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Biological variations of leukocyte numerical and morphologic parameters determined by UniCel DxH 800 hematology analyzer.

Some analytes determined in the clinical laboratory may vary during an individual's lifetime because of biological inherency involved in the aging process. For example, the numbers of leukocytes change with age and during pregnancy. On the day of birth, a newborn has a high white blood cell (WBC) count. This number falls to adult levels within 2 weeks. The percentage of neutrophils is high for the first few weeks after birth, but then lymphocyte predominance is seen. Until about 8 years of age, lymphocytes are more predominant than neutrophils. In the elderly, the total WBC count decreases slightly. Pregnancy results in leukocytosis, primarily due to an increase in neutrophils with a slight increase in lymphocytes. (1)

Two numerical measurements of WBCs commonly done in the hematology laboratory are the total number of WBCs in blood and the percentages of each of the 5 types of WBC, known as differentials. In addition to traditional numerical parameters measured, a Coulter hematology analyzer with VCS technology, such as the UniCel DxH 800 (Beckman Coulter Inc, Brea, California), uses 3 independent energy sources simultaneously to evaluate the intrinsic biophysical properties of more than 8000 leukocytes in their near-native states. Namely, it uses direct current impedance to measure cell volume for accurate size of all cell types; radio frequency opacity to characterize conductivity for internal composition of each cell; and a laser beam to measure light scatter for cytoplasmic granularity and nuclear structure. It also measures the degree of cell size variation. These measurements of cellular morphologic properties are known as cell population data (CPD).

The DxH 800 is the newest fully automatic hematology analyzer. In addition to volume and conductivity measurements, a new flow cell design supports multiple angles of light scatter measurements, enabling enhanced data acquisition for the WBC differentials. These multiple angles of light scatter measurements include median angle light scatter, lower median angle light scatter, and upper median angle light scatter, which detect granularity and membrane topography; axial light loss measurement, which analyzes cellular transparency; and low-angle light scatter, which is a cellular complexity index. This enhanced data collection allows the DxH 800 to collect 10 times more information on each sample when compared to other Coulter hematology analyzers, such as the LH 780, and give accurate determination of a WBC count even in the presence of interference. (2)

It has previously been demonstrated that neutrophil morphologic parameters change significantly in septic patients. For example, the neutrophil CPD, such as mean neutrophil volume and neutrophil volume distribution width, are significantly increased not only in septic patients with high WBC, but also in those with normal or low WBC. (3-7) The mean neutrophil volume and/or neutrophil volume distribution width, which reflect respectively neutrophil size and size variations, show superior sensitivity and specificity for predicting sepsis compared to WBC, the percentage of neutrophils, band counts, C-reactive protein, or procalcitonin, proving to be promising indicators for the diagnosis of acute bacterial infection. (8,9) In addition, the mean neutrophil volume and neutrophil volume distribution width are significantly increased in postsurgical patients with bacterial infection compared with non infected patients, although WBC was increased in both groups. (9,10) Alterations of the lymphocyte CPD in viral infection (11,12) and in chronic lymphocytic leukemia and other chronic lymphoproliferative disorders (12) as well as the monocyte CPD in malaria infection (13) have also been reported.

Although several previous studies have investigated biological variations of numerical parameters of leukocytes (14-17) and demonstrated hour-to-hour and day-to-day intraindividual fluctuation of WBC, (17) this study is the first to investigate the biological variations of morphologic parameters of leukocytes because of their increasing clinical significance. As we know, the variation can be described as random fluctuation around a homeostatic setting point. The test results of any individual may vary over time, because of 3 factors: preanalytical variation, such as preparation of the individual for sampling and sample collection itself; analytical variation (precision), such as random error and possibly systematic error (changes in bias due to calibration); and inherent biological variation. (18) Using the newest analyzer, the DxH 800, with likely improved analytical precision, we attempted to reinvestigate biological variations of leukocyte numerical parameters. The biological variations of morphologic parameters or CPD were also studied.

MATERIALS AND METHODS

Subjects

The participants were 40 healthy volunteers of Chinese ethnicity (21 women and 19 men) with ages ranging from 20 to 40 years. None of the women were menstruating. All participants maintained their normal lifestyles, including no excess of alcohol, tea, or tobacco consumption, and did not engage in strenuous exercise during the study period.

Specimen Collection

The blood samples were drawn in duplicate at 8 am, noon, and 4 pm each day for 3 consecutive days. The participants were in sitting position for at least 15 minutes before drawing. All samples were collected in EDTA-anticoagulated tubes (BD Inc, Franklin Lakes, New Jersey) by a single experienced phlebotomist and analyzed within 2 hours after specimen collection.

