Biological Variations of Hematologic Parameters Determined by UniCel DxH 800 Hematology Analyzer

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1 Biological Variations of Hematologic Parameters Determined by UniCel DxH 800 Hematology Analyzer Pianhong Zhang, PhD; Huqiang Tang, MS, MPH; Keqing Chen, MS; Yingying Chen, BS; Dongsheng Xu, MD, PhD Context. The Coulter DxH 800 hematology analyzer can determine conventional hematologic parameters. It also provides many new hematologic parameters, some of which show potential clinical utility. Objective. To study, for the first time, the biological variations of new hematologic parameters and reinvestigate the biological variations of conventional hematologic parameters using the newest Coulter hematology analyzer. Design. Forty adult volunteers (21 women and 19 men) were included. All participants maintained their normal lifestyles. Blood samples were drawn in duplicate by a single experienced phlebotomist and analyzed within 2 hours using a single analyzer. Before each batch analysis, the instrument quality controls were performed using the same lots of reagents. Results. Within-subject and between-subject biological variations for the conventional hematologic parameters were compatible with published data. The analytic variation of the DxH 800 for these parameters appeared smaller. Index of individuality (ratio of within-subject to between-subject biological variation) for all parameters was low. In addition, intraday and interday biological variations of most parameters studied are fairly constant among the population examined. Conclusions. These observations are clinically valuable. Data on within-subject biological variation and analytic precision may be used to generate objective delta-check values for use in quality management. Comparing within-subject and between-subject biological variation on new parameters may allow us to decide the utility of traditional population-based reference ranges. Furthermore, documentation of biological variations of new parameters is an essential prerequisite in the development of any clinical application in the future. (Arch Pathol Lab Med. 2013;137: ; doi: /arpa OA) The clinical laboratory test results of any individual may intraindividual fluctuation. 3,6 We revisit these areas for vary over time, because of 3 sources of variation: several reasons. First of all, we use the newest model of preanalytic variation, such as preparation of the individual for sampling, and sample collection itself; analytic variation (precision), such as random error and possibly systematic error (changes in bias due to instrument calibration); and inherent biological variation around the homeostatic setting point. 1 In terms of biological variations, some analytes may vary during an individual s lifetime, simply because of natural biological factors involved in the aging process. Some analytes have predictable biological rhythms or cycles. Most analytes, however, do not have cyclic rhythms that are of major clinical importance, such as hematologic parameters. Several previous studies have investigated the biological automated hematology analyzer, UniCel DxH 800 (Beckman Coulter, Inc, Fullerton, California). In addition to cell volume and cellular contents, 5 extralaser diffraction angles are used in this model to analyze each individual cell, allowing a specific analysis of nucleated red blood cells and detection of giant platelets and platelet clumps. With likely improved analytic precision, we can possibly minimize the imprecision due to analytic variations and give more reliable estimation of inherent biological variation. Second, almost all the reported studies in the past have been based entirely on Caucasian/white populations. 2 9 There is little knowledge about biological variation of hematologic parameters among Asian populations. Third, many new hematologic parameters have become available from modern automated variations of hematologic parameters. 2 9 Some parameters, such as hemoglobin or reticulocytes, have been demonstrated to exhibit hour-to-hour, day-to-day, or seasonal ters include low hemoglobin density (LHD), microcytic hematology analyzers in recent years. These new parame- anemia factor, mean sphered cell volume, red cell size factor, immature reticulocyte fraction, mean reticulocyte Accepted for publication October 15, volume, high light-scatter reticulocytes, reticulocyte distribution width, and mean platelet volume. To our knowledge, From the Clinical Laboratory Center, the Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang, China (Dr Zhang and Messrs Tang and K. Chen); the School of Medical biological variations of these new parameters have never Sciences and Laboratory, Jiangsu University, Jiangsu, China (Ms. Y. been investigated before. Importantly, some of these new Chen); and the Department of Hematopathology, CBLPath, Inc, Rye parameters have demonstrated useful clinical applications. For example, immature reticulocyte fraction, Brook, New York (Dr Xu). The authors have no relevant financial interest in the products or companies described in this article. which is defined as the ratio of immature to total Reprints:DongshengXu,MD,PhD,CBLPath,Inc,760Westchester reticulocytes, has been proposed as a new hematologic Ave, Rye Brook, NY ( dxu@cblpath.com). parameter in the evaluation of erythropoietic activity. 10 Its 1106 Arch Pathol Lab Med Vol 137, August 2013 Biological Variations of Hematologic Parameters Zhang et al

