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Clinical Chemistry 46: 242-247, 2000;
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(Clinical Chemistry. 2000;46:242-247.)
© 2000 American Association for Clinical Chemistry, Inc.


Articles

Leukocyte Counts in Cerebrospinal Fluid with the Automated Hematology Analyzer CellDyn 3500 and the Urine Flow Cytometer UF-100

Reinhard Ziebiga,1, Andreas Lun1 and Pranav Sinha1

1 Institut für Laboratoriumsmedizin und Pathobiochemie, Universitätsklinikum Charité, Campus Charité Mitte, Medizinische Fakultät der Humboldt Universität zu Berlin, Schumannstrasse 20-21, 10117 Berlin, Germany.
a Author for correspondence. Fax 49-030-2802-8422; e-mail reinhard.ziebig{at}charite.de


   Abstract
Top
Abstract
Introduction
Materials and Methods
Results
Discussion
References
 
Background: The counting of leukocytes and erythrocytes in cerebrospinal fluid (CSF) is still performed microscopically, e.g., using a chamber in most laboratories. This requires sufficient practical experience, is time-consuming, and may constitute a problem in emergency diagnostics. Specific automated systems for CSF cell counting are not available at present.

Methods: We tested the hematology analyzer CellDyn 3500 (CD) and the urine flow cytometer UF-100 (UF), which are not designed for CSF analysis. We studied >104 samples with both analyzers, and the counts obtained were compared with the reference method (Fuchs-Rosenthal chamber).

Results: Good linearity in the medically relevant range of 15 x 106 to 1000 x 106 leukocytes/L and a high degree of within-run accuracy were seen for both analyzers. Cell counting on the UF was excellent, especially when low cell counts were encountered (CV, 4.9% compared with 28% observed for the CD). Method comparison showed that identical results could be detected for a majority of the count pairs. For a few samples, there was a discrepancy between the results from the analyzers and the counting chamber. In most cases, these were CSF samples containing a high proportion of lymphocytes. For these samples, the CD result led to a false-positive high leukocyte count, and on the UF these cells were not allocated to the leukocyte population, thus leading to false-negative counts.

Conclusions: Both analyzers should not be used for CSF cell counting in all cases at present. However, once the technical and software problems have been solved, routine use of the two analyzers for CSF analysis should be seriously contemplated.


   Introduction
Top
Abstract
Introduction
Materials and Methods
Results
Discussion
References
 
The counting of leukocytes and of erythrocytes in cerebrospinal fluid (CSF)1 has not been automated. Cell counts are still performed microscopically, e.g., using the Fuchs-Rosenthal chamber in most laboratories. Fully automated analyzers meet time and quality requirements and are objective in material handling. On the other hand, laboratory technicians frequently must squeeze microscopic chamber counting into tight laboratory schedules (with increasing workloads), additionally needing to consider several other factors (e.g., number of samples and quality of the cells). Furthermore, insufficient practical experience in microscopic chamber counting and the subjectivity of individual laboratory workers adds to the unreliability of the results frequently observed.

The reason for the lack of more specific CSF diagnoses, including, e.g., a cell differentiation, often is insufficient sample volume and/or too few cells in a sample. Analyses performed with the modified sedimentation chamber technique according to Sayk (1) or with centrifugation (2) have contributed to the optimization of CSF cytology. They do not, however, solve the primary problem of the accurate determination of the number of cells.

Specific automated systems for CSF cell counting are not available at present. The following requirements, among others, would be necessary for such a system: (a) the ability to count small numbers of cells (e.g., 10 x 106 cells/L); (b) the ability to differentiate leukocytes into polymorphonuclear and mononuclear cell populations; and (c) the use of small sample volumes. On the basis of these requirements, we considered two analyzers appropriate for a test to determine cells in the CSF: the Abbott CellDyn 3500 (CD) and the Sysmex UF-100 (UF).

The automated hematology analyzer CD permits, according to the manufacturer, the counting and differentiation of leukocytes in <50 x 106 cells/L. This analyzer requires a sample volume of 150–200 µL. The urine flow cytometer UF was designed for the analysis of particles in urine and thus also of small numbers of leukocytes and erythrocytes. The sample volume required by this analyzer is 800 µL.


   Materials and Methods
Top
Abstract
Introduction
Materials and Methods
Results
Discussion
References
 
csf samples
One hundred four CSF samples were examined for leukocytes in a paired comparison on the CD, the UF, and in a microscopic counting chamber (Fuchs-Rosenthal).

