Lynen, Lut MD*; Teav, Syna BSc†; Vereecken, Chris BSc*; Munter, Paul De MD†*; An, Sokkab MD†; Jacques, Gary MD†; Kestens, Luc PhD*
With the reduced cost and complexity of antiretroviral therapy (ART), the number of people living with HIV/AIDS who have access to treatment is rapidly increasing in resource-limited settings. Unfortunately, optimal and affordable laboratory monitoring tools are often lacking in these settings. Clinical monitoring is considered a good basis but is too insensitive to identify patients who need ART and opportunistic infection (OI) prophylaxis.1 A rapid decline in total lymphocyte count (TLC) or hemoglobin was associated with progression to clinical AIDS in the Multicenter AIDS Cohort Study,2 but, in general, the TLC correlates only weakly with the CD4 count and scores even worse for monitoring of therapy.3-8
The classic multiparameter measurement of CD4+ T cells by flow cytometry is complicated and often too expensive for resource-limited countries.9-11 Less complex dedicated clinical instruments like the FACSCount require dedicated reagents and sample processing within 48 hours of venipuncture. This, together with numerous logistic problems, makes it is difficult for remote health centers in resource-limited settings to send their samples to a reference laboratory. The panleucogating approach has been an important step forward to extend sample storage before processing up to 96 hours after venipuncture.12 Alternative manual methods for CD4 counting are considered less appropriate in high-prevalence urban centers, where large numbers of samples have to be analyzed per day.13,14
In high-prevalence settings, there is a need for reliable CD4 counting methods in terms of accuracy and precision but at a reduced cost and allowing for high throughput analysis. Primary CD4 gating on volumetric flow cytometers, using only a single antibody (anti-CD4) has been put forward as an affordable and accurate alternative for resource-limited settings.15 We have evaluated this method under field conditions (Phnom Penh, Cambodia) using a 2-parameter (1 fluorescence [FL] and 1 side scatter [SSC] detector) volumetric flow cytometer, the CyFlow SL Green (FL1-SSC; Partec GmbH, Münster, Germany), with a lyse-no-wash CD4 staining protocol. The CyFlow SL Green provides direct absolute CD4 counts without the use of expensive reference microbeads. In addition, we evaluated whether this method could be used to analyze CD4+ T cells in unfixed whole blood stored between 24°C and 30°C for up to 96 hours after phlebotomy. Our method differs from the no-lyse-no-wash protocol described by Cassens et al.16 The lyse-no-wash procedure, in contrast to the unlysed blood procedure, does not cause perturbation of the light scatter properties of white blood cells by the presence of red blood cells. Therefore, it allows for better differentiation between CD4+ T cells and CD4-dim monocytes, particularly in aged blood stored at room temperature.
MATERIALS AND METHODS
Assessment of CD4 Counting on the CyFlow SL Green in Cambodia
To evaluate CD4 counting on the CyFlow SL Green, CD4 counting was performed on 2 different flow cytometers: (1) a dedicated clinical instrument for CD4 counting: the FACSCount (Becton Dickinson, Franklin Lakes, NJ) installed at the Pasteur Institute of Cambodia in Phnom Penh, which was the only available reference method for CD4 counting in Phnom Penh at the time of evaluation, and (2) the CyFlow SL Green (Partec), which is located at the Sihanouk Hospital Center of Hope (SHCH), a nongovernmental organization (NGO)-run hospital in Phnom Penh. The Cyflow SL Green is a compact mobile flow cytometer able to perform many types of cell analysis and absolute volumetric counting. The instrument is equipped with a 532-nm green solid-state laser, 1 FL detector, and 1 light SSC detector. Data acquisition, analysis, and real-time display were performed with a laptop computer using Windows FloMax software. A Cambodian laboratory technician from the SHCH was familiarized with the 2-parameter CyFlow SL Green and FloMax software through intensive training at the Institute of Tropical Medicine in Antwerp for 2 months. The training included proper interpretation of the light scatter and FL signals, correct setting of the gates and regions around CD4+ T cells, and calculation of the absolute number of CD4+ T cells. After training, the CyFlow SL Green was shipped to Cambodia and installed in the SHCH laboratory.
