White blood cells as a novel mortality predictor in haemodialysis patients
Donal N. Reddan1,2,,
Preston S. Klassen1,2,
Lynda A. Szczech1,2,
Joseph A. Coladonato1,2,
Susan O'Shea3,
William F. Owen Jr2,5 and
Edmund G. Lowrie2,4
1 Division of Nephrology, Department of Medicine, Duke University Medical Center, Durham, NC,
2 Duke Institute for Renal Outcomes Research and Health Policy, Durham, NC,
3 Division of Hematology/Oncology, Duke University Medical Center, Durham, NC,
4 Fresenius Medical Care, North America, Lexington, MA and
5 Renal Division, Baxter Healthcare, Gurnee, IL, USA
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Abstract
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Background. Many conventional cardiovascular risk factors in the general population are not as predictive in end-stage renal disease (ESRD). As absolute neutrophil count and total white blood cell (WBC) count are associated with adverse cardiovascular outcomes and all-cause mortality, this analysis was undertaken to explore the associations of WBC variables with mortality risk in ESRD.
Methods. Of a total study population of 44 114 ESRD patients receiving haemodialysis during 1998 at facilities operated by Fresenius Medical Care, North America, 25 661 patients who underwent differential white cell count and had complete follow-up were included. Information on case mix (age, gender, race), clinical (diabetes, body mass index), and laboratory variables (haematocrit, albumin, creatinine, potassium, calcium, phosphorus, bicarbonate, ferritin, transferrin saturation and differential WBC count) was obtained. Associations between lymphocyte count, neutrophil count and demographic and clinical variables were examined using linear regression. Associations between WBC variables and survival were estimated using Cox proportional hazard regression.
Results. A higher lymphocyte count was associated with higher serum albumin and creatinine, lower age and black race. High neutrophil count was associated with lower serum albumin and creatinine, younger age and white race (all Ps <0.0001). Cox proportional hazard regression showed an increased lymphocyte count was associated with reduced mortality risk [HR 0.86 (0.830.89) per 500/ml increase in lymphocyte count] and an increased neutrophil count was associated with increased mortality risk [HR 1.08 (1.061.09) per 1000/ml increase in neutrophil count].
Conclusions. An increased neutrophil count is strongly associated with, and reduced lymphocyte count associated less strongly with, many surrogates of both malnutrition and inflammation. An increased neutrophil count and reduced lymphocyte count are independent predictors of increased mortality risk in haemodialysis patients.
Keywords: end-stage renal disease; haemodialysis; lymphocytes; mortality; neutrophils
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Introduction
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Mortality among maintenance dialysis patients is high, and cardiovascular disease is the major cause of mortality. Mortality at 1 year for incident end-stage renal disease (ESRD) patients was 19.5% in 2000, and
50% of deaths were reported to be due to cardiac disease [1]. ESRD patients also experience significantly worse outcomes following myocardial infarction and following cardiac interventions [2,3]. Many conventional cardiovascular risk factors such as elevated cholesterol [4] and hypertension [5], which are robust predictors of adverse cardiovascular outcomes in the non-ESRD population, do not follow conventional intuitive mortality relationships in maintenance dialysis patients. Approximately one third of the general population who have suffered a myocardial infarction have no conventional risk factors for atherosclerotic disease [6] and so, in the general population, increased scrutiny has been placed on non-conventional risk factors such as prothrombotic lipids, like lipoprotein (a) or endothelial inflammation, reflected by biomarkers like C-reactive protein (CRP) [7]. Similarly, some of these non-conventional risk factors have been found to individually add to the risk profile for cardiovascular events or for overall mortality in the ESRD population. Examples include CRP [8,9], lipoprotein (a) [10], homocysteine [11] and oxidative stress [12]. In the general population, an increased total white blood cell (WBC) count has been found to correlate with increased cardiovascular mortality in elderly men [13] and with increased mortality following myocardial infarction in general [14]. Increased total WBC counts [15] and neutrophil count [16] have also been implicated as a biomarker of atherosclerosis. Alternatively, a reduced lymphocyte count has been associated with increased mortality in patients with congestive heart failure [17], and has also been identified as a bad prognostic sign in patients with coronary artery disease [18]. An association between WBC counts and mortality in ESRD has also been suggested in the past [19]. Because of the limited correlation between conventional risk factors and cardiovascular mortality in dialysis patients, and the similar profile of non-conventional risk factors and death risks for the general population and dialysis patients, we proposed that routine haematological measures like the total WBC count and selected components of the differential WBC count would predict death risk for the dialysis population.
