Abstract

Aims To determine the prevalence and incidence of renal dysfunction (RD) in patients with chronic heart failure (CHF), to identify contributory factors and predictors of worsening renal function (WRF), and to explore the relationship between RD and mortality.

Methods and results Prospective data on 1216 patients with CHF were analysed. The glomerular filtration rate (GFR) was used to determine renal function, and WRF was defined as an increase in serum creatinine of >26.5 µmol/L (>0.3 mg/dL). The prevalence of RD defined as a GFR of <60 mL/min was 57%. During 6 months, WRF occurred in 161 (13.0%) patients. Predictors of WRF were vascular disease, the use of thiazide diuretics, and a baseline urea >9 mmol/L. Two hundred and sixty-three (21.6%) patients died, and baseline RD and WRF both predicted a higher mortality (P<0.001), whereas an improvement in renal function over the first 6 months predicted a lower mortality (hazard ratio 0.8, 95% confidence interval 0.6–1.0).

Conclusion In ambulatory patients with CHF, RD is common, commonly deteriorates over a relatively short period of time, is unlikely to recover substantially, and augurs a poor prognosis.

Introduction

Chronic heart failure (CHF) is common, is an important cause of hospitalizations, and is associated with significant morbidity and mortality.1 Despite some recent successes, the prognosis of heart failure remains poor.24 Reports from randomized controlled trials suggest that renal dysfunction (RD) is common in patients with heart failure and is associated with an adverse prognosis.5,6 However, thesestudies usually enrolled younger patients with fewer co-morbidities than those managed in clinical practice. Moreover, patients with significant RD were often excluded from the trials.712 Information on the prevalence and natural history of RD in patients hospitalized due to worsening heart failure has been published,1315 but few data from epidemiologically representative cohorts of ambulatory outpatients with CHF exist.

RD in patients with heart failure is multi-factorial. Renal function may act as a barometer of cardiac function. As heart failure progresses, preferential renal vasoconstriction occurs diverting blood away from the kidney to maintain blood flow to the heart, brain, and, during exercise, skeletal muscle. A decline in arterial pressure combined with an increase in venous pressure leads to a fall in the trans-renal pressure gradient, and despite efferent arteriolar constriction, glomerular filtration declines. However, disease of the kidneys and cardiovascular system shares many common aetiologies,16,17 and therefore renal parenchymal disease, renovascular disease, and urinary obstruction may also be important causes of RD in patients with heart failure.

RD may confer an adverse prognosis because it is a marker of more severe vascular disease, greater age, worse cardiac function, or greater likelihood of life-saving treatment being withheld. Alternatively, or in addition, RD may cause salt and water retention due to impaired nephron function and reduced diuretic efficacy. RD may also lead to failure to excrete toxic substances such as oxidized catecholamines, uric acid, and other uraemic factors.18 However, the effectiveness of interventions to improve renal function in heart failure remains largely anecdotal and little advice is given by guidelines for heart failure.19,20

Our aims were to determine the prevalence and incidence ofRD in a large community-based management programme for patients with CHF due to left ventricular systolic dysfunction, to identify possible contributory factors and predictors of worsening renal function (WRF), and to explore the relationship between RD and mortality.

Methods

Ethical approval

All participants provided written informed consent, and the study was carried out in accordance with the Helsinki Declaration II and the European Standards for Good Clinical Practice. Ethical approval was granted by the Local Research Ethics Committee.

Study design

This was a single centre, observational, prospective study. Cross-sectional data were used to determine the prevalence of RD in patients with CHF and left ventricular systolic dysfunction. Longitudinal data were analysed to investigate the incidence of RD, predictors of WRF, and their inter-relationship with prognosis.

Participants

Patients were identified from a community-based heart failure programme that accepts all patients with a suspected diagnosis of heart failure or major cardiac dysfunction but retains only those in whom the left ventricular ejection fraction is <45% on echocardiography. Heart failure is defined as current symptoms of heart failure, or a history of symptoms controlled by ongoing therapy, due to cardiac dysfunction and in the absence of any more likely cause. The only exclusion criteria for this study were the inability to provide written consent, pregnancy, and renal replacement therapy with dialysis or transplantation.

Clinical assessment

Clinical information obtained included past medical history and drug and smoking history. Clinical examination included assessment of height, weight, heart rate, rhythm, and blood pressure. Diabetes was defined as a previous diagnosis of this condition and sub-categorizedas requiring insulin, oral hypoglycaemic agent, or diet-controlled. Hypertension and chronic obstructive pulmonary disease were defined as a prior history of receiving drug treatment for these conditions. Patients with previous myocardial infarction, coronary artery bypass surgery, positive tests for ischaemia, or coronary artery disease on angiography were considered to have ischaemic heart disease (IHD). Vascular disease was defined as a previous clinical diagnosis with investigations that identified one or more of the following: peripheral vascular disease, renal artery stenosis, cerebrovascular disease, or abdominal aortic aneurysm. Cardiomyopathy was defined as the presence of left ventricular systolic dysfunction in the absence of any known cause. Daily doses of diuretics were expressed in furosemide equivalents (bumetanide 1 mg=furosemide 40 mg). Doses of agents blocking the renin–angiotensin–aldosterone system [both angiotensin-converting enzyme (ACE)-inhibitors and angiotensin receptor blockers] were expressed as the percentage of the maximum recommended daily dose used in CHF patients.19,20 All medications were those that the patients were taking at the time of referral to the heart failure clinic. Blood tests included urea, creatinine, sodium, and haemoglobin.

Follow-up

Following the baseline visit, all patients with a diagnosis of heart failure were reviewed at approximately 3 and 6 months. Patients not on optimal ACE-inhibition and/or beta-blocker therapy at baseline were seen at 2, 4, and 6 weeks for up-titration of their medications and followed up as above. Data collection was performed between March 1999 and November 2003, but only patients who survived more than 6 months were included in the analysis.

Assessment of renal function

We considered two different methods of assessing renal function. These were the serum creatinine (SCr) and the glomerular filtrationrate (GFR) using the simplified modification of diet in renal disease (MDRD) prediction equation [186×SCr−1.154 (mg/dL) × Age−0.203 (years)×0.742 if female, ×1.212 if black].21,22

Definitions of RD and changes in renal function

The primary definition of RD was a GFR <60 mL/min.6,23,24 We also considered a SCr >130 µmol/L (1.5 mg/dL) as a marker of RD.25 For the presentation of data, we divided patients into three groups based upon the baseline SCr [normal ≤106 µmol/L (≤1.2 mg/dL), minor increase 106–177 µmol/L (>1.2–2.0 mg/dL), marked increase >177 µmol/L (>2.0 mg/dL)]26 and four groups based upon the baseline GFR (normal ≥90 mL/min, mild impairment 60–89 mL/min, moderate impairment 30–59 mL/min, and severe renal impairment ≤29 mL/min) to compare the clinical characteristics of patients with different severities of RD. These values were defined by the Kidney Disease Outcome Quality Initiative guidelines.27

Changes in renal function were defined using two methods. The primary method used was an increase or decrease in SCr of >26.5 µmol/L (>0.3 mg/dL), a convention previously used by other groups.1315,28 In addition, we also used a change in the GFR category between normal, mildly impaired, moderately impaired, and severely impaired, as mentioned above27 to assess changes in renal function over a period of 6 months.

Statistical analysis

Continuous variables are presented as mean±standard deviation. Categorical data are presented as percentages. Analysis of variance was used for continuous data, whereas tests for trend were applied across the three SCr categories and the four GFR categories, respectively. The evaluation for linear trend in percentages was carried out using the Cochrane–Armitage test.29

Multivariable logistic regression models for WRF were developed using all baseline variables. Data from the logistic regressions are presented as odds ratios with 95% confidence intervals (CIs). The odds ratio is an approximation to the relative risk.30,31 Models were adjusted for length of follow-up because not all patients had the same length of follow-up. We did not consider the duration of heart failure as a potential variable due to the uncertainty of defining the onset of the syndrome. All the continuous variables were assessed for linearity by including a squared term. For all but one baseline variable (GFR), the linearity assumption was satisfied. However, our preference was to analyse all the continuous variables by categories essential for presentation purposes. We checked for co-linearity by calculating Pearson's correlation coefficients. Model building was based on backwards elimination (P-value for entry=0.05; P-value for removal=0.1). Backwards elimination is preferable to forward selection.32 Models were validated using re-sampling based on 10-fold cross-validation.33 The data were divided into 10 subsets of approximately equal size while maintaining the frequency of WRF within each of the subsets. Hence, each subset contained approximately 16 cases of WRF. For each subset, we generated a model for RD, leaving out one subset at a time. The omitted subset was used to calculate the misclassification rate per model. We are aware of the problems of model building using stepwise methods and these issues are discussed later.

The matched pairs odds ratio was used to assess changes in treatment from baseline to 6 months. The 95% CIs were calculated according to McNemar's test.34 Kaplan–Meier survival curves35 are presented for mortality data using the guidance of Pocock et al.36 The log-rank test was used to assess the equality of survivor function across groups. The Cox regression model was used to calculate hazard ratios with 95% CI. The Cox regression model is semi-parametricin the sense that no assumption concerning event-free survival times is necessary. The proportional hazards model is based on the assumption that the effect of a risk factor, expressed as a hazard ratio, is constant over time. The assumption of proportionality was tested for each variable, using the method of Grambsch and Therneau.37 The output of this test is a P-value, which for all our variables tested was not significant. Hence, we did not include time-dependent variables in our Cox models. We did not validate these models using cross-validation because this method is less well developed than the logistic model. A difficulty is understanding how to deal with censoring.38 Instead, we produced a subset of variables on univariate Cox analysis that were significantly associated with follow-up time. We included all of these variables in the final Cox model whether significant or not. We further adjusted the final model for age, sex, and New York Heart Association (NYHA) functional class. Variables used in the equations to calculate renal function were not excluded in any analysis.

