proBNP and GFR added independent prognostic information to the multi-marker risk scores.

Currently, the SHFS is the best validated prognostic score for heart failure patients. However, estimation of this score is complex and time consuming. Unfortunately, even simple scores like the Framingham risk score are rarely used in routine clinical practice and therefore, risk factor analysis and control are poor [15]. A laboratory marker of prognosis in heart failure patients is needed to help simplify risk stratification in general practice medicine.

B-type natriuretic peptides (NPs) are accurate serum markers of cardiac function which also include prognostic information in many cardiological patient populations [16,17]. Particularly in patients with acute and chronic heart failure, NPs are strong predictors of event-free survival and have been shown to detect high risk patients better than routinely assessed risk parameters like EF or maximum oxygen uptake [18]. Furthermore, NPs identified high risk heart failure patients better than the Heart Failure Survival Score [18,19]. However, as this score was evaluated in 1997, it is suboptimal in patients receiving β-blockers and is less accurate than contemporary models like the SHFS [20]. The addition of BNP to the SHFS in the Val-HeFT study population showed only a slight improvement of the AUC for 1-year-mortality [6], but to date the prognostic value of NPs and GFR, respectively, has not been directly compared with clinical multi-marker scores derived from heart failure patients receiving contemporary therapy in line with current guidelines. In our patient group, a single measurement of NT-proBNP was superior and equally predictive to two current multi-marker risk scores, SHFS and CHARM, respectively. NT-proBNP discriminated worse outcome significantly better than the SHFS especially in stable ambulatory patients. This is in concordance with previous findings showing that NT-proBNP values assessed after recompensation predict outcome better than NT-proBNP values assessed at admission in patients with acute heart failure [21].

NT-proBNP is known to reflect the functional and clinical severity of heart failure characterized by EF or NYHA class very well [9]. Furthermore, NT-proBNP has been shown to correlate with several other prognostic variables also included in the examined scores like age, signs of obesity, myocardial ischaemia, anaemia, renal function, mitral regurgitation and modern heart failure therapies, so that the strong prognostic power of this single laboratory marker in comparison to a multi-marker score is comprehensive [22-27].

A possible reason for the strong prognostic performance of NT-proBNP compared to the scores in our study population, may be that with the exception of the IN-CHF registry (n=872), all remaining patients in the SHFS cohort (>10.000 patients) and in the CHARM cohort were recruited from medication trials. This limits the transmission of results to "real-life" patients with concomitant cardiac dysfunction such as valvular defects or atypical cardiomyopathies as well as non-cardiac comorbidities like renal or liver failure. These disorders are all known to impact on NT-proBNP levels [28-31]. Most importantly, as higher degree renal dysfunction was an exclusion criterion in many trials, patients with renal dysfunction were under-represented in the SHFS and CHARM populations, with mean serum creatinine levels of 1.3 mg/dl (SD 0.3) and 1.2 mg/dl (SD 0.4), respectively. This may be one reason for creatinine being not predictive in the SHFS in contrast to other studies. Our population had a mean creatinine level of 1.4 mg/dl (SD 1.6) and included a larger proportion of patients with renal insufficiency.

Another explanation for the suboptimal predictive power of the SHFS in our study is that we analysed the endpoint of heart failure hospitalisation which was not included in the generation of the SHFS. However, even after excluding this endpoint from our analyses, the results did not change significantly.

Renal dysfunction is a known prognostic marker in cardiovascular patients but serum creatinine was not an independent risk predictor in the derivation of the SHFS [32,33]. However, serum creatinine only roughly reflects renal function [34]. We therefore analysed GFR estimated by the MDRD formula which has been shown to be the most precise estimation of GFR in heart failure patients and concurrently is clinically feasible [35]. In our population GFR was significantly predictive for event-free survival whereas serum creatinine showed only borderline significance (HR 1.2 95% CI 1.01-1.4 per increase of 1 SD of creatinine, p=0.039). Furthermore, GFR added independent predictive information to the SHFS and the CHARM score if NT-proBNP was not included to the multivariate analysis. Therefore, further studies should evaluate the benefit of adding the easily assessable GFR to prognostic heart failure scores.

