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European Journal of Heart Failure 2008 10(7):682-688; doi:10.1016/j.ejheart.2008.04.006
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© 2008 European Society of Cardiology

Wall motion index, estimated glomerular filtration rate and mortality risk in patients with heart failure or myocardial infarction: A pooled analysis of 18,010 patients

Morten Schoua,b,*, Christian Torp-Pedersenc, Finn Gustafssona, Jawdat Abdullad and Lars Kobera

a Department of Cardiology, The Heart Centre, Rigshospitalet University Hospital DK-2100 Copenhagen O, Denmark
b Department of Cardiology and Endocrinology, Frederiksberg University Hospital DK-2000 Frederiksberg, Denmark
c Department of Cardiology, Gentofte University Hospital DK-2900 Hellerup, Denmark
d Department of Cardiology, Glostrup University Hospital DK-2600 Glostrup, Denmark

* Corresponding author. Department of Cardiology and Endocrinology, Frederiksberg University Hospital, DK-2000, Frederiksberg, Denmark. Tel.: +45 23 21 30 58; fax: +45 38 16 43 59. E-mail address: m.schou{at}dadlnet.dk (M. Schou).


   Abstract
Top
 Abstract
1. Introduction
 2. Material and methods
 3. Results
 4. Discussion
 5. Conclusions
 References
 
Aims: This study was designed to assess whether the prognostic significance of estimated glomerular filtration rate (eGFR) and left ventricular ejection fraction (LVEF) interact in populations with heart failure (HF) and myocardial infarction (MI).

Methods: Patients were recruited from four screening registers (N=18,010) including patients admitted with HF or MI. Ten years follow-up was recorded and formal testing for interactions between eGFR and LVEF with respect to outcome was done.

Results: Twelve-thousand-and-ninety patients died. A significant interaction (P=0.010) was found and each parameter became relatively more important when the value of the other was low. eGFR and LVEF were reparameterized to categorical variables and we observed that chronic kidney disease stage II was associated with a decreased (Hazard ratio (HR): 0.79 (95% Confidence Interval: 0.72–0.86)) and chronic kidney disease stages IV (HR: 1.60 (1.45–1.91) and V (HR: 1.91 (1.45–2.52) were associated with an increased mortality risk with an additive effect of left ventricular systolic dysfunction (LVSD).

Conclusion: The prognostic significance of eGFR and LVEF is synergistic in patients with HF or MI and the impact of one parameter is inversely related to the level of the other. Statistical interactions are scale dependent and the relationship between chronic kidney disease stages I to V and mortality risk is J-shaped with an additive effect of LVSD.

Key Words: Heart failure • Myocardial infarction • Estimated glomerular filtration rate • Left ventricular systolic dysfunction • Mortality

Received December 22, 2007; Revised March 15, 2008; Accepted April 15, 2008


   1. Introduction
Top
 Abstract
 1. Introduction
2. Material and methods
 3. Results
 4. Discussion
 5. Conclusions
 References
 
Neurohormonal activation with increased activity of the sympathetic nervous system and activation of the renin angiotensin aldosterone system are key factors in the progression of heart failure (HF) and myocardial infarction (MI) complicated by left ventricular systolic dysfunction (LVSD) [1]. Neurohormonal inhibition [2] improves outcome in these patient categories, but serious side effects can occur due to a decline in glomerular filtration rate e.g. dehydration, uraemia, hyponatraemia, hyperkalaemia, and decreased renal and metabolic clearance of metabolic agents like urate [3] and homocysteine [4]. Both left ventricular ejection fraction estimated by wall motion index (WMI) [5] and estimated glomerular filtration rate (eGFR) are associated with mortality risk in patients with HF and MI [6-10] and it may be speculated that a low eGFR may affect the prognostic significance of WMI since patients with a low eGFR may not tolerate medication in optimal doses and the frequency of renal side effects may be increased. Conversely, impaired myocardial function may be associated with increased neurohormonal activation and decreased renal blood flow, which may affect eGFR and a poor myocardial function may therefore amplify the prognostic significance of eGFR.

We therefore hypothesized that the prognostic significance of WMI and eGFR were interdependent. Previous studies evaluating the effect of WMI and eGFR in patients with heart disease have not addressed this issue. To test the hypothesis, we recorded 10 years mortality among 18,010 patients admitted with HF or MI from four screening registers (pooled analysis) and tested for interactions between eGFR and WMI and mortality risk.