SPSS (version 11) and GLIM4 statistical computer packages were used to analyse the data.39 An arbitrary level of 5% statistical significance was used throughout (two-tailed). One of the statistical issues to address is the problem of multiple testing when many variables are present and the possible inflation of Type I error. However, there is no consensus on what procedure to adopt to allow for multiple comparisons.40 Hence, in order to account for the inflation of experimentwise Type I error due to multiple testing, we have followed the recommendations of Perneger41 and not adjusted for this. There were two outcome measures: all-cause mortality and WRF.

Results

Study sample

A total of 2621 patients with suspected heart failure were assessed. Of these, 905 patients werereferredfromgeneral outpatient clinics, 1087 patients were referred by primary care physicians, and 629 patients were identified while in hospital with suspected heart failure and subsequently seen following stabilization and discharge. In 1406 patients, the diagnosis of heart failure due to left ventricular systolic dysfunction was confirmed following clinical assessment and investigations as described above and were entered into thelong-term management programme. At 6 months, 91 patients had died, 21 patients were lost to follow-up, and76 patients had missing data points. The remaining 1216 patients had complete data sets at both baseline (Table 1) and 6-months follow-up and constituted our study sample.

We compared the baseline data between the 190 patients with missing 6 months data and our study sample of 1216 patients. Four variables showed significant differences: mean heart rate [71 (SD 15) vs. 76 (SD 17) b.p.m.], patients with hypertension [96 (51%) vs. 502 (41%)], patients with dilated cardiomyopathy [23 (12%) vs. 94 (8%)] and the number of patients taking diuretics [20 (63%) vs. 893 (73%), P<0.05 for all]. Other baseline data did not differ significantly.

Prevalence

The mean age at baseline was 71 years (range 22–95 years) and 837 (69%) were male. One-fifth (n=257) were aged ≥80. Mean ejection fraction was 34% (SD 10) and CHF was due to IHD in 798 (66%) patients. The mean values for SCr and GFR were 122 µmol/L (SD 51) [1.4 mg/dL (SD 0.6)] and 57 mL/min (SD 21), respectively. Figure 1 shows the distributions for the SCr and GFR values. The prevalence of RD defined as using an SCr of >130 µmol/L (1.5 mg/dL) and a GFR of <60 mL/min was 32 and 57%, respectively.

Contributing factors and associations with renal function at baseline

Five hundred and forty-eight patients (45%) had a normal SCr, 526 (43%) had a minor increase, and 142 (12%) had a marked increase. GFR was normal (≥90 mL/min) in only 82 patients (7%), mildly reduced (60–89 mL/min) in 437 patients (36%), moderately reduced (30–59 ml/min) in 577 patients (47%), and markedly reduced (≤29 ml/min) in 120 patients (10%).

Patients with severely impaired renal function were older, more often female and had lower left ventricular ejection fractions, diastolic blood pressure, and haemoglobin levels than patients with normal or mildly impaired renal function (Table 1). As the severity of renal impairmentincreased, so did the prevalence of IHD, hypertension, vascular disease, and other common co-morbidities. The proportion of patients taking diuretics, and the dose administered, increased as renal function declined. The proportion of patients taking ACE-inhibitors or beta-blockers however was not different between the groups. As renal function worsened, the proportion of patients taking aspirin, statins, and calcium channel blockers declined.

Independent predictors of a low GFR at baseline were increasing age, low haemoglobin, poorer NYHA functional class, presence of IHD, vascular disease and hypertension, and the use of spironolactone and loop diuretics (data not shown).

Changes in renal function assessed by SCr

During the 6-month follow-up, mean SCr rose by 4 µmol/L (0.05 mg/dL) (95% CI=2–4). WRF, defined as a rise in SCr of >26.5 µmol/L (0.3 mg/dL), occurred in 161 (13.0%) patients. One hundred and twenty-one (9.7%) patients showed an improvement by this amount. Figure 2A shows these changes in SCr as frequency histograms. Figure 3A shows the number of patients in whom renal function worsened, improved, or remained the same over 6 months period, stratified by the presence or absence of baseline renal impairment, defined by the SCr values mentioned earlier. Table 2 indicates the risk factors for WRF. Factors associated with WRF were a history of vascular disease, low systolic and diastolic blood pressures, low ejection fraction, high baseline blood urea levels, and thiazide and diuretic use. The relationships with all other variables were not statistically significant. The model-building exercise produced a wide variety of models but with some commonalties (Table 3). A total of 12 variables appeared in 10 different predictor models for RD but no model was repeated more than once. Misclassification rates varied greatly (Table 4). From these models, the presence of vascular disease, use of thiazide diuretics, and blood urea levels were strongly associated with WRF.

Changes in renal function assessed by the GFR

During the 6-month follow-up period, mean GFR fell by 2 mL/min (95% CI=1–3). Figure 2B shows these changes in GFR as frequency histograms. Figure 3B shows the number of patients in each category of calculated GFR at baseline and at follow-up and changes between groups. GFR deteriorated by at least one category in 229 patients (19%) and improved by at least one category in 145 (12%).

We looked at whether drug treatment at baseline and 6months could explain the change in renal function as assessed by GFR. Table 5 shows the changes in treatment that occurred between baseline and 6 months in the 374 patients who had a change in their GFR category. Beta-blocker use increased regardless of change in GFR, probably reflecting continued efforts to optimize therapy. Patients were more likely to be taking aspirin at baseline than follow-up if their GFR worsened, suggesting that worseningGFR may provoke aspirin withdrawal. There was no significant difference in the percentage of the maximum recommended daily dose of ACE-inhibitors used at baseline (50±48%) or follow-up (54±47%) either in patients whose GFR improved [mean difference 3.0, 95% CI −4.6–10.6, P=0.44] or in patients whose GFR worsened [mean difference 4.6, 95% CI −1.1–10.3, P=0.11]. The daily dose of diuretics used was not significantly different in patients whose GFR improved [mean difference 5.6 mg, 95% CI −2.6–13.8, P=0.41]. However, in patients whose GFR worsened, the diuretic dose was higher at 6 months when compared to baseline [mean difference in furosemide-equivalent dose of 11.7 mg, 95% CI 4.8–18.6, P=0.001].

Relationships to prognosis

There were 263 deaths at follow-up representing a crude death rate of 21.6%. The median time to follow-up was 16.5 months (interquartile range 9.6–26.3 months). Kaplan–Meier survival curves for baseline SCr and GFR, along with the numbers at risk, are shown in Figure 4. Patients with worse renal function at baseline had a poorer prognosis (P<0.001). The relationship between death and renal disease was investigated by Cox regression analysis from which hazard ratios and 95% CI were generated. Patients with WRF had a poorer prognosis, although the severity of RD rather than its change appeared the most important determinant of outcome. A total of nine variables were significantly associated with follow-up time on univariate analysis. All these variables were included in the final Cox model (Table 6). GFR was eliminated from the model by SCr. When considering the joint effects of baseline SCr and WRF on death, a synergistic relationship was noted (Figure 5). Recovery in renal function (a fall in the SCr of >26.5 µmol/L (0.3 mg/dL)) was associated with a better prognosis (hazard ratio 0.8, 95% CI 0.6–1.0) adjusted for age, sex, and NYHA class).

Discussion

This study provides unique information on the prevalence and natural history of RD in a large epidemiologically representative population of outpatients with relatively stable CHF due to left ventricular systolic dysfunction. It shows that RD is common, commonly deteriorates over a relatively short period of time, is unlikely to recover substantially, and augurs a poor prognosis.

Prevalence

In patients with a recent hospitalization for worsening heart failure, the prevalence of RD has been reported to be between 24 and 75% depending on the definition used.14 One-third of our patients had evidence of renal impairment using an SCr threshold of 130 µmol/L (1.5 mg/dL) but more than half had a calculated GFR of <60 mL/min. Among patients in the SOLVD Prevention and Treatment trials, the prevalence of RD defined as a GFR of <60 mL/min was 21 and 36%, respectively, but in these studies, patients with SCr 220 µmol/L (2.5 mg/dL) and those aged >79 were excluded.6 The absence of such exclusion criteria, combined with higher rates of co-morbidity, probably accounts for the greater prevalence of RD in our population and is similar to the findings reported recently by others.42

Incidence of WRF

The natural progression of RD in outpatients with CHF is oneof the deterioration. Although the incidence of deterioration is much less than in patients admitted with an exacerbation of heart failure using the same definition for WRF,13,15 the gross rate of 13% over 6 months is substantial. Even if the rate of ‘recovery’ is subtracted, the net rate of 3% over 6 months is still of concern.