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Methods
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Patient population
Of 44 114 ESRD patients receiving haemodialysis on 1 January 1998 at facilities operated by Fresenius Medical Care, North America (FMCNA), 25 661 patients who underwent differential WBC count and had complete follow-up for the year of observation were included in the analysis. Demographic, clinical and laboratory data were collected on all patients during the months of October, November and December 1997. All laboratories were performed pre-dialysis. For patients who had more than one determination of the differential WBC count between October and December 1997, the results of all determinations during this interval were averaged for each patient. The number of differential WBC determinations made from monthly blood samples ranged between one and three per patient. All the biochemical analyses were performed by a single clinical laboratory, LifeChem Clinical Laboratory (Rockleigh, NJ).
Patients were followed until 31 December 1998. Patients who left FMCNA facilities or received a renal transplant were censored from the analyses. Date of death or censoring was recorded for time-to-event analysis of all-cause mortality.
Measurements and data analysis
Absolute neutrophil count and absolute lymphocyte count were calculated by multiplying total WBC quantitated as 109/l of blood by the percentages of neutrophils and lymphocytes, respectively. Patients were described overall and by quartile of neutrophil count and lymphocyte count. Univariate and multivariable analyses were conducted to examine associations of lymphocyte count and neutrophil count with demographic and laboratory variables. Differences among groups in continuous variables were tested by a two-tailed Student's t-test. Mean values were presented as mean±standard deviation (SD). Differences among groups in categorical variables were tested by chi-square analysis. Stepwise linear regression was performed to examine the associations of lymphocyte count and neutrophil count with clinical, demographic and laboratory variables. The correlation between neutrophil count with lymphocyte count was also assessed using a Pearson correlation coefficient. Final models were generated using the variables noted to be significant in the stepwise analysis and other variables thought to be clinically relevant.
Survival was estimated using the KaplanMeier method for patients with and without WBC data and compared using the log-rank test. KaplanMeier survival curves among patients across quartiles of lymphocyte count and neutrophil count were compared similarly. The associations of neutrophil count and lymphocyte count with mortality were subsequently assessed in adjusted and unadjusted models using Cox proportional hazards regression. Variables entered into the models included all variables found to be predictive of lymphocyte count and neutrophil count in the linear regression models and other variables thought to be clinically relevant. The regression models were built using both forward addition and backward elimination stepwise methods (entry threshold: P<0.05; retention threshold: P<0.1) and subsequent models were also generated with imputed values for missing lymphocyte and neutrophil values.
Finally, patients were stratified into a matrix of 81 groups (nine groups of neutrophil count stratified by nine groups of lymphocyte count), and using Cox proportional hazards modelling, unadjusted hazard ratios (HRs) were derived for each subgroup. The selection of nine subgroups for each variable was for ease of data presentation. The analysis was then repeated adjusting for demographic and clinical variables and adjusted HRs derived for each sub-group. The interaction between neutrophil count and lymphocyte count was tested in both the models with lymphocyte count and neutrophil count as linear variables and the model where neutrophil count and lymphocyte count were categorized into a matrix of 81 groups.
All P-values reported are two-sided, and all confidence intervals (CIs) reported are 95% intervals. Analyses were performed using SAS software version 8.1 (SAS Institute Inc., Cary, NC).
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Results
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Of the 25 661 patients included in the analysis, 4087 died by the end of 1998, and 4006 patients were censored from the analysis due to transplantation, loss to follow-up, or because they moved to another dialysis unit. In comparison to the 18 453 excluded because of missing WBC data, the final analytic group of 25 661 patients were slightly younger (mean age 59.9 and 60.4 years, respectively), had higher serum albumin concentrations (mean albumin 39 and 38 g/l, respectively), and higher serum ferritin concentrations (mean 1191 and 1045 pmol/ml). The final analytical group of patients was also disproportionately of black race (45 and 39%, for included and excluded patients, respectively). There were no other clinically significant differences noted between included and excluded patients with respect to baseline characteristics. Mortality was significantly higher among patients included than in those excluded. Over the year of follow-up, 18.9% of uncensored patients included in the analysis died, in comparison to 15.4% of the uncensored patients that were excluded because of missing data. A difference in mortality was confirmed using KaplanMeier analysis P<0.001 (data not shown).
