Skip Navigation



NDT Advance Access published online on April 23, 2007
Nephrology Dialysis Transplantation, doi:10.1093/ndt/gfm222
This Article
Right arrow Abstract Freely available
Right arrow FREE Full Text (PDF) Freely available
Right arrow All Versions of this Article:
22/9/2586    most recent
gfm222v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Right arrow Disclaimer
Google Scholar
Right arrow Articles by Glynn, L. G.
Right arrow Articles by Murphy, A. W.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Glynn, L. G.
Right arrow Articles by Murphy, A. W.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author [2007]. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Chronic kidney disease and mortality and morbidity among patients with established cardiovascular disease: a West of Ireland community-based cohort study

Liam G. Glynn1, Donal Reddan2, John Newell3, John Hinde3, Brian Buckley1 and Andrew W. Murphy1

1Department of General Practice, National University of Ireland, Galway, 2Department of Medicine, University College Hospital, Galway and 3Department of Mathematics, National University of Ireland, Galway, Ireland

Correspondence and offprint requests to: Dr Liam G. Glynn, Department of General Practice, National University of Ireland, Galway, Ireland. Email: liam.glynn{at}nuigalway.ie



   Abstract
Top
 Abstract
Introduction
 Subjects and methods
 Results
 Discussion
 Conclusion
 Acknowledgements
 References
 
Background. The importance of chronic kidney disease as an independent risk factor for morbidity and mortality in patients with cardiovascular disease in the community is not widely recognized.

Methods. A retrospective cohort study based in the West of Ireland followed a randomized practice-based sample of patients with cardiovascular disease. A database of 1609 patients with established cardiovascular disease was established in 2000. This was generated from a randomized sample of 35 general practices in the West of Ireland. The primary endpoint was a cardiovascular composite endpoint, which included death from a cardiovascular cause or any of the cardiovascular events of myocardial infarction (MI), heart failure, peripheral vascular disease and stroke. The secondary endpoint was death from any cause.

Results. Of the original community-based cohort of 1609 patients with cardiovascular disease, 1272 (79%) had one or more serum creatinine measurements during the study period and 31 (1.9%) patients were lost to follow-up. Median follow-up was 2.90 years (SD 1.47) and the risk of the cardiovascular composite endpoint (total of 219 events) was significantly increased in those patients with reduced estimated glomerular filtration rate (GFR) [log rank (Mantel–Cox) 26.74, P < 0.001] as was the risk of death from any cause (total of 214 deaths) [Log Rank (Mantel–Cox) 56.97, P < 0.001]. On the basis of the proportional hazards model, while adjusting for other significant covariates, reduced estimated GFR was associated with a significant increase in risk of the primary and secondary outcomes (P < 0.01). For every 10 ml decrement in estimated GFR there was a corresponding 20% increase in hazard of the cardiovascular composite endpoint and a 33% increase in hazard of death from any cause.

Conclusions. Estimated GFR appears to discriminate prognosis between patients with established cardiovascular disease. These results emphasise the importance of recognising chronic kidney disease as a significant risk factor in patients with cardiovascular disease in the community.

Keywords: coronary disease; kidney; mortality; primary care



   Introduction
Top
 Abstract
 Introduction
Subjects and methods
 Results
 Discussion
 Conclusion
 Acknowledgements
 References
 
Physicians worldwide recognize the importance of cardiovascular risk factors and strive to identify and improve them in their patients. Despite the high prevalence of chronic kidney disease in this population [1], chronic kidney disease remains outside the group of well-recognized cardiovascular risk factors in many healthcare settings. Indeed where chronic kidney disease is recognized in this population, patients are often nihilisitically treated [2,3]. Despite the recognized association between reduced estimated glomerular filtration rate (GFR) and poorer prognosis, risk factor management is often not optimized for such patients in the community setting [3] while screening for chronic kidney disease is frequently limited to a measurement of serum creatinine, which does not accurately reflect GFR [4,5], the best indicator of renal function in health and disease [6]. This is despite the growing body of evidence demonstrating increased cardiovascular risk associated with chronic kidney disease in the general population [7,8] as well as in patients post-MI [1,9–11], post-cardiac intervention [12] and those with diabetes [13]. However, the health systems of only a minority of countries [14,15] have emphasized the importance of chronic kidney disease screening (categorized using estimated GFR) within primary care and recommendations relating to chronic kidney disease in primary care are largely based on data generated outside this setting. Previous studies have also been limited by patient selection methods [1,9–12]; use of serum creatinine as a proxy for renal function [16–18]; short follow-up periods [1,11,12] and lack of uniformity in definition of chronic kidney disease [1,7, 8,10–13].