A limitation of our study is the small sample size. Further studies are necessary to confirm these results and to reliably derive cut-off values. However, the prognostic thresholds we assigned for NT-proBNP are in the same dimension as found in the literature confirming that our cohort seems to be representative [36,37]. Furthermore, the AUC of the SHFS for detecting 1-year event-free survival was similar to that published for the validation cohorts (0.68-0.82), again confirming our population and analysis to be representative [6].

In summary, the laboratory parameters NT-proBNP and GFR were strong prognostic markers in hospitalised and ambulatory patients with systolic heart failure. NT-proBNP was more predictive for event-free survival than the SHFS and was similarly predictive compared to the CHARM score. NT-proBNP and GFR added independent prognostic information to the multi-marker scores. Assessment of these laboratory markers can simplify risk stratification in heart failure patients and addition to risk scores might further improve the prognostic power.


   References
Top
 Abstract
 1. Introduction
 2. Methods
 3. Results
 4. Discussion
 References
 

  1. McMurray J.J., Ostergren J., Swedberg K., et al. Effects of candesartan in patients with chronic heart failure and reduced left-ventricular systolic function taking angiotensin-converting-enzyme inhibitors: the CHARM-Added trial. Lancet (2003) 362:767–771.[CrossRef][Web of Science][Medline]
  2. Dries D.L., Sweitzer N.K., Drazner M.H., Stevenson L.W., Gersh B.J. Prognostic impact of diabetes mellitus in patients with heart failure according to the etiology of left ventricular systolic dysfunction. J Am Coll Cardiol (2001) 38:421–428.[Abstract/Free Full Text]
  3. Gustafsson F., Torp-Pedersen C., Brendorp B., Seibaek M., Burchardt H., Kober L. Long-term survival in patients hospitalized with congestive heart failure: relation to preserved and reduced left ventricular systolic function. Eur Heart J (2003) 24:863–870.[Abstract/Free Full Text]
  4. Lewis E.F., Moye L.A., Rouleau J.L., et al. Predictors of late development of heart failure in stable survivors of myocardial infarction: the CARE study. J Am Coll Cardiol (2003) 42:1446–1453.[Abstract/Free Full Text]
  5. Nohria A., Tsang S.W., Fang J.C., et al. Clinical assessment identifies hemodynamic profiles that predict outcomes in patients admitted with heart failure. J Am Coll Cardiol (2003) 41:1797–1804.[Abstract/Free Full Text]
  6. Levy W.C., Mozaffarian D., Linker D.T., et al. The Seattle Heart Failure Model: prediction of survival in heart failure. Circulation (2006) 113:1424–1433.[Abstract/Free Full Text]
  7. Swedberg K., Cleland J., Dargie H., et al. Guidelines for the diagnosis and treatment of chronic heart failure: executive summary (update 2005): The Task Force for the Diagnosis and Treatment of Chronic Heart Failure of the European Society of Cardiology. Eur Heart J (2005) 26:1115–1140.[Free Full Text]
  8. Wang T.J., Evans J.C., Benjamin E.J., Levy D., LeRoy E.C., Vasan R.S. Natural history of asymptomatic left ventricular systolic dysfunction in the community. Circulation (2003) 108:977–982.[Abstract/Free Full Text]
  9. Pfister R., Scholz M., Wielckens K., Erdmann E., Schneider C.A. Use of NT-proBNP in routine testing and comparison to BNP. Eur J Heart Fail (2004) 6:289–293.[Abstract/Free Full Text]
  10. Stevens L.A., Coresh J., Greene T., Levey A.S. Assessing kidney function-measured and estimated glomerular filtration rate. N Engl J Med (2006) 354:2473–2483.[Free Full Text]
