canine automated differential leukocyte count: study using a hematology analyzer system

Canine Automated Differential Leukocyte Count: Study Using a Hematology Analyzer System Harold Tvedten‘ Carolyn Haines’

Key Words: canine, differential leukocyte count, Technicon H-1 E

Introduction

One advantage of the Technicon H-1 E hematology analyzer (H1 :Miles, Diagnostics Division, Tarrytown, NY) is the availability of an automated differential leukocyte count (Adiff). The principles of how the instrument determines the Adiff have been previously de- scribed, induding veterinary applications.8~‘0 Briefly, the system uses two systems to identify cells. One is based on peroxidase staining of intact cells and the other is based on the appearance of nuclei of leukocytes after a basophil reagent has stripped off the cytoplasm of leukocytes with the exception of human basophils.

An evaluation of some performance characteristics of the canine Adiff was made by comparing it to the routine microscopic differential leukocyte count (Mdiff). An understanding of the characteristics and limitations of the Adiff is needed for recommendations on appropriate applications of the Adiff. An automated hematd- ogy report for canine blood samples obviating the labor intensive microscopic evaluation of blood smears for a microscopic differential leukocyte count has great cost savings. Prlor experience with this instrument using human software indicated that of the four common domestic species, the Adiff was best for dogs, so the initial evaluation was of canine blood samples.

Material and Methods

The results of analysis of 700 canine blood samples determined by an automated hematology analyzer with new species specific software (H-1 E Multi-Species V3.0- Software:Miles, Inc.) were compared to the microscopic differential leukocyte count reported on the same submissions to the veterinary clinical pathology laboratory

at Michigan State University’s Veterinary Medical Com- plex. The microscopic differential leukocyte count (M- diff), based on counting 100 leukocytes (WBC), was routinely performed by medical techndogists in the laboratory. The Mdiff was reviewed for accuracy by a clinical pathdogist when a leukocytosis or leukopenia was present or when atypical WBC were noted. Two selected observations from other clinical samples are noted in this report. One reference is also made to an earlier, similar unpublished evaluation on the Adiff and 602 canine blood samples analyzed with the original human software (Basic Operating System, Domestic, V1.2.2, 113-b497-11 :Miles, Inc.) 5 years earlier.

The H1 was calibrated to manufacturer’s specifica- tions with a few modifications. A hematology calibrator (R + D Tech-Cal H-1 Hematology Calibratory [TCVIIO]:Miles, Inc.) was used for most of the calibration rather than Technicon’s normal control blood. At the time this calibrator was chosen, Technicon did not have a separate control and calibrator. We chose not to use the normal control reagent both for calibration and then as the control, too. We continued to use this calibrator for the sake of consistency.

Five canine blood samples were used as calibrators to determine the CHCM calibration factor and the WBCB calibration factor. This optimized the system to canine blood which comprised about 2/3 of the routine submissions to the laboratory. Human blood was re- commended for optimizing these factors, but our laboratory did not perform tests on human blood for safe- ty reasons. The CHCM calibration factor adjusted the instrument to the MCHC of the canine blood samples. The WBCB calibration factor adjusted the total WBC count determined by the basophil channel to best match the WBC count determined by the peroxidase channel.

’Veterinary Clinical Center, Michigan State University, East Lansing, Michigan 48824-1314 Confinued

PAGE 90 0 Vol. 23, No. 3 0 VETERINARY CLINICAL PATHOLOGY

Automated Differential Leukocyte Counts

FIG. 1 -Peroxidase cytogram illustrating typical separation of different types of leukocytes into cell clusters which are well outlined by computer defined fields. The P is left of the platelet and debris cluster at the lower left corner of the cytogram. Note much of this platelet field is empty and extends to the left and up to include a few platelet clumps which extend at a 45 degree angle up and to the left. The L is left of the lymphocyte cluster. U is in the field for large unstained cells (LUC) containing only one dot. The N is near the neutrophil cluster. The M is over monocytes. The lower right field (E) contains the eosinophils.

The statistical analysis of the paired data included comparison between descriptive statistics such as mean, standard deviation (SD), and range for each type of WBC by A-diff and Mdiff. Least squares para- meters slope, intercept and standard error of estimate, Pearson correlation coefficient, and paired Student t- test values were also determined by a computer pro- gram (SAS' System:SAS Institute, Cary, NC).

