Clinical features and outcome of chronic dialysis patients admitted to an intensive care unit

Géraud Manhes1, Anne Elisabeth Heng2, Bruno Aublet-Cuvelier3, Nicole Gazuy1, Patrice Deteix2 and Bertrand Souweine1

1 Service de Réanimation Médicale Polyvalente and 2 Service de Néphrologie, Hôpital Gabriel Montpied and 3 Service d'Epidémiologie et Santé Publique, Hôtel-Dieu, Centre Hospitalier Universitaire de Clermont-Ferrand, France

Correspondence and offprint requests to: Bertrand Souweine, Service de Réanimation Médicale Polyvalente, Centre Hospitalier Universitaire Gabriel Montpied, BP 69, 63003 Clermont-Ferrand Cedex 1, France. Email: bsouweine{at}chu-clermontferrand.fr



   Abstract
 Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 References
 
Background. Information about chronic dialysis (CD) patients admitted to intensive care units (ICU) is scant. This study sought to determine the epidemiology and outcome of CD patients in an ICU setting and to test the performance of the Simplified Acute Physiology Score (SAPS II) to predict hospital mortality in this population.

Methods. All consecutive CD patients admitted to an adult, 10 bed medical/surgical ICU at a university hospital between January 1996 and December 1999 were included in this prospective observational study. Demographics, characteristics of the underlying renal disease, admission diagnosis, the number of organ system failures (OSFs) excluding renal failure and SAPS II, both calculated 24 h after admission, the duration of mechanical ventilation, ICU survival and survival status at hospital discharge and 6 months after discharge were recorded.

Results. A total of 92 CD patients, 16 on peritoneal dialysis and 76 on haemodialysis, were included. The main reason for ICU admission was sepsis and the mean ICU length of stay 6.2±9.9 days. ICU mortality was 26/92 (28.3%) and was associated in multivariate analysis with SAPS II (P<0.001), duration of mechanical ventilation (P<0.01) and abnormal values of serum phosphorus (high or low; P<0.05). Hospital mortality was 35/92 (38.0%) and was accurately predicted by SAPS II [receiver operating characteristics curve: 0.86±0.04; goodness-of-fit test: C = 6.86, 5 degrees of freedom (df), P = 0.23 and H = 4.78, 5 df, P = 0.44]. The 6 month survival rate was 48/92 (52.2%).

Conclusions. CD patients admitted to the ICU are a subgroup of patients with high mortality and SAPS II can be used to assess their probability of hospital mortality. The severity of the acute illness responsible for ICU admission and an abnormal value of serum phosphorus are determinants for ICU mortality.

Keywords: chronic dialysis; intensive care unit; outcome



   Introduction
 Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 References
 
The current population of patients with end-stage renal (ESRD) disease has increased rapidly in the past two decades owing to the rising number of older patients with diabetic nephropathy and vascular renal disease. If this tendency persists, the incidence of ESRD will roughly double in the next 10 years [1]. The majority of these patients will undergo dialysis. In the last decade, the number of patients treated for chronic uraemia has grown worldwide and, in Europe, for example, the data available indicate an increase in both the incidence and prevalence of dialysis-treated ESRD patients of the average order of 4% per year [2].

The epidemiology of chronic dialysis (CD) patients shows growing numbers of patients who are getting older and sicker. These patients frequently develop cardiovascular complications, such as coronary artery disease, cerebrovascular disease, haematological abnormalities, gastrointestinal bleeding and bacterial infections, which are the principal cause of morbidity and a major cause of death in CD patients [1,3]. CD patients present a high risk of multiorgan dysfunction stemming from pre-existing medical problems and complications secondary to renal replacement therapy. Thus, as the number of older patients starting dialysis increases, so will that of patients requiring admission to the intensive care unit (ICU). In a recent study it was calculated that 2% of CD patients require ICU admission every year [4]. In addition, ESRD providers in the US [5] reported that an acute medical/surgical complication in CD patients is a listed cause of dialysis discontinuation prior to death. To describe the epidemiology and the outcome of ICU patients is of prime importance in helping CD patients and their family to plan what kind of care they would want in the event of critical illness. To date the few data available on the outcome of CD patients admitted to ICUs derive from studies comparing patients with ESRD and those with acute renal failure [4,6].