  11. Baer F.M., Voth E., Deutsch H.J., et al. Predictive value of low dose dobutamine transesophageal echocardiography and fluorine-18 fluorodeoxyglucose positron emission tomography for recovery of regional left ventricular function after successful revascularization. J Am Coll Cardiol (1996) 28:60–69.[Abstract]
  12. Schneider C.A., Voth E., Gawlich S., et al. Significance of rest technetium-99m sestamibi imaging for the prediction of improvement of left ventricular dysfunction after Q wave myocardial infarction: importance of infarct location adjusted thresholds. J Am Coll Cardiol (1998) 32:648–654.[Abstract/Free Full Text]
  13. Pocock S.J., Wang D., Pfeffer M.A., et al. Predictors of mortality and morbidity in patients with chronic heart failure. Eur Heart J (2006) 27:65–75.[Abstract/Free Full Text]
  14. DeLong E.R., DeLong D.M., Clarke-Pearson D.L. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics (1988) 44:837–845.[CrossRef][Web of Science][Medline]
  15. Hall L.M., Jung R.T., Leese G.P. Controlled trial of effect of documented cardiovascular risk scores on prescribing. BMJ (2003) 326:251–252.[Free Full Text]
  16. Pfister R., Tan D., Thekkanal J., Hellmich M., Erdmann E., Schneider C.A. NT-pro-BNP measured at discharge predicts outcome in multimorbid diabetic inpatients with a broad spectrum of cardiovascular disease. Acta Diabetol (2007) 44:91–97.[CrossRef][Web of Science][Medline]
  17. Pfister R., Tan D., Thekkanal J., Hellmich M., Schneider C.A. NT-pro-BNP is associated with long-term outcome in a heterogeneous sample of cardiac inpatients. Eur J Intern Med (2007) 18:215–220.[CrossRef][Web of Science][Medline]
  18. Gardner R.S., Ozalp F., Murday A.J., Robb S.D., McDonagh T.A. N-terminal pro-brain natriuretic peptide. A new gold standard in predicting mortality in patients with advanced heart failure. Eur Heart J (2003) 24:1735–1743.[Abstract/Free Full Text]
  19. Koglin J., Pehlivanli S., Schwaiblmair M., Vogeser M., Cremer P., vonScheidt W. Role of brain natriuretic peptide in risk stratification of patients with congestive heart failure. J Am Coll Cardiol (2001) 38:1934–1941.[Abstract/Free Full Text]
  20. Lund L.H., Aaronson K.D., Mancini D.M. Predicting survival in ambulatory patients with severe heart failure on beta-blocker therapy. Am J Cardiol (2003) 92:1350–1354.[CrossRef][Web of Science][Medline]
  21. Bettencourt P., Azevedo A., Pimenta J., Frioes F., Ferreira S., Ferreira A. N-terminal-pro-brain natriuretic peptide predicts outcome after hospital discharge in heart failure patients. Circulation (2004) 110:2168–2174.[Abstract/Free Full Text]
  22. Mayer S.A., de Lemos J.A., Murphy S.A., Brooks S., Roberts B.J., Grayburn P.A. Comparison of B-type natriuretic peptide levels in patients with heart failure with versus without mitral regurgitation. Am J Cardiol (2004) 93:1002–1006.[CrossRef][Web of Science][Medline]
  23. Bibbins-Domingo K., Ansari M., Schiller N.B., Massie B., Whooley M.A. B-type natriuretic peptide and ischemia in patients with stable coronary disease: data from the Heart and Soul study. Circulation (2003) 108:2987–2992.[Abstract/Free Full Text]
  24. Das S.R., Drazner M.H., Dries D.L., et al. Impact of body mass and body composition on circulating levels of natriuretic peptides: results from the Dallas Heart Study. Circulation (2005) 112:2163–2168.[Abstract/Free Full Text]
  25. Fruhwald F.M., Fahrleitner-Pammer A., Berger R., et al. Early and sustained effects of cardiac resynchronization therapy on N-terminal pro-B-type natriuretic peptide in patients with moderate to severe heart failure and cardiac dyssynchrony. Eur Heart J (2007) 28:1592–1597.[Abstract/Free Full Text]
  26. Hogenhuis J., Voors A.A., Jaarsma T., et al. Anaemia and renal dysfunction are independently associated with BNP and NT-proBNP levels in patients with heart failure. Eur J Heart Fail (2007) 9:787–794.[Abstract/Free Full Text]