The raw data were inspected for discrepancies which seemed large. This was to find those samples which might identify any serious errors in the A-diff or M-diff. For neutrophils a discrepancy large enough to evalu- ate was arbitrarily set at 10 or more percentage points, with neutrophil counts of 72 and 82% on the same sample, for example. This is close to the inherent variability of the M-diff.' A percentage count of 50% has a 95% confidence range of 40 to 60% if 100 WBC are counted. Samples with the most apparent discrepancies were evaluated for alterations which may explain the error. All explanation for each was speculated based on the appearance of the peroxidase cytograms compared to that considered appropriate (Fig. 1).

TABLE 1 Comparison of the Automated and Mlcroscoplc Differential

Leukocyte Counts in 700 Canine Samples'

Paired WBC Mean Deviation Range r

1. ASeg

MSeg 2. ASeg

MSegNseg 3. ALymph

MLymph

4. ALymLUC MLymph

5. AEosin MEosin

6. AMono

7. AMonLUC

8. ABaso

9. Hct

MMono

MMono

MBaso

PCV

72.4

75.3

72.4

76.2

18.3

15.1

18.8 15.1

3.73 3.27

4.97

5.31

5.4

5.31

0.1

0.0

43.5

42.8

12.9

14.1

12.9

13.9

10.8

12.0

11.3

12.0

4.0

3.9

3.8

4.6

4.1

4.6

0.2

0.1

10.0

9.4

4-93

4-97

4-93 4-97

1-88

0-94

2-90

0-94

0-35

0-28

0-76

0-55

0-76

0-55

0-3

0-2

8-77

8-75

0.88

0.88

0.87

0.87

0.87

0.87

0.85

0.85

0.82

0.82

0.24

0.24

0.37

0.37

0.00

0.00

0.99

0.99

'Note: ASeg Is the percentage of neutrophils (Seg) from the Technicon H-1 automated dlfferentlel count (A). Similarly, MSeg 1s the percentage of segmented neutrophils determined by the microscopic differential count (M). NSeg IS nonsegmentea neutrophils. ALym is lymphocytes by the automated differential, Mon or Mono is monocytes. LUC Is large unstained cells, and Bas0 is basophils

Certain patterns on the peroxidase cytogram were then selected if they seemed consistent enough that one could recognize them prospectively on new samples. A potential use would be to use certain patterns to prospectively identify samples with potential errors in the Adiff or M-diff. All H1 reports were then reexam- ined and those samples with these patterns were deleted. This second, groomed data set of 654 samples was statistically analyzed as the original data set. One pattern, that of lipemia, was also evaluated as a separate data pool.

Results and Discussion There are different methods to compare a new test

to a test in routine use. Often mean, standard deviation, range and Pearson correlation coefficient (r) have been used.'S2 The r value is sensitive to random error but not constant error or proportional error so other common statistical tests such as least squares analysis (slope, intercept, and standard error of estimate), standard deviation of difference, Student's t-test, and

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VETERINARY CLINICAL PATHOLOGY 0 Vol. 23, NO. 3 0 PAGE 91


TABLE 2 Regression Analysis for Technlcon H-1 and Microscopic

Leukocyte Differential Counts in 700 Dogs and a Human Study’

Cell Type Species a b SEY

ASeg:MSeg

ALym:MLym

AMon: MMon

AEos:MEos

ABas: MBas

Neutrophils

Lymphocytes

Monocytes

Eosinophils

Basoph i Is

Canine

Canine

Canine

Canine

Canine

Humana

Human’

Humana

Humana

Humana

12.98 6.34 3.62 0.96 0.13 2.66 1.59 2.07 0.00 0.30

0.79 0.77 0.24 0.85 0.01 0.95 0.94 0.75 1.04 0.69

1.14 0.31 0.15 0.11 0.00 2.65 2.60 1.82 0.80

NA

*Coefficients for regression equation y = a + b(x) with y being the dependent variable, the automated differential count in percentage. The a is the intercept and b IS

slope. SEy IS the standard error of estimate in the y direction. The canine values are from the current evaluation and the humana values are from the literature? Abbreviations are as in Table 1. NA is not available.

f-test help identify these three types of errors3 Inter- cept is sensitive to constant error and slope is sensitive to proportional error. Simply plotting the difference between the two tests compared to the average of the two tests can visually illustrate a consistent bias7 If there is a consistent difference in results of two tests, either the new test or established test may be in error, so the average of the two tests is assumed to be closer to the true value.