Different scoring systems have been designed to estimate the probability of hospital mortality and to compare the performance of different ICUs. The Simplified Acute Physiology Score II (SAPS II) was developed to grade the severity of individual ICU patients and to assess prognosis [7]. While most studies show a good SAPS II discrimination, several report calibration as disappointing [8]. Generic scores may fail to adequately predict outcome in ICU-admitted CD patients, since most allocate high points for several clinical and laboratory data that are usually out of the physiological range in CD patients, irrespective of the acute event leading to ICU admission. To our knowledge, only one case-control study, involving a relatively small number of CD patients, has investigated the adequacy of SAPS II to predict outcome [4]. However, calibration was not tested and, therefore, it is not known whether SAPS II is valid for this specific cohort.

The aim of the study was to describe the clinical features and outcome of CD patients admitted to an ICU and to confirm that SAPS II is able to predict their hospital mortality.



   Subjects and methods
 Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 References
 
Design and setting
This prospective observational study was made in the Gabriel Montpied hospital in Clermont-Ferrand, France, which is a 740 bed, teaching hospital serving a large surrounding population. In the area, all critically ill ESRD patients on CD are referred to the 10 bed medical/surgical ICU, because it is the only ICU with renal replacement therapy facilities.

CD patients
All consecutive CD patients, either on chronic haemodialysis or on chronic peritoneal dialysis, admitted to the ICU between January 1996 and December 1999 were enrolled in the study. For CD patients readmitted to the ICU during the study period, only the first ICU admission was taken into account. During their ICU stay, renal replacement therapy using intermittent haemodialysis was administered on average every other day. Haemodialysis was performed using bicarbonate dialysate and biocompatible membranes, essentially polyacrylonitrile membranes. The vascular accesses for haemodialysis procedures were the permanent arteriovenous fistula in the majority of patients and dialysis catheters in the others.

The Ethics Committee of the hospital was informed about the study design and raised no objections. Informed consent from patients was not requested, but patients were informed about the study.

Data collection
We recorded diagnosis on admission, the variables necessary to calculate SAPS II [7] and the number of organ system failures (OSFs), other than renal failure [9] during the first 24 h after ICU admission. SAPS II was calculated as indicated by its developers [7]. OSF was defined on the basis of the following features. Cardiovascular failure was defined by the presence of one or more of the following: heart rate ≤54/min, mean arterial blood pressure ≤49 mmHg, occurrence of ventricular tachycardia, occurrence of ventricular fibrillation and serum pH ≤7.24 with PaCO2 of ≤49 mmHg. Respiratory failure was defined by the presence of one or more of the following: respiratory rate ≤5/min or ≥49/min, PaCO2 ≥50 mmHg, AaDO2 ≥350 mmHg and need for mechanical ventilation (MV). Haematological failure was defined by one or more of the following: white blood cell count ≤1000/mm3, platelet count ≤20 000/mm3 and haematocrit ≤20%. Neurological failure was defined by a Glasgow coma score ≤6 in the absence of sedation. One point was given for each OSF and, thus, the number of OSFs per patient ranged from 0 to 4. Additional laboratory factors recorded on ICU admission were serum uric acid, serum phosphorus and serum cholesterol levels before a new dialysis. Severe sepsis was defined as reported previously by Bone et al. [10]. The following clinical and laboratory data related to ESRD were collected: cause of renal disease, prior duration of CD and mode of dialysis, either haemodialysis or peritoneal dialysis. Diabetes mellitus and cardiovascular disease (arterial hypertension, history of myocardial infarction or coronary revascularization procedure or angiographic evidence of coronary stenosis, peripheral vascular disease and prior vascular surgery for atherosclerosis and history of cerebral infarction) were recorded. We also noted the use and the duration of MV, the ICU length of stay and survival status at hospital discharge and 6 months after ICU discharge.