   2. Material and methods
Top
 Abstract
 1. Introduction
 2. Material and methods
3. Results
 4. Discussion
 5. Conclusions
 References
 
2.1. Patient selection
Data from four Danish registries (TRACE, DIAMOND-MI, DIAMOND-HF and ECHOS) were pooled. Each of these registries consists of consecutive patients who were hospitalised for either MI or HF and screened for a trial. Patients were obtained from screening registries involved in the Trandolapril in Cardiac Evaluation (TRACE) study [11], the Danish Investigations of Arrhythmia and Mortality HF (DIAMOND-HF) study [12], the DIAMOND-Acute Myocardial Infarction (DIAMOND-MI) study [13] and finally the Echo Cardiography and Heart Outcome Study (ECHOS) [14]. The designs of these studies have been published previously [11-14]. The TRACE (n=6676) and DIAMOND-MI (n=7974) registries included patients after MI, while DIAMOND-HF (n=5177) and ECHOS (n=2904 Danish patients) registries included patients with HF. WMI and estimated eGFR were available in 18,010 patients (79% of the available patients in the four registries). We chose to conduct a pooled analysis to include WMI- and eGFR-data in a broad range of patients with the highest possible sample size in all subgroups (N=108 in chronic kidney disease stage V in the present analyses (Table 1)). We considered a pooled analysis reasonable since all patients could be considered as patients at risk due to the verified MI or HF diagnosis. Before pooling of data we tested for interaction between eGFR, WMI and mortality risk in the MI and HF cohorts and significant interactions were observed (P=0.020 for interaction in the MI cohort and P=0.004 for interaction in the HF cohort, respectively).


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  		Table 1 Baseline characteristics according to the presence of chronic kidney disease stages I-V

 
Baseline characteristics were available for all patients. Left ventricular ejection fraction was evaluated centrally from echocardiograms for all patients. Echocardiograms were recorded on videotape and WMI was calculated in a core laboratory by experienced investigators [5]. LVSD was defined as WMI≤1.4 ({approx} a left ventricular ejection fraction ≤40-45%). Information on hypertension, previous chronic heart failure, diabetes, smoking, previous MI, chronic ischaemic heart disease and chronic obstructive lung disease was based on patient interview and chart review. Estimated eGFR was calculated from the four-component Model of Disease in Renal Disease (MDRD) equation incorporating age, race, sex and serum creatinine level [9]: estimated eGFR=186 * (serum creatinine [in milligrams per decilitre])–1.154 * (age [in years])–0.203. For women and Afro-American patients the product of the equation must be multiplied by a correction factor of 0.742 and 1.21, respectively. Chronic kidney disease was defined as an eGFR<60 ml/min/1.73 m2 in accordance with the National Kidney Foundation. Chronic kidney disease stages I to V were also defined in accordance with the National Kidney Foundation: chronic kidney disease stage I: >90 ml/min/1.73 m2, chronic kidney disease stage II: 60-90 ml/min/1.73 m2, chronic kidney disease stage III: 30-59 ml/min/1.73 m2, chronic kidney disease stage IV: 15-29 ml/min/1.73 m2 and chronic kidney disease stage V: <15 ml/min/1.73 m2. Plasma concentrations of creatinine were measured at the participating centers at admission.

The Ethics Committee approved all the trials and all patients gave informed consent (oral and written) to participate in the studies according to the Helsinki Declaration II.