Specimen Analysis

All samples were analyzed using a single DxH 800 hematology analyzer. Before each batch sample analysis, the instrument quality controls were performed using the same lots of Coulter SCAL Calibrator (lot 112753780), and Coulter 6C Cell Control with 3 levels at different concentrations (for level 1, lot 122755170; for level 2, lot 132757540; and for level 3, lot 142755190) to allow consistent determination during the course of the study.

The study protocol was approved by the hospital ethics committee.

Automated Hematologic Data Collection

Data collected from the DxH 800 included total WBC, leukocyte differentials, and CPD, which were generated by each individual cell passing through the aperture and optically and electronically being measured by the analyzer. The CPD included volume, conductivity, and 5 different light scatters (median angle light scatter, lower median angle light scatter, upper median angle light scatter, axial light loss measurement, and low-angle light scatter) for neutrophils, lymphocytes, and monocytes.

Statistical Analysis

Nested analysis of variance and coefficients of variation were performed using SPSS software, version 10.0 (SPSS, Chicago, Illinois) and Microsoft (Redmond, Washington) Excel 2003. Analytical coefficient of variation was calculated from 10 independent tests using Coulter 6C Cell Control (Beckman Coulter). Comparison between 2 means was performed by Student t test. A P value less than .05 was considered significant.

RESULTS

Within-Subject and Between-Subjects Biological Variations

We initially studied within-subject (CVI) and between-subjects (CVG) biological variations on both leukocyte numerical parameters and morphologic parameters. As shown in Table 1, although we achieved similar CVI (10.97% versus 10.9%) and CVG (19.3% versus 19.6%) values for total leukocyte counts compared with published data, (14,18) the CVI values for absolute neutrophil, monocyte, and lymphocyte differential counts were much smaller than previously reported (14,18). 6.7% versus 16.1%, 10.7% versus 17.8%, and 9.8% versus 12.3%, respectively. The CVG values for absolute neutrophil, monocyte, and lymphocyte counts were also smaller than previously reported (14,18): 13.8% versus 32.8%, 25.7% versus 49.8%, and 18.8% versus 21.3%, respectively. The smaller CVI and CVG values in this study are likely due to much improved analytical precision (analytical coefficient of variation) of the DxH 800 for leukocyte differential counts (0.93% versus 8.1% for neutrophil, (18) 0.9% versus 6.9% for lymphocyte, (1,4) and 2.9% versus 8.9% for monocyte (18)). Compared to numerical parameters, the morphologic parameters or CPD showed much smaller overall CVI and CVG (Table 2). The coefficients of variation for most parameters were less than 5%, suggesting that these parameters are less variable around the homeostatic set point intraindividually and interindividually. The index of individuality, calculated as the simple ratio of the [CV.sub.I]/[CV.sub.G], (18) for all morphologic parameters was low, ranging from 0.3 to 0.92 with a mean of 0.71 (Table 2). A low index of individuality indicates that conventional reference values for these parameters may be of little utility, particularly when deciding whether changes in an individual have occurred. (18)

Intraday and Interday Biological Variations

The intraday and interday biological variations on both leukocyte numerical parameters and morphologic parameters were next investigated. As shown in Table 3, the biological coefficients of variation of total leukocytes and their differential counts are constant except for the 4 PM time point on day 3. We do not have explanations for this. The results are slightly different from the previous observation, which showed hour-to-hour variations and subject-specific diurnal variation. (17) One explanation for this is that the current study uses a more advanced analyzer with likely improved precision, which may minimize the interference of likely analytical variation. On the other hand, all coefficients of variation for morphologic parameters or CPD are less than 10%, with most less than 5%, suggesting that these parameters are more constant in biological homeostatic conditions (Table 4).