Causes of RD and reasons for WRF

Haemodynamic and renal factors

Patients with co-morbid vascular disease, higher levels of baseline blood urea, and lower ejection fractions were at greater risk of developing WRF. Two previous reports analysing WRF in hospitalized patients with CHF describe systemic hypertension, diabetes, history of CHF, and high baseline SCr values as independent risk factors for the development of WRF (using the same definition as us).13,15 These data are consistent with the concept that WRF in patients with heart failure is multi-factorial. First, there is a potential ‘haemodynamic’ element related to declining cardiac function, renal blood flow, and perfusion pressure leading to a fall in GFR. Secondly, there is an intrinsic renal element. Many patients, especially those with peripheral vascular disease, have renovascular disease and these patients are at greater risk of developing WRF either due to progression of arterial disease or in response to haemodynamic deterioration.9,43

Medications

RD could also be a side effect of treatments for heart failure. Loop and thiazide diuretics and spironolactone were all associated with WRF, although in an observational study such as this cause and effect cannot usually be distinguished. There must have been a reason to alter diuretic therapy and that will usually have been an attempt to improve symptoms. Also, as GFR declines, less diuretic will be filtered and intra-tubular delivery will decline with a loss of diuretic effect. Higher doses may be required in the presence of RD to achieve the same diuretic effect.

We showed no difference in the frequency or dose of ACE-inhibitors or angiotensin receptor blocker use in patients with varying degrees of RD, which contrasts with previous reports.44,45 Although ACE-inhibitors may reduce renal perfusion pressure, efferent arteriolar tone, and GFR in patients with heart failure in the short-term, there is evidence that they retard long-term deterioration of renal function in other clinical settings.46,47 The net long-term effect of ACE-inhibition on renal function in most patients with heart failure appears fairly neutral. Similarly, beta-blockers did not exert marked effects on the incidence of RD.

Statins have been reported to reduce the risk of WRF in patients with renal artery disease.48 In our study, patients with moderate or severe RD were less likely to be taking a statin, suggesting that chronic use of these agents might retard progression of atherosclerotic renal disease, although other mechanisms of benefit should not be discounted. During 6 months of follow-up, we were unable to confirm a protective effect of statins on WRF but statins were associated with a markedly lower mortality. The hypothesis that the observed association between better prognosis and statin treatment is cause and effect is being tested in two large randomized controlled trials.49

Relationships to prognosis

The relationship between RD and survival seen in our cohort of patients has been observed in several other multi-centre studies of CHF and, more recently, in clinical practice.42 In the SOLVD treatment trial,6 patients with CHF who had a GFR <60 mL/min were more likely to die, especially from worsening CHF. Similar findings were reported by Mahon et al.50 These studies show that even minor reductions in GFR, which do not necessarily increase SCr above the normal range, are associated with a worse outcome. The seven-fold increase in the risk of death in the presence of a markedly raised baseline SCr and WRF when compared with a normal baseline SCr and no WRF observed in our cohort implies a synergistic association between these two variables (Figure 5). Interestingly, it did not appear that GFR was a better predictor of prognosis than SCr and therefore it seems appropriate when using markers of RD to assess prognosis to use the simplest available measure. In our study, the severity of RD appeared more prognostically important than the change.

The reasons for the increased mortality in patients with RD and CHF are multi-factorial and complex. RD may limit the use of life-saving interventions such as ACE-inhibitors, angiotensin receptor blockers, and beta-blockers, although this was not obvious in the current study. Patients with RD have more peripheral vascular disease and may be at higher risk of vascular events. RD may lead to diuretic resistance and sodium and water retention leading to an increase in cardiac filling pressures, progressive ventricular dilation, and hyponatraemia.51,52 Electrolyte disturbances may increase the risk of arrhythmias,53 and it has been reported that an increase in mortality is observed in certain groups of patients with CHF who are prescribed spironolactone and ACE-inhibitors due to the adverse effects of hyperkalaemia.54 Other reasons for a higher mortality include abnormal calcium metabolism,55 hyperparathyroidism,56 increased coaguability, hyperhomocysteinaemia,57 and ureamic cardiomyopathy.

The improvement in prognosis seen with recovery of renal function has not been reported before in a stable, community-based population with heart failure. The reasons are multi-factorial and probably due to a reverse of the mechanisms discussed above. Reports of resolution of ventricular dysfunction after renal transplantation in patients with renal failure58,59 or after the correction of bilateral renal artery stenosis60 further suggest that RD may cause or exacerbate CHF.

Model selection procedures

In epidemiological studies, statistical models are often determined by data-driven selection methods. Such methods usually have only a heuristic basis and their sampling properties are largely unknown. Forward selection and backward elimination are two of the most widely used (and abused) selection methods. Indeed, many computer packages have such automated stepwise methods built into their software. Statistical objections to automated selection methods have long been known. For example, results lead to standard errors that may be too low.6163 If important variables are omitted from the final model, the regression coefficients are known to be biased.64 Finally, the selection method affects the properties of the tests of the final model itself.65 Many ‘solutions’ to these objections have been proposed with most focusing on resampling. Perhaps the best known resampling methods are cross-validation, the jack-knife, and bootstrapping. It is not our aim here to discuss which of these resampling methods is best but an excellent overview is given Sauerbrei.32

In the past, it may have been true that a single model was typically fitted to a given data set.66 However, modern day computing resources mean that it is possible to explore many models simultaneously so that the ‘one-fit’ model is no longer appropriate. This fits in with the notion of having a portfolio of plausible models.67 Finally, Copas68 stated that ‘a good predictor may include variables which are not significant, exclude others which are, and may involve coefficients which are systematically biased’, sentiments with which we agree. In conclusion, stepwise methods are not designed to select ‘best’ models or to indicate their relative importance but rather designed to select subsets from data sets ‘padded with extraneous variables, for example, those that contain everything we could measure’.69

Study limitations

The definition of WRF we used has arisen by convention rather than been justified scientifically.1315 An increase in SCr of as little as 17.7 µmol/L (0.2 mg/dL) is associated with an adverse outcome.14 Other investigators have used a rise in SCr above a threshold to define renal insufficiency [e.g. SCr >221 µmol/L (>2.5 mg/dL) or a percentage increase from baseline (e.g. >25% increase)].70,71 Each definition of WRF has its merits and problems. For instance, the final SCr may still be in the normal range even if there is a substantial increase. This may not carry the same adverse prognosis as a similar rise in SCr that results in an increased final value. Likewise the definition used for an improvement in renal function is not evidence based. The study analysed data at two defined points during patient follow-up, but does not take into account the factors in the intervening periods that could effect the renal function such as hospitalizations with significant haemorrhage, dehydration, or exposure to intravenous contrast. We also do not have temporal information as to when the doses of diuretics were changed in relationship to changes in renal function. Finally, because of the inherent nature of the study, patients who died or had incomplete data sets at follow-up were not included in the analysis. This could have the potential to introduce a selection bias.

Conclusion

RD is common in CHF and is associated with a poor prognosis, which is only partly explained by its association with poorer ventricular function. Renovascular disease, perhaps both of extra- and intra-renal vessels, may be an important under-recognized risk factor for WRF in patients with heart failure. Whether routine investigation for and treatment of renal artery stenosis in this setting is beneficial is uncertain, but is being tested in a subset of patients with heart failure in a randomized controlled trial (ASTRAL, Angioplasty and Stent for Renal Artery Lesions).72

Conflict of interest: none declared.

Figure 1 (A) Distribution of SCr in 1216 patients with heart failure. Mean 122 (+51) µmol/L. Sixteen patients with values >300 µmol/L are not shown. (B)Distribution of the GFR calculated by the MDRD prediction equation in 1216 patients with heart failure. Mean 57.3 (+21.4) mL/min.

Figure 1 (A) Distribution of SCr in 1216 patients with heart failure. Mean 122 (+51) µmol/L. Sixteen patients with values >300 µmol/L are not shown. (B)Distribution of the GFR calculated by the MDRD prediction equation in 1216 patients with heart failure. Mean 57.3 (+21.4) mL/min.

Figure 2 (A) Distribution of the change in SCr over a period of 6 months (15patients with an improvement in their SCr by >100 µmol/L and 16 patients with a worsening of their SCr by >100 µmol/L are not shown). (B) Distribution of the change in the GFR (in mL/min) over a period of 6months (five patients with an improvement in their GFR by >50 mL/min and eight patients with a worsening of their GFR by >50 mL/min are not shown).

Figure 2 (A) Distribution of the change in SCr over a period of 6 months (15patients with an improvement in their SCr by >100 µmol/L and 16 patients with a worsening of their SCr by >100 µmol/L are not shown). (B) Distribution of the change in the GFR (in mL/min) over a period of 6months (five patients with an improvement in their GFR by >50 mL/min and eight patients with a worsening of their GFR by >50 mL/min are not shown).

Figure 3 (A) The number of patients in whom renal function worsened, improved, or remained the same over the 6-month period. Definitions use the SCr and are as follows: RD=SCr >130 µmol/L, WRF=an increase in SCr by 26.5 µmol/L, improving renal function = a decrease in SCr by 26.5 µmol/L, no change in renal function=change in SCr of less than ±26.5 µmol/L. Numbers outside the boxes represent the number of patients. (B) Change in the GFR in millilitres per minute over 6 months for 1216 patients. Figures are mean (standard deviation) for GFR. Numbers outside the boxes represent the number of patients.