2.2. Statistics
The individual patient data reported in the four registries were combined and analyzed using the SAS (SAS Institute, Cary, North Carolina, USA) version 9.1. Patients were divided into four categories according to the absence or presence of LVSD and chronic kidney disease. Continuous baseline variables are presented as medians with confidence intervals (CI=90%) and were analyzed using Kruskal-Wallis test, while the discrete variables are presented as proportions and were analyzed by Chi square tests. Mortality risk according to eGFR and WMI (a change of 0.3 in WMI=a change of 10 in left ventricular ejection fraction) as continuous variables was studied with Cox Proportional hazard models. In multivariate analyses we adjusted for age, sex, history of hypertension, chronic heart failure, diabetes and chronic obstructive pulmonary disease. Estimated eGFR is adjusted for body surface area in the MDRD formula and consequently body mass index was not included in the multivariable models. The assumptions underlying the Cox proportional-hazards model (proportional hazards, lack of interaction, and linearity of continuous variables) were tested and found valid unless otherwise indicated. eGFR fulfilled the criteria for linearity by a LogRank test if eGFR were divided into quartiles. eGFR also fulfilled the criteria for linearity according to chronic kidney disease stages in an unadjusted analysis, but not in an adjusted Cox analysis (see Results). eGFR and WMI interacted and these variables were therefore reparameterized to: I) WMI categories and absence or presence of chronic kidney disease, II) chronic kidney disease stages I to V and absence or presence of LVSD and III) absence or presence of LVSD and chronic kidney disease. To present the interaction between WMI, eGFR and mortality risk we kept one variable as a continuous variable and presented it according to the other one as categorical variable. Differences in mortality between groups were examined by the use of log-rank tests and presented as Kaplan-Meier curves. The primary end-point was death from all causes and was reported from the Danish Central Personal Registry, where all deaths in Denmark are registered within 2 weeks. Thus, in May 2006 the central registry provided 10 years (median) of follow-up (range: 3.2 to 16.4 years) for patients included in the current analyses. Survival data were censored at time of hospital discharge for 19 patients who were lost to follow-up and censored at time of emigration for 38 patients who had emigrated.


   3. Results
Top
 Abstract
 1. Introduction
 2. Material and methods
 3. Results
4. Discussion
 5. Conclusions
 References
 
Baseline characteristics of the 18,010 patients according to chronic kidney disease stages I-V are presented in Table 1. Age and the prevalence of hypertension were associated with chronic kidney disease stages I to V. The prevalence of ischaemic heart disease, chronic heart failure, previous myocardial infarction, chronic obstructive pulmonary disease and diabetes increased progressively in chronic kidney disease stages I through IV. WMI was inversely associated with chronic kidney disease stages I to IV. Patients in chronic kidney disease stage I were younger and more frequently female and smokers than patients in chronic kidney disease stages II to V.

Twelve-thousand-and-ninety patients (67%) died during the follow-up period. We observed a significant interaction between eGFR and WMI (P=0.010) in the multivariate Cox proportional hazard model. To evaluate whether illustration of data supported the interaction we presented eGFR and WMI as continuous variables according to the other one as a categorical variable. We observed that the effect of eGFR on mortality risk increased when WMI decreased (Fig. 1) and that the effect of WMI increased slightly when eGFR decreased (Hazard ratio for WMIincrease of 0.3 was 0.87 (95% confidence interval (CI): 0.82-0.92 (P<0.001) in chronic kidney disease stage I and 0.82 (95% CI: 0.81-0.83, P<0.001) in chronic kidney disease stages II-V).


Figure 01
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  		Fig. 1 Adjusted relationship between 10 year mortality risk and glomerular filtration rate (eGFR) as continuous variable according to wall motion index (WMI) as categorical variable.

 
Due to the interaction between eGFR and WMI as continuous variables these variables were reparameterized to categorical variables. We observed a J-shaped relationship between chronic kidney disease stages I to V and mortality risk with an additive effect of LVSD (Fig. 2) and a curvilinear relationship between WMI categories and mortality risk with an additive effect of chronic kidney disease (Fig. 3). Patients in chronic kidney disease stage I with a WMI>1.4 had a 21% (95% CI: 13-28%, P<0.001) increased mortality risk compared to patients in chronic kidney disease stage II with a WMI>1.4 and the same outcome as patients in chronic kidney disease stage III with a WMI>1.4 (P=0.751) (Fig. 2).


Figure 02
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  		Fig. 2 Adjusted relationship between chronic kidney disease stages (CKD) I to V and absence and presence of left ventricular systolic dysfunction (LVSD) and 10 year mortality risk. Reference: CKD stage I and wall motion index (WMI) >1.4. Bars indicate 95% confidence interval. *P<0.001 and NS = not significant.

 


Figure 03
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  		Fig. 3 Adjusted relationship between wall motion index (WMI) categories and absence and presence of chronic kidney disease (CKD) (=estimated glomerular filtration rate (eGFR) <60 ml/min/1.73 m2) and 10 year mortality risk. WMI categories: I: >1.6, II: 1.3-1.6, III: 1.0-1.2, IV: 0.7-0.9 and V: <0.7. Reference: WMI>1.6 and eGFR>/=60 ml/min/1.73 m2). Bars indicate 95% confidence interval. *P<0.001.