2 main clinical value is that the immature reticulocyte fraction has been shown to be an earlier and more sensitive indicator of bone marrow stimulation than other traditionally used reticulocyte parameters, such as the absolute reticulocyte count and the reticulocyte percentage feature is particularly important in certain clinical circumstances, such as evaluation of bone marrow recovery after chemotherapy or stem cell transplantation; the response to therapy with iron, folate, and vitamin B 12 in anemic patients; and neonatal monitoring Low hemoglobin density is another new parameter available from the DxH800; it is derived from the mean corpuscular hemoglobin concentration (MCHC) using the pmathematical sigmoid transformation [LHD ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ð1 ð1=1þeð1:8ð30 MCHCÞÞÞÞ]. Low hemoglobin density has been shown to be a reliable parameter for the detection of patients with iron-deficiency anemia even in the presence of inflammation. 14 Investigation on biological variations has important clinical implication. Data on biological variation may 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. 1 study will provide useful information about the biological variations of traditional and newly described hematologic parameters using the newest hematology analyzer. 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 old. None of the women were menstruating. All participants maintained their normal lifestyles, including no excess of alcohol, tea, and tobacco consumption, and did not participate in strenuous exercise during the study period. Specimen Collection The blood samples were drawn in duplicate at 8:00 AM, noon, and 4:00 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 anticoagulation 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 (Beckman Coulter Inc, Brea, California). Before each batch sample analysis, instrument quality controls were performed using the same lots of Coulter S-CAL Calibrator (lot No ; Beckman Coulter), and Coulter 6C Cell Control with 3 levels at different concentrations (lot No. for level 1, ; for level 2, ; for level 3, ; Beckman Coulter) to allow consistent determination during the course of the study. study protocol was approved by the hospital ethics committee. Automated Hematologic Data Collection Data collected from the DxH 800 included the conventional parameters of red blood cells, reticulocytes, and platelets, as well as many newly described hematologic parameters, such as LHD, microcytic anemia factor, mean sphered cell volume, red cell size factor, immature reticulocyte fraction, mean reticulocyte volume, high light-scatter reticulocytes, reticulocyte distribution width, and mean platelet volume. Immature reticulocyte fraction was determined by using a supravital stain (new methylene blue) to highlight cytoplasmic RNA and a new flow cell design to support multiple angles of light scatter measurements, enabling enhanced data acquisition to allow not only for measuring the reticulocytes from the entire red blood cell population, but also for identification of a subpopulation of the immature reticulocytes with high light scatter. The ratio of the immature reticulocytes to the total reticulocyte population is defined as the immature reticulocyte fraction. Mean platelet volume was determined using volume, conductivity, and light scatter technology by measuring direct current impedance. Other parameters, such as LHD, microcytic anemia factor, mean sphered cell volume, and red cell size factor, were mathematically calculated by the instrument. Statistical Analysis Nested analysis of variance and coefficients of variation (withinsubject [CV I ] and between-subject [CV G ]) were performed using SPSS software, version 10.0 (SPSS, Chicago, Illinois) and Microsoft (Redmond, Washington) Excel Analytic coefficient of variation (CV A ) was calculated from 10 independent tests using Coulter 6C Cell Control. Reference change values (RCVs) were calculated using the formula RCV ¼ 2 1/2 * Z * (CV A2 þ CV I2 ) 1/2, where Z scores are 1.65, 1.96, and 2.58 for probabilities of 90%, 95% and 99%, respectively. Comparison between 2 means was performed by Student t test. A P value less than.05 was considered significant. RESULTS Within-Subject and Between-Subject Biological Variations We initially studied within-subject (CV I ) and betweensubject (CV G ) biological variations on hematologic parameters. As shown in Table 1, we achieved similar CV I and CV G for the conventional erythrocyte or platelet parameters compared with published data. 1,2 The analytic variations (CV A ) of DxH 800 for these parameters appeared smaller than or comparable to those found in previous studies. 