Most primary and secondary prevention of cardiovascular disease happens in the community, and therefore, it is important that we investigate the risk relationship between cardiovascular disease and chronic kidney disease in its most relevant setting. Cardiovascular disease remains a leading cause of morbidity and mortality in the developed world, with rates in Ireland where this study took place among the highest in Europe [19]. Remarkably, with chronic kidney disease representing the group at highest risk from cardiovascular complications, there has been a systematic exclusion of patients with chronic kidney disease from therapeutic trials [20]. We utilized a cohort of patients from a random sample of practices with the full spectrum of cardiovascular disease in the community and examined whether a dose response relationship exists between estimated GFR and mortality and new cardiovascular morbidity.



   Subjects and methods
Top
 Abstract
 Introduction
 Subjects and methods
Results
 Discussion
 Conclusion
 Acknowledgements
 References
 
Study population and measurement of renal function
A comprehensive description of how the cohort was assembled has been described previously [21,22]. In short, the study sample consisted of a cohort of 1609 patients with cardiovascular disease who were identified in 2000 from a stratified random sample of 35 general practices in the West of Ireland. In order to generate this sample, practices were randomly selected, after stratification by practice type (single-handed or group) and location (rural or urban), from the Western and North-Western health board areas and asked to participate in the study. Thirty five (60%) practices chose to do so and these practices were asked to generate a list of all their patients with established cardiovascular disease using a combination of multiple methods including practice disease registers, patient database searches, prescribing records, prospective recording of patient attendance and opportunistic practitioner recall. Patients were defined as having cardiovascular disease if they had a history of myocardial infarction (MI); angina; or revacularization by percutaneous coronary intervention (PCI) or coronary artery bypass grafting (CABG). Patients were included in the study analysis if they had a serum creatinine sample measurement from the regional laboratory within the study period or up to 30 days prior to recruitment. The first date of measurement of serum creatinine was considered the patient's index date for purposes of analysis. Follow-up data on the cohort were collected after a period of 5 years and patients not experiencing events were censored at this point. Data on subjects were also censored where follow-up data ceased to be available. Renal function was assessed using both serum creatinine and estimated GFR. Due to the nature of the study, it was not possible to obtain information on proteinuria or albuminuria. The abbreviated Modified Diet in Renal Disease (MDRD) equation [23] was used to calculate estimated GFR:

Estimated GFR (ml/min) = 186 x [serum creatinine level (in milligrams per decilitre)]–1.154 x [age (in years)]–0.203.

For women and those of Afro-Caribbean descent (ethnicity data was collected during follow-up), the product of this equation was multiplied by correction factors of 0.742 and 1.21, respectively [23].

Outcomes
The primary endpoint was a cardiovascular composite endpoint, which included death from a cardiovascular cause (cardiovascular disease included as either a primary or a contributing factor on the death certificate, but not if simply a co-existing morbidity) [24] or any of the cardiovascular events of MI, heart failure, peripheral vascular disease and stroke. The secondary endpoint was death from any cause. Death data was collected from a search of practice records and the General Register Office, which is the central civil repository for records relating to births, marriages and deaths in the Republic of Ireland.