The characteristics of the final study population are described in Table 1
. This population had a slightly higher proportion of black patients (45%) when compared to the total US ESRD population [1]. The proportion of males (51%), mean haematocrit (0.33) and albumin concentrations (38 g/l) were all consistent with those of the US ESRD population [1]. Table 2
portrays differences in patient characteristics across quartiles of lymphocyte count. Patients with higher lymphocyte count were more often black, female, diabetic and younger, and had a greater body mass index (BMI). Patients with a higher lymphocyte count had higher serum creatinine and serum albumin concentrations. Total WBC and neutrophil counts increased monotonically in parallel with higher lymphocyte count. Table 3
portrays differences in patient characteristics across quartiles of neutrophil count. Patients with higher neutrophil count were more often white, female, diabetic and older. Patients with higher neutrophil count had lower serum creatinine and serum albumin concentrations. Higher neutrophil count was accompanied by a monotonic increase in ferritin concentration and WBC and lymphocyte counts.
The results of the stepwise linear regression analyses, which describe independent predictors of both neutrophil count and lymphocyte count, are presented in Tables 4
and 5
, respectively. Significant independent predictors of a higher lymphocyte count included younger age, female gender, presence of diabetes, increased BMI, higher serum creatinine, lower serum potassium and higher transferrin saturation (TSAT) and haematocrit. The total F-value for the linear model predicting lymphocyte count was 32.7. Significant independent predictors of higher neutrophil count included younger age, white race, presence of diabetes, higher BMI, lower serum creatinine and albumin concentrations, higher potassium and calcium concentrations, and lower bicarbonate and transferrin saturation and higher ferritin. For the linear model predictive of neutrophil count, the total F-value was 327.3. The Pearson correlation coefficient for the correlation of neutrophil count with lymphocyte count was 0.074.
Table 6
displays the results of the Cox proportional hazards modelling. All variables tested in the stepwise process were found to be significant except for race, and this variable was found to be significant when added back to the final model. The variables found to be predictive of mortality included lower lymphocyte count, higher neutrophil count, older age, male gender, diabetes, white race, lower BMI, lower serum creatinine and albumin concentrations, higher serum potassium, calcium, and phosphate concentrations, lower serum bicarbonate concentration, and lower transferrin saturation. Each 50x109/l decrement in lymphocyte count was associated with a 14% increase in mortality, and each 100x109/l increase in neutrophil count was associated with an 8% increase in mortality. Mortality risk was highest for those with a high neutrophil count and a low lymphocyte count. When the interaction between neutrophil count and lymphocyte count was added to the multivariable survival model, it was not found to be significant (chi-square=1.30, P=0.25). The interaction was also not significant when further tested in the survival models incorporating nine categories of lymphocyte count and nine categories of neutrophil count (P=0.3). When survival models were generated that also incorporated imputed data for missing neutrophil and lymphocyte counts, results were remarkably similar to those reported here (data not shown).
Figures 1
and 2
describe the relationships between neutrophil count and lymphocyte count and mortality in adjusted and unadjusted analyses. Figure 1
illustrates an increase in hazard of death with increasing neutrophil count and decreasing lymphocyte count in an unadjusted analysis. Figure 2
, which is an adjusted model, illustrates similar relationships, even after adjustments for the aforementioned mortality predictors.
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Discussion
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This analysis demonstrates a novel finding of significant mortality risk associated with selected white cell components in ESRD patients receiving maintenance haemodialysis. Higher neutrophil count and lower lymphocyte count are each independently associated with increased risk of death. These relationships are similar to findings in patients with cardiovascular disease [14,17], and suggest that examination of the differential WBC count may be useful in assessing mortality risk profiles in ESRD patients.