Figure 3 (A) The number of patients in whom renal function worsened, improved, or remained the same over the 6-month period. Definitions use the SCr and are as follows: RD=SCr >130 µmol/L, WRF=an increase in SCr by 26.5 µmol/L, improving renal function = a decrease in SCr by 26.5 µmol/L, no change in renal function=change in SCr of less than ±26.5 µmol/L. Numbers outside the boxes represent the number of patients. (B) Change in the GFR in millilitres per minute over 6 months for 1216 patients. Figures are mean (standard deviation) for GFR. Numbers outside the boxes represent the number of patients.

Figure 4 (A) SCr and relationship to prognosis. (B) Calculated GFR and relationship to prognosis.

Figure 4 (A) SCr and relationship to prognosis. (B) Calculated GFR and relationship to prognosis.

Figure 5 Relationship between baseline SCr, WRF, and death. WRF defined as an increase in SCr of >26.5 µmol/L (>0.3 mg/dL), SCr low≤106 µmol/L (≤1.2 mg/dL), medium=106–177 µmol/L (1.2–2.0 mg/dL), and high ≥ 177 µmol/L (>2.0 mg/dL).

Figure 5 Relationship between baseline SCr, WRF, and death. WRF defined as an increase in SCr of >26.5 µmol/L (>0.3 mg/dL), SCr low≤106 µmol/L (≤1.2 mg/dL), medium=106–177 µmol/L (1.2–2.0 mg/dL), and high ≥ 177 µmol/L (>2.0 mg/dL).

Table 1

Baseline characteristics for 1216 patients, as classified by calculated GFR

Variable Total (n=1216) Normal 
 >≥90 mL/min (n=82) Mild
 >60 to <90 mL/min (n=437) Moderate 
 >30 to <60 mL/min (n=577) Severe 
 ><30 mL/min (n=120) P-value for trend 
Age (years) 71 (10.8) 59.6 (11.9) 68.1 (11.4) 73.8 (8.9) 75.9 (8.9) <0.0001 
Male sex 837 (69%) 66 (81.6%) 321 (70.4%) 387 (65.2%) 63 (52.2%) <0.0001 
Weight (kg) 77.6 (17) 82.7 (17.1) 79.4 (17.3) 75.9 (16.3) 76.0 (18.0) <0.0001 
BMI 27.8 (5.6) 28.1 (5.1) 27.6 (5.9) 27.4(5.5) 27.9(5.5) 0.64 
NYHA classes       
 Class I 160 (12.8%) 14 (17.1%) 68 (14.9%) 65 (11.0%) 7 (5.9%) 0.0009a 
 Class II 772 (61.7%) 53 (64.6%) 280 (61.4%) 346 (58.3%) 68 (57.0%)  
 Class III 304 (24.3%) 15 (18.3%) 86 (18.9%) 159 (26.8%) 40 (33.1%)  
 Class IV 16 (1.3%) 2 (0.4%) 9 (1.5%) 5 (4.1%)  
Heart rate (b.p.m.) 75.6 (16.8) 77.1 (19.3) 76.2 (17.0) 75.4 (16.6) 74.6 (16.2) 0.21 
Systolic BP (mmHg) 134.9 (25.8) 135.1 (25.1) 135.6 (25.0) 134.6 (26.1) 135.3 (28.4) 0.87 
Diastolic BP (mmHg) 76.8 (25.1) 78.7 (14.6) 77.0 (36.2) 75.9 (14.6) 71.4 (13.6) <0.0001 
Pulse pressure (mmHg) 59.2 (21.3) 56.1 (20.7) 58.1 (20.2) 58.6 (21.3) 63.9 (22.9) <0.0001 
Ejection fraction 34.2 (10.2) 36.9 (9.9) 35.4 (10.5) 33.1 (9.8) 33.2 (10.2) <0.0001 
Baseline characteristics       
 IHD 798 (65.6%) 41 (50.0%) 265 (58.1%) 400 (67.4%) 92 (76.0%) <0.0001 
 Cardiomyopathy 94 (7.7%) 8 (9.8%) 50 (11.0%) 25 (4.2%) 11 (9.1%) 0.012 
 Past h/o hypertension 502 (41.3%) 33 (41.3%) 150 (32.9%) 256 (43.2%) 63 (52.1%) 0.0001 
 Current smoker 114 (9.4%) 17 (20.7%) 40 (9.0%) 46 (8.0%) 11 (9.1%) 0.024 
 Past smoker 627 (51.7%) 35 (43.7%) 218 (47.8%) 309 (52.0%) 65 (54.4%) 0.11 
 Diabetes 257 (21.1%) 17 (20.7%) 87 (21.2%) 123 (20.5%) 30 (24.8%) 0.27 
 AF 307 (25.2%) 19 (24.2%) 91 (20.0%) 170 (28.8%) 27 (23.0%) 0.18 
 Vascular diseaseb 204 (16.8%) 5 (6.1%) 56 (12.3%) 111 (18.9%) 32 (26.4%) <0.0001 
 Chronic obstructive pulmonary disease 109 (9.0%) 10 (12.2%) 34 (7.5%) 59 (10.0%) 6 (5.0%) 0.58 
Medications       
 RAAS blockade 918 (76.0%) 61 (74.4%) 330 (72.6%) 453 (76.4%) 74 (62.0%) 0.19 
 RAAS blocker dose/day 50.3 (47.5) 48.4 (44.4) 51.4 (49.0) 51.3 (46.7) 40.0 (45.9) 0.21 
 Beta-blocker 625 (51.4%) 44 (53.7%) 237 (51.9%) 287 (48.4%) 57 (47.8%) 0.12 
 Diuretic 893 (73.4%) 45 (55.9%) 272 (59.4%) 469 (79.0%) 107 (89.2%) <0.0001 
 Diuretic (dose/day) 49.9 (48.1) 32.5 (40.7) 36.3 (40.9) 57.3 (50.0) 71.9 (53.1) <0.0001 
 Thiazide 60 (5%) 3 (3.7%) 22 (4.8%) 31 (5.3%) 4 (3.3%) 0.74 
 Spironolactone 244 (20.1%) 9 (11.0%) 66 (14.5%) 134 (22.6) 35 (28.9%) <0.0001 
 Aspirin 591 (48.6%) 38 (47.4%) 226 (49.6%) 281 (47.3%) 46 (38.0%) 0.074 
 Statin 406 (33.4%) 32 (39.0%) 172 (37.7%) 169 (28.5%) 33 (27.3%) 0.0008 
 Calcium channel blocker 133 (10.9%) 16 (19.5%) 59 (13.5%) 48 (8.3%) 10 (8.3%) 0.006 
 NSAID 59 (4.9%) 4 (4.9%) 19 (4.3%) 29 (5.0%) 7 (5.8%) 0.56 
Blood tests       
 Haemoglobin (g/dL) 13.1 (1.6) 13.8 (1.5) 13.8 (1.6) 13.1 (1.8) 11.7 (1.5) <0.001 
 SCr (µmol/L) 122.5 (50.7) 71.1 (9.7) 90.8 (13.3) 130.7 (26.0) 231.5 (66.8) <0.0001 
 Serum sodium (mmol/L) 138.3 (3.8) 137.6 (3.5) 138.6 (3.3) 138.5 (3.8) 136.9 (5.4) 0.078 
 Urea (mmol/L) 9.0 (5.5) 5.4 (2.4) 6.3 (2.0) 9.6 (4.3) 18.1 (9.0) <0.0001 
Variable Total (n=1216) Normal 
 >≥90 mL/min (n=82) Mild
 >60 to <90 mL/min (n=437) Moderate 
 >30 to <60 mL/min (n=577) Severe 
 ><30 mL/min (n=120) P-value for trend 
Age (years) 71 (10.8) 59.6 (11.9) 68.1 (11.4) 73.8 (8.9) 75.9 (8.9) <0.0001 
Male sex 837 (69%) 66 (81.6%) 321 (70.4%) 387 (65.2%) 63 (52.2%) <0.0001 
Weight (kg) 77.6 (17) 82.7 (17.1) 79.4 (17.3) 75.9 (16.3) 76.0 (18.0) <0.0001 
BMI 27.8 (5.6) 28.1 (5.1) 27.6 (5.9) 27.4(5.5) 27.9(5.5) 0.64 
NYHA classes       
 Class I 160 (12.8%) 14 (17.1%) 68 (14.9%) 65 (11.0%) 7 (5.9%) 0.0009a 
 Class II 772 (61.7%) 53 (64.6%) 280 (61.4%) 346 (58.3%) 68 (57.0%)  
 Class III 304 (24.3%) 15 (18.3%) 86 (18.9%) 159 (26.8%) 40 (33.1%)  
 Class IV 16 (1.3%) 2 (0.4%) 9 (1.5%) 5 (4.1%)  
Heart rate (b.p.m.) 75.6 (16.8) 77.1 (19.3) 76.2 (17.0) 75.4 (16.6) 74.6 (16.2) 0.21 
Systolic BP (mmHg) 134.9 (25.8) 135.1 (25.1) 135.6 (25.0) 134.6 (26.1) 135.3 (28.4) 0.87 
Diastolic BP (mmHg) 76.8 (25.1) 78.7 (14.6) 77.0 (36.2) 75.9 (14.6) 71.4 (13.6) <0.0001 
Pulse pressure (mmHg) 59.2 (21.3) 56.1 (20.7) 58.1 (20.2) 58.6 (21.3) 63.9 (22.9) <0.0001 
Ejection fraction 34.2 (10.2) 36.9 (9.9) 35.4 (10.5) 33.1 (9.8) 33.2 (10.2) <0.0001 
Baseline characteristics       
 IHD 798 (65.6%) 41 (50.0%) 265 (58.1%) 400 (67.4%) 92 (76.0%) <0.0001 
 Cardiomyopathy 94 (7.7%) 8 (9.8%) 50 (11.0%) 25 (4.2%) 11 (9.1%) 0.012 
 Past h/o hypertension 502 (41.3%) 33 (41.3%) 150 (32.9%) 256 (43.2%) 63 (52.1%) 0.0001 
 Current smoker 114 (9.4%) 17 (20.7%) 40 (9.0%) 46 (8.0%) 11 (9.1%) 0.024 
 Past smoker 627 (51.7%) 35 (43.7%) 218 (47.8%) 309 (52.0%) 65 (54.4%) 0.11 
 Diabetes 257 (21.1%) 17 (20.7%) 87 (21.2%) 123 (20.5%) 30 (24.8%) 0.27 
 AF 307 (25.2%) 19 (24.2%) 91 (20.0%) 170 (28.8%) 27 (23.0%) 0.18 
 Vascular diseaseb 204 (16.8%) 5 (6.1%) 56 (12.3%) 111 (18.9%) 32 (26.4%) <0.0001 
 Chronic obstructive pulmonary disease 109 (9.0%) 10 (12.2%) 34 (7.5%) 59 (10.0%) 6 (5.0%) 0.58 
Medications       
 RAAS blockade 918 (76.0%) 61 (74.4%) 330 (72.6%) 453 (76.4%) 74 (62.0%) 0.19 
 RAAS blocker dose/day 50.3 (47.5) 48.4 (44.4) 51.4 (49.0) 51.3 (46.7) 40.0 (45.9) 0.21 
 Beta-blocker 625 (51.4%) 44 (53.7%) 237 (51.9%) 287 (48.4%) 57 (47.8%) 0.12 
 Diuretic 893 (73.4%) 45 (55.9%) 272 (59.4%) 469 (79.0%) 107 (89.2%) <0.0001 
 Diuretic (dose/day) 49.9 (48.1) 32.5 (40.7) 36.3 (40.9) 57.3 (50.0) 71.9 (53.1) <0.0001 
 Thiazide 60 (5%) 3 (3.7%) 22 (4.8%) 31 (5.3%) 4 (3.3%) 0.74 
 Spironolactone 244 (20.1%) 9 (11.0%) 66 (14.5%) 134 (22.6) 35 (28.9%) <0.0001 
 Aspirin 591 (48.6%) 38 (47.4%) 226 (49.6%) 281 (47.3%) 46 (38.0%) 0.074 
 Statin 406 (33.4%) 32 (39.0%) 172 (37.7%) 169 (28.5%) 33 (27.3%) 0.0008 
 Calcium channel blocker 133 (10.9%) 16 (19.5%) 59 (13.5%) 48 (8.3%) 10 (8.3%) 0.006 
 NSAID 59 (4.9%) 4 (4.9%) 19 (4.3%) 29 (5.0%) 7 (5.8%) 0.56 
Blood tests       
 Haemoglobin (g/dL) 13.1 (1.6) 13.8 (1.5) 13.8 (1.6) 13.1 (1.8) 11.7 (1.5) <0.001 
 SCr (µmol/L) 122.5 (50.7) 71.1 (9.7) 90.8 (13.3) 130.7 (26.0) 231.5 (66.8) <0.0001 
 Serum sodium (mmol/L) 138.3 (3.8) 137.6 (3.5) 138.6 (3.3) 138.5 (3.8) 136.9 (5.4) 0.078 
 Urea (mmol/L) 9.0 (5.5) 5.4 (2.4) 6.3 (2.0) 9.6 (4.3) 18.1 (9.0) <0.0001 

