Derivation and validation of a disease-specific risk score for cardiac risk stratification in chronic kidney disease
Kirsten A. Armstrong,
Dhrubo J. Rakhit,
Colin Case,
David W. Johnson,
Nicole M. Isbel and
Thomas H. Marwick
Department of Medicine, University of Queensland, Brisbane, Australia
Correspondence and offprint requests to: Thomas H. Marwick, MBBS, PhD, University of Queensland, Department of Medicine, Princess Alexandra Hospital, Ipswich Road, Brisbane Qld 4102, Australia. Email: tmarwick{at}soms.uq.edu.au
 |
Abstract
|
---|
Objective. Cardiac events (CE; cardiac death, non-fatal myocardial infarction and acute coronary syndrome) are the principal causes of death in patients with chronic kidney disease (CKD). We sought to devise and validate a cardiac risk score to risk-stratify patients with CKD.
Methods. Clinical history and biochemical data were obtained in 167 CKD patients. CE were recorded over a median follow-up of 22 months. The hazard ratio (HR) of each independent variable using Cox regression analysis was used to derive a cardiac risk score for the prediction of events. The cardiac risk score was then applied to a validation population of 99 CKD patients to confirm its validity in predicting CE.
Results. CE occurred in 20 patients in the derivation group. The independent predictors of CE were cardiac history (HR 9.83, P = 0.001), body mass index (BMI; HR 1.15, P = 0.002), dialysis duration (HR 1.24, P = 0.004) and serum phosphate (HR 4.29, P = 0.001). The resulting cardiac risk score (range 2667) gave an area under the receiver operating characteristic curve of 0.86. CE occurred in 25 patients in the validation group; the ROC curve area was similar (0.84, P = 0.11). An optimal cardiac risk score cut-off of 50 assigned high risk to 29% of the derivation and 35% of the validation group (P = 0.26). CE occurred in 35 and 57% of the high-risk derivation and validation groups, respectively (P = 0.09), and in 2 and 8% of the low-risk groups (P = 0.15).
Conclusion. Application of a cardiac risk score using cardiac history, dialysis duration, BMI and phosphate identifies CKD patients at risk of future CE.
Keywords: screening; chronic kidney disease; cardiac risk score
 |
Introduction
|
---|
Cardiovascular disease is the principal cause of morbidity and mortality in patients with chronic kidney disease (CKD), with a mortality rate approaching 40 times that of the general population [1]. Specifically, cardiac disease occurs in up to 40% patients on dialysis and accounts for 45% of all-cause mortality [2]. Cardiac disease is also common in pre-dialysis CKD patients and occurs with increasing frequency, severity and mortality-associated risk as renal function deteriorates [3]. This process frequently continues post-transplantation, with cardiac disease accounting for up to 50% of cardiovascular deaths in renal transplant recipients [4].
Cardiac screening of potential transplant recipients is essential to pre-select those at high risk of future cardiac events (CE), but there is no consensus about how best to accomplish this [5]. Cardiac disease is often subclinical and patients are frequently asymptomatic, despite having fairly advanced coronary artery disease (CAD). Many renal units perform cardiac stress imaging at the time of transplant evaluation in most CKD patients, but a universal screening strategy may result in unnecessary testing in patients who are at low risk of a CE, and false-positive results. An alternative approach would be to select higher risk patients on the basis of a clinical screening algorithm, thereby better defining patients who may benefit from further cardiac investigations. Such an algorithm, based on cardiac and risk factor data, has been shown to identify low-risk patients who may not require further cardiac evaluation prior to renal transplantation [6,7]. However, cardiac risk in CKD also pertains to non-cardiac phenomena, including the duration and severity of renal impairment, as well as the biochemical sequelae of CKD. We therefore sought the clinical and biochemical variables that were predictive of CE in CKD patients, using these to devise and validate a cardiac risk score (CRS) that would better identify those patients in whom further pre-transplant cardiac investigations might be avoided.
 |
Subjects and methods
|
---|
Study design
This was an analysis of two series of prevalent CKD patients from the Renal Unit at the Princess Alexandra Hospital, Brisbane, Australia. In a prospective series (derivation population), we evaluated which clinical parameters were predictive of CE in patients with CKD, using the hazard ratio (HR) of each independent variable from a Cox regression analysis to derive a CRS for the prediction of events. This score was then applied to a second series of patients (validation population) using the same clinical parameters in order to confirm the validity of the score.