A comparison of A-diff and M-diff data is given in Table 1. The comparison of mean, SD, and range for the percentages of differential WBC counts by cell type(s) are similar when one considers the inherent variability of microscopic differential leukocyte counts. For example the reported 95% confidence limits of a lymphocyte count of 15% would be 8 to 24% if a 100 WBC M-diff were perf~rmed.~ Increasing the number of WBC counted to 200 only would improve these limits to 10 to 21%. If 500 cells were counted the limits still should be 12 to 19%.

The comparison of the hematocrit (Hct) determined by the H1 and a packed cell volume (PCV) determined by the microhematocrit method was included to illustrate two methods with essentially the same mean, SD, and range and with a Pearson correlation coefficient of 0.99 where 1 .O is perfect. The slope was 1.05 and intercept was -1.49 with Hct being the dependent variable (y).

Based on the Pearson correlation coefficients the A-diff correlated adequately well to the standard M-diff method for neutrophils, lymphocytes, and eosinophils. There was poor correlation between the A-diff and M-

a.. . . . . . . . I. . . . . . . . :, . . . . . . . . . I.. . . . . . . ., I.. . . . . . . . I

i I ................................................. L - . . *.

I

FIG. 2 -The peroxidase cytogram to the left labelled PER0X:Dog illustrates canine blood analyzed with canine software. A cell cluster thoughtto representmonocytes extendsfrom the monocyteareainto the top of the lymphocyte area. Note a distinct lymphocyte cluster in the lower half of the lymphocyte field. The peroxidase cytogram on the right, labelled PEROX, is canine blood analyzed with the human software used earlier in our laboratory. Note the lower border between the lymphocyte and LUC areas. It appears the monocyte cluster extends from the monocyte field into the LUC field.

diff for monoctyes. There was no correlation between the A-diff and M-diff for basophils. These are similar results to three other studies on the H1 A-diff. A pre- vious correlation study of the canine Adiff in our laboratory using our original human software and Mdiff (unpublished data) had Pearson correlation coeff i- cients: neutrophils 0.87, lymphocytes 0.92, eosinophils 0.79, monocytes 0.38, and basophils -0.05. A study of human blood had Pearson correlation coefficients (and y intercept, %) of: neutrophils 0.86 (12.6), lymphocytes 0.83 (5), eosinophils 0.77 (7), monocytes 0.44 (4.9), and basophils 0.20 (0.4).’ Another study of human blood had Pearson correlation coefficients of: neutrophils 0.94, lymphocytes 0.94, eosinophils 0.81, monocytes 0.64, and basophils 0.23.’

The slope and intercept values for canine and human blood are given in Table 2. The slope values (b) were closer to the optimal value of 1.0 and the intercept values (a) were closer to 0.0 in the human study than our evaluation of canine blood. Constant errors are estimated from the intercept. Proportional errors are estimated from the slope. The standard error of the estimate in the y direction (SEy) indicates random error and appeared smaller in our canine results than the reported human results. The Student’s t-test indicated the data sets of pairs for all WBC were signifi- cantly different (p > 0.0001). Thus one should not interpret results from the A-diff while using reference ranges established from the M-diff. Reference ranges for the canine A-diff using the multi-species software were reported.6

COC4ifllJd



. .........,......... Il.:. ..... 1 .........,......... . -! .. I!

I? i I'

.......... I .........,......... .................... .

.......... I ......... .............................. r FIG. 3- Peroxidase and basophil cytograms illustrate the lipemia pattern. In the peroxidase cytogram the particles extend up from the platelet clusterasa rightforkdividing awayfromthe lymphocytecluster but remaining in the lymphocyte field and extending up into the neutrophil field. On the basophil cluster these particles form a lazy S on the lower right side of the regular worm-shaped basophil cytogram.