Continuous variables are presented as mean (standard deviation), whereas categorical variables are expressed as numbers (percentage). P-values are for differences between GFR groups (columns 3, 4, 5, and 6). See text for detailed definitions. BMI, body mass index; BP, blood pressure; h/o, history of; PMH, past medical history; AF, atrial fibrillation; RAAS, renin–angiotensin–aldosterone system; NSAID, non-steroidal anti-inflammatory drugs.

aNYHA class I vs. II–IV.

bVascular disease is a composite of stroke, transient ischaemic attacks, peripheral vascular disease, renal artery stenosis, or abdominal aortic aneurysms.

Table 2

Risk factors for WRF [defined as a rise in SCr of >26.5 µmol/L (0.3 mg/dL)] in 1216 patients with heart failure

Variable Baseline variable Number without WRF Number with WRF OR 95% CI 
Age (years) ≤65 292 40  
 65–74 317 42 0.9 0.6–1.5 
 75+ 446 79 1.3 0.9–1.9 
Sex Female 325 54  
 Male 730 107 0.9 0.6–1.3 
Ejection fraction (%) 40+ 334 35  
 31–40 444 74 1.6 1.0–2.4 
 ≤30 277 52 1.8 1.0–2.8 
BMI Underweight <21.7 112 18 1.0  
 Ideal 21.7–27.1 447 75 1.0 0.6–1.8 
 Overweight 27.1–31 252 67 1.7 1.0–3.0 
 Obese >31 244 37 0.9 0.5–1.7 
NYHA grade 137 17  
 II 654 93 1.1 0.7–2.0 
 III/IV 264 51 1.6 0.9–2.9 
Heart rate (b.p.m.) ≤64 287 45  
 65–84 482 73 0.6–1.4 
 85+ 286 43 0.6–1.5 
Systolic blood pressure (mmHg) 150+ 305 42  
 121–150 451 60 0.6–1.5 
 ≤120 299 59 1.4 0.9–2.2 
Diastolic blood pressure (mmHg) 80+ 466 55  
 71–79 252 36 1.2 0.8–1.9 
 ≤70 337 70 1.8 1.2–2.6 
Pulse pressure (mmHg) <50 365 57  
 50–69 415 53 0.8 0.6–1.2 
 ≥70 311 51 1.1 0.7–1.6 
Baseline haemoglobin (g/dL) >14.5 242 30  
 13.3–14.5 276 45 1.3 0.8–2.2 
 12–13.2 284 43 1.2 0.7–2.0 
 <11.9 253 43 1.4 0.8–2.3 
Baseline sodium (mmol/L) ≥140 437 62  
 137–139 360 52 0.7–1.5 
 ≤136 258 47 1.3 0.8–2.0 
Baseline SCr (µmol/L) ≤106 500 82 1.0  
 106.1–177 444 88 1.2 0.9–1.7 
 >177 111 27 1.5 0.9–2.4 
Urea (mmol/L) ≤6 342 36  
 6.1–9 390 54 1.3 0.8–2.1 
 >9 323 71 2.1 1.4–3.2 
Baseline GFR (mL/min) ≥90 66 16 1.0  
 60 to <90 395 61 0.6 0.3–1.1 
 30 to <60 498 95 0.8 0.4–1.4 
 <30 96 25 1.1 0.5–2.2 
Co-morbidity      
 Diabetes No 837 122  
 Yes 218 39 1.2 0.8–1.8 
 Past history of hypertension No 627 87  
 Yes 428 74 1.2 0.9–1.4 
 Atrial fibrillation No 788 121  
 Yes 267 40 0.7–1.4 
 Current smoker No 961 141  
 Yes 94 20 1.5 0.9–2.4 
 Past smoker No 509 80  
 Yes 546 81 0.9 0.7–1.3 
 Chronic obstructive pulmonary disease No 962 145  
 Yes 93 16 1.1 0.7–2.1 
 IHD No 368 50  
 Yes 687 111 1.2 0.8–1.7 
 Cardiomyopathy No 970 152  
 Yes 85 0.7 0.3–1.4 
 Vascular disease No 890 122  
 Yes 165 39 1.7 1.2–2.6 
Treatment      
 RAAS blockade No 256 42  
 Yes 799 119 0.9 0.6–1.3 
 Spironolactone No 852 120  
 Yes 203 41 1.4 1.0–2.1 
 Beta-blockers No 511 80  
 Yes 544 81 0.9 0.7–1.3 
 Diuretics No 290 33  
 Yes 765 128 1.5 1.0–2.2 
 Thiazides No 1008 148  
 Yes 47 13 1.9 1.0–3.6 
 Statins No 705 105  
 Yes 350 56 1.1 0.8–1.5 
 Aspirin No 540 85  
 Yes 515 76 0.9 0.7–1.3 
 Calcium channel blocker No 933 150  
 Yes 122 11 0.6 0.3–1.1 
 NSAID No 1003 154  
 Yes 53 0.9 0.6–1.3 
Variable Baseline variable Number without WRF Number with WRF OR 95% CI 
Age (years) ≤65 292 40  
 65–74 317 42 0.9 0.6–1.5 
 75+ 446 79 1.3 0.9–1.9 
Sex Female 325 54  
 Male 730 107 0.9 0.6–1.3 
Ejection fraction (%) 40+ 334 35  
 31–40 444 74 1.6 1.0–2.4 
 ≤30 277 52 1.8 1.0–2.8 
BMI Underweight <21.7 112 18 1.0  
 Ideal 21.7–27.1 447 75 1.0 0.6–1.8 
 Overweight 27.1–31 252 67 1.7 1.0–3.0 
 Obese >31 244 37 0.9 0.5–1.7 
NYHA grade 137 17  
 II 654 93 1.1 0.7–2.0 
 III/IV 264 51 1.6 0.9–2.9 
Heart rate (b.p.m.) ≤64 287 45  
 65–84 482 73 0.6–1.4 
 85+ 286 43 0.6–1.5 
Systolic blood pressure (mmHg) 150+ 305 42  
 121–150 451 60 0.6–1.5 
 ≤120 299 59 1.4 0.9–2.2 
Diastolic blood pressure (mmHg) 80+ 466 55  
 71–79 252 36 1.2 0.8–1.9 
 ≤70 337 70 1.8 1.2–2.6 
Pulse pressure (mmHg) <50 365 57  
 50–69 415 53 0.8 0.6–1.2 
 ≥70 311 51 1.1 0.7–1.6 
Baseline haemoglobin (g/dL) >14.5 242 30  
 13.3–14.5 276 45 1.3 0.8–2.2 
 12–13.2 284 43 1.2 0.7–2.0 
 <11.9 253 43 1.4 0.8–2.3 
Baseline sodium (mmol/L) ≥140 437 62  
 137–139 360 52 0.7–1.5 
 ≤136 258 47 1.3 0.8–2.0 
Baseline SCr (µmol/L) ≤106 500 82 1.0  
 106.1–177 444 88 1.2 0.9–1.7 
 >177 111 27 1.5 0.9–2.4 
Urea (mmol/L) ≤6 342 36  
 6.1–9 390 54 1.3 0.8–2.1 
 >9 323 71 2.1 1.4–3.2 
Baseline GFR (mL/min) ≥90 66 16 1.0  
 60 to <90 395 61 0.6 0.3–1.1 
 30 to <60 498 95 0.8 0.4–1.4 
 <30 96 25 1.1 0.5–2.2 
Co-morbidity      
 Diabetes No 837 122  
 Yes 218 39 1.2 0.8–1.8 
 Past history of hypertension No 627 87  
 Yes 428 74 1.2 0.9–1.4 
 Atrial fibrillation No 788 121  
 Yes 267 40 0.7–1.4 
 Current smoker No 961 141  
 Yes 94 20 1.5 0.9–2.4 
 Past smoker No 509 80  
 Yes 546 81 0.9 0.7–1.3 
 Chronic obstructive pulmonary disease No 962 145  
 Yes 93 16 1.1 0.7–2.1 
 IHD No 368 50  
 Yes 687 111 1.2 0.8–1.7 
 Cardiomyopathy No 970 152  
 Yes 85 0.7 0.3–1.4 
 Vascular disease No 890 122  
 Yes 165 39 1.7 1.2–2.6 
Treatment      
 RAAS blockade No 256 42  
 Yes 799 119 0.9 0.6–1.3 
 Spironolactone No 852 120  
 Yes 203 41 1.4 1.0–2.1 
 Beta-blockers No 511 80  
 Yes 544 81 0.9 0.7–1.3 
 Diuretics No 290 33  
 Yes 765 128 1.5 1.0–2.2 
 Thiazides No 1008 148  
 Yes 47 13 1.9 1.0–3.6 
 Statins No 705 105  
 Yes 350 56 1.1 0.8–1.5 
 Aspirin No 540 85  
 Yes 515 76 0.9 0.7–1.3 
 Calcium channel blocker No 933 150  
 Yes 122 11 0.6 0.3–1.1 
 NSAID No 1003 154  
 Yes 53 0.9 0.6–1.3 