Study populations
The derivation population comprised 167 CKD patients >18 years of age, who were either on maintenance dialysis therapy (haemodialysis or peritoneal dialysis) or had a calculated glomerular filtration rate (GFR) of
30 ml/min using the CockcroftGault equation [8]. Patients were excluded from the study if they had a pre-existing condition which was expected to limit their life expectancy to <6 months. Observational data were collected in this population between 2000 and 2001, in all patients who gave written, informed consent to participate in the study, which was approved by the Human Ethics Committee of the University of Queensland and Princess Alexandra Hospital.
The validation population comprised all patients categorized as dialysis dependent at the start of January 1999, or with a calculated GFR of
30 ml/min recorded for the first time during 1999. Data were recorded retrospectively on the clinical variables that comprised the CRS (derived from the study population) from prospectively obtained data entered into the Nephrology Database, supplemented by medical and dialysis records. Measurements and clinical assessments were assessed relative to the categorization date.
Clinical data
Demographic data were recorded including age, gender, race and cause of renal disease. For patients on maintenance dialysis therapy, dialysis duration was recorded. For pre-dialysis patients, the GFR was recorded. Cardiovascular risk factors documented were a previous cardiac event (PCE), peripheral vascular disease (PVD; defined as angioplasty, bypass or amputation), cerebrovascular disease (defined as transient ischaemic attack or stroke with neurological deficit), hypertension (defined as the use of anti-hypertensive agents or self-reported), hyperlipidaemia (defined as the use of lipid-lowering therapy or self-reported), diabetes mellitus (DM; defined as the use of oral hypoglycaemic agents, insulin or self-reported), smoking status (never, former or current) and a family history of cardiovascular disease or hyperlipidaemia. A PCE was defined as (i) a non-fatal myocardial infarction (MI; defined as abnormal cardiac enzymes and one of the following: ischaemic symptoms, development of pathological Q waves on ECG, ECG changes suggestive of ischaemia or coronary intervention); (ii) acute coronary syndrome (ACS; defined as no elevation of cardiac enzymes, ischaemic symptoms±ECG changes suggestive of ischaemia requiring hospitalization); (iii) angina not requiring hospitalization; (iv) coronary artery bypass graft; or (v) percutaneous coronary intervention. Current use of anti-hypertensive agents [ß-blockers, calcium channel blockers and angiotensin-converting enzyme (ACE) inhibitors], aspirin and statins were recorded. Weight and height were measured to the nearest 0.1 kg and 1 cm, respectively, and the mean of three arterial blood pressure readings, each taken at 1 min intervals, was recorded.
Biochemical data
Blood for biochemical analysis was obtained from fasting venous samples. Serum concentrations of creatinine, glucose, lipids [total cholesterol, low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol and triglycerides], albumin, haemoglobin, calcium (corrected for albumin), phosphate, C-reactive protein (CRP), homocysteine and intact parathyroid hormone were analysed using standard laboratory techniques.
End-points
Patients in the derivation and validation groups were followed for 2 years. In the derivation group, exit from the study during the follow-up period was recorded as due to death, renal transplantation, loss to follow-up or patient choice. The primary end-point was the first recorded CE [non-fatal MI, ACS or cardiac death, defined as that confirmed secondary to ischaemic heart disease (IHD)] occurring during the follow-up period and the secondary end-points were all-cause mortality and cardiac mortality. In the derivation group, end-points were determined either by direct communication with the patient or, in the case of death, by review of the death certificate or autopsy report. In the validation group, end-points were determined from medical records, telephone enquiries, death certificates and autopsy reports.
Statistical analysis
Statistical analyses were performed using standard statistical software (SPSS Version 11.5, SPSS, Chicago, IL). For normally distributed continuous data, results are expressed as mean±SD; for non-parametric continuous data, results are expressed as median and interquartile range (IQR); for categorical data, results are expressed as frequencies and percentages. Comparisons of means, where appropriate, were made using unpaired t-tests for normally distributed data and the MannWhitney test for non-parametric data; P-values <0.05 were considered statistically significant.