One hundred twenty fresh whole-blood samples from patients for whom CD4 counting was requested and performed at the Pasteur Institute of Cambodia in Phnom Penh in May through June 2003 were used in this evaluation. All samples were sent by various doctors and organizations to the Pasteur Institute with the request to perform a CD4 count. The samples were not (re)tested for HIV in this study. In Cambodia, CD4 counting is done to assess the immune status of HIV patients. Therefore, the samples were categorized as likely to be HIV-positive. The blood samples were collected in EDTA-Vacutainer tubes (Becton Dickinson) and processed within 5 hours after collection. CD4 counting was performed on the same day, in a masked way, in parallel on the FACSCount at the Pasteur Institute and on the CyFlow SL Green at the SHCH. Transport between the Pasteur Institute and SHCH was organized once a day in the afternoon by a laboratory technician using an air-conditioned car. Samples were put in a rack in a safe container, and transport lasted 10 to 15 minutes. On arrival at the SHCH, the samples were immediately processed for the CyFlow SL Green. At the end of the evaluation, the absolute CD4 counts provided by the FACSCount were compared with those of the CyFlow SL Green.
Accuracy of CD4 Counting on the CyFlow SL Green
The FACSCount was used as predicate instrument for CD4 counting in this study. CD4 counting was performed according to the instructions provided by the manufacturer. In short, 50 μL of EDTA-blood was added to the dedicated FACSCount vials, which were then vortexed and left to incubate for 60 minutes at room temperature in the dark. Subsequently, a fixative was added, and the samples were incubated for 30 minutes at room temperature and then run on the FACSCount.
Direct volumetric CD4 measurements were performed on the CyFlow SL Green as follows: 50 μL of whole EDTA-blood was pipetted into polystyrene test tubes. Ten microliters of phycoerythrin (PE)-conjugated anti-CD4 monoclonal antibody (Becton Dickinson) was added to the blood in the test tubes and left to incubate for 15 minutes at room temperature. Before acquisition, 2 mL of red blood cell lysing solution (Becton Dickinson) was added for direct lyse-no-wash volumetric CD4 measurements on the CyFlow SL Green. The acquisition and analysis of lysed whole blood were done according to a strategy based on CD4 versus SSC gating, described as primary CD4 gating by Janossy et al.15 The concentration of CD4+ T cells per microliter was calculated by multiplying the counted CD4+ T cells by the dilution factor (41.2 = 50 μL of blood in a final volume of 2060 μL) and dividing the result by 200 (the CyFlow SL Green counts cells in a fixed volume of 200 μL).
Assessment of Precision of CD4 Counting
The precision of CD4 counting on the CyFlow SL Green instrument was assessed using 2 different blood samples. Intrarun (instrument) precision was determined by preparing a large volume (10 mL) of CD4-stained blood and repeating sample acquisition on the instrument at least 13 times to determine the instrument's precision. Interrun precision, which includes the variation induced by pipetting errors made by the technician (tube-to-tube variability), was assessed by repeating the entire CD4 staining procedure at least 10 times: pipetting, sample preparation, staining, lysing of red blood cells, and sample acquisition. By comparing the intrarun and interrun precision, a fairly good impression of instrument's and technician's performance can be obtained. Interperson variation (between different technicians) was not assessed. Precision is expressed as the coefficient of variance obtained by dividing SD of all the measurements by the mean (CV% = SD × 100/mean).
CD4 Counting in Aged Blood
We analyzed the accuracy of CD4 counting in EDTA-whole-blood samples from HIV-positive patients that were stored for up to 4 days (96 hours) at room temperature (24°C-30°C) without fixatives. Two sets of blood samples, 12 blood samples in the first set and 15 blood samples in the second set, were analyzed at an interval of 2 weeks. They were stained and analyzed by primary CD4 gating (CD4 vs. SSC) on the CyFlow SL Green after 0, 1, 2, 3, or 4 days of storage.
Correlations between the absolute CD4 counts obtained by the different methods were analyzed by the Passing and Bablok method, which, in common with all nonparametric methods, is less sensitive to outliers.17 This method provides a test of the agreement of 2 analytic methods. Difference plots are given as proposed by Bland and Altman.18,19 The Bland-Altman method examines, in a discriminative fashion, whether the methods agree sufficiently well to be used interchangeably. The average of values seen by the 2 methods is displayed on the x axis and plotted against the difference between the 2 methods shown on the y axis.
The average difference between the 2 methods, referred to as bias, was marked on the graph by a horizontal line, and the mean difference and the limits of agreement with a 95% confidence interval (CI) were also calculated. The level of significance for linear regression was set at α < 0.05. MedCalc (Medcalc Software, Mariakerke, Belgium) version 220.127.116.11 statistical software was used to perform the Bland-Altman analyses. A modified Bland-Altman plot calculating the difference between 2 results and expressing it as a percentage of the mean of the 2 measurements was performed as well.20
Blood stability testing was analyzed using 27 samples, by comparing the results of absolute CD4 counting on days 1, 2, 3, and 4 with the results of day 0. To this end, we applied a repeated-measures linear regression model on log-transformed data with random intercept for each patient fitted by restricted maximum likelihood using Stata version 9.0 statistical software (StataCorp, College Station, TX).