Statistical analysis
For analysis of baseline characteristics and survival analysis, patients were classified according to National Kidney Foundation guidelines [25] with a further subdivision of stage III incorporating more recent recommendations [8]: Estimated GFR ≥60 ml/min/1.73 m2 (stages I & II); 45–59 ml/min/1.73 m2 (stage IIIa); 30–44 ml/min/1.73 m2 (stage IIIb); and <30 ml/min/1.73 m2 (stages IV & V). For descriptive purposes, estimated GFR is presented as a categorical variable, but was also treated as a continuous measure in statistical tests. Baseline characteristics were analysed with the use of one-way ANOVA for continuous variables and Chi-square test for categorical variables. Patient variables included 20 baseline characteristics (Table 1). To control for the large number of covariates, variable selection techniques were used to identify the most parsimonious model containing significant explanatory variables while including the covariate of interest (estimated GFR). Stepwise Cox proportional-hazards models [forward selection (Wald criterion)] were used to evaluate the prognostic effect of estimated GFR over the study period while controlling for all explanatory variables. Adjusted hazard ratios, categorized according to estimated GFR, for the cardiovascular composite endpoint and for death from any cause were determined while adjusting for all other significant explanatory variables. The proportional hazards assumption was checked using suitable residual plots and interactions for the key categorical variables such as gender, smoking status, previous MI and diabetes were investigated. In order to investigate whether patients without serum creatinine data could be considered ‘missing at random’, the analysis was conducted incorporating those patients as a separate category. All statistical test values were two-sided, and a P-value of <0.05 was considered to indicate statistical significance. Analysis was carried out using SPSS (14.0) and R statistical software.


View this table:
[in this window]
[in a new window]

  		Table 1. Baseline characteristics of 1272 patients with cardiovascular disease according to estimated GFR

 


   Results
Top
 Abstract
 Introduction
 Subjects and methods
 Results
Discussion
 Conclusion
 Acknowledgements
 References
 
Baseline characteristics
Among the original 1609 patients in the study, 31 (1.9%) patients were lost to follow-up and 1272 (79%) had one or more serum creatinine measurements during the study period. Median follow-up was 2.90 years (SD 1.47) and there were no significant differences in age, gender, social status, smoking status, diabetic status and previous cardiovascular morbidity between those patients with, and without, serum creatinine measurements. Table 1 describes the baseline characteristics of the patients according to estimated GFR levels. The estimated GFR at baseline for the 1272 patients with a serum creatinine measurement was normally distributed (Figure 1). The mean (+/–SD) estimated GFR was 65.6 (17.4) ml/min/1.73 m2 with a range of 5.6–182.4. A total of 809 (63.6%) patients had a normal estimated GFR of ≥60 ml/min/1.73 m2; 335 (26.3%) patients had an estimated GFR 45–59 ml/min/1.73 m2; 100 (12.7%) patients had an estimated GFR 30–44 ml/min/1.73 m2; and 28 (2.2%) patients had an estimated GFR <30 ml/min/1.73 m2. Patients with reduced estimated GFR at baseline were older and more likely to be female and had a higher prevalence of heart failure. Conversely, these patients were less likely to be receiving lipid-lowering agents.


Figure 1
View larger version (23K):
[in this window]
[in a new window]
[Download PowerPoint slide]
  		Fig. 1. Distribution of estimated GFR at baseline for the 1272 patients with a serum creatinine measurement.

 
Outcomes
Of the 1578 patients with follow-up data, 1272 (80%) had one or more serum creatinine measurement during the study period. During follow-up there were 214 deaths and 219 cardiovascular events. The risk of the cardiovascular composite endpoint was significantly increased in those patients with reduced estimated GFR compared with those with normal GFR [Log Rank (Mantel–Cox) 26.74, P < 0.001] (Figure 2 and Table 2) as was the risk of death from any cause (Figure 3 and Table 3) [Log Rank (Mantel–Cox) 53.29, P < 0.001]. In the examination of the relationship between the primary and secondary outcomes and estimated GFR, while controlling for all significant explanatory variables, patients with normal estimated GFR (≥60 ml/min/1.73 m2) were used as the reference group (Table 4). After adjustment for age, gender, social status, smoking status, salt intake, body mass index, systolic blood pressure, diastolic blood pressure, total cholesterol level, previous MI, angina, heart failure, stroke, peripheral vascular disease, thromboembolic events, prior percutaneous transfemoral coronary angioplasty, prior CABG, diabetes mellitus and medication use, the adjusted hazard ratio for the cardiovascular composite endpoint for patients with an estimated GFR of 45–59 ml/min/1.73m2 was 1.61 (95% Confidence interval 1.14–2.26) for the event as compared with 3.60 (95% confidence interval 1.74–7.46) for patients with an estimated GFR of <30 ml/min/1.73 m2. Similarly, for death from any cause, the adjusted hazard ratio was 1.63 (95% confidence interval 1.01–2.63) for patients with an estimated GFR of 45–59 ml/min/1.73 m2 while this increased to 5.74 (95% confidence interval 2.40–13.77) for patients with an estimated GFR of <30 ml/min/1.73 m2. When estimated GFR was entered as a continuous variable in the Cox proportional hazards model, it was found for every unit decrement in estimated GFR, there were corresponding estimated hazard ratios of 1.018 (95% confidence interval 1.005–1.032, P = 0.006) for risk of the cardiovascular composite endpoint and 1.029 (95% confidence interval 1.015–1.043, P = 0.000) for risk of death from any cause. This corresponds to a 20% [exp(10 x 0.018) = 1.20] [26] increase in hazard of the cardiovascular composite endpoint, and a 33% [exp(10 x 0.029) = 1.33] [26] increase in hazard of death from any cause for every 10 ml/min decrement in estimated GFR.