There was a standard procedure for collecting medical data, which were recorded by two physicians (A.E.H. and N.G.). All data gathered during the study period were systematically audited by a third physician (G.M.). In the event of disagreement, another physician (B.S.) was invited to give his opinion. The data included in the quality assessment were SAPS II variables, duration of hospitalization, outcomes at ICU and hospital discharge and use and duration of MV.

The SAPS II score, age, ICU length of stay and vital status at hospital discharge of all the non-CD patients admitted to the ICU during the same period were extracted from the general database of the Clermont-Ferrand Hospital registry.

Rationale and methodology of statistical analysis
It was decided to use univariate analysis to identify the variables to be included in a preliminary logistic regression model. All values were expressed as means±SD. The CD patients’ variables, including SAPS II score, were analysed by univariate analysis to compare survivors and non-survivors at ICU discharge. Univariate statistical analysis was performed by chi-square or Fisher tests when necessary to compare categorical variables and by Mann–Whitney U-test to compare continuous variables. Multivariate logistic regression analysis was performed, with survival at ICU discharge being the dependent variable and the variables found in the univariate analysis with a P-value of <0.15 for survivors and non-survivors being the independent variables. When the degree of correlation between the variables entered in the multiple regression equation is high, the regression model can produce incorrect variance estimates and may give a misleading interpretation of the model. To address the problem of multicolinearity, a correlation matrix was used to assess the overlap between the data. A high Spearman correlation (r>0.5) indicates that the data are highly correlated with each other [11]. One way of mitigating the harmful effect of multicolinearity is to delete offending variables from the regression model. This was done on the basis that one or more variables are redundant [11].

We then tested the performance of SAPS II in predicting hospital mortality. The individual specific probability of death derives from the SAPS II model. The following variables have been included in the equation: SAPsII, the number of OSF's, the duration of MV, serum phosphorus, and dialysis category. The probability of ICU death was calculated as follows:

We calculated the predicted hospital death rates by summing predicted risks of dying for individual patients and dividing by the number of patients. The predicted and actual death rates were compared using the chi-square test. The ability of the model to discriminate between survivors and non-survivors was studied by receiver operating characteristic (ROC) curves. The ROC curves were constructed by plotting the true positive rate (sensitivity) against the false positive rate (100 – specificity) at different risk thresholds. The ability of the standard SAPS II to accurately reflect the probability of hospital mortality was assessed using the Hosmer–Lemeshow goodness-of-fit calibration test (H statistic and C statistic) and calibration curves [12]. Calibration evaluates the degree of correspondence between a model-estimated probability of mortality and the observed mortality rate. A low P-value suggests that the model does not correctly reflect the actual outcome [12].

Data analyses and statistics analysis were performed using SAS software (SAS Institute Inc). A P-value of <0.05 was considered statistically significant.



   Results
 Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 References
 
Demographic characteristics of the ICU population
During the study period, 1257 ICU admissions were recorded, including 108 admissions of CD patients and 1149 admissions of non-CD patients. The mean age, SAPS II, ICU length of stay and hospital mortality did not vary over time (Figure 1). In the non-CD ICU-admitted patients, the mean age was 60.4±17.6 years, the sex ratio was 1.3 and the four main admission diagnoses were acute respiratory failure (20.9%), acute renal failure (16.6%), septic shock or other shock (12.6%) and neurological disorders (8.8%). The mean SAPS II on ICU admission was 43.7±24.6, ICU length of stay was 9.1±16.9 days, ICU mortality was 22.2% and hospital mortality was 28%.



View larger version (26K):
[in this window]
[in a new window]
 
Fig. 1. Characteristics of the overall population admitted to the ICU during the study period.