To assess the clinical impact of using the CyFlow SL Green instead of the FACSCount in this setting, we calculated the sensitivity and the specificity of the CyFlow SL Green to identify patients who had a CD4 count <200 cells/μL with the FACSCount. A nonparametric ROC (receiver operating characteristic) curve was constructed to identify the optimal CD4 cutoff point of the CyFlow SL Green to detect a CD4 count <200 cells/μL on the FACSCount with MedCalc version 18.104.22.168 statistical software.21
Accuracy of Direct Volumetric CD4 Measurements on the CyFlow SL Green Using Lysed Whole Blood
Parallel CD4 measurements on both instruments, the FACSCount and CyFlow SL Green, were available for 115 of 120 tested blood samples. CD4 counting was automatically aborted by the FACSCount during sample acquisition of 5 blood samples, which were excluded from further analysis. The average CD4 count for 115 blood samples tested on the FACSCount was 289 ± SD 336 CD4+ T cells/μL, and results ranged from 1 to 2000 cells/μL (median = 183 cells/μL). Primary CD4 gating on the CyFlow SL Green using the same blood samples resulted in an average CD4 count of 268 ± SD 310 cells/μL, ranging from 1 to 1792 cells/μL (median = 170 cells/μL). An excellent correlation (R2 = 0.993) was observed between the absolute CD4 counts obtained by the FACSCount and those by the CyFlow SL Green via primary CD4 gating (Fig. 1A). The CyFlow SL Green CD4 results, however, showed a significant relative bias of −9.5% (95% CI: −11.8 to −7.1%) as compared to the FACSCount with limits of agreement between −32.5% and 13.6% after excluding CD4 counts of <10 cells/μL (FACSCount). This was done to avoid interference of large relative differences at low CD4 counts with little or no clinical impact. As shown in Figure 1B, the bias and limits of agreement were significantly distorted by the inclusion of low CD4 counts (<10 cells/μL). Considering the performance over the most relevant range between 50 and 750 CD4+ T cells/μL (n = 78), the relative bias was −8.1% (95% CI: −10.1 to −6.1%) when compared with the FACSCount, with limits of agreement between −25.4% and 9.4%. To investigate whether this bias would have a significant impact on clinical decision making during ART, we calculated the sensitivity and specificity of primary CD4 counting on the CyFlow SL Green to identify HIV patients having less than (or more than) 200 CD4+ T cells/μL by ROC analysis. At a cutoff of 200 CD4+ T cells/μL, the CyFlow SL Green has a sensitivity of 100% and a specificity of 96% in this population to identify patients with CD4 counts <200 cells/μL when compared with the FACSCount. The optimal cutoff would be 184 CD4+ T cells/μL (100% sensitivity and specificity).
Precision of Direct Volumetric CD4 Measurements on the Cyflow SL Green Using Primary CD4 Gating of Lysed Whole Blood
The intra- and interrun precision were tested using 2 blood samples. The intrarun precision of the instrument, expressed as the coefficient of variance, was 2.9% for the first sample (mean: 324 ± SD 9 CD4+ T cells/μL) and 5.1% for the second sample (mean: 182 ± SD 9 CD4+ T cells/μL). The interrun precision performed on the same 2 samples was 6% (319 ± SD 20 CD4+ T cells/μL) and 5.5% (mean: 172 ± SD 9 CD4+ T cells/μL), respectively.