Cox regression survival analysis and time to event was used to devise a multivariate model of predictors of CE from clinical variables in the derivation population. The following variables were used first in a univariate model: age, gender, weight, BMI, dialysis modality, dialysis duration, GFR (if pre-dialysis), DM, duration of DM, PCE, PVD, cerebrovascular disease, hypertension, hyperlipidaemia, smoking status, family history of cardiovascular disease or hyperlipidaemia, systolic blood pressure, diastolic blood pressure, total cholesterol, LDL cholesterol, HDL cholesterol, triglycerides, calcium, phosphate, albumin, haemoglobin, homocysteine, CRP, parathyroid hormone and medications (aspirin, statins, ß-blockers, calcium channel blockers and ACE inhibitors). Variables with a P-value <0.1 on univariate analysis were then entered into a multivariate regression analysis and the final multivariate model was obtained using a stepwise backwards elimination procedure of variables with a P-value >0.05. A CRS was calculated for each patient in the derivation population using the HRs for each clinical variable in the multivariate model. A receiver operator characteristic (ROC) analysis was performed to determine the best cut-off attributing high risk and low risk of CE. A Cox proportional hazards model was used to compare events between the high- and low-risk groups in the derivation population. Using the same HRs and clinical variables, each patient in the validation population was attributed a CRS and, using the same cut-off of 50, patients were stratified into a high-risk group or a low-risk group. Comparison between the high- and low-risk groups in the validation population, and then between each risk level in the different populations was made using the Cox model.
 |
Results
|
---|
Clinical characteristics
The 167 patients in the derivation population are compared with the 99 patients in the validation population in Table 1. Patients in the validation population were older (P = 0.006), lighter (P = 0.02) and had been on dialysis for longer (P = 0.007). Prevalence of cardiovascular risk factors was similar between the two groups, although hyperlipidaemia was more common in the derivation population (P = 0.02). The use of ß-blockers, ACE inhibitors and statins was significantly less in the validation population. A history of a PCE was similar in the two groups (P = 0.57). Mean levels of all biochemical parameters were comparable between the populations.
Outcomesderivation group
The median duration of follow-up in the derivation group was 22 months (IQR 1125). There were 20 initial CE: four non-fatal MIs, 12 admissions with ACS and four cardiac deaths. Causes of cardiac death were reported as myocardial ischaemia and infarction (n = 3) and congestive cardiac failure secondary to IHD (n = 1). There were 24 deaths in total (all-cause mortality 14%) and, because of death of two patients initially presenting with a non-fatal event, there were a total of six cardiac deaths (cardiac mortality 4%). Of the non-cardiac deaths, eight were due to sepsis, six were due to withdrawal of therapy, two were due to bowel infarction and two were due to liver ischaemia.
Outcomesvalidation group
In the validation group, median duration of follow-up was 22 months (IQR 730). There were 25 initial CE: nine non-fatal MIs, six admissions with ACS and 10 cardiac deaths. Causes of cardiac death were reported as MI (n = 9) and cardiorespiratory arrest presumed secondary to IHD (n = 1). There were 50 deaths in total (all-cause mortality 51%) and, because of death of three patients initially presenting with a non-fatal event, there was a total of 13 cardiac deaths (cardiac mortality 13%). The main causes of non-cardiac death were reported as withdrawal of therapy (n = 10), septicaemia (n = 6), malignancy (n = 2), cerebrovascular event (n = 2), peritonitis (n = 2), pulmonary oedema (n = 2), subarachnoid haemorrhage (n = 1), bowel perforation (n = 1) and suicide (n = 1). There were 10 deaths recorded as being of uncertain aetiology.