Routine CSF samples were brought from the ward on ice-water and were stored in the laboratory until the analyses were performed. The samples were obtained from patients after brain surgery (n = 76), patients suffering from viral and bacterial meningitis (n = 12) and/or encephalitis (n = 5), and patients in remission who had undergone control examinations (n = 11). Because only the material that was leftover from routine analyses was used, special consent from the patients was not required.

Initial analyses in defined cell suspensions prepared in our laboratory were used to check the accuracy and linearity. For this purpose, cell-rich plasma (buffy coat) was obtained from EDTA-blood samples after their spontaneous sedimentation, and its composition was checked on the H3 hematology analyzer (Bayer Diagnostics) in repeated tests. Thereafter, these samples were diluted with physiological saline solution to cell concentrations typical in the CSF in patients suffering from encephalitis and meningitis.

A special CSF pool was prepared to test the within-run imprecision in CSF. Ten samples were mixed and analyzed.

analyzers
The counting principles, specifications, and evaluations of the CD and the UF have been published previously (3)(4)(5)(6)(7)(8). For counting and differentiation of leukocytes, the CD uses a multiangle polarization scatter separation technology in the optical channel [white blood cell optical count (WOC)], combined with a second channel with impedance count (white blood cell impedance count). The erythrocyte counting is based on the impedance principle (3)(4)(5). On the UF, cells in the urine are determined by light scatter (small-angle and wide-angle scattering) and the fluorescence of the cell membrane and the chromatin after staining with phenanthridine and carbocyanine as well as by impedance (6)(7)(8).

In preparation for CSF cell counting, the CD was flushed three times with saline to obtain cell counts of <3 x 106 cells/L. Only the leukocytes counted in the WOC channel were taken into consideration for the evaluation. All scattergrams including lobularity of 90 degrees and complexity of 10 degrees that showed the separation of polymorphonuclear cells from mononuclear cells were scrutinized carefully. In addition, the counts rate summary for leukocytes in the optical channel was printed. This indicated the stability of leukocytes.

As with the CD, the UF was flushed three times to obtain cell counts of <3 x 106 cells/L. The CSF samples were measured either directly or were first diluted with a physiological saline solution when the sample volumes were <800 µL. Samples were also prediluted when the number of cells (particles) counted was flagged by the analyzer as >40 000 particles.

interpretation of counting protocols
When interpreting the findings, it was helpful to take the following alarms as well as some other results into account: For the CellDyn 3500, "VARIANT LYM" indicates atypical lymphocytes and/or pathological cells; "FWBC" indicates fragile white blood cells (WBCs); "KWOC" indicates a kinetically corrected value of the optical counting channel (WOC) caused by a nonlinear counting pulse rate. For the UF-100, the only alarm that was considered was "Other Particles", which indicates particles that can not be allocated to any population in the scattergram.

statistical evaluation
The test results were evaluated by the regression method of Passing and Bablok (9) as well as by demonstrating the count difference of two methods in relation to the mean value of the two value pairs, according to the Bland-Altman method (10).


   Results
Top
Abstract
Introduction
Materials and Methods
Results
Discussion
References
 
The within-run imprecision for the CD and the UF for three series of tests with different cell suspensions and a special CSF pool (UF only) are shown in Table 1 . The UF had surprisingly low within-run CVs (4.9–3.5%) for the investigated cell suspensions (x = 28.9 x 106, 82.8 x 106, and 387.4 x 106 WBC/L). The measurement in the CSF pool produced a CV of 6.8% (x = 37.4 x 106 WBC/L). With the CD, the cell suspensions with low leukocyte counts (x = 28.1 x 106 and 86.6 x 106 WBC/L) showed acceptable CVs (28% and 16%), and the CV of the third test series was 3.3% (x = 363.7 x 106 WBC/L), identical to that of the UF.


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Table 1. Within-run imprecision of leukocyte counting on the CellDyn 3500 (CD) and the UF-100 (UF).

The linearity of leukocyte counting was carried out in serial dilutions prepared from a defined cell suspension. Fig. 1 shows that of the leukocyte counts on the UF were linear even in the lower range. Furthermore, the CD also produced leukocyte counts with acceptable linearity in this range. The Cusum test produced no significant deviation in both cases.



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Figure 1. Linearity of leukocyte counting of cell suspensions on the CellDyn 3500 and the UF-100.