A limitation of this data set is that it does not contain information about the cause of mortality. The fact that cardiovascular disease is the major cause of mortality in ESRD suggests that the increased mortality risk is a consequence of cardiovascular disease, but future evaluation of these relationships among other data sets are needed to validate or refute this supposition. This study has a number of additional limitations germane to observational database research. The absence of follow-up beyond 1 year is a limitation. There is a possible bias in that those patients who had a differential WBC evaluation performed were demographically different than those who did not. Although suggested, it does not mean that their comorbid conditions were different. Moreover, WBC counts are often performed as part of a routine laboratory panel in dialysis units, and so it is unclear how much bias by indication has occurred in this data set. The patients who had WBC counts performed had a higher mortality than those patients who did not, which suggests that the strength of the relationships may be affected in a broader patient base. However, the finding that the other parameter estimates of death were in the same direction and magnitude as had been seen previously suggests that the leukocyte/mortality relationship is likely genuine, too. Recent results from the Dialysis Outcomes and Practice Patterns Study (DOPPS) are also consistent with the observation of higher neutrophil count and lower lymphocyte count predicting mortality [20]. The clinical predictive importance of an elevated WBC count in an individual patient may not be readily derived from these data and the relevance of such individual findings are best interpreted at a clinical level.
The similarity of these findings to those in the general population with cardiovascular disease is not surprising in view of the weak associations of cardiovascular disease in ESRD patients with conventional cardiovascular risk factors. Cardiovascular disease remains the most important contributor toward ESRD mortality [1] and just as in the non-ESRD population a correlation between inflammation and cardiovascular disease in ESRD has been suggested [9]. The pathobiology of this correlation may involve the interaction of soluble mediators such as interleukin-6 [21,22], advanced glycation end products (AGEs) and lipoxidation products [23,24], and oxidative stress [9]. The leukocytes examined herein are likely affected by an inflammatory process, and so may serve as a biomarker for it. Data was not captured on other inflammatory biomarkers like CRP or fibrinogen. Nor were blood cytokine levels or their antagonists measured directly or from isolated stimulated leukocytes. So, it is unclear if the strength of these relationships is affected in models in which other biomarkers or provocateurs are included. However, these are not routinely performed laboratory tests, and are less available as clinical tools to identify patients at risk.
Leukocyte counts are also affected by the patient's nutritional status. For example, lymphopaenia is well described in malnourished patients [25]. So it is unsurprising that a higher lymphocyte count was associated with greater anthropometric attributes and higher serum creatinine values. The relationship between protein-calorie malnutrition, inflammation, and mortality is noteworthily complex, especially for scrutiny by a cross-sectional study design and the finding that neutrophil count and lymphocyte count display opposing relationships with mortality suggests that they may indicate different pathobiologic relationships. For example, we posit that a low lymphocyte count may reflect protein-calorie malnutrition and increased death risk secondary to this co-morbid condition; an increased death risk associated with an increased neutrophil count may reflect subclinical infection and/or inflammation and be a consequence of these processes. The identified predictors of an increased neutrophil count from the linear regression model are consistent with its proposed association with inflammation and poor nutrition in that higher neutrophil count is associated with lower serum creatinine, lower serum albumin, lower transferrin saturation and higher serum ferritin. The predictors of lymphocyte count selected from an identical variable pool were strikingly less robust in terms of the overall model and in terms of the individual predictors identified. The fact that lower lymphocyte count was a strong mortality predictor but was not well predicted by the variables in the models elucidated suggests that the pathobiology underlining the relationship between lymphocyte count and mortality remains somewhat unclear. Although there was a correlation the observation that neutrophil count and lymphocyte count did not correlate highly with each other also suggests that they are associated with different disease processes.
Based on the data offered herein, we suggest that higher neutrophil count and lower lymphocyte count are independent predictors of mortality risk in haemodialysis patients, and simple measures to be followed.
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Acknowledgments
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D.N.R. was supported by a grant from the National Kidney Foundation, Inc., with matching funds from the National Kidney Foundation of North Carolina. W.F.O. Jr received support for this work through an unrestricted educational grant from Amgen, Inc. (Thousand Oaks, CA). L.A.S. is supported by grant DK02724-01A1 from the NIH. P.S.K. received support from an American Kidney Fund Clinical Scientist in Nephrology Fellowship.
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Notes
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Correspondence and offprint requests to: Dr Donal Reddan, Duke Institute of Renal Outcomes Research and Health Policy, Box 3646, Duke University Medical Center, Durham, NC 27710, USA. Email: redda001{at}mc.duke.edu 
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Received for publication: 13. 5.02
Accepted in revised form: 20.12.02