1,2,5 The CV I and CV G for newly described erythrocyte parameters, such as microcytic anemia factor, mean sphered cell volume, or red cell size factor, were less than 5%, except for LHD, which showed greater intraindividual and interindividual variations (Table 1). On the other hand, CV I and CV G for most reticulocyte parameters were higher than those of erythrocytes or platelets, likely because of higher analytic imprecision for these parameters (Table 1). The index of individuality, calculated as the simple ratio of CV I :CV G, 1 for most parameters examined, was less than 0.5 (Table 1). A low index of individuality indicates that conventional reference values for these parameters may be of little utility, particularly when deciding whether changes observed in an individual are clinically significant. 1 Intraday and Interday Biological Variations The intraday and interday biological variations on hematologic parameters were next investigated. As shown in Table 2, intraday biological variations for most hematologic parameters examined were constant except for reticulocyte distribution width coefficient of variation and reticulocyte distribution width SD, which showed a statistically significant difference between 8:00 AM and noon measurements. We do not have good explanations for these fluctuations. No significant interday biological variations of the hematologic parameters examined were observed (data not shown). Determination of the RCV or Critical Difference Monitoring hematologic parameters allows the detection of various physiologic or pathologic states when their values are increased or decreased longitudinally in relation to Arch Pathol Lab Med Vol 137, August 2013 Biological Variations of Hematologic Parameters Zhang et al 1107

3 Analytes Table 1. Biological Coefficients of Variation of Hematologic Parameters CV A,% CV I,% CV G,% II RBC Hemoglobin Hematocrit MCV MCH MCHC RDW CV RDW SD 0.87 NA 1.27 NA 4.45 NA NA LHD 4.54 NA NA NA NA MAF 0.35 NA 3.72 NA NA NA MSCV 0.86 NA 1.75 NA 7.45 NA NA RSF 0.45 NA 0.74 NA 6.31 NA NA Reticulocyte No NA 9.60 NA NA NA Reticulocyte % 9.02 NA 9.47 NA NA NA IRF 6.79 NA 8.45 NA NA NA MRV 0.87 NA 1.64 NA 5.80 NA NA HLR No NA NA NA NA HLR % 9.52 NA NA NA NA RDWR CV 1.61 NA 5.72 NA 8.08 NA NA RDWR SD 1.47 NA 6.07 NA 6.96 NA NA Count MPV PDW NA NA Abbreviations: CV, coefficient of variation; CV A, analytical coefficient of variation; CV G, between-subject biological coefficient of variation; CV I, within-subject biological coefficient of variation; HLR, high light-scatter reticulocytes; II, index of individuality (ratio of CV I :CV G ); IRF, immature reticulocyte fraction; LHD, low hemoglobin density; MAF, microcytic anemia factor; MCH, mean corpuscular hemoglobin; MCHC, mean corpuscular hemoglobin concentration; MCV, mean corpuscular volume; MPV, mean platelet volume; MRV, mean reticulocyte volume; MSCV, mean sphered cell volume; NA, not available; PDW, platelet distribution width; RBC, red blood cell; RDW, red cell distribution width; RDWR, reticulocyte distribution width; RSF, red cell size factor. Analytes Table 2. Intraday Biological Variations of Hematologic Parameters Hour to Hour 8 AM Noon 4 PM P Value RBC /L Hemoglobin g/dl Hematocrit % MCV fl MCH pg MCHC g/l RDW CV % RDW SD fl LHD % MAF g MSCV fl RSF fl Reticulocyte /L Reticulocyte % IRF % MRV fl HLR /L HLR % RDWR CV % RDWR SD fl Count 10 9 /L MPV fl PDW fl Abbreviations: CV, coefficient of variation; HLR, high light-scatter reticulocytes; IRF, immature reticulocyte fraction; LHD, low hemoglobin density; MAF, microcytic anemia factor; MCH, mean corpuscular hemoglobin; MCHC, mean corpuscular hemoglobin concentration; MCV, mean corpuscular volume; MPV, mean platelet volume; MRV, mean reticulocyte volume; MSCV, mean sphered cell volume; PDW, platelet distribution width; RBC, red blood cell; RDW, red cell distribution width; RDWR, reticulocyte distribution width; RSF, red cell size factor Arch Pathol Lab Med Vol 137, August 2013 Biological Variations of Hematologic Parameters Zhang et al