{diamondsuit}, theoretical leukocyte value vs leukocyte counts on the CD 3500 (y = 0.93x + 0.19); •, theoretical leukocyte value vs leukocyte counts on the UF-100 (y = 0.99x + 1.31).

The results of the comparison of the methods used for the determination of leukocytes in the CSF are shown in Fig. 2 . Leukocyte counts >1000 x 106/L were not taken into account for statistical evaluation so that the examinations were narrowed to the medically critical range for decisionmaking.



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Figure 2. Regression analysis between leukocyte counting by the UF-100 and CD 3500.

(a), UF-100 vs CD 3500; regression line: y = 0.99x - 4.09; r = 0.91; n = 104. (b), CD 3500 vs counting chamber; regression line: y = 1.11x + 5.68; r = 0.92; n = 73. (c), UF-100 vs counting chamber; regression line: y = 1.35x - 1.39; r = 0.87; n = 73. n, number of value pairs.

The comparison of the CD, UF, and microscopic chamber counting methods yielded correlation coefficients (r) between 0.87 and 0.91, and the slope of the regression line in the comparison between CD and UF was almost identical with the identity line (Fig. 2aUp ). In contrast, the regression lines between the chamber and CD (Fig. 2bUp ) and the chamber and UF (Fig. 2cUp ) methods show larger deviations from the identity line. In Fig. 2cUp , it is the slope of 1.35 (intercept = -1.39 x 106 WBC/L) that is primarily responsible for the difference in the leukocyte values between chamber and UF. It should be critically stated that the extended range of leukocyte counts (0 to 980 x 106 WBC/L) makes the correlation look more favorable than it is.

A simple graphic presentation of pair differences in method comparison (difference plot after Bland and Altman) clarifies this fact. Paired values between CD and UF were chosen for this presentation. In Fig. 3 , the differences between the value pairs are depicted on the y-axis and the mean is depicted on the x-axis. The mean difference of the comparative counting as well as the standard deviation (± 2 SD) of the differences is shown parallel to the x-axis. From Fig. 3 , it is apparent that some of the value pairs show larger differences in different directions. Based on these differences, medical acceptance is impossible. Because the results obtained with microscopic chamber counting were similar to the results obtained with CD or UF, they are not shown here.



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Figure 3. Comparison of CellDyn 3500 and UF-100 counts of leukocytes for 104 CSF cell samples.

The Bland-Altman difference plot depicts the difference of the value pairs on the y-axis and the mean on the x-axis. The mean difference of the comparative counting (solid line) as well as the SD (± 2 SD) of the differences (dotted lines) is shown parallel to the x-axis.

The following results obtained for one sample illustrate the problems with the validity of the cell counts and the impact of interfering factors on CSF cell counting with automated analyzers. The macroscopic appearance of this sample was without any apparent abnormalities. Cell counts yielded the following results for leukocytes: chamber counting, 150 x 106 WBC/L; CellDyn 3500, 215 x 106 WBC/L; UF-100, 24 x 106 WBC/L.

The protocols of the CD indicated an abnormal lymphocyte population with KWOC, VARIANT LYM, and FWBC flags. KWOC in this case means the mathematical extrapolation of the number of leukocytes with assumed cell degeneration during the count period. This mathematical correction algorithm leads to a higher number of leukocytes. The percentage of the lymphocyte population registered here was 97%.

The same sample yielded a lower leukocyte count (24 x 106 WBC/L) on the UF. The scattergram (forward scattered light intensity vs fluorescent light intensity; Fig. 4 ) shows in addition to the blue cluster representing the leukocytes, a second cluster marked in yellow. These cells (169 x 106 WBC/L) are not assigned to any of the populations defined in the UF scattergram. The addition of these 169 x 106 cells to the leukocyte count would produce a corrected value of 193 x 106 WBC/L. A strictly defined yellow cluster in the scattergram such as the one shown for this sample was always found in the presence of atypical lymphocytes.



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Figure 4. UF-100 scattergram for a CSF cell sample with atypical lymphocyte characteristic.

Fsc, forward scattered light intensity; Fl2, fluorescent light intensity; RBC, erythrocytes (red); WBC, leukocytes (blue); Bact, bacteria (green). The cells in the yellow clusters are not allocable to any of the population defined in the UF scattergram.

Six additional samples yielded similar results. Method comparisons between the leukocyte value obtained with the chamber and the UF revealed poor correlation (r = 0.20). A comparison of the corrected leukocyte value and chamber counting showed good correlation (r = 0.94). Fig. 5 shows the corresponding regression line for seven CSF samples (circles).