OR, odds ratio; BMI, body mass index; RAAS, renin–angiotensin–aldosterone system; NSAID, non-steroidal anti-inflammatory drugs. ORs calculated adjusting for length of follow-up (months). Example interpretation: patients taking thiazides are 1.9 times as likely to have a WRF when compared with those not taking them.

Table 3

Predictor models for WRF

Baseline variable Subset (excluding) Number of times included 
 10  
Vascular disease  
Thiazides   
Urea    
Haemoglobin     
GFR      
Ejection fraction       
Pulse pressure       
SBP        
Current smoking         
BMI          
DBP          
Spironolactone          
Baseline variable Subset (excluding) Number of times included 
 10  
Vascular disease  
Thiazides   
Urea    
Haemoglobin     
GFR      
Ejection fraction       
Pulse pressure       
SBP        
Current smoking         
BMI          
DBP          
Spironolactone          

+ means that the variable has been included in the model. Subset1 means all subsets excluding subset 1, and so on. SBP, systolic blood pressure; BMI, body mass index; DBP, diastolic blood pressure.

Table 4

Misclassification rates

Omitted subset Per cent misclassified 
37.7 
39.7 
37.3 
29.1 
38.2 
24.1 
44.3 
37.4 
36.1 
10 41.1 
Omitted subset Per cent misclassified 
37.7 
39.7 
37.3 
29.1 
38.2 
24.1 
44.3 
37.4 
36.1 
10 41.1 
Table 5

Treatment changes in patients whose GFR category (see text and Figure 3B) either improved (n==145) or deteriorated (n=229) over a period of 6 months

Treatment at baseline Treatment at 6 months 
 GFR improved GFR worsened 
 No Yes ORmp (95% CI) No Yes ORmp (95% CI) 
RAAS blockade 
 No 26 14   28 28   
 Yes 15 90 0.9 (0.4–2.0) 29 144 (0.6–1.8) 
Spironolactone 
 No 105 7   156 22   
 Yes 13 20 0.5 (0.2–1.4) 16 35 1.4 (0.7–3.3) 
Beta-blocker 
 No 49 19   62 59   
 Yes 9 68 2.1 (0.9–5.0) 17 91 3.5 (2.0–5.0) 
Diuretic 
 No 28 6   53 18   
 Yes 13 98 0.5 (0.1–1.2) 17 141 1.1 (0.5–2.0) 
Thiazide 
 No 138 4   206 6   
 Yes 2 2.0 (0.3–20.0) 6 11 (0.3–3.7) 
Statin 
 No 91 13   127 15   
 Yes 4 37 3.3 (0.9–14.3) 25 62 0.6 (0.3–1.1) 
Aspirin 
 No 68 10   104 5   
 Yes 14 53 0.7 (0.3–1.7) 39 81 0.1 (0.04–0.3) 
CCB 
 No 131 0   204 0  
 Yes 9  15 10   
NSAID 
 No 138 0   221 2   
 Yes 4  3 0.7 (0.07–5.3) 
Treatment at baseline Treatment at 6 months 
 GFR improved GFR worsened 
 No Yes ORmp (95% CI) No Yes ORmp (95% CI) 
RAAS blockade 
 No 26 14   28 28   
 Yes 15 90 0.9 (0.4–2.0) 29 144 (0.6–1.8) 
Spironolactone 
 No 105 7   156 22   
 Yes 13 20 0.5 (0.2–1.4) 16 35 1.4 (0.7–3.3) 
Beta-blocker 
 No 49 19   62 59   
 Yes 9 68 2.1 (0.9–5.0) 17 91 3.5 (2.0–5.0) 
Diuretic 
 No 28 6   53 18   
 Yes 13 98 0.5 (0.1–1.2) 17 141 1.1 (0.5–2.0) 
Thiazide 
 No 138 4   206 6   
 Yes 2 2.0 (0.3–20.0) 6 11 (0.3–3.7) 
Statin 
 No 91 13   127 15   
 Yes 4 37 3.3 (0.9–14.3) 25 62 0.6 (0.3–1.1) 
Aspirin 
 No 68 10   104 5   
 Yes 14 53 0.7 (0.3–1.7) 39 81 0.1 (0.04–0.3) 
CCB 
 No 131 0   204 0  
 Yes 9  15 10   
NSAID 
 No 138 0   221 2   
 Yes 4  3 0.7 (0.07–5.3) 

Figures highlighted in bold represent changes in therapy. RAAS, renin–angiotensin–aldosterone system. ORmp, matched pairs odds ratio; CCB, calcium channel blockers; NSAID, non-steroidal anti-inflammatory drugs. Data are presented as a series of 2×2 tables. ORmp with corresponding 95% CIs was calculated for all 2×2 combinations. For example, of the 229 patients whose GFR deteriorated, 81 were taking aspirin both at baseline and at 6 months, 104 were not taking aspirin at either baseline or 6 months, 39 were taking aspirin at baseline but not at 6 months, and five were taking aspirin at 6 months but not at baseline. Hence, for aspirin, the matched pairs odds ratio is 5/39=0.1, calculated as the ratio of the discordant (for treatment) pairs. An odds ratio>1 means that the drug use is more likely at 6 months of follow-up; an odds ratio<1 means that the drug use is less likely at 6 months of follow-up; an odds ratio=1 means that drug use did not change from baseline to 6 months of follow-up.