Clinical variables predictive of events
The correlates of CE in the derivation group are summarized in Table 2. On univariate analysis, significant predictors of a CE were a PCE (HR 8.32, P = 0.001), history of PVD (HR 3.34, P = 0.008), weight (HR 1.03, P = 0.03), BMI (HR 1.09, P = 0.02), dialysis duration (HR 1.19, P = 0.005) and serum phosphate (HR 3.83, P = 0.002). Clinical variables that were not significant on univariate analysis were age, gender, systolic blood pressure, diastolic blood pressure, dialysis modality, dialysis duration, history of DM, hypertension or hyperlipidaemia, smoking status and type or number of medications. No biochemical parameters other than phosphate were predictive of events. In the final multivariate model, independent predictors of a CE were a PCE (HR 9.83, P = 0.001), BMI (HR 1.15, P = 0.002), dialysis duration (HR 1.24, P = 0.004) and phosphate (HR 4.29, P = 0.001).
View this table:
[in this window]
[in a new window]
|
Table 2. Univariate and multivariate predictors of cardiac events using Cox regression survival analysis and time to event in the derivation population
|
|
Role of diabetes
In the derivation population, 44 patients (26%) had a history of DM, and BMI was greater in this subgroup (29.9±5.4 vs 26.2±5.1 kg/m2, P<0.001). Patients with DM were also more likely to have had a PCE compared with those without DM (57 vs 25%, P<0.001). While there were more events in the diabetic patients (20 vs 9%), and a trend towards a significant correlation between DM and CE on univariate analysis [HR 2.31, 95% confidence interval (CI) 0.965.59; P = 0.06], DM was not an independent predictor of CE in the multivariate model. Only in the absence of BMI and PCE did a history of DM correlate with CE (HR 2.70, 95% CI 1.066.87; P = 0.04) together with dialysis duration (HR 1.19, 95% CI 1.051.35; P = 0.005) and phosphate (HR 2.28, 95% CI 1.094.77; P = 0.03).
Cardiac risk score
A CRS was calculated for each patient in the derivation group as shown in Table 3. The area under the ROC curve was 0.86 (Figure 1a); a score of 50 allocated low risk (CRS <50) to 119 (71%) and high risk (CRS
50) to 48 patients (29%). This cut-off of 50 gave a sensitivity of 85% and specificity of 80% for prediction of CE over 22 months follow-up. The area under the ROC curve for the validation group was 0.84 (Figure 1b) and was similar to that for the derivation group (P = 0.11). Using ROC analyses for each variable, the strongest predictor of a future CE in both the derivation and the validation groups was a PCE (Figure 2a and b).

View larger version (15K):
[in this window]
[in a new window]
|
Fig. 1. Receiver operating characteristic curves for cardiac risk score in the derivation population (a) and the validation population (b). The AUC was similar in both populations (0.86 vs 0.84, P = 0.11).
|
|

View larger version (26K):
[in this window]
[in a new window]
|
Fig. 2. Receiver operating characteristic curves for phosphate, previous cardiac event, dialysis duration and body mass index in the derivation population (a) and validation population (b).
|
|
Event ratesderivation group
The results for primary and secondary events are shown in Table 4. The CE rate was significantly higher in the high-risk group when compared with the low-risk group (35 vs 2%, P<0.001). The annualized event rate was 19% in the high-risk group and 1% in the low-risk group. There were three CE in the low-risk groupthese were recorded as due to ACS (n = 2) and death due to congestive cardiac failure secondary to IHD (n = 1).
View this table:
[in this window]
[in a new window]
|
Table 4. Event rates in the derivation population and validation population assigned low risk and high risk by cardiac risk score
|
|
Rather than optimizing sensitivity and specificity, a screening approach could be selected to maximize sensitivity. Use of a lower cut-off (45), with a sensitivity of 90%, allowed prediction of one additional CE, but at the cost of lower specificity (66%), which increased patients in the high-risk group by 13%.
Event ratesvalidation group
Using the optimal score cut-off of 50, 64 patients (64%) in the validation group were stratified into the low-risk group. Event rates for all end-points are shown in Table 4. The CE rate was significantly higher in the high-risk group when compared with the low-risk group (57 vs 8%, P<0.001). The annualized event rate was 31% in the high-risk group and 4% in the low-risk group. Fifteen patients in the high-risk group did not have a CE during the follow-up period. However, seven (47%) died of another cause and one patient had an MI beyond the 2 year follow-up. Seven patients did not have a CE despite having a CRS >50. There were five CE in the low-risk group. These were recorded as due to ACS (n = 1), non-fatal MI (n = 1) and fatal MI (n = 3).
