Skip Navigation


NDT Advance Access first published online on December 14, 2007
This version published online on December 21, 2007
Nephrology Dialysis Transplantation, doi:10.1093/ndt/gfm879
This Article
Right arrow Abstract Freely available
Right arrow FREE Full Text (PDF) Freely available
Right arrow All Versions of this Article:
23/6/1955    most recent
gfm879v2
gfm879v1
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 Meyer, A.
Right arrow Articles by Reinecke, H.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Meyer, A.
Right arrow Articles by Reinecke, H.
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 e-mail: journals.permissions@oxfordjournals.org


Impact of Different Stages of Chronic Kidney Disease on In-Hospital Costs in Patients with Coronary Heart Disease

Anne Meyer1, Holger Bunzemeier2, Martin Hausberg3, Michael Walter4, Norbert Roeder2, Günter Breithardt1 and Holger Reinecke1

1 Department of Cardiology and Angiology, Medizinische Klinik und Poliklinik C, Albert-Schweitzer-Str. 33, 48149 Münster, Germany 2 DRG-Research Group, Albert-Schweitzer-Str. 33, 48149 Münster, Germany 3 Department of Nephrology, Medizinische Klinik und Poliklinik D, Albert-Schweitzer-Str. 33, 48149 Münster, Germany 4 Institut für Klinische Chemie und Laboratoriumsmedizin, Hospital of the University of Münster, Albert-Schweitzer-Str. 33, 48149 Münster, Germany

Correspondence and offprint requests to: Holger Reinecke, Medizinische Klinik und Poliklinik C, 48129 Münster, Germany. Tel: +49-251-834-7617; Fax: +49-251-834-7864; E-mail: hreinecke{at}gmx.net



   Abstract
Top
 Abstract
Introduction
 Subjects and methods
 Results
 Discussion
 References
 
Background. Chronic kidney disease (CKD) is associated with markedly increased in-hospital morbidity and mortality. Its effect on in-hospital costs for the treatment of coronary heart disease (CHD) has not been assessed, although it is of interest due to the exponential increase in its prevalence.

Methods. Clinical and costing data were retrospectively assessed from 765 consecutive patients who suffered from CHD requiring percutaneous coronary interventions. Based on their estimated glomerular filtration rate (eGFR), patients were classified in accordance with the National Kidney Foundation. Patient-level in-hospital costs for this single hospitalization were thoroughly calculated from precise in-house assessments for the national DRG database.

Results. In univariate analysis, the average total in-hospital costs increased with each stage of CKD [{euro}2926; {euro}3466; {euro}4208; {euro}9687 (stages 4 and 5 combined), P < 0.0001]. Treating patients with CKD stages 4 and 5 utilized markedly more resources than patients with ST-elevation myocardial infarction ({euro}4916), coronary three-vessel disease ({euro}4659), severely impaired left ventricular function ({euro}6072) or diabetes ({euro}4495). Multivariate analyses identified, even after adjustment for confounding comorbidities, that CKD was a significant and independent predictor of in-hospital costs; with each loss of 1 ml/min in the eGFR, the expenses for this hospitalization increased by {euro}18 (95% CI, {euro}13–23).

Conclusions. Although the absolute amount of costs may vary between different countries, this work showed, for the first time, that in all stages of CKD, there is a significant increase of in-hospital costs when treating patients with both CHD and CKD.

Keywords: chronic kidney disease; coronary heart disease; costs; mortality



   Introduction
Top
 Abstract
 Introduction
Subjects and methods
 Results
 Discussion
 References
 
During recent years, the prevalence of chronic kidney disease (CKD) has markedly increased and is expected to double continuously every 8 to 10 years [1,2,3,4,5]. The massive increases in the prevalence of obesity, diabetes and hypertension will further augment this dramatic development [2,5,6]. This increase in affected patients is associated with markedly higher utilization of resources and costs which must be borne by the health care systems [3,5,6]. Apart from the direct costs of treating patients in all stages of CKD (specific drugs, renal replacement therapy), the high risk for the development of coronary heart disease (CHD) represents the major cause for morbidity and mortality in this population [7,8,9], thus, being responsible for additional markedly higher expenses. However, although the high economic burden has been outlined by many authors [2,7,8,9], there are no concrete calculations available about the impact of CKD on direct in-hospital costs when treating patients with CHD. This is even more of interest since the presence of CKD is currently not taken into account by any of the national reimbursement systems.