Table 6

A Cox-regression model predicting mortality (excluding patients who died in the first 6 months of follow-up or who did not have repeat SCr measurement)

Variable Level HR (95% CI) P-value 
Vascular disease No 1.0  
 Yes 1.3 (0.8–2.1) 0.35 
Chronic obstructive No 1.0  
 pulmonary disease Yes 1.8 (1.0–3.1) 0.05 
Loop diuretics No 1.0  
 Yes 1.4 (0.8–2.4) 0.35 
Spironolactone No 1.0  
 Yes 1.7 (1.1–2.6) 0.017 
Ejection fraction (%) >40 1.0  
 30–39 1.9 (1.1–3.3)  
 <30 1.6 (1.1–2.4) 0.02 
Baseline urea (mmol/L) ≥6 1.0  
 6.1–9.0 0.8 (0.4–1.5)  
 >9 1.5 (0.8–2.9) 0.06 
Baseline SCr (µmol/L) <106 1.0  
 106–177 1.1 (0.6–1.8)  
 >177 1.5 (0.8–2.9) 0.81 
Baseline sodium (mmol/L) ≥140 1.0  
 137–139 0.9 (0.6–1.5)  
 ≤136 1.2 (0.8–2.0) 0.75 
Haemoglobin (g/dL) >14 1.0  
 13.3–14.5 1.1 (0.6–2.0)  
 12.0–13.2 1.6 (0.9–2.9)  
 ≤11.9 0.9 (0.5–1.8) 0.18 
Variable Level HR (95% CI) P-value 
Vascular disease No 1.0  
 Yes 1.3 (0.8–2.1) 0.35 
Chronic obstructive No 1.0  
 pulmonary disease Yes 1.8 (1.0–3.1) 0.05 
Loop diuretics No 1.0  
 Yes 1.4 (0.8–2.4) 0.35 
Spironolactone No 1.0  
 Yes 1.7 (1.1–2.6) 0.017 
Ejection fraction (%) >40 1.0  
 30–39 1.9 (1.1–3.3)  
 <30 1.6 (1.1–2.4) 0.02 
Baseline urea (mmol/L) ≥6 1.0  
 6.1–9.0 0.8 (0.4–1.5)  
 >9 1.5 (0.8–2.9) 0.06 
Baseline SCr (µmol/L) <106 1.0  
 106–177 1.1 (0.6–1.8)  
 >177 1.5 (0.8–2.9) 0.81 
Baseline sodium (mmol/L) ≥140 1.0  
 137–139 0.9 (0.6–1.5)  
 ≤136 1.2 (0.8–2.0) 0.75 
Haemoglobin (g/dL) >14 1.0  
 13.3–14.5 1.1 (0.6–2.0)  
 12.0–13.2 1.6 (0.9–2.9)  
 ≤11.9 0.9 (0.5–1.8) 0.18 

HR, hazard ratio. Adjusted for age, sex, and NYHA class. All variables included were significant on univariate analysis.

References

1
Cowie MR, Mosterd A, Wood DA, Deckers JW, Poole-Wilson PA, Sutton GC, Grobbee DE. The epidemiology of heart failure.
Eur Heart J
 
1997
;
18
:
208
–225.
2
Cleland JG, Gemmell I, Khand A, Boddy A. Is the prognosis of heart failure improving?
Eur J Heart Fail
 
1999
;
1
:
229
–241.
3
Cowie MR, Wood DA, Coats AJ, Thompson SG, Suresh V, Poole-Wilson PA, Sutton GC. Survival of patients with a new diagnosis of heart failure: a population based study.
Heart
 
2000
;
83
:
505
–510.
4
Ho KK, Anderson KM, Kannel WB, Grossman W, Levy D. Survival after the onset of congestive heart failure in Framingham Heart Study subjects.
Circulation
 
1993
;
88
:
107
–115.
5
Hillege HL, Girbes AR, de Kam PJ, Boomsma F, de Zeeuw D, Charlesworth A, Hampton JR, van Veldhuisen DJ. Renal function, neurohormonal activation, and survival in patients with chronic heart failure.
Circulation
 
2000
;
102
:
203
–210.
6
Dries DL, Exner DV, Domanski MJ, Greenberg B, Stevenson LW. The prognostic implications of renal insufficiency in asymptomatic and symptomatic patients with left ventricular systolic dysfunction.
J Am Coll Cardiol
 
2000
;
35
:
681
–689.
7
Hall WD. Abnormalities of kidney function as a cause and a consequence of cardiovascular disease.
Am J Med Sci
 
1999
;
317
:
176
–182.
8
Levin A, Foley RN. Cardiovascular disease in chronic renal insufficiency.
Am J Kidney Dis
 
2000
;
36
(Suppl. 3):
S24
–S30.
9
MacDowall P, Kalra PA, O'Donoghue DJ, Waldek S, Mamtora H, Brown K. Risk of morbidity from renovascular disease in elderly patients with congestive cardiac failure.
Lancet
 
1998
;
352
:
13
–16.
10
McCullough PA, Soman SS, Shah SS, Smith ST, Marks KR, Yee J, Borzak S. Risks associated with renal dysfunction in patients in the coronary care unit.
J Am Coll Cardiol
 
2000
;
36
:
679
–684.
11
Zanchetti A, Stella A. Cardiovascular disease and the kidney: an epidemiologic overview.
J Cardiovasc Pharmac
 
1999
;
33
(Suppl. 1):
S1
–S6.
12
Packer M, Carver JR, Rodeheffer RJ, Ivanhoe RJ, DiBianco R, Zeldis SM, Hendrix GH, Bommer WJ, Elkayam U, Kukin ML. Effect of oral milrinone on mortality in severe chronic heart failure. The PROMISE Study Research Group.
N Engl J Med
 
1991
;
325
:
1468
–1475.
13
Krumholz HM, Chen YT, Vaccarino V, Wang Y, Radford MJ, Bradford WD, Horwitz RI. Correlates and impact on outcomes of worsening renal function in patients > or =65 years of age with heart failure.
Am J Cardiol
 
2000
;
85
:
1110
–1113.
14
Smith GL, Vaccarino V, Kosiborod M, Lichtman JH, Cheng S, Watnick SG, Krumholz HM. Worsening renal function: what is a clinically meaningful change in creatinine during hospitalization with heart failure?
J Card Fail
 
2003
;
9
:
13
–25.
15
Forman DE, Butler J, Wang Y, Abraham WT, O'Connor CM, Gottlieb SS, LohE, Massie BM, Rich MW, Stevenson LW, Young JB, Krumholz HM. Incidence, predictors at admission, and impact of worsening renal function among patients hospitalized with heart failure.
J Am Coll Cardiol
 
2004
;
43
:
61
–67.
16
Brown A, Cleland JGF. Influence of concomitant disease on patterns of hospitalisation in patients with heart failure discharged from Scottish hospitals in 1995.
Eur Heart J
 
1998
;
19
:
1063
–1069.
17
Khand AU, Gemmell I, Rankin AC, Cleland JGF. Clinical events leading to the progression of heart failure: insights from a national database of hospital discharges.
Eur Heart J
 
2001
;
22
:
153
–164.
18
Boure T, Vanholder R. Biochemical and clinical evidence for uremic toxicity.
Artif Organs
 
2004
;
28
:
248
–253.
19
Remme WJ, Swedberg K. Task Force for the Diagnosis Treatment of Chronic Heart Failure European Society of Cardiology. Guidelines for the diagnosis and treatment of chronic heart failure.
Eur Heart J
 
2001
;
22
:
1527
–1560.
20
Hunt SA, Baker DW, Chin MH, Cinquegrani MP, Feldman AM, Francis GS, Ganiats TG, Goldstein S, Gregoratos G, Jessup ML, Noble RJ, Packer M, Silver MA, Stevenson LW, Gibbons RJ, Antman EM, Alpert JS, Faxon DP, Fuster V, Jacobs AK, Hiratzka LF, Russell RO, Smith SC Jr. ACC/AHA guidelines for the evaluation and management of chronic heart failure in the adult: executive summary. A report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines (Committee to revise the 1995 Guidelines for the Evaluation and Management of Heart Failure).
J Am Coll Cardiol
 
2001
;
38
:
2101
–2113.
21
Levey AS, Bosch JP, Lewis JB, Greene T, Rogers N, Roth D for the Modification of Diet in Renal Disease Study Group. A More accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation.
Ann Intern Med
 
1999
;
130
:
461
–470.
22
Levey AS. A simplified equation to predict GFR from serum creatinine.
Am J Kid Dis
 
2002
;
39
(Suppl. 1):
S76
–S110.
23
Sadeghi HM, Stone GW, Grines CL, Mehran R, Dixon SR, Lansky AJ, Fahy M, Cox DA, Garcia E, Tcheng JE, Griffin JJ, Stuckey TD, Turco M, Carroll JD. Impact of renal insufficiency in patients undergoing primary angioplasty for acute myocardial infarction.
Circulation
 
2003
;
108
:
2769
–2775.
24
O'Hare AM, Glidden DV, Fox CS, Hsu C. High prevalence of peripheral arterial disease in persons with renal insufficiency.
Circulation
 
2004
;
109
:
320
–323.
25
Ruilope LM, van Veldhuisen DJ, Ritz E, Luscher TF. Renal function: the cinderella of cardiovascular risk profile.
J Am Coll Cardiol
 
2001
;
38
:
1782
–1787.
26
Gibson CM, Pinto DS, Murphy SA, Morrow DA, Hobbach HP, Wiviott SD, Giugliano RP, Cannon CP, Antman EM, Braunwald E. Association of creatinine and creatinine clearance on presentation in acute myocardial infarction with subsequent mortality.
J Am Coll Cardiol
 
2003
;
42
:
1535
–1543.
27
National Kidney Foundation Kidney Disease Outcome Quality Initiative Advisory Board. Kidney Disease Outome Quality Initiative (K/DOQI) clinical practice guidelines for chronic kidney disease: evaluation, classification and stratification.
Am J Kidney Dis
 