 
Kaplan-Meier plots showing mortality rates according to the presence or absence of LVSD and chronic kidney disease are presented in Fig. 4 (Log Rank P<0.001). In an unadjusted analysis the patients with chronic kidney disease had a higher risk of dying than patients with LVSD, but in a multivariate Cox proportional hazard model LVSD was associated with a 51% (95% CI: 43-59%, P<0.001) increased mortality risk and chronic kidney disease was associated with a 30% (95% CI: 23-37%, P<0.001) increased mortality risk. The combination was associated with a doubled (HR: 2.02, 95% CI: 1.92-2.13, P<0.001) increased mortality risk.


Figure 04
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  		Fig. 4 Kaplan-Meier plot according to presence or absence of left ventricular systolic dysfunction (LVSD) (=Wall motion index≤1.4) and chronic kidney disease (CKD) (=estimated glomerular filtration rate<60 ml/min/1.73 m2). LogRank: P<0.001.

 

   4. Discussion
Top
 Abstract
 1. Introduction
 2. Material and methods
 3. Results
 4. Discussion
5. Conclusions
 References
 
In these analyses of a large cohort admitted with either MI or HF, we observed that the prognostic significance of WMI and eGFR is synergistic. We further explored that the relationship between mortality risk and chronic kidney disease stages I to V is J-shaped with an additive effect of LVSD.

It is noteworthy that the prognostic significance of eGFR and WMI is synergistic if the variables are considered as continuous variables, but only additive if they are considered as categorical variables. The fact that statistical interactions can be scale dependent is well described [15]. Our results indicate that the effect of eGFR on mortality risk increases when WMI decreases and that the effect of WMI on mortality risk increases slightly when eGFR decreases. Therefore, when the value of one of the parameters is low the other one becomes relatively more important and given the interaction it is not statistically reasonable to consider a general effect of either WMI or eGFR (as continuous variables) on mortality risk, rather an effect must be interpreted in the light of the value of the other variable. The mechanism(s) behind this observed statistical interaction cannot be deduced from our results.

In order to analyze the effect of eGFR and WMI on mortality risk we reparameterized the continuous variables to categorical variables in two ways and we then observed a J-shaped relationship between chronic kidney disease stages I-V and mortality risk with an additive effect of LVSD (Fig. 2) and a curvilinear relationship between WMI categories and mortality risk - like in the CHARM programme [16] - with an additive effect of chronic kidney disease (Fig. 3). According to our knowledge this study is the first to report that patients with eGFR>90 ml/min/1.73 m2 (chronic kidney disease stage I) have a poorer long-term prognosis than patients with an eGFR between 60 and 90 ml/min/1.73 m2 (chronic kidney disease stage II) in an adjusted analysis. Smith et al. [17] observed higher 1-year mortality rates in MI and HF patients with high eGFR, but in adjusted Cox analyses according to deciles of eGFR a high eGFR was not associated with an increased 1-year mortality risk. The discrepancy between their results and ours may be explained by length of follow-up. The potential mechanism behind this finding is not clear. It could, obviously, be a matter of chance, but the number of patients in this group is high, making this explanation less likely. Theoretically, eGFR>90 ml/min/1.73 m2 (chronic kidney disease stage I) may reflect glomerular hyperfiltration induced by early glomerular damage [18], implying that diabetes might be involved as a mechanism because diabetic nephropathy is in its early phase characterised by glomerular hyperfiltration [18]. We did not find that diabetes was more common among patients in chronic kidney disease stage I than in chronic kidney disease stage II but under diagnosing of diabetes in HF is common and the proportions observed in the current study may not reflect the real prevalence of abnormal glucose metabolism [19]. We observed a higher frequency of smoking in patients with an eGFR>90 ml/min/1.73 m2 (chronic kidney disease stage I) (Table 1). An association between glomerular hyperfiltration and smoking has previously been reported in patients with diabetes [20]. It should also be noted that although patients in chronic kidney disease stage I are younger and more frequently female than patients in chronic kidney disease stage II (Table 1) they have a poorer outcome [21-23], supporting the suggestion that undiagnosed diabetes (residual confounding) could be the explanation for the increased mortality risk associated with a high eGFR [24]. However, our data also support the notion that glomerular hyperfiltration could constitute a new metabolic risk marker [25]. The increased mortality risk in chronic kidney disease stage I could also reflect low serum creatinine levels induced by muscle wasting and therefore false high eGFR levels. However, patients in chronic kidney disease stage I were younger, had similar WMI's and body-mass-indices and a similar frequency of comorbidities as patients with chronic kidney disease stages II to V (Table 1) making this explanation less likely.