COMMENT

Although several previous studies have investigated the biological variations of leukocyte numerical parameters, (14-17) such as total leukocytes and their differential counts, this carefully designed study is the first attempt to our knowledge to document the biological variations of leukocyte morphologic parameters, also known as CPD. The CPD are generated by Coulter hematology analyzers with VCS technology. Using the newest analyzer, the Coulter UniCel DxH 800, we have demonstrated that CVI and CVG for leukocyte numerical parameters are much smaller than previously reported, (14,18) likely because of improved analytical precision of the analyzer (analytical coefficient of variation = 0.93% versus 8.1% for neutrophils, (18) 0.9% versus 6.9% for lymphocytes, (14) and 2.99% versus 8.9% for monocytes (18)). We also evaluated the biological variations of leukocyte morphologic parameters or CPD, mainly because of an increasing number of publications (3-13) in recent years demonstrating the potential clinical utility of CPD in diagnosing many pathologic conditions, particularly in acute bacterial infection or sepsis. (3-10) We showed that the CPD have much smaller overall CVI and CVG compared to numerical parameters, suggesting that these morphologic parameters are less variable around the homeostatic set point intraindividually and interindividually. In addition, the index of individuality for all morphologic parameters was low. A low index of individuality suggests that these parameters may have marked individuality and the conventional reference values for these parameters may be of little utility. (18) We also show that intraday and inter day biological variations of leukocyte numerical parameters and CPD are fairly constant in the age group examined, although the study only spans the 3-day period and the population studied is limited to Chinese ethnicity. However, there was no previous report regarding biological variations on leukocyte numerical parameters using an Asian population. Further studies with a similar ethnic population or other ethnic groups may be warranted for validating the current findings.

These observations are clinically important. Data on CVI and analytical precision may be used to determine the change that occur in an individual's serial results before the change is significant, and to generate objective delta-check values for use in quality management. (18) Comparing [CV.sub.I] and [CV.sub.G] on CPD may allow us to decide the utility of traditional population-based reference ranges. (18) Data on biological variation may also be used for determining the number of samples needed to get an estimate of the homeostatic setting point within a certain percentage with a stated probability, and deciding the best way to report test results, the best sample to collect, and the test procedure of greatest potential use. (18) Lastly, documentation of CPD data on biological variations is an essential prerequisite in the development of any new application of CPD clinically. (18)

In summary, [CV.sub.I], [CV.sub.G], and intraday and interday biological variations measured by the DxH 800 for leukocyte numerical parameters in healthy adults are smaller than those previously reported. Biological variations for CPD show even less fluctuation at homeostatic set point, making them potentially more reliable parameters for clinical use. The index of individuality for CPD was low, indicating that reference values for these parameters may have little utility.

References

(1.) Sibylle V, Klaus L. Homeostatic regulation of blood neutrophil counts. J Immunol. 2008; 181(8):5183-5188.

(2.) Jean A, Boutet C, Lenormand B, et al. The new hematology analyzer DxH 800: an evaluation of the analytical performances and leukocyte flags, comparison with the LH755. Int J Lab Hematol. 2011; 33(2):138-145.

(3.) Chaves F, Tierno B, Xu D. Quantitative determination of neutrophil VCS parameters by the Coulter automated hematology analyzer: new and reliable indicators for acute bacterial infection. Am J Clin Pathol. 2005; 124(3):440-444.

(4.) Chaves F, Tierno B, Xu D. Neutrophil volume distribution width: a new automated hematologic parameter for acute infection. Arch Pathol Lab Med. 2006; 130(3):378-380.

(5.) Lee JC, Ahern TP, Chaves FP, Quillen K. Utility of hematologic and volume, conductivity, and scatter parameters from umbilical cord blood in predicting chorioamnionitis. Int J Lab Hematol. 2010; 32(3):351-359.

(6.) Mardi D, Fwity B, Lobmann R, Ambrosch A. Mean cell volume of neutrophils and monocytes compared with C-reactive protein, interleukin-6 and white blood cell count for prediction of sepsis and nonsystemic bacterial infections. Int J Lab Hematol. 2010; 32(4):410-418.

(7.) Park DH, Park K, Park J, et al. Screening of sepsis using leukocyte cell population data from the Coulter automatic blood cell analyzer DxH800. Int J Lab Hematol. 2011; 33(4):391-399.

(8.) Bagdasaryan R, Zhou ZR, Tierno B, Rosenman D, Xu D. Neutrophil VCS parameters are superior indicators for acute infection. Lab Hematol. 2007; 13(1):12-16.

(9.) Charafeddine KM, Youssef AM, Mahfouz RA, Sarieddine DS, Daher RT. Comparison of neutrophil volume distribution width to C-reactive protein and procalcitonin as a proposed new marker of acute infection. Scand J Infect Dis. 2011; 43(10):777-784

(10.) Zhu Y, Cao X, Chen Y, Wang Y, Yuan K, Xu D. Neutrophil cell population data: useful indicators for post-surgical bacterial infection [published online ahead of print December 29, 2011]. Int J Lab Hematol. doi: 10.1111/j.1751553X.2011.01394.x.