Figure 2
View larger version (33K):
[in this window]
[in a new window]
[Download PowerPoint slide]
  		Fig. 2. Kaplan–Meier estimates for cumulative survival function for cardiovascular composite endpoint according to baseline estimated GFR.

 

View this table:
[in this window]
[in a new window]

  		Table 2. Summary statistics for cardiovascular composite endpoint according to baseline estimated GFR

 

Figure 3
View larger version (32K):
[in this window]
[in a new window]
[Download PowerPoint slide]
  		Fig. 3. Kaplan–Meier estimates for cumulative survival function for all-cause mortality according to baseline estimated GFR.

 

View this table:
[in this window]
[in a new window]

  		Table 3. Summary Statistics for all-cause mortality according to baseline estimated GFR

 

View this table:
[in this window]
[in a new window]

  		Table 4. Adjusted hazard ratios for the composite cardiovascular endpoint and death from any cause among 1578 patients according to estimated GFRa

 


   Discussion
Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
Conclusion
 Acknowledgements
 References
 
In a representative community-based population of patients with cardiovascular disease, reduced estimated GFR appears to be a significant risk factor for long-term mortality and new cardiovascular morbidity. Greater than one-third of these patients had an estimated GFR consistent with stage III chronic kidney disease or worse according to National Kidney Foundation guidelines [25]. This is equivalent to the prevalence reported by Anavekar for patients enrolled in the VALIANT trial [1].

Relationship of the study to other published work
The relationship between chronic kidney disease and cardiovascular disease is important and complex. Cardiovascular disease is frequently associated with chronic kidney disease and individuals with chronic kidney disease are more likely to die of cardiovascular disease than to develop end-stage renal disease (ESRD) [27]; cardiovascular disease in chronic kidney disease is treatable and potentially preventable and chronic kidney disease appears to be a risk factor for cardiovascular disease [27]. Additionally, the few community-based studies that have been undertaken, do suggest an inverse relationship between renal function and certain adverse cardiovascular outcomes [8,13]. This study appears to confirm this relationship and demonstrates the importance of estimated GFR as a measurable and potentially modifiable risk factor in cardiovascular disease.

Following cardiovascular events, greater degrees of chronic kidney disease are associated with increased adverse outcomes [28]. The one year mortality of elderly patients with mild chronic kidney disease (creatinine 132–211 µmol/l) after MI is nearly doubled compared with those with normal renal function (46% vs 24%) while moderate chronic kidney disease (serum creatinine 220–343 µmol/l) results in an even higher mortality of 66% [29]. Thus, chronic kidney disease appears to strongly predict mortality after an MI. The evidence in this study suggests that this risk is not just confined to the post-MI population, but rather exists in the cardiovascular population as a whole, which adds to previous evidence obtained in community-based populations [7,8,13] and populations with pre-identified cardiovascular risk [9–12,30].

In the general population, for each 5 ml/min fall in estimated GFR, there is a corresponding rise in cardiovascular mortality of 11% [13] while for patients with cardiovascular disease undergoing cardiac catheterization there is an approximate 17% increase in relative risk for every 10 unit decrease in estimated GFR [12]. The present study suggests that estimated GFR is a valid and important prognostic marker in patients in the community with cardiovascular disease with each 10 ml decrement associated with a 33% increase in relative risk of death from any cause. This has important implications for application of secondary preventative measures as early identification of chronic kidney disease in patients at cardiovascular risk and could facilitate more aggressive and targeted management of hypertension, hyperlipidemia, microalbuminuria and other risk factors.