Prior studies have focussed on the prognostic significance of eGFR and WMI in HF populations reporting that they are independently associated with an increased mortality risk [6,7] and emphasizing that the presence of a combination of the two is associated with a very poor prognosis [6]. However, the interaction seen in the present study has not, to our knowledge, been reported previously. In HF patients with LVSD, Hillege et al. [6] observed that left ventricular ejection fraction and estimated creatinine clearance carried independent prognostic information and that the hazard ratio for estimated creatinine clearance was higher than that for left ventricular ejection fraction. In the CHARM programme, where estimated eGFR was used, the hazard ratio for left ventricular ejection fraction and estimated eGFR were in the same range [7]. We chose to stratify the patients according to absence and presence of chronic kidney disease and LVSD to obtain a relative impression of the prognostic significance of these prognostic factors (see Results and Fig. 4). We observed that a combination of LVSD and chronic kidney disease was associated with a doubled mortality risk and that LVSD and chronic kidney disease were associated with a 51% and 30% increased mortality risk, respectively, indicating a more pronounced effect of LVSD than chronic kidney disease in an adjusted analysis when a single reference was applied.

Based on our results, it may be argued that MI and HF patients with chronic kidney disease and preserved left ventricular ejection fraction should receive treatments such as angiotensin converting enzyme inhibitors, because they should be considered as high-risk patients [26,27]. Particularly in patients with chronic kidney disease, an accelerated decline in eGFR may occur after an MI; in the CATS study post MI treatment with an angiotensin converting enzyme inhibitor protected against such a decline [28]. Hou et al. [29] also showed that an angiotensin converting enzyme inhibitor slowed the decline in eGFR in patients with chronic kidney disease without diabetes. However, to date, there is no evidence on mortality benefit from clinical trials to support the use of angiotensin converting enzyme inhibitors in patients with HF and preserved left ventricular ejection fraction based on renal function. In patients with both LVSD and chronic kidney disease, data from the SAVE study [30] indicate that patients with chronic kidney disease benefit from treatment with angiotensin converting enzyme inhibitors.

Some limitations of the present study should be noted. The TRACE, DIAMOND-MI, DIAMOND-HF and ECHOS trials were conducted in 1990s and early 2000s and treatment of MI and HF has changed in the interim. However, to test the hypothesis of the current study, long-term follow-up was needed, making this issue an inherent problem in the study design. Our study cohort consisted of patients screened for entry into clinical trials. The generalizability of our results may therefore be questioned. However, the registers encompass all screened patients not merely patients subsequently included in the trials. Furthermore, consecutive screening of patients was emphasised in all four studies, increasing the generalizability of the results. It should also be noted that the study cohort consisted of patients with either a MI and/or HF. Before pooling of data all analyses were conducted in both the MI and HF sub cohorts and the same results as in the pooled cohort were observed. This is a cohort study and we have therefore no guarantee for balancing of unmeasured confounders like micro- and macroalbuminuria, troponins, brain natriuretic peptides, anaemia and measures of diastolic dysfunction. Chronic kidney disease is often accompanied by anaemia [31], high levels of brain natriuretic peptides [32] and troponins [33], as well as diastolic dysfunction [34]. Consequently, the observed interaction may in theory partly reflect an interaction between eGFR combined with one or more of these confounders and WMI and outcome. However, it seems unlikely that unmeasured confounding explain our results completely. The difference between estimated eGFR and true eGFR may have lead to misclassification of some patients due to underestimation of true eGFR by estimated eGFR [9]. Since plasma concentrations of creatinine were measured at the study sites and not in a core laboratory like WMI, analytical variation is likely to be present. Statistical analyses have limitations and it may be argued that the observed interaction is merely an effect of chance (Type I error). The significance of the interaction is supported by observed significant interactions in the MI and HF subgroups. Finally, it cannot be excluded that the J-shaped curve may reflect measurement bias, misclassification or differences in drug therapy in different groups of renal function over time. The J-shaped relationship between chronic kidney disease stages and mortality risk is biologically plausible, but should be considered hypothesis generating.