 
During the study period there were 108 ICU admissions of CD patients. Three admissions corresponding to readmissions during the same hospital stay, one admission of a patient who died within minutes following admission and 12 admissions corresponding to ICU readmission after hospital discharge were not taken into account. A total of 92 patients were analysed. Nearly all (98.2%) necessary data were collected: serum cholesterol levels were available in only 68/92 cases. Quality control was applied to SAPS II variables, duration of hospitalization, outcomes at ICU discharge and hospital discharge, MV and duration of MV. Of the 1840 items checked, eight errors, solely concerning the SAPS II, were corrected (six errors in 1996, one in 1997 and one in 1998), yielding an average improvement of 2.75 points per error for SAPS II. The demographic characteristics of the 92 CD patients are shown in Table 1. The reasons for ICU admission were severe sepsis (n = 28), heart failure or fluid overload (n = 17), haemorrhage (n = 12), surgery (n = 8), cardiac arrest (n = 6), arterial thrombosis (n = 5), stroke (n = 5), hyperkalaemia (n = 4), abdominal crisis (n = 2), drug overdose (n = 2), pancreatitis (n = 1), malaise (n = 1) and anaphylactic shock (n = 1). OSF presented by the patients on ICU admission were cardiovascular failure (n = 27), respiratory failure (n = 56), haematological failure (n = 8) and neurological failure (n = 15). In 30 patients no OSF other than renal failure was observed on ICU admission. The mean SAPS II was 49.4±20.7. The mean contributions of urinary output, serum urea, serum potassium and serum bicarbonate to the SAPS II value were 10.0±2.9, 6.4±2.5, 1.5±1.5 and 1.2±2.0, respectively, corresponding to a mean overall value of 19.1±5.1. In 35 patients (38.0%) MV was required for a mean duration of 5.1±5.4 days. ICU length of stay was 6.2±9.9 days and ICU mortality was 26/92 (28.3%). Nine additional deaths were recorded before hospital discharge. Thus, the overall hospital mortality was 35/92 (38.0%). Five of the 57 hospital survivors (8.8%) were lost to follow-up after hospital discharge. Of the remaining 52 patients, four died during the 6 month follow-up period, all of whom were diabetics and/or had vascular disease. Thus, the 6 month ICU survival rate was 48/92 (52.2%).


View this table:
[in this window]
[in a new window]
 
Table 1. Demographic characteristics of the 92 CD patients

 
Prognostic analysis in the 92 ICU-admitted CD patients
According to the number of OSFs (other than renal failure) on ICU admission, the observed ICU mortality was 0/30 in patients with no OSF, 5/31 in patients with one OSF, 9/19 in patients with two OSFs, 11/11 in patients with three OSFs and 1/1 in the patient with four OSFs. The univariate prognostic analysis identified the number of OSFs on ICU admission, an abnormal value of serum phosphorus (high or low), SAPS II and MV duration as being associated with ICU mortality at the 5% level (Table 2). The results of multivariate analysis including the selected variables are given in Table 3 (Model 1). However, there could be a mathematical coupling between MV duration and SAPS II or between MV duration and the number of OSFs. Similarly, multicolinearity may arise between the number of OSFs and SAPS II, since multiple data used to define OSF are also components of SAPS II (heart rate, blood pressure, MV, white blood cell count and Glasgow coma scale). Pairwise correlations were therefore developed for MV duration, the number of OSFs and SAPS II. The Spearman correlation was 0.41 between MV duration and SAPS II, 0.60 between MV duration and the number of OSFs and 0.69 between the number of OSFs and SAPS II, which is an indication of high multicolinearity and a suggestion that Model 1 is unable to manipulate all predictor variables independently. Since the highest correlations were observed between the number of OSFs and SAPS II and between the number of OSFs and MV duration, the prognosis factor analysis was rerun using a multivariate model without OSFs (Model 2). As shown in Table 3, SAPS II, MV duration and an abnormal value of serum phosphorus level were significantly associated with ICU survival (P<0.001, P<0.01 and P<0.05, respectively).


View this table:
[in this window]
[in a new window]
 
Table 2. Variables associated with ICU mortality in univariate analysis

 

View this table:
[in this window]
[in a new window]
 
Table 3. Results of multivariate models

 
SAPS II and hospital mortality were higher in CD patients than in non-CD patients (P<0.001 and P = 0.04, respectively). The discriminative power of SAPS II for predicting hospital mortality was good in CD patients, with an area under the ROC curve of 0.86±0.04. There was no significant difference between observed and predicted hospital mortality: 38.0% vs 41.6% (P = 0.65). The calibration of SAPS II was good [C = 6.86, 5 degrees of freedom (df), P = 0.23 and H = 4.78, 5 df, P = 0.44; Figure 2]. We found that advanced age and a high value for SAPS II on ICU admission predicted a low 6 month survival: P = 0.01 and P = 0.0001, respectively.