As a fundamental change in the hospital reimbursement system in Germany during the last few years, the Australian system of diagnosis-related groups (DRGs) has been introduced [10]. This now allows us to obtain detailed costing data. Some selected German hospitals calculate their costs for each individual patient very thoroughly including all expenses for personnel, drugs, procedures, implants and others, e.g. hospital overhead. These patient-level costing data together with all diagnoses and procedures are given to the national DRG institute, which thereby calculates the treatment costs for more than 1000 DRGs. These are then used as prices for the revenues in the 2000 German hospitals [10,11].

Since our hospital is one of the hospitals delivering data to the national DRG institute, we have access to very precise data to evaluate the impact of different stages of CKD on the in-hospital costs of patients with significant CHD requiring revascularization by percutaneous coronary intervention (PCI).



   Subjects and methods
Top
 Abstract
 Introduction
 Subjects and methods
Results
 Discussion
 References
 
Patients
All consecutive patients who underwent a PCI procedure at our department from 1 January 2002 to 31 December 2002 were retrospectively identified from our data management system. The database contained information on the indication for the intervention as well as patient characteristics including potential risk factors. Data not available online, for concomitant diseases and drug use, were retrieved from the medical records. During the above period, all patients admitted to our institution for elective or emergency PCI were consecutively included in the analysis.

Renal function
Serum creatinine values determined by the Jaffe method (standardized by isotope-dilution mass spectrometry, ID-MS) were retrieved from the computer database of the Institute of Clinical Chemistry. Only values that were determined 1– 3 days before the procedure were considered for analyses (missing values in five patients). To estimate the glomerular filtration rate (GFR), the simplified Modification of Diet in Renal Disease (MDRD) equation was used [4,12]:


Formula

Based upon these results, the patients were then classified into five stages of CKD in accordance with the classification of the National Kidney Foundation [12]. Since only 22 patients were in stage 4 and 11 patients were in stage 5, these were analysed together in an aggregated group.

Interventional procedures
PCIs were performed using arterial access from the femoral or brachial arteries. The coronary segment, the type of coronary stenosis dilated and angiographic success were classified in accordance with the revised classification of the ACC/AHA guidelines [13]. Angiographic success after PCI was assumed if a residual stenosis in the vessel diameter of <30% could be achieved [13]. Left ventricular ejection fraction was assessed from the pre-PCI angiogram and determined from 30° right anterior oblique projections. Drug eluting stents (DES) were not available in Germany at the time of the analysis.

Definition of cardiovascular risk factors
Cardiovascular risk factors for PCI were assessed at presentation and were defined as follows: history of smoking was defined if patients had smoked within the last 10 years; hypertension if blood pressure >140/90 mmHg had been documented; hyperlipidaemia if total cholesterol or triglycerides levels were >200 mg/dl or levels of lipoprotein(a) >20 mg/dl; family history of cardiovascular disease if stroke, myocardial infarction or a coronary intervention had occurred in a first-degree relative; diabetes was assumed if the patients required a specific diet or took oral antidiabetic medication or received insulin.

Cost calculations
Patient-level in-hospital costing data were derived from the hospital cost accounting and were available for all patients included in the analysis. All cost calculations refer to the hospitalization cost for the in-hospital stay necessary for the index PCI. The costing followed the principle of full cost accounting including actual direct costs and hospital overhead costs. A bottom-up approach for the cost calculation was performed including patient-specific utilization factors for calculating medical and nursing staff costs were taken into consideration. Furthermore, individual items of patient consumption were assessed for each single case including blood components, antibiotics, antimycotics, thrombolysis or glycoprotein IIb/IIIa inhibitors, amount of contrast media and all catheter material including balloons and stents. The costing methodology corresponded with the guidelines of the German National DRG Costing Study [14]. Thus, our data as one of 140 German hospitals were included in this nationwide DRG census that is conducted annually to calculate the German DRG system representing the basis for hospital reimbursement in the entire country.