  27. Latini R., Masson S., Anand I., et al. Effects of valsartan on circulating brain natriuretic peptide and norepinephrine in symptomatic chronic heart failure: the Valsartan Heart Failure Trial (Val-HeFT). Circulation (2002) 106:2454–2458.[Abstract/Free Full Text]
  28. Gerber I.L., Stewart R.A., Legget M.E., et al. Increased plasma natriuretic peptide levels reflect symptom onset in aortic stenosis. Circulation (2003) 107:1884–1890.[Abstract/Free Full Text]
  29. Henriksen J.H., Gotze J.P., Fuglsang S., Christensen E., Bendtsen F., Moller S. Increased circulating pro-brain natriuretic peptide (proBNP) and brain natriuretic peptide (BNP) in patients with cirrhosis: relation to cardiovascular dysfunction and severity of disease. Gut (2003) 52:1511–1517.[Abstract/Free Full Text]
  30. Luchner A., Hengstenberg C., Lowel H., Riegger G.A., Schunkert H., Holmer S. Effect of compensated renal dysfunction on approved heart failure markers: direct comparison of brain natriuretic peptide (BNP) and N-terminal pro-BNP. Hypertension (2005) 46:118–123.[Abstract/Free Full Text]
  31. Mutlu B., Bayrak F., Kahveci G., Degertekin M., Eroglu E., Basaran Y. Usefulness of N-terminal pro-B-type natriuretic peptide to predict clinical course in patients with hypertrophic cardiomyopathy. Am J Cardiol (2006) 98:1504–1506.[CrossRef][Web of Science][Medline]
  32. Hillege H.L., Nitsch D., Pfeffer M.A., et al. Renal function as a predictor of outcome in a broad spectrum of patients with heart failure. Circulation (2006) 113:671–678.[Abstract/Free Full Text]
  33. Dries D.L., Exner D.V., Domanski M.J., Greenberg B., Stevenson L.W. The prognostic implications of renal insufficiency in asymptomatic and symptomatic patients with left ventricular systolic dysfunction. J Am Coll Cardiol (2000) 35:681–689.[Abstract/Free Full Text]
  34. Levey A.S., Coresh J., Balk E., et al. National Kidney Foundation practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Ann Intern Med (2003) 139:137–147.[Abstract/Free Full Text]
  35. Smilde T.D., van Veldhuisen D.J., Navis G., Voors A.A., Hillege H.L. Drawbacks and prognostic value of formulas estimating renal function in patients with chronic heart failure and systolic dysfunction. Circulation (2006) 114:1572–1580.[Abstract/Free Full Text]
  36. Hartmann F., Packer M., Coats A.J., et al. Prognostic impact of plasma N-terminal pro-brain natriuretic peptide in severe chronic congestive heart failure: a substudy of the Carvedilol Prospective Randomized Cumulative Survival (COPERNICUS) trial. Circulation (2004) 110:1780–1786.[Abstract/Free Full Text]
  37. Kallistratos M.S., Dritsas A., Laoutaris I.D., Cokkinos D.V. N-terminal prohormone brain natriuretic peptide as a marker for detecting low functional class patients and candidates for cardiac transplantation: linear correlation with exercise tolerance. J Heart Lung Transplant (2007) 26:516–521.[CrossRef][Web of Science][Medline]

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H. Wedel, J. J.V. McMurray, M. Lindberg, J. Wikstrand, J. G.F. Cleland, J. H. Cornel, P. Dunselman, A. Hjalmarson, J. Kjekshus, M. Komajda, et al.
Predictors of fatal and non-fatal outcomes in the Controlled Rosuvastatin Multinational Trial in Heart Failure (CORONA): incremental value of apolipoprotein A-1, high-sensitivity C-reactive peptide and N-terminal pro B-type natriuretic peptide
Eur J Heart Fail, March 1, 2009; 11(3): 281 - 291.
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W. C. Levy
Can B-type natriuretic peptides replace heart failure risk models?
Eur J Heart Fail, March 1, 2008; 10(3): 224 - 225.
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