Clinical implications
Automated searching of general practice computer records can provide a reliable and valid way of identifying people with chronic kidney disease who could benefit from interventions readily available in primary care [31]. However, poor physician-awareness of chronic kidney disease and its association with excess cardiovascular morbidity and mortality remains a significant problem [32]. The very low rate of recording of chronic kidney disease in patients found to have chronic kidney disease indicates scope for improving detection and early intervention [33]. It appears that both traditional (diabetes mellitus, hypertension and smoking, etc.) and non-traditional risk factors (elevated inflammatory markers such as C-reactive protein and interleukin-6 [34]; elevated plasma homocysteine and abnormal apolipoprotein levels [35]; anaemia [36]; and arterial calcification [37] among others) present in the chronic kidney disease population, promote the frequent development of cardiovascular disease, which means that therapies targeting both progression of chronic kidney disease and comorbidities such as cardiovascular disease are required to reduce mortality among these patients [32]. As chronic disease management moves to the community, there are understandable concerns regarding the increasing numbers of patients and associated workload. Our results suggest that estimated GFR may be a useful tool to prioritize the management of certain patients with established heart disease in the community. As the cardiovascular disease/chronic kidney disease population continues to rise, strategies to improve survival for this group become increasingly critical. In fact, several studies suggest that these individuals may derive as much, if not more, benefit from evidence-based cardiovascular therapies and strategies [30]. In the post-MI population where more aggressive therapies can be used, these therapies are associated with improved rates of in-hospital and post-discharge survival at all levels of renal function [10,11]. These findings emphasize the importance of chronic kidney disease detection in prioritizing secondary cardiovascular risk prevention in the community setting. Despite the increasing awareness of chronic kidney disease as a risk factor, it is, however, important to recognize that aggressive risk factor modification has not yet been inextricably linked to improved outcomes. Results of recent studies of cholesterol reduction in patients with type 2 diabetes undergoing haemodialysis [38] and anaemia correction in patients with chronic kidney disease [39] are important in this context. On the other hand, it is clear that aggressive blood pressure reduction with either ACE inhibitors or ARBs in diabetics or non-diabetics [40,41] can improve long-term renal outcomes. In any event, whether certain therapies are more or less effective in chronic kidney disease patients, may be less important than the increased awareness of potential targets that comes from patients being identified to be in a group at risk. These results also highlight the potential role of primary care physicians in risk factor assessment and management decision-making for patients with chronic kidney disease.

Strengths and weaknesses of the study
This study represents one of the first truly representative samples of a community-based cardiovascular disease population and so should be generalizable to general practice in this country and internationally. With the increasing availability of estimated GFR in everyday clinical practice, this is of particular importance [42]. Only patients with a confirmed diagnosis of cardiovascular disease have been included, which avoids the use of less accurate methods of identification of patient groups within the community [43] and very few patients were lost to follow-up. However, our study had a number of limitations. Firstly, the data collection was retrospective even if the study hypothesis was formulated before starting analyses. Secondly, although serum creatinine was measured in regional laboratories using the Kinetic Jaffe reaction identical to the Cleveland Clinic laboratory where the MDRD equation was developed, we did not calibrate our methods directly against the reference laboratory assay used to develop the MDRD equation. Thirdly, the study was conducted among a relatively homogenous population of community-based patients in the West of Ireland, a geographic area with a relatively high incidence of cardiovascular disease. Fourthly, the study sample was overwhelmingly Caucasian and the prevalence of chronic kidney disease in this study may underestimate the true prevalence in other ethnic groups such as African Americans in whom prevalent ESRD rates are four to five times higher than Caucasian Americans [44]. Fifthly, follow-up varied among patients as the index date for inclusion in the study was the first date within the study period on which patients had a recorded serum creatinine. This reflects the limitation in data-availability, which can hamper chronic disease management and indeed research within primary care. Finally, stepwise procedures were used to account for the variation in presence or absence of other explanatory variables for cardiovascular disease in different estimated GFR categories. However, as reduced renal function may contribute to the development of hypertension, it is possible that adjusting for elevated systolic and diastolic blood pressure may underestimate the effect of reduced renal function on outcomes.