Visual inspection of peroxidase cytograms sug- gested a possible explanation for the inadequate correlation of the A-diff for monocytes (Fig. 2). Those data pairs for monocytes with the greatest discrepancy often had peroxidase cytograms where a cell cluster, apparently representing the monocytes, extended from the monocyte field into the lymphocyte field. The left peroxidase cytogram in Figure 2 determined with new canine software illustrates an example. A portion of the monocytes would be counted as lymphocytes. The mean percentage of lymphocytes was higher with the A-diff than the M-diff and the mean percentage of monocytes was lower with the A-diff than the M-diff (Table 1) . The new canine software set a higher thres- hold between lymphocytes and large unstained cells (LUC) than the original human software (Fig. 2). Cells classified as LUC by the H1 usually include large lymphocytes, reactive lymphocytes, and monocytes which stain poorly for peroxidase. Blast/leukemic cells also are classified LUC. Monocytes which on the human software often extended to some extent into the LUC field now seem to extend into an expanded lympho-

r' .........I.. ........................... 1 .......... I ';

i BASO:Dog ' I i

,......... ........................................ . FIG. 4 - Peroxidase and basophil cytograms illustrate the leukemia pattern. Note the dark cell cluster extending from the lymphocyte cluster up through the LUC field on the peroxidase cytogram. On the basophil cytogram the leukemic cells extend at a 45" angle up from a dark area at the left of the regular basophil cytogram.

cyte field on the canine software (Fig. 2). The sum of the percentage of LUC and monocytes of the Adiff compared to the monocyte percentage of the M-diff had an improved Pearson correlation coefficient (Table 1). A software adjustment by the instrument's devel- oper to lower the 1ymphocyte:LUC boundary may put more of the misclassified monocytes into the LUC category. It would be easier to detect a population of monocytes with poor peroxidase staining by noting an increase in the percentage LUC rather than some monocytes being hidden among the lymphocytes.

The patterns selected which might indicate an error in the A-diff included the fdlowing: (These were also used to exclude samples to form the groomed data pod). 1) The linear pattern reported to be lipemia (Fig. 3).' This is a dense cdumn of dots on the peroxidase cytogram curving from the platdet/debris cluster through the lymphocyte and neutrophil fields. These dots would be classified in the A-diff as WBC, depending on the WBC counting field in which they were found. Dots usually represent cells. In this case the dots are thought to be lipid droplets. When a 1% suspension of vegetable

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VETERINARY CLINICAL PATHOLOGY 0 Vol. 23. No. 3 a PAGE 93


i BflS01Dog - . . .

~......... 1 ......... .............................. . I

..

.. -

~......... I ......... .............................. .

i . ........r.........l.........~.........,......... r

......... ........................................ . . . . . I ...

.......... I ......... .............................. .

: . ' I .

i ......... 1 ......... .............................. . FIG. 5- Peroxidase cytogram and basophil cytograms illustrate the location of nucleated erythroid cells (NRBC). This dog with sep- ticemia had no neutrophils, 90% lymphocytes, 10% monocytes, and 436 NRBC/100 WBC. In the peroxidase cytogram the NRBC fill the space between the lymphocyte and platelet/debris clusters. The lack of a space or valley between the clusters probabty interferes with proper identification of the lymphocyte cluster. In the basophil cytogram the normal pattern resembles a worm (Figs. 7 & 8). The head of the worm contains lymphocytes and monocytes and the body of the worm normally contains granulocytes, consisting mainly of neutrophils. This dog had no neutrophils, so the body of the worm con- sists of NRBC. A few NRBC extended up into the large upper box where they would be identified as basophils.

oils is analyzed by the instrument, it will reproduce the same curvilinear pattern. An indistinct row of few dots was accepted and the sample was not deleted. The lipemia pattern on the basophil cytogram appears as a lazy S-shaped curve to the right of the "worm" (Fig. 3). The worm is the descriptive name (jargon) for the shape of the normal basophil cytogram, which normally is shaped like a worm with a round head on the left and a long body extending to the right. 2) A strong, dark extension of cells from the lymphocyte area high into the LUC area indicated a leukemia pattern (Fig. 4). On the basophil cytogram, the blast cells extend up and to the right of the "head of the worm" a: a 45" angle into the basophil box where they are classified as basophils. 3) Heavy filling of the space usually found between the platelets and lymphocyte cluster may obscure where to

FIG. 6 - Peroxidase and basophil cytogram of canine blood with 9% basophils. The cytograms appear basically normal and there are essentially no basophils found in the large basophil box labelled BASO: Dog.

divide the cluster dots normally representing lymphocytes, platelets, and cell debris (Fig. 5). Nucleated RBC, when numerous, fill the space between lymphocytes and platelets/debris. The presence of NRBC was usually supported by the sprinkling of dots above the worm in the basophil cytogram (Fig. 5). 4) Significant extension of dots into the basophil box of the basophil cytogram indicated probable error with the A-diff (Figs. 4 & 5). The magnitude of the error was indicated by the magnitude of the percentage of basophils in the Adiff. Discussion on canine basophils and Figure 6 follows. 5) One pattern was perceived when many dots, inter- preted to be platelet clumps, extended from the platelet area into the lymphocyte field or were more randomly distributed and seemed to disrupt the usual, proper placement of computer defined fields around WBC clusters. The number of dots was important and a few isdated platelet dumps in WBC fields were ignored. 6) Inadequate separation of eosinophil cluster and neutrophil duster so that a portion of either cell cluster was included in the other cell's field occurred.