View larger version (18K):
[in this window]
[in a new window]
 
Fig. 2. Comparison of observed and expected mortality of ICU-admitted CD patients. The diagonal line is the line of ideal correspondence between observed and expected mortality.

 


   Discussion
 Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 References
 
The aim of this study was to describe the epidemiology and outcome of CD patients admitted to an ICU. The results of the study demonstrate that (i) the severity of the acute illness responsible for ICU admission and abnormal admission values of serum phosphorus are major determinants for ICU survival; (ii) although CD patients are a subgroup of critically ill patients with a high mortality rate, 52% of them survived >6 months after ICU discharge; and (iii) SAPS II can be used to assess the severity of ICU CD patients and adequately predict their hospital mortality.

Although this study was limited to one centre and the ICU-admitted CD population was small (n = 92), the characteristics of the CD patients according to mean age, mode of dialysis and mean primary renal disease were representative of a general CD population [13].

To our knowledge, only two studies have reported the outcome of CD patients admitted to an ICU. The first study evaluated the impact of acute renal failure with regard to the total loss of renal function on outcome [6]. The CD and acute renal failure patients had similar APACHE III scores with no difference between the observed and predicted hospital mortality. Despite this similarity in APACHE III scores, the observed hospital mortality was significantly higher in acute renal failure patients than in CD patients. The study involved 57 CD patients with an 11% ICU mortality and a 14% hospital mortality. These lower ICU and hospital mortality rates may be related to differences in case mix and in admission diagnoses, but any explanations remain speculative because of the lack of information on ERSD patients and on their ICU admission characteristics. The other study involved 38 acutely ill CD patients with a mean age of 44.9 years [4]. The first reason for ICU admission was sepsis. As reported by the authors, many CD patients were referred to ICU for post-operative care. This study did not report factors associated with ICU mortality. The mean SAPS II score of CD patients was 44.7 with an observed hospital mortality of 34.2%. In our study, CD patients were older with a slightly higher mean SAPS II and hospital mortality. Most CD patients were admitted because they required life-sustaining support. Sepsis was the leading cause for ICU admission and the observed ICU mortality in CD patients admitted for severe sepsis (37.5%) was comparable to reported ICU mortality for this type of admission [8].

In our study, the CD patients formed a subgroup of patients with a greater illness severity and a higher mortality than that observed in the non-CD ICU patients. This higher severity may be related to the frequency of associated comorbidity, especially cardiovascular comorbidity, which is recorded in almost all of this population. However, half of the CD patients were 6 month survivors, which shows that intensive care can be successful and should not be dismissed out of hand. As far as we know, no previous study has reported long-term survival after ICU discharge of CD patients.

Prognostic analysis at ICU discharge
In our study, univariate analysis identified four variables related to ICU mortality: SAPS II, the number of OSFs, MV duration and high or low serum phosphorus concentration. We found no significant differences in CD patient characteristics between survivors and non-survivors with regard to the distribution of primary renal disease and the prior dialysis duration. Given the sample size, ICU mortality did not seem to be related to the type of dialysis. However, since we did not evaluate all the important predictors of long-term mortality in CD patients, such as nutritional status and serum albumin concentration [14,15], we cannot exclude that some of them may have had an impact on ICU mortality. In addition, formal analysis efficiency either by measurement of dialysis dose (Kt/V) or by urea reduction ratios was not performed and, thus, the impact of dialysis modality and dose per se on the ICU outcome of these patients cannot be determined from our data.