   Conclusion
Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 Conclusion
Acknowledgements
 References
 
In a community-based population of patients with cardiovascular disease, reduced estimated GFR appears to be a graded, independent and significant risk factor for mortality and new cardiovascular morbidity. This suggests that estimated GFR should be added to the list of dyslipidemia, hypertension, smoking, central obesity and sedentary lifestyle as a significant risk factor for cardiovascular disease. The fact that estimated GFR appears to discriminate prognosis, emphasizes the importance of including estimated GFR in any cardiovascular risk factor profile for this group of patients.

Ethical Approval
Ethical approval was granted by the research ethics committee of the Irish College of General Practitioners (Protocol No: REC0904-4).



   Acknowledgements
Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 Conclusion
 Acknowledgements
References
 
The cohort was originally assembled through a Health Research Board Health Services Research Fellowship grant by Molly Byrne and Andrew Murphy. The current study was supported by a grant from the Irish College of General Practitioners Research and Education Foundation. Both grants were received through a peer-reviewed application process from which all authors remain independent and the authors’ work was carried out independently of the funding organizations. Damian Griffen, Liam Connell, Eoin Clerkin and Mary Diver of the Clinical Biochemistry Departments in University College Hospital Galway, Mayo General Hospital, Sligo General Hospital and LetterKenny General Hospital, respectively. Ethna Shryane and Claire Hennigan for practice data collection and Michael O’Keefe for data collection within the hospital setting. Finally all our participating general practitioners and patients without whom this study could never have taken place.

Conflict of interest statement. A.W.M. has received funding from Pfizer to support educational meetings for general practitioners who teach medical students from the Department of General Practice at NUI, Galway. L.G.G. and D.R. have received an honorarium from Roche laboratories for contribution to the development of chronic kidney disease guidelines for primary care. J.N., J.H. and B.B. declare no conflict of interest. The results presented in this paper have not been published previously in whole or part, except in abstract format.



   References
Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 Conclusion
 Acknowledgements
 References
 