Statistics
Differences between the four subgroups were analysed by the ANOVA F-test for continuous variables, and the chi-square test for categorical parameters. Parameters with skewed distribution were compared by the Kruskal–Wallis test. Univariate analyses of parameters associated with costs were performed by logistic regression. Furthermore, multivariate analyses using linear logistic regression models were performed and adjusted for confounding factors.

For all tests, P-values < 0.05 were taken as significant. All statistical analyses were performed with SPSS 14.0 for Windows.



   Results
Top
 Abstract
 Introduction
 Subjects and methods
 Results
Discussion
 References
 
In 2002, a total of 770 patients consecutively underwent PCI at our institution. Current (1–3 days old) creatinine values before catheterization were not available in five patients; therefore, all subsequent analyses are based on the data of 765 patients.

Basic clinical and laboratory characteristics of the population are summarized in Table 1. The proportion of women, smokers, diabetics and patients with previous PCI increased significantly with advanced stages of CKD.


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

  		Table 1. Baseline characteristics

 
Cardiac and angiographic characteristics, as well as in-hospital outcomes, are summarized in Table 2. The proportion of patients with acute coronary syndromes was not higher with advanced stages of CKD, but multi-vessel disease was significantly more frequent in the subgroups with reduced estimated GFR.


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

  		Table 2. In-hospital treatment and outcome

 
With regard to in-hospital outcome (Table 2), the length of in-hospital stay and the rate of in-hospital deaths increased significantly with higher stages of CKD. While the proportion of patients with CKD who required treatment in the intensive care unit (ICU) was not higher, those patients with CKD who had to be treated in the ICU remained there significantly longer.

Predictors of in-hospital costs
The average in-hospital costs for this cohort of patients with CHD and PCI were {euro}3885 (median {euro}2829; range {euro}908–67 307). Univariate analyses of predictors for in-hospital costs by logistic regression models are presented in Table 3. As expected, higher age showed a trend to higher costs; however, these differences were not significant regardless of whether four classes of age (as in Table 3) or only three classes (<50 years, 50–75 years, >75 years; data not shown, P = 0.4) were analysed.


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

  		Table 3. Univariate analyses of predictors of in-hospital costs

 
To identify whether eGFR was an independent predictor of in-hospital costs, a multivariate analysis using a logistic regression model was performed. This was adjusted for a number of clinically important, potentially confounding factors (Table 4). However, eGFR remained even after adjustment as an independent predictor of in-hospital costs; in detail, each loss of 1 ml/min in the eGFR was associated with an increase of {euro}18 in expenses.


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

  		Table 4. Multivariate analysis of predictors of in-hospital costs

 
The association of different stages of CKD (based on the eGFR) and in-hospital costs are displayed in Figure 1. Thus, in the univarate analysis as well as in the multivariate analysis adjusted for confounding factors (see Table 4 and the legend to Figure 1) the stages of CKD were significant predictors of in-hospital costs.


Figure 1
View larger version (22K):
[in this window]
[in a new window]
[Download PowerPoint slide]
  		Fig. 1. The impact of different stages of CKD on total in-hospital costs. Panel (A) displays the unadjusted average costs for the distinct stages of CKD calculated from univariate analyses by the ANOVA F-test. This demonstrates the continuous and marked increase in costs with each step of CKD. Panel (B) visualizes the impact of different stages of CKD on in-hospital costs after multivariate logistic regression analyses, adjusted for age, gender, diabetes, number of diseased coronary vessels, number of dilated segments, smoking, acute coronary syndromes and left ventricular function. Thus, even after adjustment for the other confounding factors, CKD had a significant and independent impact on in-hospital costs, with a decrease in CKD stage 1 while all other stages of CKD were associated with an increase in costs.