When SAPS II, the number of OSFs and MV duration were included in the multivariate analysis, their significance level decreased sharply, suggesting an overlap between the variables. A high degree of correlation between data entered in a multivariate analysis may distort the relationship between outcome and vital explanatory variables. This is probably why in the first multivariate model SAPS II and an abnormal value of serum phosphorus level were not identified as prognostic factors. Given the need to address multicolinearity, we used Spearman correlation to diagnose redundant data. Matrix analysis revealed a high correlation between the number of OSFs and both SAPS II and MV duration. The number of OSFs was, therefore, withdrawn from the multivariate model [11]. The second multivariate model, taking into account multicolinearity, retained the following predictive variables: SAPS II, MV duration and abnormal value of serum phosphorus level.

The severity of the critical illness as defined by the generic prognostic scores, the number of acute organ dysfunctions and MV are classically reported as important adverse prognostic factors in the overall population. Similar results have been reported after ICU admission of patient groups with chronic diseases [16,17]. The impact of abnormal (high or low) serum phosphorus levels on ICU survival needs to be highlighted. Hypophosphataemia is a multifactorial event and may be related to the severity of acute illness [18]. In addition, it often reflects severe malnutrition, which affects up to half of CD patients, and malnutrition is a well-known factor of morbidity and mortality in this population [14]. In contrast, elevated serum phosphorus is of poor prognostic value in CD patients and preventing hyperphosphataemia appears to reduce morbidity and mortality from cardiovascular disease, which is the leading cause of death in CD patients [19]. According to new recommendations, the control of serum phosphorus levels is one of the targets set for the management of mineral homeostasis in ESRD patients [20]. Thus, the results of the multivariate analysis suggest that both the severity of the acute illness that motivated ICU admission and the underlying renal disease and its treatment are predictive of ICU mortality in CD patients.

Validity of SAPS II
This report demonstrates that SAPS II is a reliable predictor of hospital mortality for CD patients, although it includes several variables that are usually abnormal in non-critically ill CD patients, i.e. urinary output, serum urea level, serum potassium level and serum bicarbonate level. These variables can count for up to 30 points in the calculation of SAPS II and in our CD patients accounted for an average of 19.1 points, which corresponds to 38.7% of the overall score. Calibration measures how closely mortality prognosis fits the observed mortality. We found that observed and predicted mortality agreed well over the whole range of SAPS II and, hence, SAPS II adequately estimates the severity of ICU-admitted CD patients. However, we must point out that the use of goodness-of-fit is generally more reliable for calibration when the number of patients included is higher than in our sample. Thus, given the small size of the population, we cannot exclude that the goodness-of-fit test failed to show a difference between observed and estimated hospital mortality.

In summary, this study provides evidence that in ICU-admitted CD patients the severity of the acute illness, the duration of MV and abnormal values (high or low) of serum phosphorus are reliable prognostic markers for ICU mortality. Our findings suggest that the appropriateness of intensive care for these patients should be discussed on the same basis as for the general population.



   Acknowledgments
 
The authors thank Dr Valerie Tournilhac and Mr Jeffrey Watts for their help in preparing the manuscript. Research support was provided by Centre Hospitalier Universitaire de Clermont-Ferrand, France.

Conflict of interest statement. None declared.