2002
;
39
(Suppl.):
S1
–S246.
28
Pitt B, Segal R, Martinez FA, Meurers G, Cowley AJ, Thomas I, Deedwania PC, Ney DE, Snavely DB, Chang PI. Randomised trial of losartan vs. captopril in patients over 65 with heart failure (Evaluation of Losartan in the Elderly Study, ELITE).
Lancet
 
1997
;
349
:
747
–752.
29
Corcoran C, Mehta C, Senchaudhuri P. Power comparisons for tests of trend in dose-response studies.
Stat Med
 
2000
;
19
:
3037
–3050.
30
Rigby AS. Statistical methods in epidemiology. III. The odds ratio as an approximation to the relative risk.
Disabil Rehabil
 
1999
;
21
:
145
–151.
31
Morris JA, Gardner MJ. Calculating confidence intervals for relative risks (odds ratios) and standardised ratios and rates.
Br Med J
 
1990
;
296
:
1313
–1316.
32
Sauerbrei W. The use of resampling methods to simplify regression models in medical statistics.
Appl Statist
 
1999
;
48
:
313
–329.
33
Brieman L, Spector P. Submodel selection and evaluation in regression: the X random case.
Int Stat Rev
 
1992
;
60
:
291
–319.
34
McNemar Q. Note on the sampling error of the difference between correlated proportions and percentages.
Psychometrika
 
1947
;
12
:
153
–157.
35
Kaplan EL, Meier P. Nonparametric estimation from incomplete observations.
J Am Stat Assoc
 
1958
;
53
:
457
–481.
36
Pocock SJ, Clayton TC, Altman DG. Survival plots of time-to-event outcomes in clinical trials: good practice and pitfalls.
Lancet
 
2002
;
359
:
1686
–1689.
37
Grambsch PM, Therneau TM. Proportional hazards tests and diagnostics based on weighted residuals.
Biometrika
 
1994
;
82
:
515
–526.
38
Kuk AYC. All subsets regression in a proportional hazards model.
Biometrika
 
1984
;
71
:
587
–592.
39
Francis B, Green M, Payne C (eds).
The GLIM System. Release 4 Manual
 . Oxford: Clarendon Press;
1994
.
40
Altman DG, Machin D, Bryant TN, Gardner MJ.
Statistics with Confidence
 . 2nd ed. London: BMJ;
2000
.
41
Perneger TV. What's wrong with Bonferonni adjustments?
BMJ
 
1998
;
316
:
1236
–1238.
42
McAlister FA, Ezekowitz J, Tonelli M, Armstrong PW. Renal insufficiency and heart failure. Prognostic and Therapeutic Implications from a Prospective Cohort Study.
Circulation
 
2004
;
109
:
1004
–1009.
43
Choudhri AH, Cleland JGF, Rowlands PL. Unsuspected renal artery stenosis in peripheral vascular disease.
BMJ
 
1990
;
78
:
879
–892.
44
McMurray JJV. Failure to practice evidence-based medicine: why do physicians not treat patients with heart failure with angiotensin-converting enzyme inhibitors?
Eur Heart J
 
1998
;
19
(Suppl. L):
L15
–L21.
45
Houghton AR, Cowley AJ. Why are ACE-inhibitors under-utilised in the treatment of heart failure by general practitioners?
Int J Cardiol
 
1997
;
59
:
7
–10.
46
Brenner BM, Cooper ME, de Zeeuw D, Keane WF, Mitch WE, Parving HH, Remuzzi G, Snapinn SM, Zhang Z, Shahinfar S; RENAAL Study Investigators. Effects of losartan on renal and cardiovascular outcomes in patients with type 2 diabetes and nephropathy.
N Engl J Med
 
2001
;
345
:
861
–869.
47
The EUCLID Study Group. Randomised placebo-controlled trial of lisinopril in normotensive patients with insulin-dependent diabetes and normoalbuminuria or microalbuminuria.
Lancet
 
1997
;
349
:
1787
–1792.
48
Muntner P, Coresh J, Smith JC, Eckfeldt J, Klag MJ. Plasma lipids and risk of developing renal dysfunction: the atherosclerosis risk in communities study.
Kidney Int
 
2000
;
58
:
293
–301.
49
Tavazzi L, Tognoni G, Franzosi MG, Latini R, Maggioni AP, Marchioli R, Nicolosi GL, Porcu M; GISSI-HF Investigators. Rationale and design of the GISSI heart failure trial: a large trial to assess the effects of n-3 polyunsaturated fatty acids and rosuvastatin in symptomatic congestive heart failure.
Eur J Heart Fail
 
2004
;
6
:
635
–641.
50
Mahon NG, Blackstone EH, Francis GS, Starling RC, Young JB, Lauer MS. The prognostic value of estimated creatinine clearance alongside functional capacity in ambulatory patients with chronic congestive heart failure.
J Am Coll Cardiol
 
2002
;
40
:
1106
–1113.
51
Al-Ahmad A, Sarnak MJ, Salem DN, Konstam MA. Cause and management of heart failure in patients with chronic renal disease.
Semin Nephrol
 
2001
;
21
:
3
–12.
52
Lee WH, Packer M. Prognostic importance of serum sodium concentration and its modification by converting-enzyme inhibition in patients with severe chronic heart failure.
Circulation
 
1986
;
73
:
257
–267.
53
Leier CV, Cas LD, Metra M. Clinical relevance of management of themajorelectrolyte abnormalities in congestive heart failure: hyponatraemia, hypokalaemia and hypomagnesaemia.
Am Heart J
 
1994
;
128
:
564
–574.
54
Juurlink DN, Mamdani MM, Lee DS, Kopp A, Austin PC, Laupacis A, Redelmeier DA. Rates of hyperkalemia after publication of the Randomized Aldactone Evaluation Study.
N Engl J Med
 
2004
;
351
:
543
–551.
55
Goodman WG, Goldin J, Kuizon BD, Yoon C, Gales B, Sider D, Wang Y, Chung J, Emerick A, Greaser L, Elashoff RM, Salusky IB. Coronary-artery calcification in young adults with end-stage renal disease who are undergoing dialysis.
N Engl J Med
 
2000
;
342
:
1478
–1483.
56
Block GA, Port FK. re-evaluation of risks associated with hyperphosphatemia and hyperparathyroidism in dialysis patients: recommendations for a change in management.
Am J Kidney Dis
 
2000
;
35
:
1226
–1237.
57
Boushey CJ, Beresford SAA, Omenn GS, Motulsky AG. A quantitative assessment of plasma homocysteine as a risk factor for vascular disease.
JAMA
 
1995
;
274
:
1049
–1057.
58
Burt RK, Gupta-Burt S, Suki WN, Barcenas CG, Ferguson JJ, Van Buren CT. Reversal of left ventricular dysfunction after renal transplantation.
AnnIntern Med
 
1989
;
111
:
635
–640.
59
Parfrey PS, Harnett JD, Foley RN, Kent GM, Murray DC, Barre PE, Guttmann RD. Impact of renal transplantation on uremic cardiomyopathy.
Transplantation
 
1995
;
60
:
908
–914.
60
de Silva R, Nikitin NP, Bhandari S, Nicholson A, Clark AL, Cleland JGF. Atherosclerotic renovascular disease in chronic heart failure-should we intervene?
Eur Heart J
 
2005
;
26
:
1596
–1605.
61
Murray GD. A cautionary note on selection of variables in discriminate analysis.
Appl Statist
 
1977
;
26
:
246
–250.
62
Derkson S, Keselman HJ. Backward, forward and stepwise automated subset selection algorithms: frequency of obtaining authentic and noise variables.
B J Mathemat Stat Psychol
 
1992
;
45
:
265
–282.
63
Freedman LS, Pee D, Midthune DN. The problem of underestimating theresidual error variance in forward stepwise regression.
Statistician
 
1992
;
41
:
405
–412.
64
Miller AJ. Selection of subsets of regression variables (with discussion).
JRoy Stat Soc Ser A
 
1984
;
147
:
389
–425.
65
Freedman LS. A note on screening regression equations.
Am Stat
 
1983
;
37
:
152
–155.
66
Chatfield C. Model uncertaintly, data mining and statistical inference (with discussion).
J Roy Stat Soc Ser A
 
1995
;
158
:
419
–466.
67
Poskitt DS, Tremayne AR. Determining a portfolio of linear time series models.
Biometrika
 
1987
;
74
:
125
–137.
68
Copas JB. Regression, prediction and shrinkage (with discussion).
J Roy Stat Soc Ser A
 
1983
;
145
:
311
–354.
69
Hoerl RW, Schuenemeyer JH, Hoerl AE. A simulation of biased estimation and subset regression techniques.
Technometrics
 
1983
;
28
:
369
–380.
70
Gottlieb SS, Abraham W, Butler J, Forman DE, Loh E, Massie BM, O'Connor CM, Rich MW, Stevenson LW, Young J, Krumholz HM. The prognostic importance of different definitions of worsening renal function in congestive heart failure.
J Card Fail
 
2002
;
8
:
136
–141.
71
Leithe ME, Margorien RD, Hermiller JB, Unverferth DB, Leier CV. Relationship between central haemodynamics and regional blood flow in normal subjects with congestive heart failure.
Circulation
 
1984
;
69
:
57
–64.
72
Wheatley K. ASTRAL—the story so far.
J Renovasc Dis
 
2003
;
2
:
1
–2.

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