  1. Anavekar NS, McMurray JJV, Velazquez EJ, et al. (2004) Relation between renal dysfunction and cardiovascular outcomes after myocardial infarction. N Engl J Med 351:1285–1295.[Abstract/Free Full Text]
  2. Ezekowitz J, McAlister FA, Humphries KH, et al. (2004) The association among renal insufficiency, pharmacotherapy and outcomes in 6427 patients with heart failure and coronary artery disease. Journal of the American College of Cardiology 44:1587–1592.[Abstract/Free Full Text]
  3. McCullough PA, Sandberg KR, Borzak S, Hudson MP, Garg M, Manley HJ. (2002) Benefits of aspirin and beta-blockade after myocardial infarction in patients with chronic kidney disease. Am Heart J 144:226–232.[CrossRef][Web of Science][Medline]
  4. Levey AS. (1990) Measurement of renal function in chronic renal disease. Kidney Int 38:167–184.[Web of Science][Medline]
  5. Swedko PJ, Clark HD, Paramsothy K, Akbari A. (2003) Serum creatinine is an inadequate screening test for renal failure in elderly patients. Arch Intern Med 163:356–360.[Abstract/Free Full Text]
  6. Smith H. (1951) Diseases of the kidney and urinary tract. The Kidney: Structure and Function in health and disease(Oxford University Press, New York) pp. 836–887.
  7. Muntner P, He J, Hamm L, Loria C, Whelton PK. (2002) Renal insufficiency and subsequent death resulting from cardiovascular disease in the United States. J Am Soc Nephrol 13:745–753.[Abstract/Free Full Text]
  8. Go AS, Chertow GM, Fan D, McCulloch CE, Hsu C-Y. (2004) Chronic kidney disease and the risks of death, cardiovascular events and hospitalization. N Engl J Med 351:1296–1305.[Abstract/Free Full Text]
  9. Tonelli M, Jose P, Curhan G, et al. (2006) Proteinuria, impaired kidney function and adverse outcomes in people with coronary disease: analysis of a previously conducted randomised trial. Br Med J 332:1426.[Abstract/Free Full Text]
  10. Wright RS, Reeder GS, Herzog CA, et al. (2002) Acute myocardial infarction and renal dysfunction: a high-risk combination. Ann Intern Med 137:563–570.[Abstract/Free Full Text]
  11. Al Suwaidi J, Reddan DN, Williams K, et al. (2002) Prognostic implications of abnormalities in renal function in patients with acute coronary syndromes. Circulation 106:974–980.[Abstract/Free Full Text]
  12. Hemmelgarn BR, Southern DA, Humphries KH, et al. (2006) Refined characterization of the association between kidney function and mortality in patients undergoing cardiac catheterization. Eur Heart J –ehi846.
  13. Henry RMA, Kamp O, Kostense PJ, et al. (2005) Mild renal insufficiency is associated with increased left ventricular mass in men, but not in women: An arterial stiffness-related phenomenon: the Hoorn study. Kidney Int 68:673–679.[CrossRef][Web of Science][Medline]
  14. BMA. (2006) Quality and outcomes framework guidance: summary of indicators—clinical domain. (BMA, London) http://www.bma.org.uk/ap.nsf/Content/qof06~summclinical#ChronicKidneyDisease.
  15. Levey AS, Coresh J, Balk E, et al. (2003) National Kidney Foundation Practice Guidelines for chronic kidney disease: evaluation, classification, and stratification. Ann Intern Med 139:137–147.[Abstract/Free Full Text]
  16. Wannamethee SG, Shaper AG, Perry IJ. (1997) Serum creatinine concentration and risk of cardiovascular disease: a possible marker for increased risk of stroke. Stroke 28:557–563.[Abstract/Free Full Text]
  17. Culleton BF, Larson MG, Wilson PW, Evans JC, Parfrey PS, Levy D. (1999) Cardiovascular disease and mortality in a community-based cohort with mild renal insufficiency. Kidney Int 56:2214–2219.[CrossRef][Web of Science][Medline]
  18. Shulman NB, Ford CE, Hall WD, et al. (1989) Prognostic value of serum creatinine and effect of treatment of hypertension on renal function. Results from the hypertension detection and follow-up program. The Hypertension Detection and Follow-up Program Cooperative Group. Hypertension 13:[5 Suppl], I80–I93.[Medline]
  19. Department of Health, and Children. (1999) Building healthier hearts: national cardiovascular health strategy. (Stationery Office, Dublin).
  20. Reddan DN. (2002) Therapy for cardiovascular disease in patients with chronic kidney disease: appropriate caution or the absence of data. Am Heart J 144:206–207.[CrossRef][Web of Science][Medline]
  21. Byrne M, Murphy AW, Walsh J. (2005) Secondary prevention of coronary heart disease: patients’ beliefs and health related behaviour. J Psychosomatic Res 58:403–415.[CrossRef][Web of Science][Medline]
  22. Byrne M, Murphy AW, Walsh JC, Shryane E, McGroarty M, Kelleher CC. (2006) A cross-sectional study of secondary cardiac care in general practice: impact of personal and practice characteristics. Fam Pract 23:295–302.[Abstract/Free Full Text]
  23. Levey AS, Bosch JP, Lewis JB, Greene T, Rogers N, Roth D. (1999) A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of Diet in Renal Disease Study Group. Ann Intern Med 130:461–470.[Abstract/Free Full Text]
  24. Murchie P, Campbell NC, Ritchie LD, Simpson JA, Thain J. (2003) Secondary prevention clinics for coronary heart disease: four-year follow-up of a randomised controlled trial in primary care. Br Med J 326:84.[Abstract/Free Full Text]