4 themselves. It is important to know the RCV or critical difference, which defines the percentage change that should be exceeded given the analytic and biological variations inherent to a particular test, in that there is a significant difference between the 2 consecutive measurements. Using the formula described in Materials and Methods, we calculated RCV for all hematologic parameters investigated, as shown in Table 3. The percentage changes that were significant (95%) or highly significant (99%) for most conventional red cell parameters were smaller than or compatible with previous data. 1, 2 The smaller RCV was likely due to the smaller CV A of the current analyzer, as illustrated in Table 1. The RCV for new red cell parameters except for LHD was also smaller. On the other hand, the RCV for most reticulocyte parameters was larger (Table 3), likely because of greater CV A and intraindividual variations (Table 1). COMMENT The implications of a decreased analytic variation in gaining greater confidence in the inherent biological variations in healthy individuals should be obvious, especially in using an individual s baseline values to compute that individual s personal reference interval. Using the newest Coulter hematology analyzer, the DxH800, we demonstrated that CV I or intraindividual and CV G or interindividual biological variations of most conventional hematologic parameters were fairly constant among the subjects and during the time points investigated. The CV I and CV G for most newly described erythrocyte parameters except for reticulocyte parameters were small, suggesting these parameters are less variable around the homeostatic setting point intraindividually and interindividually. The higher CV I and CV G for most reticulocyte parameters were likely due to higher analytic imprecision caused by random error (Table 1). may be due to low quantity of the reticulocytes, as we usually see poor correlation for basophil measurements between 2 instruments. In addition, the index of individuality for the newly described hematologic 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. 1 Our results are slightly different from the previously published observations, which showed hour-to-hour and subject-specific diurnal fluctuation. 5,6 For example, the study by Sennels et al 5 demonstrated diurnal variation of hematology parameters. In this study, samples were collected every third hour through 24 hours, 9 time points in total, in contrast to 3 time points during an 8-hour interval in our study. The greatest changes for most parameters were observed around 9:00 PM to midnight. One of the explanations for these differences is that the current study uses a more advanced analyzer with likely improved precision, which may minimize the fluctuations due to analytic variations. Another possibility would be that because the study investigated the changes at only 3 daytime points each day for a 3-day period, some changes, such as seasonal variations, might not have been observed. 3,5 Interestingly, the biological variations during the similar time points (from 9:00 AM 3:00 PM) were also small in that study 5 compared with our study (from 8:00 AM 4:00 PM). The numbers from two 9:00 AM points were also similar, suggesting smaller interday variation. 5 Table 3. Reference Change Values of Hematologic Parameters Probability That Rise or Fall Is Significant, % Analytes 90% 95% 99% RBC Hemoglobin Hematocrit MCV MCH MCHC RDW CV RDW SD LHD MAF MSCV RSF Reticulocyte No Reticulocyte % IRF MRV HLR No HLR % RDWR CV RDWR SD Counts MPV PDW Abbreviations: CV, coefficient of variation; HLR, high light-scatter reticulocytes; IRF, immature reticulocyte fraction; LHD, low hemoglobin density; MAF, microcytic anemia factor; MCH, mean corpuscular hemoglobin; MCHC, mean corpuscular hemoglobin concentration; MCV, mean corpuscular volume; MPV, mean platelet volume; MRV, mean reticulocyte volume; MSCV, mean sphered cell volume; PDW, platelet distribution width; RBC, red blood cell; RDW, red cell distribution width; RDWR, reticulocyte distribution width; RSF, red cell size factor. Using the data on CV I and analytic precision, we have also established the RCV or critical difference for both conventional and newer hematologic parameters (Table 3). The RCV may be used as reference to determine the changes 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. 1 For example, a difference between 2 consecutive analyses for LHD that is 42.4% or greater, or 55.8% or greater, will be considered as a significant or highly significant change, respectively (Table 3). These results may be flagged, which suggest that they may have failed the delta check. The delta-check failure may be due to real change in the patient s condition or errors associated with samples (either the first or the second). Therefore, there are considerable advantages to using RCV to alert laboratory staffs that serial results in an individual have changed significantly (95%) or highly significantly (99%). 1 Another potential clinical application is to use the RCV in everyday practice just as we frequently use conventional population-based reference values. For example, a recent study 14 demonstrated that the median value of LHD for healthy subjects (n ¼ 90) was 2.3, with a range of 0.9 to 4.1, which is similar to our mean values of LHD ( , , and at 8:00 AM, noon, and 4:00 PM time points, respectively) (Table 2). However, the median value of LHD for the patients with iron-deficiency Arch Pathol Lab Med Vol 137, August 2013 Biological Variations of Hematologic Parameters Zhang et al 1109