(11.) Zhu Y, Cao X, Xu D. Detection of morphologic changes of peripheral mononuclear cells in Hepatitis B virus infection using Coulter LH750. Lab Hematol. 2011; 17(3):22-26.

(12.) Silva M, Fourcade C, Fartoukh C, et al. Lymphocyte volume and conductivity indices of the haematology analyser Coulter GEN.S in lymphoproliferative disorders and viral diseases. Clin Lab Haematol. 2006; 28(1):1-8.

(13.) Briggs C, DaCosta A, Freeman L, Aucamp I, Ngubeni B, Machin SJ. Development of an automated malaria discriminant factor using VCS technology. Am J Clin Pathol. 2006; 126(5):691-698.

(14.) Dot D, Miro J, Fuentes-Arderiu X. Biological variation of the leukocyte differential count quantities. Scand J Clin Lab Invest. 1992; 52(7):607-611.

(15.) Dot D, Miro J, Fuentes-Arderiu X. Within-subject biological variation of hematological quantities and analytical goals. Arch Pathol Lab Med. 1992; 116(8):825-826.

(16.) Statland BE, Winkel P, Harris SC, Burdsall MJ, Saunders AM. Evaluation of biologic sources of variation of leukocyte counts and other hematologic quantities using very precise automated analyzers. Am J Clin Pathol. 1978; 69(1):48-54.

(17.) Winkel P, Statland BE, Saunders AM, Osborn H, Kupperman H. Withinday physiologic variation of leukocyte types in healthy subjects as assayed by two automated leukocyte differential analyzers. Am J Clin Pathol. 1981; 75(5):693700.

(18.) Fraser CG. Biological Variation: From Principles to Practice. Washington, DC: AACC Press; 2001.

Huqiang Tang, MS, MPH; Jiyong Jing, MS; Dandan Bo, BS; Dongsheng Xu, MD, PhD

Accepted for publication January 31, 2012.

From the Clinical Laboratory Center, the Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang, China (Tang and Jing); the School of Medical Sciences and Laboratory, Jiangsu University, Jiangsu, China (Ms Bo); and the Hematopathology Program, CBLPath, Inc, Rye Brook, New York (Dr Xu).

The authors have no relevant financial interest in the products or companies described in this article.

Reprints: Dongsheng Xu, MD, PhD, CBLPath, Inc, 760 Westchester Ave, Rye Brook, NY 10573 (e-mail: dxu@cblpath.com).
Table 1. Biological Coefficients of Variation of Numerical Parameters

Analytes CVa, % CVi, % CVg, % II

Leukocyte count 0.77 10.97 19.3 0.57
Neutrophil count 0.93 6.77 13.8 0.49
Lymphocyte count 0.9 9.85 18.85 0.52
Monocyte count 2.99 10.73 25.69 0.42

Abbreviations: CVa, analytical coefficient of variation; CVg,
between/subjects biological variation; CVi, within/subject
biological variation; II, index of individuality (CVi/CVg).

Table 2. Biological Coefficients of Variation of Morphologic
Parameters

Analytes CVa, % CVi, % CVg, % II

Neutrophils
 Volume
 Mean 0.44 1.71 2.93 0.58
 SD 1.58 4.42 6.44 0.69

Conductivity 0.56 0.9 1.18 0.76
MALS 0.52 1.5 2.62 0.57
UMALS 0.73 0.78 2.63 0.3
LMALS 0.43 2.19 3.25 0.67
LALS 0.43 2.73 4.3 0.63
AL2S 0.61 0.73 1.73 0.42

Lymphocytes
 Volume
 Mean 1.26 1.47 2.84 0.52
 SD 5.35 4.45 8.68 0.51

Conductivity 0.86 0.95 1.08 0.88
MALS 1.34 2.34 3.55 0.66
UMALS 1.74 3.95 5.66 0.7
LMALS 1.59 3.2 4.31 0.72
LALS 1.12 3.53 4.17 0.85
AL2S 2.87 3.52 3.92 0.89

Monocytes
 Volume
 Mean 1.38 1.41 2.28 0.62
 SD 4.19 5.31 8.78 0.61

Conductivity 1.01 1.09 1.18 0.92
MALS 1.6 2.16 3.06 0.71
UMALS 1.43 1.49 2.65 0.56
LMALS 3.43 3.71 4.68 0.79
LALS 6.36 6.54 8.33 0.78
AL2S 2.76 3.16 3.61 0.88

Abbreviations: AL2S, axial light loss; CVa, analytical coefficient of
variation; CVg, between-subject biological variation; CVi,
within-subject biological variation; II, index of individuality
(CVi/CVg); LALS, low angle light scatter; LMALS, lower median angle
light scatter; MALS, median angle light scatter; UMALS, upper median
angle light scatter.