  25. National Kidney Foundation. (2002) K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Am J Kidney Dis 39:[2 Suppl 1], S1–S266.[CrossRef][Web of Science][Medline]
  26. Hosmer DW and Lemeshow S. (1999) Applied Survival Analysis: regression modelling of time to event data(Wiley Inter-Science, New York).
  27. Sarnak MJ, Levey AS, Schoolwerth AC, et al. (2003) Kidney disease as a risk factor for development of cardiovascular disease: a statement from the American Heart Association Councils on kidney in cardiovascular disease, high blood pressure research, clinical cardiology, and epidemiology and prevention. Hypertension 42:1050–1065.[Free Full Text]
  28. McCullough PA, Soman SS, Shah SS, et al. (2000) Risks associated with renal dysfunction in patients in the coronary care unit. J Am Coll Cardiol 36:679–684.[Abstract/Free Full Text]
  29. Shlipak MG HP, Noguchi H, Chertow GM, Browner WS, McClellan MB. (2002) Association of renal insufficiency with treatment and outcomes after myocardial infarction in elderly patients. Ann Intern Med 137:555–562.[Abstract/Free Full Text]
  30. Anandarajah S, Tai T, de Lusignan S, et al. (2005) The validity of searching routinely collected general practice computer data to identify patients with chronic kidney disease (CKD): a manual review of 500 medical records. Nephrol Dial Transplant 20:2089–2096.[Abstract/Free Full Text]
  31. Perazella MA and Khan S. (2006) Increased mortality in chronic kidney disease: a call to action. [Review]. American Journal of the Medical Sciences 331:150–153.[CrossRef][Web of Science][Medline]
  32. de Lusignan S, Chan T, Stevens P, et al. (2005) Identifying patients with chronic kidney disease from general practice computer records. Fam Pract 22:234–241.[Abstract/Free Full Text]
  33. Shlipak MG, Fried LF, Crump C, et al. (2003) Elevations of inflammatory and procoagulant biomarkers in elderly persons with renal insufficiency. Circulation 107:87–92.[Abstract/Free Full Text]
  34. Muntner P, Hamm LL, Kusek JW, Chen J, Whelton PK, He J. (2004) The prevalence of non-traditional risk factors for coronary heart disease in patients with chronic kidney disease. Ann Intern Med 140:9–17.[Abstract/Free Full Text]
  35. Hsu C-y, McCulloch CE, Curhan GC. (2002) Epidemiology of anemia associated with chronic renal insufficiency among adults in the United States: results from the Third National Health and Nutrition Examination Survey. J Am Soc Nephrol 13:504–510.[Abstract/Free Full Text]
  36. Raggi P, Boulay A, Chasan-Taber S, et al. (2002) Cardiac calcification in adult hemodialysis patients: a link between end-stage renal disease and cardiovascular disease? J Am Coll Cardiol 39:695–701.[Abstract/Free Full Text]
  37. Wanner C, Krane V, Marz W, et al. (2005) Atorvastatin in patients with type 2 diabetes mellitus undergoing hemodialysis. N Engl J Med 353:238–248.[Abstract/Free Full Text]
  38. Singh AK, Szczech L, Tang KL, et al. (2006) Correction of anemia with epoetin alfa in chronic kidney disease. N Engl J Med 355:2085–2098.[Abstract/Free Full Text]
  39. Lewis EJ, Hunsicker LG, Clarke WR, et al. (2001) Renoprotective effect of the angiotensin-receptor antagonist irbesartan in patients with nephropathy due to type 2 diabetes. N Engl J Med 345:851–860.[Abstract/Free Full Text]
  40. Brenner BM, Cooper ME, de Zeeuw D, et al. (2001) Effects of losartan on renal and cardiovascular outcomes in patients with type 2 diabetes and nephropathy 10.1056/NEJMoa011161. N Engl J Med 345:861–869.[Abstract/Free Full Text]
  41. Traynor J, Mactier R, Geddes CC, Fox JG. (2006) How to measure renal function in clinical practice. Br Med J 333:733–737.[Free Full Text]
  42. Mant J, McManus RJ, Hare R, Mayer P. (2003) Identification of stroke in the community: a comparison of three methods. Br J Gen Pract 53:520–524.[Web of Science][Medline]
  43. U.S. Renal Data System. (2005) USRDS 2005 Annual Data Report. Atlas of end-stage renal disease in the United States(National Institutes of Health: National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD).
Received for publication: 18.12.06
Accepted in revised form: 22. 3.07


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
 BMJHome page
A W Murphy, M E Cupples, S M Smith, M Byrne, M C Byrne, J Newell, and for the SPHERE study team
Effect of tailored practice and patient care plans on secondary prevention of heart disease in general practice: cluster randomised controlled trial
BMJ, October 29, 2009; 339(oct29_4): b4220 - b4220.
[Abstract] [Full Text] [PDF]

This Article
Right arrow Abstract Freely available
Right arrow FREE Full Text (PDF) Freely available
Right arrow All Versions of this Article:
22/9/2586    most recent
gfm222v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Right arrow Disclaimer
Google Scholar
Right arrow Articles by Glynn, L. G.
Right arrow Articles by Murphy, A. W.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Glynn, L. G.
Right arrow Articles by Murphy, A. W.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?