 


   Discussion
Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
References
 
The prevalence of CKD is rising dramatically [1,2,3,4] and has been predicted to become ‘a global challenge’ [5]. With this increase in the prevalence of this chronic disease, a marked increase in overall health care costs has to be expected. This was confirmed meanwhile by a few reports evaluating the ambulatory and hospitalization costs in patients with end-stage renal disease, e.g. for medical attendance; for drugs like ACE-inhibitors, diuretics and erythropoietin; and for chronic renal replacement therapies [2,15,16]. All these cost analyses referred only to patients with end-stage renal failure that in fact represent only a very small subgroup (~2%) of the large CKD population [12], and are moreover only in part identical with patients in CKD stage 5. However, there are no data available on the impact of stages 2 to 4 on expenses. Furthermore, the impact of CKD on indirect costs, e.g. for the treatment of CHD, which is a very frequent comorbidity and the major cause for morbidity and mortality in CKD, has not been assessed previously.

Previous analyses had already identified multi-vessel disease, acute coronary syndromes, left ventricular impairment, peripheral occlusive disease and diabetes to be significant predictors of in-hospital costs for the treatment of patients with CHD [17,18,19,20]. Our present analyses additionally found that CKD was a significant and major predictor of in-hospital costs. The distribution of patients in the distinct CKD stages is not identical to other reports, which [12] is probably due to the fact that we assessed not the general population but consecutive patients with significant CHD who underwent PCI. Univariate analyses showed that there was a significant and marked stepwise rise in in-hospital costs depending on the stage of CKD as defined by the National Kidney Foundation. Treating patients with CKD stage 4 or 5 consumed two- to threefold higher resources than, for instance, treating patients with ST-elevation myocardial infarctions, severely reduced left ventricular function or diabetes (Table 3 and Figure 1A). Moreover, this marked impact of CKD on costs could also be seen after adjustment for relevant comorbidities such as age, diabetes, left ventricular function and others (see Table 4 and Figure 1B). This demonstrates that not the comorbidities of CKD but other inherent factors were associated with higher expenses.

Despite this clear and marked association of advanced stages of CKD with higher in-hospital costs, CKD is at present not taken into account by any of the current health care systems even if they are based on DRGs or other approaches for hospital reimbursement. Some DRG systems (e.g. Australia, Germany, Switzerland) include so-called patient comorbidity and complexity level (PCCL) scores. However, suffering from CKD solely is not sufficient to reach a higher PCCL score. In all cases, other comorbidities such as chronic heart failure, diabetes and severe infections must also be present in combination to reach a higher PCCL score and thereby to be grouped in a DRG with higher revenues. Moreover, only few DRGs do, at present, include the PCCL score as a split criterion. Therefore, even if multimorbidity including CKD is present, this leads, only in a small number of cases, to a higher reimbursement for the hospitals.

In summary, our analyses found CKD to be a significant and independent predictor of markedly higher in-hospital costs treating patients with significant CHD by PCI. Patients with CKD stages 4 and 5 consumed the highest resources of all subgroups and cost predictors evaluated in this study. Although these data could not necessarily be transferred to other cohorts with CKD and CHD (e.g. those with conservative treatment or those undergoing coronary bypass surgery), it might be reasonable to expect that comparable assessments in such patient cohorts will find similar results. Since the presence of CKD is not taken into account as a confounding factor by any of the health care systems in the western countries, those hospitals that treat a higher proportion of patients with CKD (usually academic hospitals or larger centres) have markedly higher expenses that are not covered by adequate revenues. The price that has to be paid for CKD will in the future be further augmented by the climbing prevalence of the ‘global challenge’ that is CKD.

Conflict of interest statement. There is no conflict of interest or any interest to be disclosed for all authors.