   References
 Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 References
 

  1. Ruggenenti P, Schieppati A, Remuzzi G. Progression, remission, regression of chronic renal diseases. Lancet 2001; 357: 1601–1608[CrossRef][ISI][Medline]
  2. Berthoux F, Jones E, Gellert R, Mendel S, Saker L, Briggs D. Epidemiological data of treated end-stage failure in European Union during the year 1995: report of the European Renal Association Registry and the National Registries. Nephrol Dial Transplant 1999; 14: 2332–2342[Abstract/Free Full Text]
  3. Sarnak MJ, Jaber BL. Mortality caused by sepsis in patients with end-stage renal disease compared with the general population. Kidney Int 2000; 58: 1758–1764[CrossRef][ISI][Medline]
  4. Uchino S, Morimatsu H, Bellomo R, Silvester W, Cole L. End-stage renal failure patients requiring renal replacement therapy in the intensive care unit: incidence, clinical features, and outcome. Blood Purif 2003; 21: 170–175[CrossRef][ISI][Medline]
  5. Cohen LM, Germain MJ, Poppel DM. Practical considerations in dialysis withdrawal: ‘to have that option is a blessing’. JAMA 2003; 289: 2113–2119[Abstract/Free Full Text]
  6. Clermont G, Acker CG, Angus DC, Sirio CA, Pinsky MR, Johnson JP. Renal failure in the ICU: comparison of the impact of acute renal failure and end-stage renal disease on ICU outcomes. Kidney Int 2002; 62: 986–996[CrossRef][ISI][Medline]
  7. Le Gall JR, Lemeshow S, Saulnier FA. New Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study. JAMA 1993; 270: 2957–2963[Abstract]
  8. Le Gall JR, Lemeshow S, Leleu G et al. Customized probability models for early severe sepsis in adult intensive care patients. Intensive Care Unit Scoring Group. JAMA 1995; 273: 644–650[Abstract]
  9. Knaus WA, Draper EA, Wagner DP, Zimmerman JE. Prognosis in acute organ-system failure. Ann Surg 1985; 202: 685–693[ISI][Medline]
  10. Bone RC, Balk RA, Cerra FB et al. Definitions for sepsis and organ failure and guidelines for the use of innovative therapies in sepsis. The ACCP/SCCM Consensus Conference Committee. American College of Chest Physicians/Society of Critical Care Medicine. Chest 1992; 101: 1644–1655[Abstract]
  11. Van Steen K, Curran D, Kramer J et al. Multicolinearity in prognostic factor analyses using the EORTC QLQ-C30: identification and impact on model selection. Stat Med 2002; 21: 3865–3884[CrossRef][ISI][Medline]
  12. Lemeshow S, Hosmer DW. A review of goodness of fit statistics for use in the development of logistic regression models. Am J Epidemiol 1982; 115: 92–106[Abstract]
  13. Loran MJ, McErlean M, Eisele G, Raccio-Robak N, Verdile VP. The emergency department care of hemodialysis patients. Clin Nephrol 2002; 57: 439–443[ISI][Medline]
  14. Owen WF, Lew NL, Liu Y, Lowrie EG, Lazarus JM. The urea reduction ratio and serum albumin concentration as predictors of mortality in patients undergoing hemodialysis. N Engl J Med 1993; 329: 1001–1006[Abstract/Free Full Text]
  15. Kaysen GA, Rathore V, Shearer GC, Depner TA. Mechanisms of hypoalbuminemia in hemodialysis patients. Kidney Int 1995; 48: 510–516[ISI][Medline]
  16. Casalino E, Mendoza-Sassi G, Wolff M et al. Predictor of short and long term survival in HIV-infected patient admitted to the ICU. Chest 1998; 113: 421–429[Abstract/Free Full Text]
  17. Staudinger T, Stoiser B, Mullner M et al. Outcome and prognostic factors in critically ill cancer patients admitted to the intensive care unit. Crit Care Med 2000; 28: 1322–1328[CrossRef][ISI][Medline]
  18. Giovannini I, Chiarla C, Nuzzo G. Pathophysiologic and clinical correlates of hypophosphatemia and the relationship with sepsis and outcome in postoperative patients after hepatectomy. Shock 2002; 18: 111–115[CrossRef][ISI][Medline]
  19. Cizman B. Hyperphosphataemia and treatment with sevelamer in haemodialysis patients. Nephrol Dial Transplant 2003; 18[Suppl 5]: v47–v49[Medline]
  20. Locatelli F. The need for better control of secondary hyperparathyroidism. Nephrol Dial Transplant 2004; 19[Suppl 5]: v15–v19[Medline]
Received for publication: 8. 6.04
Accepted in revised form: 2. 2.05





This Article
Abstract
Full Text (PDF)
All Versions of this Article:
20/6/1127    most recent
gfh762v1
Alert me when this article is cited
Alert me if a correction is posted
Services
Email this article to a friend
Similar articles in this journal
Similar articles in ISI Web of Science
Similar articles in PubMed
Alert me to new issues of the journal
Add to My Personal Archive
Download to citation manager
Disclaimer
Request Permissions
Google Scholar
Articles by Manhes, G.
Articles by Souweine, B.
PubMed
PubMed Citation
Articles by Manhes, G.
Articles by Souweine, B.