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Figure 5. Comparison of leukocyte counts between chamber and UF-100 (corrected value: summary of original leukocyte counts plus "other particles") and comparison of leukocyte counts between chamber and CD 3500 (raw data of measurements as corrected values).

{circ}, value pairs of chamber and UF-100; (——–), regression line between chamber and UF-100: y = 0.929x + 29.515; r = 0.94; n = 7. {blacksquare}, value pairs of chamber and CD 3500; (- - - - -), regression line between chamber and CD 3500: y = 1.005x + 6.572; r = 0.95; n = 20. (· · · · · · · ·), line of identity. n, number of value pairs.

To study the influence of lymphocytes on the leukocyte count obtained with the UF, defined cell suspensions whose leukocyte concentration and composition had been determined earlier on the H3 were analyzed on the UF (Fig. 6 ). We can demonstrate that an increase in the lymphocyte population leads to reduced leukocyte counts. Typically, these lymphocytes are not allocated to any defined cell population.



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Figure 6. Dependence of recovery of leukocytes (%) by the UF-100 on lymphocyte portion (%) in defined cell suspensions.

The leukocyte concentration and composition had been determined earlier on the H3 analyzer.

If one correlates leukocyte differences (between CD and UF, CD and chamber, or UF and chamber) with the percentage of lymphocytes in the CSF sample, the correlation coefficients (r) are between 0.42 and 0.71 (Table 2 ). Evidence of the same correlation was found when cell suspensions were used instead of CSF (Table 2 , values in parentheses).


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Table 2. Comparison of the differences1 of counted leukocytes (WBC 106/L) with the lymphocyte portion (%) indicated on the CellDyn 3500 in CSF cell samples.

Better correlations between CD and the counting chamber for CSF samples high in lymphocytes were achieved when raw uncorrected values measured on the CD were used. Fig. 5Up shows the regression line resulting from the comparison (squares) of CD (raw leukocyte values) and counting chamber results for 20 CSF samples high in lymphocytes (50–97%). It is evident from these results that in this way, it is possible to considerably minimize the differences of the counts. This is, however, a tedious way of obtaining correct results.

The evaluation of the erythrocyte counts obtained with the UF (y) and by chamber counting (x) showed a correlation coefficient (r) of 0.83 (n = 48) and the following regression line: y = 0.873x + 1.518. Erythrocyte values of >1000 x 106/L were not included in the analysis. Because the CD does not print erythrocyte values <1000 x 106/L, no correlation in the relevant erythrocyte range could be determined between the CD and the UF as well as between the CD and the counting chamber.


   Discussion
Top
Abstract
Introduction
Materials and Methods
Results
Discussion
References
 
The examinations of the accuracy and linearity of the two analyzers showed that it was not irrelevant to test them for CSF analyses although they had not been designed specifically for that purpose. The comparison of the methods used for CSF samples produced correlation coefficients (r) between 0.87 and 0.91 that further corroborate our approach.

The high degree of the scattering of the residuals around the identity line and/or the high degree of the scattering of the count differences with a still acceptable mean difference (bias) manifests the fault liability of the automatic cell counting. One possible fault was indicated during analyses of CSF samples with high lymphocyte populations and was confirmed in analyses of defined cell suspensions obtained from sedimented EDTA samples.

As far as CD is concerned, there is a spurious extrapolation of the number of leukocytes in the WOC channel with a recorded high lymphocyte percentage of the CSF sample in most cases indicated by the alarms VARIANT LYM and KWOC. The CD performed this erroneous extrapolation of the leukocyte count when certain samples containing atypical lymphocytes were measured repeatedly.

Thus, it must be assumed that the algorithm of the CD is sensitive to certain conditions (CSF) and/or that is susceptible to certain coincidences. In some subsequent examinations, it was possible to show that in counting atypical lymphocytes, the rough counts of the WOC channel lead to a better conformity with the chamber counts.

The UF does not classify certain lymphocyte populations in CSF and allocates them to a different particle population ("Other"). The number of leukocytes that is determined is falsely low. In some cases, it was possible to correct the leukocytes count by simple addition (leukocytes x 106/L + number of cells x 106/L in the "Other" population). This method of correction, however, requires additional confirmation before being used.