Using the Cox proportional hazards model, there was no difference in primary CE at 22 months between the low-risk patients in both derivation and validation groups (P = 0.15) or between the high-risk patients in both the derivation and validation groups (P = 0.09) (Figure 3a and b).

View larger version (17K):
[in this window]
[in a new window]
|
Fig. 3. Event-free survival of low-risk patients (a) and high-risk patients (b) in the derivation population (continuous line) and validation population (interrupted line) over 22 months of follow-up.
|
|
 |
Discussion
|
---|
The results of this study suggest that application of a CRS allows accurate cardiovascular risk stratification in patients with CKD. Patients with a CRS <50 are at low risk for future CE and may avoid further cardiac evaluation prior to transplantation. Patients with a CRS of
50, however, represent a high-risk cohort, with an annualized event rate of up to 30%, in whom supplementary cardiac assessment is indicated.
Risk scores in CKD
A handful of risk scores for CKD patients have been reported in the literature, but few have been validated [6,7,9,10]. Foley et al. specifically addressed mortality risk in 325 prevalent dialysis patients by devising a score using age and co-morbid conditions (including severe PVD, advanced neoplasia and systemic sepsis), which correctly attributed 100% mortality at 6 months to those with a score >9, compared with 5% mortality to those with a score <4 [9]. The factors in this score are often used informally, in the selection of patients for dialysis, so this score is difficult to validate.
The designation of cardiac risk is a specific concern in pre-transplant evaluation, and there have been a few attempts to devise an easily reproducible screening tool to pre-select high-risk patients. Seyfert et al. reported on a clinical screening model to predict CAD risk in CKD patients [10]. Based on a retrospective analysis of coronary angiography findings in 42 dialysis patients, they devised a risk score from age, dialysis duration, history of angina or transient ischaemic attacks, ejection fraction, PVD and previous shunt thrombosis. While the sensitivity and specificity of the score were high (90 and 80%, respectively), it was a retrospective analysis and has not been validated.
The only validated clinical score was devised as part of a two-tiered cardiac risk stratification algorithm for patients undergoing pre-transplant evaluation [6]. One hundred and eighty-nine CKD patients were stratified into high- and low-risk groups based on five clinical variables (age >50 years, insulin-dependent diabetes, abnormal ECG, history of cardiac failure or angina). Patients with one or more risk factors were considered high risk and had thallium imaging. At 46 months, cardiac mortality was higher in the high-risk group (17 vs 1%, P<0.001) and an abnormal thallium scan in the high-risk patients conferred added risk (27 vs 5%, P<0.001). It was concluded that thallium scanning would not have assisted cardiovascular risk stratification in 50% of CKD patients who were low risk on clinical grounds and could therefore be avoided pre-transplantation. Although these findings have been validated [7], this approach ignores non-cardiovascular risk factors that determine the development of CAD.
Constituents of the CRS
The four clinical variables that were independently predictive of future CE in the derivation population were PCE, increasing BMI, increasing phosphate and longer dialysis history. The strongest predictor of a future CE was a PCE [relative risk (RR) 9.83, 95% CI 2.7035.76], a finding congruent with the literature, in which a PCE is recognized as the strongest predictor for a future CE in CKD patients [12]. In a study of 1846 haemodialysis patients, IHD at baseline was implicated in 62% of cardiac deaths and any cardiac disease at baseline was highly predictive of cardiac death during a mean follow-up of 2.8 years (RR 2.57, 95% CI 1.733.83) [12].
The dialysis process is associated with accelerated development of atherosclerosis, and the longer the dialysis history, the greater the risk of cardiac events [13]. A median longer dialysis history in the validation population may explain in part why the validation population had significantly more CE compared with the derivation population (25 vs 12%, P = 0.005). Consistent with the literature [14], this study demonstrated that hyperphosphataemia is an independent risk factor for CE in CKD patients. In one study, patients with serum phosphate >6.5 mg/dl had a 52% higher risk of death from CAD, a 26% higher risk of sudden death, a 34% higher risk from other cardiac causes and a 39% higher risk of death from cerebrovascular accidents compared with those with a serum phosphate <6.5 mg/dl [14].