   References
Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 References
 

  1. US Renal Data System: USRDS. Annual Data Report: Atlas of End-Stage Renal Disease in the United States. (2006) Bethesda, MD. National Institutes of Health, National Institute of Diabetes, and Digestive and Kidney Diseases,
  2. Hamer RA, El Nahas AM. The burden of chronic kidney disease. BMJ (2006) 332:563–564.[Free Full Text]
  3. Barsoum RS. Chronic kidney disease in the developing world. N Engl J Med (2006) 354:997–999.[Free Full Text]
  4. Brosius FC III, Hostetter TH, Kelepouris E, et al. Detection of chronic kidney disease in patients with or at increased risk of cardiovascular disease: a science advisory from the American Heart Association Kidney and Cardiovascular Disease Council; the Councils on High Blood Pressure Research, Cardiovascular Disease in the young, and Epidemiology and prevention; and the Quality of Care and Outcomes Research Interdisciplinary Working Group: developed in collaboration with the National Kidney Foundation. Circulation (2006) 114:1083–1087.[Abstract/Free Full Text]
  5. Levey AS, Andreoli SP, DuBose T, et al. Chronic kidney disease: common, harmful, and treatable—World Kidney Day 2007. J Am Soc Nephrol (2007) 18:374–378.[Free Full Text]
  6. Meguid El, Nahas A, Bello AK. Chronic kidney disease: the global challenge. Lancet (2005) 365:331–340.[Web of Science][Medline]
  7. Go AS, Chertow GM, Fan D. Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization. N Engl J Med (2004) 351:1296–1305.[Abstract/Free Full Text]
  8. Anavekar NS, McMurray JJ, Velazquez EJ, et al. Relation between renal dysfunction and cardiovascular outcomes after myocardial infarction. N Engl J Med (2004) 351:1285–1295.[Abstract/Free Full Text]
  9. Reinecke H, Trey T, Matzkies F, et al. Grade of chronic renal failure, and acute and long-term outcome after percutaneous coronary interventions. Kidney Int (2003) 63:696–701.[CrossRef][Web of Science][Medline]
  10. Furstenberg T, Rochell B, Roeder N, et al. Ambulatory and hospital care in the Federal Republic of Germany. Am Heart Hosp J (2007) 5:22–26.[CrossRef][Medline]
  11. Furstenberg T, Bunzemeier H, Roeder N, et al. Cardiovascular medicine in the German diagnosis-related group (G-DRG) system 2005. Z Kardiol (2005) 94:360–370.[CrossRef][Web of Science][Medline]
  12. Levey AS, Coresh J, Balk E, et al. (National Kidney Foundation). National Kidney Foundation practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Ann Intern Med (2003) 139:137–147.[Abstract/Free Full Text]
  13. Smith SC Jr, Dove JT, Jacobs AK, et al. ACC/AHA guidelines for percutaneous coronary intervention: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines (Committee to Revise the 1993 Guidelines for Percutaneous Transluminal Coronary Angioplasty). J Am Coll Cardiol (2001) 37:2239i–lxvi.
  14. Institut für das Entgeltsystem im Krankenhaus. Leitlinien zur Kostenkalkulation. www.g-drg.de (only online available).
  15. Smith DH, Gullion CM, Nichols G, et al. Cost of medical care for chronic kidney disease and comorbidity among enrollees in a large HMO population. J Am Soc Nephrol (2004) 15:1300–1306.[Abstract/Free Full Text]
  16. Gregory DD, Sarnak MJ, Konstam MA, et al. Impact of chronic kidney disease and anemia on hospitalization expense in patients with left ventricular dysfunction. Am J Cardiol (2003) 92:1300–1305.[CrossRef][Web of Science][Medline]
  17. Ellis SG, Brown KJ, Ellert R, et al. Cost of cardiac care in the three years after coronary catheterization in a contained care system: critical determinants and implications. J Am Coll Cardiol (1998) 31:1306–1313.[Abstract/Free Full Text]
  18. Selby JV, Ray GT, Zhang D, et al. Excess costs of medical care for patients with diabetes in a managed care population. Diabetes Care (1997) 20:1396–1402.[Abstract]
  19. Favarato D, Hueb W, Gersh BJ, et al. First year follow-up of MASS II study. Relative cost comparison of treatments for coronary artery disease: the first year follow-up of MASS II study. Circulation (2003) 108:II21–II23.
  20. Cohen DJ, Becker ER, Culler SD, et al. Impact of patient characteristics, complications, and facility volume on the costs and time of cardiac catheterization and coronary angioplasty in 70 catheterization laboratories. Am J Cardiol (2000) 86:595–601.[CrossRef][Web of Science][Medline]
Received for publication: 10. 9.07
Accepted in revised form: 19.11.07


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



This Article
Right arrow Abstract Freely available
Right arrow FREE Full Text (PDF) Freely available
Right arrow All Versions of this Article:
23/6/1955    most recent
gfm879v2
gfm879v1
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 Meyer, A.
Right arrow Articles by Reinecke, H.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Meyer, A.
Right arrow Articles by Reinecke, H.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?