Effect on Blood Stability of Direct Volumetric CD4 Measurements on the CyFlow SL Green Using Primary CD4 Gating of Lysed Whole Blood
The absolute CD4 count was determined using 27 blood samples and resulted in an average CD4 count of 241 ± SD 186 CD4+ T cells/μL and a standard error of the mean (SEM) of 36. The average CD4 counts (±SEM) on days 1, 2, 3, and 4 were 235 (±37), 233 (±35), 230 (±35), and 236 (±34), respectively, but remained within the range of the SEM (8.5%) of the average CD4 count on day 0. Figure 2A represents the individual CD4 counting in aging blood of 27 individuals with various CD4 counts at day 0. A repeated-measures linear regression model applied on log-transformed data from all 27 patients showed no significant change over the 5 consecutive days (P = 0.572). Overall, a relative change of 0.42% (95% CI: −1.0% to 1.9%) per day was observed. Repeating the same analysis per CD4 stratum, <300 CD4+ T cells and ≥300 CD4+ T cells, resulted in nonstatistically significant (P = 0.260) and borderline significant (P = 0.052) relative daily changes of 1.3% (95% CI: −0.98% to 3.7%) and −0.91% (95% CI: −1.9% to 0.01%), respectively. The cumulative relative daily changes were within the range of interrun precision using fresh blood (<6%). The interassay precision of absolute CD4 counting using aged blood (0-4 days), expressed as the coefficient of variance, was superior to the interrun precision using fresh blood, except for the case in which CD4 counts were lower than 100 cells/μL (see Fig. 2B). Figure 3 illustrates the deterioration of the quality of aged blood. Separation between CD4+ T cells in region 1 (RN1) and CD4-dim monocytes located at the left of RN1 was excellent on day 0 but became less clear on subsequent days. The separation between CD4+ T cells (RN1) and CD4-dim monocytes located at the lower right of RN1 in aged blood in our study was clearly facilitated by the use of 2 parameter (anti-CD4 and SSC) dot plots (left). This separation would have been much more problematic in single-parameter (anti-CD4) analyses, as illustrated by the histograms (right) in the same figure. Here, the operator has to select the optimal position of the left marker to delineate the position of CD4+ T cells (RN1). Because of the important overlap between CD4+ T cells and CD4-dim monocytes in aged blood, improper positioning of the left marker would result in an over- or underestimation of the absolute CD4 count.
The World Health Organization has published guidelines for scaling up ART using simple monitoring tools.22 These guidelines state that an absolute CD4 count is desirable to start and monitor patients on ART. Therefore, the development of affordable CD4 counting using compact and robust low-cost cytometers is mandatory. Clinical state-of-the-art flow cytometers used in the West cost approximately $100,000 US, and companies easily charge $15 to $25 US per test. Many of these instruments routinely measure CD4 percentage (CD4%) and can only provide absolute CD4 counts by using reagents that contain expensive reference beads (single-platform result). Alternatively, absolute CD4 counts can be obtained by multiplying the CD4% with the TLC (divided by 100) obtained from a hematology analyzer (double-platform result). The new generation of simplified flow cytometers can operate as single-platform volumetric instruments without the use of expensive microbeads (eg, CyFlow, Guava [Guava Technologies, Hayward, CA], Pointcare [PointCare Technologies, Marlborough, MA], Apogee A40 Analyzer [Amplimedical SpA, Assago, Italy]).23-25 The CyFlow SL Green and CyFlow Counter measure absolute CD4 counts by using a single monoclonal antibody and, as a consequence, are more affordable per CD4 test than instruments that require multiple monoclonal antibodies. At the time of the study, the FACS-Count cost more than $30,000 US and required expensive dedicated bead-based reagents that cost $15 US per test. The investment cost for a CyFlow instrument with 1 or 2 parameters is currently between $21,800 US (CyFlow Counter) and $26,975 US (CyFlow SL Green). These 1- and 2-parameter CyFlow versions can be operated using reagents from various manufacturers to reduce the cost to as low as $1 to $2 US per test.
Compact low-range flow cytometers are much more affordable and mobile, and they require less training of personnel than the expensive and sophisticated high-end clinical flow cytometers.10 We have shown that primary CD4 gating using lysed whole blood is an affordable method that can be implemented under field conditions on a 2-parameter CyFlow SL Green. Furthermore, analysis of unfixed aged blood stored at room temperature for several days (96 hours) was proven to be feasible on this instrument. A well-trained technician can use this method with low intra- and interrun variability comparable to other published CyFlow data.9,16,26,27 The blood stability study was performed on 27 randomly selected blood samples. The observed relative change of −0.91% per day in aged blood with CD4 >300 cells/μL was close to statistical significance (P = 0.052). The cumulative relative change over 4 days was −3.64%, however, which remains within the 6% coefficient of variance we obtained during our precision measurements using fresh blood samples. Therefore, we consider this relative change over 4 days of blood storage to be biologically insignificant. A weakness of this study is that the HIV serostatus of the patients was not (re)tested. The stability of the CD4 results over time in the group of patients with CD4 counts <300 cells/μL, representing those individuals most likely to be HIV-seropositive, was not worse than in the group with CD4 counts >300 cells/μL.