5 anemia (n ¼ 110) was 22.3, with a range of 5.5 to 54, which was much greater than the RCV (.99%) from this study, indicating a highly significant difference in LHD between healthy individuals and the patients with iron-deficiency anemia. 14 Lastly, RCV may vary with changes in analytic precision and bias and can be lowered by improving precision. Therefore, if possible the analytic variations should be eliminated or certainly minimized by careful attention to quality management practices. These observations may be clinically important. Monitoring an individual s health often requires assessment of serial laboratory test results. Repeat results are seldom identical. Changes in laboratory values may be due to preanalytic variation, analytic imprecision, biological variation, or a change in the individual s health condition. Comparing CV I and CV G may allow us to decide the utility of traditional population-based reference ranges. 1 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. 1 Lastly, documentation of biological variations for newly described hematologic parameters is an essential prerequisite in the development of any new application clinically. 1 References 1. Fraser CG. Biological variation: from principles to practice. Washington, DC: AACC Press; Ricos C, Alvarez V, Cava F, et al. Current databases on biologic variation: pros, cons and progress. Scand J Clin Lab Invest. ;59: Maes M, Scharpé S, Cooreman W, et al. Components of biological, including seasonal, variation in hematological measurements and plasma fibrinogen concentrations in normal humans. Experientia. 1995;51(2): Fraser CG, Wilkinson SP, Neville RG, Knox JD, King JF, MacWalter RS. Biologic variation of common hematologic laboratory quantities in the elderly. Am J Clin Pathol. 1989;92(4): Sennels HP, Jrgensen HL, Hansen AL, Goetze JP, Fahrenkrug J. Diurnal variation of hematology parameters in healthy young males: the Bispebjerg study of diurnal variations. Scand J Clin Lab Invest. 2011;71(7): 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): Jones AR, Twedt D, Swaim W, Gottfried E. Diurnal change of blood count analytes in normal subjects. Am J Clin Pathol. 1996;106(6): Dot D, Miró J, Fuentes-Arderiu X. Within-subject biological variation of hematological quantities and analytical goals. Arch Pathol Lab Med. 1992; 116(8): Winkel P, Statland BE, Saunders AM, Osborn H, Kupperman H. Within-day physiologic variation of leukocyte types in healthy subjects as assayed by two automated leukocyte differential analyzers. Am J Clin Pathol. 1981;75(5): Davis BH. Immature reticulocyte fraction (IRF): by any name, a useful clinical parameter of erythropoietic activity. Lab Hematol. 1996;2: Chang CC, Kass L. Clinical significance of immature reticulocyte fraction determined by automated reticulocyte counting. Am J Clin Pathol. 1997;108(1): Bagdasaryan R, Glasser L, Quiller K, Chaves F, Xu D. Effect of hydroxyurea on immature reticulocyte fraction in sickle cell anemia. Lab Hematol. 2007; 13(3): Torres Gomez A, Casano J, Sanchez J, Madrigal E, Blanco F, Alvarez MA. Utility of reticulocyte maturation parameters in the differential diagnosis of macrocytic anemias. Clin Lab Haematol. 2003;25(5): Urrechaga E, Unceta M, Borque L, Escanero JF. Low hemoglobin density potential marker of iron availability. Int J Lab Hematol. 2012;34(1): Broséus J, Visomblain B, Guy J, Maynadié M, Girodon F. Evaluation of mean sphered corpuscular volume for predicting hereditary spherocytosis. Int J Lab Hematol. 2010;32(5): Urrechaga E. Clinical utility of the new Beckman-Coulter parameter red blood cell size factor in the study of erithropoiesis. Int J Lab Hematol. 2009;31(6): Zini G, Di Mario A, Garzia M, Bianchi M, d Onofrio G. Reticulocyte population data in different erythropoietic states. J Clin Pathol. 2011;64(2): Latger-Cannard V, Hoarau M, Salignac S, Baumgart D, Nurden P, Lecompte T. Mean platelet volume: comparison of three analysers towards standardization of platelet morphological phenotype. Int J Lab Hematol. 2012;34(3): Arch Pathol Lab Med Vol 137, August 2013 Biological Variations of Hematologic Parameters Zhang et al