Table 3. Intraday and Interday Biological Coefficients of Variation of
Numerical Parameters

Analytes Day 1 Day 2

 8 AM Noon 4 PM 8 AM Noon 4 PM
Leukocyte count 19.1 17.92 20.18 16.55 16.74 19.17

Neutrophil count 13.52 14.16 13.63 12.97 13.94 14.71

Lymphocyte 18.07 18.83 18.23 17.65 17.88 19.39
 count

Monocyte count 28.85 31.45 31.7 23.39 25.46 22.82

Analytes Day 3

 8 AM Noon 4 PM
Leukocyte count 21.56 17.52 14

Neutrophil count 14.48 14.49 11.91

Lymphocyte 19.74 20.85 18
 count

Monocyte count 23.54 21.84 17.98

Table 4. Intraday and Interday Biological Coefficients of Variation of
Morphologic Parameters

Analytes Day 1 Day 2
 8 AM Noon 4 PM 8 AM Noon 4 PM

Neutrophils
 Volume
 Mean 3.09 2.76 3.10 2.39 2.82 2.69
 SD 7.23 6.29 6.85 4.93 5.59 5.94

Conductivity 1.01 0.90 0.93 0.99 1.05 0.83
MALS 2.70 2.40 2.59 2.32 2.18 2.64
UMALS 2.71 2.59 2.75 2.64 2.49 2.51
LMALS 3.16 2.85 3.10 2.45 2.47 3.27
LALS 3.67 3.29 3.80 3.50 3.46 4.17
AL2S 1.75 1.83 1.91 1.40 1.81 1.61

Lymphocytes
 Volume
 Mean 2.93 2.89 2.86 2.84 2.80 2.57
 SD 7.48 9.61 7.70 6.61 7.20 9.59

Conductivity 0.77 0.85 1.01 0.90 0.85 0.84
MALS 2.70 3.13 4.56 2.70 2.88 3.01
UMALS 4.23 4.87 6.17 4.50 4.76 5.90
LMALS 3.00 3.42 5.03 2.71 3.08 3.20
LALS 2.85 2.97 3.61 2.40 2.60 3.11
AL2S 2.34 2.59 3.11 2.25 2.46 2.17

Monocytes
 Volume
 Mean 2.15 2.00 2.61 1.87 1.98 2.11
 SD 9.70 8.18 8.93 9.66 9.35 9.20

Conductivity 0.74 0.73 0.94 0.79 0.74 0.73
MALS 2.40 2.73 3.30 2.23 2.34 2.50
UMALS 2.53 2.35 2.90 2.43 2.24 2.90
LMALS 3.51 3.94 5.03 2.85 3.09 3.49
LALS 5.98 6.10 9.97 4.45 4.74 6.72
AL2S 2.00 2.18 3.76 1.80 1.72 2.66

Analytes Day 3
 8 AM Noon 4 PM

Neutrophils
 Volume
 Mean 3.00 2.85 3.01
 SD 6.59 6.17 6.85

Conductivity 1.11 1.08 1.03
MALS 2.68 2.47 2.76
UMALS 2.54 2.56 2.69
LMALS 3.57 3.48 3.63
LALS 4.52 5.06 4.64
AL2S 1.52 1.78 1.75

Lymphocytes
 Volume
 Mean 2.59 2.71 2.49
 SD 8.81 8.94 9.75

Conductivity 0.65 0.70 0.86
MALS 2.59 2.86 2.92
UMALS 4.32 4.66 5.80
LMALS 2.90 3.24 3.38
LALS 2.92 2.91 3.64
AL2S 2.46 2.20 2.16

Monocytes
 Volume
 Mean 1.95 2.13 2.17
 SD 8.89 6.93 7.42

Conductivity 1.11 0.73 0.90
MALS 3.09 2.31 2.78
UMALS 2.52 2.42 2.51
LMALS 5.15 3.17 4.42
LALS 9.38 5.02 8.74
AL2S 3.58 1.83 2.91

Abbreviations: AL2S, axial light loss; LALS, low angle light scatter;
LMALS, lower median angle light scatter; MALS, median angle light
scatter; UMALS, upper median angle light scatter.
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Author:Tang, Huqiang; Jing, Jiyong; Bo, Dandan; Xu, Dongsheng
Publication:Archives of Pathology & Laboratory Medicine
Date:Nov 1, 2012
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