The absolute CD4 counts obtained with the CyFlow SL Green are, on average, 8.1% lower than those from the FACSCount for CD4 counts between 50 and 750 cells/μL. The tendency of the CyFlow SL Green to give lower CD4 counts than the predicate instruments is in agreement with other published data and is independent of the method (lyse-no-wash vs. no-lyse-no-wash).26-28
Fortunately, the bias we observed did not adversely affect clinical management or clinical decision making. All patients who had a CD4 count less than 200 cells/μL were correctly identified as such, and the 2 patients who were misclassified as having low CD4 counts on the CyFlow SL Green had CD4 counts of 214 and 219 cells/μL on the FACSCount and would benefit from treatment with ART according to most international ART guidelines.
Partec is currently promoting the CyFlow Counter as a dedicated instrument for absolute CD4 counting, which is a simplified system in which laser alignment by the operator is not required (Partec's AlignFree technology) as opposed to the CyFlow SL Green. Laser alignment requires high technical skills and regular maintenance. This prerequisite would be a drawback if the CyFlow SL Green is used by inexperienced or insufficiently trained laboratory staff. An advantage of the CyFlow SL Green is that the standard version is equipped with at least 2 detectors, which, as we have shown, allows for CD4 counting in aged blood for up to 4 days after blood collection. This is similar to the performance of the panleucogating protocol, which is based on CD45/SSC and subsequent CD4 gating.12 Because we do not know whether transport of blood samples would affect their stability over time, this should be tested. It would be possible for a mobile laboratory with a skilled laboratory technician and an instrument like the CyFlow SL Green to travel between different sites and perform analyses at the point of care with an interval of 5 days, as suggested by the manufacturer. The robustness of the CyFlow SL Green during transport should be formally tested before implementing this strategy, however. If proven successful, 1 well-trained laboratory technician and a single CyFlow SL Green instrument could cover several sites, which would further reduce the cost of ART monitoring.
Single-platform flow cytometers, such as the CyFlow Counter, the 2-parameter (SSC × FL) CyFlow SL Green, or the FACSCount, can only provide absolute CD4 counts. In children less than 6 years of age, however, it is recommended to assess the CD4% instead of the absolute CD4 count. For pediatric ART projects, it might be worthwhile to expand the basic configuration of the CyFlow SL Green instrument with a second FL detector (CD45) or a forward scatter (FSC) detector. This would enable the technician to obtain the TLC and total CD4 count from a single analysis on the same instrument, from CD45/SSC or FSC/SCC gating and subsequent CD4 gating. Although both gating strategies provide CD4%, CD45/SSC gating is preferred. It is more expensive (extra anti-CD45 antibody required) than FSC/SSC gating, but it allows for the use of aged blood.12
The CyFlow SL Green is a precise and fairly accurate instrument to measure absolute CD4 counts under field conditions if operated by well-trained technicians. Because intensive and lengthy training cannot always be provided to technicians working in the field, the analysis of the FL and SSC signals should ideally be performed by automated software and not manually by the technician. Clinical CD4 instruments, such as the FACSCount and the Pointcare, have built-in software that automatically analyses FL and SSC signals and only report CD4 results to the technician as CD4+ T cells/μL. In case the automated software cannot correctly identify CD4+ T-cell clusters, the instrument aborts the analysis and reports an error code. As a consequence, the results of these instruments are less dependent on training, which may be an important asset for ART roll-out. During our study we did not assess the technician-related variability of the CD4 results on the CyFlow SL Green. This could be done by comparing the CD4 results provided by different technicians with different levels of training who analyzed the same samples on the same instrument. To date, the CyFlow SL Green is one of the most affordable single-platform volumetric flow cytometers with low running costs. It has the potential to be an affordable solution for resource-limited settings in which high numbers of CD4 tests are needed at the lowest cost possible. Although the manufacturer states that the instrument does not require expensive preventive maintenance by engineers, daily maintenance and careful cleaning of the delicate flow cell by the operator is imperative for continued proper functioning of the instrument.
Whatever system is put in place, worldwide networks are urgently needed to validate the accuracy and, in particular, the robustness of this new generation of instruments under true field conditions so that technical support for these electronic and delicate instruments can be ascertained.
The authors are grateful to the SHCH for making this evaluation possible and to the Pasteur Institute of Cambodia for performing the analyses on the FACSCount. They thank Professor Robert Colebunders for useful comments on the manuscript and Joris Menten for statistical review.
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© 2006 Lippincott Williams & Wilkins, Inc.