If the leukocytes count determined for sample mentioned in the Results is considered from a medical point of view, the possible diagnoses may range from suspected herpes simplex encephalitis (24 x 106 WBC/L on the UF) to meningitis (215 x 106 WBC/L on the CD).

The difference shown in this specific case is reflected in the described series of tests by a considerable number of clinically relevant differences in the number of leukocytes. The critical difference between two counts may be derived roughly from three times the time-dependent standard deviations (dk {cong} 3 SDT). With 100 x 106 WBC/L and a SDT of 15 x 106 WBC/L, the resulting difference of almost 50 x 106 WBC/L would still be acceptable.

The use of automated analyzers for cell counting in CSF is certainly feasible. The material to be analyzed is handled by the fully automated analyzers, meeting time and quality requirements, and in an objective manner. There is a good correlation between the three methods studied (Fuchs-Rosenthal chamber, CD, and UF) in most cases. In low ranges, the UF performs better than the CD (CV, 4.9% as opposed to 28%). Difficulties will be encountered when fragile cells, especially lymphocytes, are present in the sample. In these cases, the Fuchs-Rosenthal chamber is regarded as the reference method (although it is not known how many cells are destroyed during the preparation procedure). Here, the leukocyte cell counts on the CD lead to falsely high values. In contrast, the UF leads to falsely low values. In both analyzers, the presence of the atypical fragile lymphocyte population can easily be suspected.

Regression analyses showed from the high degree of scattering of the data points around the line of identity that both analyzers do not offer a sufficient degree of safety for analyzing CSF as yet.

The high degree of accuracy and linearity that is offered by both analyzers should prompt us and the manufacturers to remedy the interfering factors as described by improving the algorithms these analyzers have to offer. Once this is done, these analyzers may be very useful for cell counts in CSF.


   Acknowledgments
 
We thank the Sysmex Corporation for financial assistance and for helpful advice during the course of the study.


   Footnotes
 
Dedicated to Professor Eckart Köttgen on his 60th birthday.

1 Nonstandard abbreviations: CSF, cerebrospinal fluid; CD, CellDyn 3500; UF, UF-100 urine flow cytometer; WOC, white blood cell optical count; FWBC, fragile white blood cell; and KWOC, kinetically corrected white blood cells in optical channel.


   References
Top
Abstract
Introduction
Materials and Methods
Results
Discussion
References
 

  1. Sayk J. The sorption ring chamber for spontaneous all sedimentation and cyto-centrifugation. Felgenhauer K Holzgraefe M Prange HW eds. CNS barriers and modern CSF diagnostics 1993:355-359 VCH Verlag Weinheim. .
  2. Lehnitz R, Meyer-Rienecker H. Comparison of Hettich centrifuga- tion and Sayk sedimentation for examination of cerebrospinal fluid cells. J Neurol 1992;239(Suppl 3):91-94. [Medline] [Order article via Infotrieve]
  3. Rosier H, Vaupel HA, Körber W, Savic J, Keller R. Experiences with the CellDyn 3000, an analyzer performing a cell blood count and a five-part leukocyte differentiation. Lab Med 1993;17:47-57.
  4. Dörner K, Schulze S, Reinhardt M. First evaluation results obtained with the hematology system Cell-Dyn 3500. Klin Lab 1993;39:39-44.
  5. Burchert-Graeve M, Kock R. Automatische Leukozytendifferenzierung bei 292 Patienten mit Leukopenie. Eine Bewertung des Blutbildzählgerätes Abbott Cell-Dyn 3500 (CD3500). Clin Lab Haematol 1996;18:253-259. [Medline] [Order article via Infotrieve]
  6. IUS Product Development Division, TOA Medical Electronics Co, Ltd. Performance of the Sysmex UF 100 fully automated urine cell analyzer. Sysmex J Int 1996;6:41–5..
  7. Keijzer MH, Brandts RW. Flow cytometry and the urine laboratory: field evaluation of the Sysmex UF-100. Sysmex J Int 1997;2:117-122.
  8. Ben-Ezra J, Bork L, McPherson RA. Evaluation of the Sysmex UF-100 automated urinalysis analyzer. Clin Chem 1998;44:92-95. [Abstract/Free Full Text]
  9. Passing M, Bablok V. A new biometrical procedure for testing the equality of measurements from two different analytical methods. Application of linear regression procedures for method comparison studies in clinical chemistry. Part I. J Clin Chem Clin Biochem 1993;21:709-720.
  10. Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1986;i:307-310.



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