   5. Conclusions
Top
 Abstract
 1. Introduction
 2. Material and methods
 3. Results
 4. Discussion
 5. Conclusions
References
 
In this analysis of a large cohort admitted with MI or HF the effects on prognosis of eGFR and WMI were interdependent, such that the prognostic significance of each parameter becomes relatively more important when the value of the other is low. The relationship between chronic kidney disease stages I-V and mortality risk is J-shaped with an additive effect of LVSD whereas the relationship between WMI categories and mortality risk is curvilinear with an additive effect of chronic kidney disease. The combination of LVSD and chronic kidney disease is associated with a doubled 10-year mortality risk. The finding that glomerular hyperfiltration seems to be associated with an adverse outcome in these patient groups deserves further investigation and a high eGFR may reflect early glomerular damage in these patient categories.


   References
Top
 Abstract
 1. Introduction
 2. Material and methods
 3. Results
 4. Discussion
 5. Conclusions
 References
 

  1. Packer M. The neurohormonal hypothesis: a theory to explain the mechanism of disease progression in heart failure. J Am Coll Cardiol (1992) 20:248–254.[Abstract]
  2. Swedberg K., Cleland J.G., Dargie H.J., 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]
  3. Ochiai M.E., Baretto A.C., Oliveira M.T. Jr., Munhoz M.T., Morgado P.C., Ramires J.A. Uric acid renal excretion and renal insufficiency in decompensated severe heart failure. Eur J Heart Fail (2005) 7:468–474.[Abstract/Free Full Text]
  4. van Guldener C., Stam F., Stehouwer C.D. Homocysteine metabolism in renal failure. Kidney Int Suppl (2001) 78:S234–S237.[Medline]
  5. Kober L., Torp-Pedersen C., Carlsen J., Videbaek R., Egeblad H. Echocardiographic methods for selecting high risk patients shortly after acute myocardial infarction, for inclusion in multi-centre studies (as used in the TRACE study). TRAndolapril CardiacEvaluation. Eur Heart J (1994) 15:1616–1620.[Abstract/Free Full Text]
  6. Hillege H.L., Girbes A.R., de Kam P.J., et al. Renal function, neurohormonal activation, and survival in patients with chronic heart failure. Circulation (2000) 102:203–210.[Abstract/Free Full Text]
  7. 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]
  8. Anavekar N.S., McMurray J.J., Velazquez E.J., et al. Relation between renal dysfunction and cardiovascular outcomes after myocardial infarction. N Engl J Med (2004) 351:1285–1295.[Abstract/Free Full Text]
  9. Smilde T.D., van Veldhuisen V.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) 10(114):1572–1580.
  10. 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]
  11. Kober L., Torp-Pedersen C., Carlsen J.E., et al. A clinical trial of the angiotensin-converting-enzyme inhibitor trandolapril in patients with left ventricular dysfunction after myocardial infarction. Trandolapril Cardiac Evaluation (TRACE) Study Group. N Engl J Med (1995) 333:1670–1676.[Abstract/Free Full Text]
  12. Torp-Pedersen C., Moller M., Bloch-Thomsen P.E., et al. Dofetilide in patients with congestive heart failure and left ventricular dysfunction. Danish Investigations of Arrhythmia and Mortality on Dofetilide Study Group. N Engl J Med (1999) 341:857–865.[Abstract/Free Full Text]
  13. Kober L., Bloch Thomsen P.E., Moller M., et al. Effect of dofetilide in patients with recent myocardial infarction and left-ventricular dysfunction: a randomised trial. Lancet (2000) 356:2052–2058.[CrossRef][Web of Science][Medline]
  14. Cleland J.G., Coletta A.P., Clark A.L., Velavan P., Ingle L. Clinical trials update from the European Society of Cardiology Heart Failure meeting and the American College of Cardiology: darbepoetin alfa study, ECHOS, and ASCOT-BPLA. Eur J Heart Fail (2005) 7:937–939.[Abstract/Free Full Text]
  15. Clayton D., Hills M. Statistical models in epidemiology. (1993) First Edition. Oxford Science Publications.
  16. Solomon S.D., Anavekar N., Skali H., et al. Influence of ejection fraction on cardiovascular outcomes in a broad spectrum of heart failure patients. Circulation (2005) 112:3738–3744.[Abstract/Free Full Text]