Canine basophils did not appear to be detected by the canine A-diff (Fig. 6). Blood samples of three dogs with strong basophilia did not have basophils in the

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PAGE 94 Vol. 23, No. 3 VETERINARY CLINICAL PATHOLOGY


TABLE 3 Comparison of the Automated and Microscopic

bMwential Leukocyte Counta in 654 Canine Samples Atter Selected Deletions'

~

Paired WBC Mean Deviation Range r

1. ASeg

MSeg

2. ASeg

MSegNseg

3. ALymph

MLymph

4. ALymLUC

MLymph

5. AEosin

MEosin

6. AMono

MMono

7. AMonLUC

MMono

MBaso

8. ABaso

9. Hct

PCV

72.8

75.3

72.8

76.2

18.1

15.3

18.5

15.3

3.8

3.3

4.9

5.2

5.2

5.2

0.1

0.0

43.8

43.0

12.2

14.1

12.2

13.9

10.5

12.1

10.8

12.1

4.1

4.0

2.6

4.2

2.9

4.2

0.2

0.1

9.6

9.0

6-93

4-97

6-93

4-97

1-87

0-94

2-90

0-94

0-35

0-28

3-26

0-46

0-27

0-46

0-2

0-2

9-77

9-75

0.90

0.90

0.91

0.91

0.88

0.88

0.88

0.88 0.83

0.83

0.39

0.39

0.46

0.46

0.83

0.83

0.99

0.99

'Abbreviations are as in Table 1

basophil field of the basophil cytogram nor in the A- diff. Canine basophils apparently do not react to the basophil reagent like human basophils. Canine basophils appear different from basophils of other species. They have few indistinct granules and are often misidentified on an M-diff as monocytes.

Another unique and rare canine cell is the grey eosinophil or vacudated eosinophil. Most canine eosinophils have large and variable eosinophilic granules and are well detected by the A-diff. The grey eosinophil has clear appearing vacuoles rather than eosinophilic granules which may not be detected as eosinophils on the Adiff (Fig. 7). They are often associated with the Greyhound breed of dogs. The canine grey eosinophil is often misidentified as toxic or vacuolated neutrophils by inexperienced microscopists.

Table 3 illustrates the mean, SD, range, and r values for the groomed data pool. Compared to the full data pool in TaMe 1 there was slight improvement in the r values for neutrophils, lymphocytes, monocytes, and eosinophils. This improvement suggests that some selection criteria can be predetermined to indicate when an M-diff should be used to confirm an A-diff. Use of

.......... I . . . . . . . . . I . . . . . . . . . , . . . . . . . . . I......... ' .. . _ I (I .:-. I' ,::- .:..:. : F- . (!. . . . .

- .-.oh : .- -

-

>... ..... .,... .. ..!%!?OX!!M .... 1 ........ .: ..........,.........I......... I .........,......... . .~ i 8ASO:Dog ?

-

~......... I ......... ~.........l.........l......... r

FIG. 7 - Peroxidase and basophil cytogram of blood from a Greyhound with 18% "grey" eosinophils. Grey eosinophils in oc- casional dogs have clear vacuoles instead of routine eosinophilic granules. The peroxidase cytogram had no WBG in the eosinophil field (lower left area) of the peroxidase cytogram.

these or other selection criteria for accepting the A-diff can slightly improve the correlation of the A-diff. It is not clear whether the magnitude of improvement ob- served merits routine use of these selection criteria.

The descriptive statistics for 25 samples deleted with a prominent lipemia pattern included the following: The correlation between the percentage of eosinophils by the A-diff and M-diff, as indicated by an r value of 0.43, was much worse than the original or groomed sample pods. The r value of 0.72 for neutrophils was noticeably worse. The r value for lymphocytes was about the same at 0.86, though the cdumn of particles often appeared to be included in the lymphocyte field on peroxidase cytograms. The correlation between the percentages of monocytes was surprisingly high (r = 0.88).