This study demonstrated a significant association between increasing BMI and risk of future CE. While this is consistent with data from the general population, it is contrary to many reports which suggest that malnutrition is linked with cardiovascular mortality in dialysis patients [15] while a more favourable nutritional status may confer a survival benefit [16]. One study, however, found that low BMI (<18.5 kg/m2), while a strong predictor of all-cause mortality, was not associated with a higher incidence of ACS episodes [17]. In addition, coronary calcification, and thus cardiovascular risk, is strongly associated with high rather than low BMI in dialysis patients [18]. This may provide a plausible explanation for the observed association between increasing BMI and CE, particularly in the context of hyperphosphataemia.
Interestingly, DM was not independently predictive of CE in the derivation population, although it approached significance on univariate analysis. First, this may reflect the greater importance of hyperphosphataemia and dialysis duration as contributors to atherosclerotic risk [19]. Secondly, it may also be explained by BMI and history of a PCE acting as surrogate markers for DM in the multivariate modelas expected, patients with DM had a higher BMI and more PCE than the remainder of the group. Indeed, the association of DM with weight may not only override any positive benefit of increased BMI in terms of survival advantage, but may also explain why BMI was an independent predictor of CE. Finally, it may reflect survivor bias given that the observed frequency of diabetic patients was less than might be expected.
Limitations
There were a number of limitations to this study: both populations (which were relatively small) were heterogeneous; the follow-up of the groups was relatively short; outcome was based on prevalent rather than incident data; and the validation group was temporally remote from the derivation group. The combination of all CKD (pre-dialysis, haemodialysis and peritoneal dialysis) as a single cohort may influence outcomes because cardiac risk may be amplified by both decreasing renal function and requirement for renal replacement therapy [20]. The validation group was from an earlier cohort than the derivation group and included incomplete data regarding the levels of hyperlipidaemia and hypertension. Nonetheless, despite this and differences in cardiovascular risk factor parameters between the two groups, the CRS model worked as well in the validation group as it did in the derivation grouparguing that the score is transportable between groups and that the CRS constituents are applicable to all CKD patients. Consideration, however, could be given to developing a separate model for each of the three CKD groups in future analyses. Similarly, analysis of patients with DM in the same group as those without DM may have influenced the predictive constituents of the CRS. Again, this could be developed further in future analyses by analysing patients with DM separately from those without. While our single centre design has advantages of homogeneous patient evaluation, the consequence was the limitation of both groups to relatively small numbers with the possibility of missing some associations.
Ideally, a screening test would identify all patients at risk of events. Potentially, the three CE in the low-risk derivation group could have been anticipated if a lower CRS had been chosen. However, this increase in sensitivity of the CRS would have been at the cost of a substantial increase in the number of patients in the high-risk group (i.e. lower specificity). Despite these limitations, the CRS fulfils most of the criteria for a screening test and its validation in a second cohort of CKD patients justifies its potential as a screening tool, an advantage over the many other risk scores that have not been validated.
 |
Conclusions
|
---|
Cardiac disease is extremely common in patients with CKD, accounting for significant morbidity and mortality. The use of stress-imaging studies in all patients before transplantation risks false-positive test results, especially in low-risk patients. Preliminary evaluation with the CRS may obviate the need for further cardiac imaging in 67% of CKD patients who are low risk on clinical grounds while maximizing detection of patients liable to have future CE, in whom further cardiac evaluation should be undertaken.
 |
Acknowledgments
|
---|
The authors gratefully acknowledge the statistical advice of Ms Elaine Beller and Dr Carmel Hawley, the help with data recording provided by Ms Dale Bergman and Ms Kylie Reiger, and the assistance of the staff and patients in the Renal Unit at the Princess Alexandra Hospital, Brisbane. Supported in part by Project Grant (102471) and a Clinical Centre of Research Excellence Award from the National Health and Medical Research Council of Australia.