  17. Smith G.L., Shilpak M.G., Havranek E.P., et al. Serum urea nitrogen, creatinine, and estimators of renal function: mortality in older patients with cardiovascular disease. Arch Intern Med (2006) 166:1134–1142.[Abstract/Free Full Text]
  18. Taal M.W., Brenner B.M. Predicting initiation and progression of chronic kidney disease: developing renal risk scores. Kidney Int (2006) 70:1694–1705.[CrossRef][Web of Science][Medline]
  19. Bartnik M., Ryden L., Malmberg K., et al. Oral glucose tolerance test is needed for appropriate classification of glucose regulation in patients with coronary artery disease: a report from the Euro Heart Survey on Diabetes and the Heart. Heart (2007) 93:72–77.[Abstract/Free Full Text]
  20. Ekberg G., Grefberg N., Larsson L.O., Vaara I. Cigarette smoking and glomerular filtration rate in insulin-treated diabetics without manifest nephropathy. J Intern Med (1990) 228:211–217.[Web of Science][Medline]
  21. Gustafsson F., Torp-Pedersen C., Burchardt H., et al. Female sex is associated with a better long-term survival in patients hospitalized with congestive heart failure. Eur Heart J (2004) 25:129–135.[Abstract/Free Full Text]
  22. O Meara E., Clayton T., McEntagart M.D., et al. Sex differences in clinical characteristics and prognosis in a broad spectrum of patients with heart failure: results of the Candesartan in Heart failure: Assessment of Reduction in Mortality and morbidity (CHARM) program. Circulation (2007) 115:3111–3120.[Abstract/Free Full Text]
  23. Gustafsson F., Torp-Pedersen C., Seibaek M., Burchardt H., Kober L. Effect of age on short and long-term mortality in patients admitted to hospital with congestive heart failure. Eur Heart J (2004) 25:1711–1717.[Abstract/Free Full Text]
  24. Gustafsson I., Brendorp B., Seibaek M., et al. Influence of diabetes and diabetes-gender interaction on the risk of death in patients hospitalized with congestive heart failure. J Am Coll Cardiol (2004) 43:771–777.[Abstract/Free Full Text]
  25. Tomaszewski M., Charchar F.J., McClure J., et al. Glomerular hyperfiltration: a new marker of metabolic risk. Kidney Int (2007) 71:816–821.[CrossRef][Web of Science][Medline]
  26. Solomon S.D., Rice M.M., A Jablonski K., et al. Renal function and effectiveness of angiotensin-converting enzyme inhibitor therapy in patients with chronic stable coronary disease in the Prevention of Events with ACE inhibition (PEACE) trial. Circulation (2006) 114:26–31.[Abstract/Free Full Text]
  27. de Zeeuw D., Hillege H.L., de Jong P.E. The kidney, a cardiovascular risk marker, and a new target for therapy. Kidney Int Suppl (2005) 98:S25–S29.[Medline]
  28. Hillege H.L., van Gilst G.H., van Veldhuisen D.J., et al. Accelerated decline and prognostic impact of renal function after myocardial infarction and the benefits of ACE inhibition: the CATS randomized trial. Eur Heart J (2003) 24:412–420.[Abstract/Free Full Text]
  29. Hou F.F., Zhang X., Zhang G.H., et al. Efficacy and safety of benazepril for advanced chronic renal insufficiency. N Engl J Med (2006) 354:131–140.[Abstract/Free Full Text]
  30. Tokmakova M.P., Skali H., Kenchaiah S., et al. Chronic kidney disease, cardiovascular risk, and response to angiotensin-converting enzyme inhibition after myocardial infarction: the Survival And Ventricular Enlargement (SAVE) study. Circulation (2004) 110:3667–3673.[Abstract/Free Full Text]
  31. O Meara E., Clayton T., McEntegart M.D., et al. Clinical correlates and consequences of anemia in a broad spectrum of patients with heart failure: results of the Candesartan in Heart Failure: Assessment of Reduction in Mortality and Morbidity (CHARM) Program. Circulation (2006) 113:986–994.[Abstract/Free Full Text]
  32. 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]
  33. Abbas N.A., John R.I., Webb M.C., et al. Cardiac troponins and renal function in nondialysis patients with chronic kidney disease. Clin Chem (2005) 51:2059–2066.[Abstract/Free Full Text]
  34. de Almeida E.A., de Oliveira O.I., Lopes J.A., Almeida A.G., Lopes M.G., Prata M.M. Diastolic function in several stages of chronic kidney disease in patients with autosomal dominant polycystic kidney disease: a tissue Doppler imaging study. Kidney Blood Press Res (2007) 30:234–239.[CrossRef][Web of Science][Medline]

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