The use of the A-diff as well as many other features of an automated hematology analyzer have the advantages of reduced labor costs, increased precision, and computer generated graphic display of cell populations in a blood sample. This comparison of the Mdiff and A-diff indicates the A-diff is reasonably ac- curate in dogs for selected applications when some problems are recognized and avoided. These prob-

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VETERINARY CLINICAL PATHOLOGY 0 Vol. 23, No. 3 0 PAGE 95


lems include determination of monocytes, basophils, and grey eosinophils. Monocytes, however, are usually low in number and even in this hospital population only averaged 5% of the leukocytes, so the magnitude of this error should usually be relatively small. Baso- philia or the presence of grey eosinophils in dogs is rare and thus an uncommon source of error.

Other concerns not evaluated in this study include detection of the presence and magnitude of a left shift or toxic change in neutrophils. The appearance of the basophil cytogram can indicate a severe left shift or toxicity of neutrophilsg but this is probably not very sensitive in detecting these alterations. The left shift flag of the H1 correlated poorly to increased band neutrophils in human blood.'

The Adiff should not be considered as an all or nothing alternative to M-diff and evaluation of the blood smear. Microscopic evaluation of the blood smear still provides some information unavailable from the automated evaluation of blood. These advantages include a more complete evaluation of erythrocyte morphology, evaluation of distribution and clumping of platelets, evaluation of leukocytes for toxic change, left shifts, diagnosis of leukemia and less common morphologic alterations like viral inclusion bodies, etc.

The Adiff appears appropriate to use in several situ- ations. The A-diff appears quite satisfactory for screen- ing dogs in research projects for alteration in WBC populations, especially in research projects in which most control and treated animals have few or no alterations in WBC. This could lead to significant cost savings considering the large number of hematologic examinations required.

In a veterinary hospital setting there are such a diverse population of dogs with such a wide variety of problems that microscopic evaluation of blood smears is advisable at least during the initial evaluation of an ill dog. Following a case through treatment and recovery, however, may be an excellent applica- tion of the automated features of the H-1 E. The A-diff should be quite useful in a series begun by an M- diff when none of the alterations poorly detected by an A-diff were noted. The savings could allow more frequent testing of the patient or reduce the hospital costs.

REFERENCES 1. Nelson L, Charache S. Wingfield S, Keyser E: Laboratory Evaluation of Differential

White Blood Cell Count Information From the Coulter S-Plus IV and Technicon H-1 in Patient Populations Requiring Rapid Turnaround Time. Am J Ciin Path 91 563.569, 1989.

2. Wenz 0, Ramierez MA, Burns ER: The H.1 Hematology Analyzer. Its Performance Characteristics and Value in the Diagnosis of Infectious Disease. Arch Path Lab Med 111:521-524, 1987.

Method-Comoarison Studies. Clin Chem 1949.57. 1973. 3. Westgard JO. Hunt MR: Use and Interpretation of Common Statistical Tests in

4. Jenson'AL: Comparing Laboratory Tests Using the Difference Plot Method. Vet Clin Path 2246-48, 1993.

5. Koepke JA. Standardization of the Manual Differential Count. Lab Med 11371-375 1960.

6. Swaim WR: Laboratory and Clinical Evaluation of White Blood Cell Differential Counts Comparison of the Coulter VCS, Technicon H-I, and 800-Cell Manual Method. Am J Clin Path 95:381-388, 1991.

7. Tvedten HW Reference Values in Small Animal Clinical Diagnosis by Laboratory Methods. ed 2. Ed by Willard. Tvedlen, Turnwald. WB Saunders Co. Philadelphia, p 359, 1994.

8. Tvedten HW Automated Cytochemical Hematology: "Case A-1 Lipemia' in Multi- Species Hemalology Atlas: Technicon H-1 E System. Miles Inc, Tarrytown NY. pp 126-127, 1993.

9. Tvedten HW Automated Cylochemical Hematology: Animal Applications in Multi- Species Hematology Atlas: Technicon H-1E System. Miles, Inc. Tarrytown, NY, pp 18-27 and 98-99. 1993.

10. Tvedten HW: Advanced Hematology Analyzers Interpretation of Results. Vet Clin Path 22:72-80. 1993.


canine automated differential leukocyte count: study using a hematology analyzer system

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