Conflict of interest statement. None declared.
 |
References
|
---|
- McCullough PA. Cardiovascular disease in chronic kidney disease from a cardiologist's perspective. Curr Opin Nephrol Hypertens 2004; 13: 591600[CrossRef][ISI][Medline]
- Herzog CA. Cardiac arrest in dialysis patients: approaches to alter an abysmal outcome. Kidney Int Suppl 2003; S197S200
- Yeo FE, Villines TC, Bucci JR, Taylor AJ, Abbott KC. Cardiovascular risk in stage 4 and 5 nephropathy. Adv Chronic Kidney Dis 2004; 11: 116133[CrossRef][ISI][Medline]
- Marcen R, Morales JM, Fernandez-Juarez G et al. Risk factors of ischemic heart disease after renal transplantation. Transplant Proc 2002; 34: 394395[CrossRef][ISI][Medline]
- Ramos EL, Kasiske BL, Alexander SR et al. The evaluation of candidates for renal transplantation. The current practice of U.S. transplant centers. Transplantation 1994; 57: 490497[ISI][Medline]
- Le A, Wilson R, Douek K et al. Prospective risk stratification in renal transplant candidates for cardiac death. Am J Kidney Dis 1994; 24: 6571[ISI][Medline]
- Lewis MS, Wilson RA, Walker KW et al. Validation of an algorithm for predicting cardiac events in renal transplant candidates. Am J Cardiol 2002; 89: 847850[CrossRef][ISI][Medline]
- Cockcroft DW, Gault MH. Prediction of creatinine clearance from serum creatinine. Nephron 1976; 16: 3141[ISI][Medline]
- Foley RN, Parfrey PS, Hefferton D et al. Advance prediction of early death in patients starting maintenance dialysis. Am J Kidney Dis 1994; 23: 836845[ISI][Medline]
- Seyfert UT, Gluntz HG, Albert FW. Cardiac risk score for coronary artery disease and preparation for kidney transplantation. Nephron 1990; 56: 105106[ISI][Medline]
- Barrett BJ, Parfrey PS, Morgan J et al. Prediction of early death in end-stage renal disease patients starting dialysis. Am J Kidney Dis 1997; 29: 214222[ISI][Medline]
- Cheung AK, Sarnak MJ, Yan G et al. Cardiac diseases in maintenance hemodialysis patients: results of the HEMO Study. Kidney Int 2004; 65: 23802389[CrossRef][ISI][Medline]
- Harper SJ, Bates DO. Endothelial permeability in uremia. Kidney Int Suppl 2003; S41S44
- Levin NW, Hulbert-Shearon TE, Strawderman RL. Which causes of death are related to hyperphosphataemia in hemodialysis (HD) patients? J Am Soc Nephrol 1998; 9: 217A
- Stenvinkel P, Heimburger O, Paultre F et al. Strong association between malnutrition, inflammation, and atherosclerosis in chronic renal failure. Kidney Int 1999; 55: 18991911[CrossRef][ISI][Medline]
- Johnson DW, Herzig KA, Purdie DM et al. Is obesity a favorable prognostic factor in peritoneal dialysis patients? Perit Dial Int 2000; 20: 715721[ISI][Medline]
- Beddhu S, Pappas LM, Ramkumar N, Samore MH. Malnutrition and atherosclerosis in dialysis patients. J Am Soc Nephrol 2004; 15: 733742[Abstract/Free Full Text]
- Goodman WG, Goldin J, Kuizon BD et al. Coronary-artery calcification in young adults with end-stage renal disease who are undergoing dialysis. N Engl J Med 2000; 342: 14781483[Abstract/Free Full Text]
- Kennedy R, Case C, Fathi R et al. Does renal failure cause an atherosclerotic milieu in patients with end-stage renal disease? Am J Med 2001; 110: 198204[CrossRef][ISI][Medline]
- Nishizawa Y, Shoji T, Maekawa K et al. Intima-media thickness of carotid artery predicts cardiovascular mortality in hemodialysis patients. Am J Kidney Dis 2003; 41: S76S79[CrossRef][ISI][Medline]
Received for publication: 23. 2.05
Accepted in revised form: 1. 6.05