CD4 Monitoring in Resource-Limited Settings: The State of the Art at Bangkok

With conventional CD4 T-cell counts and viral load tests all but unaffordable for routine use in resource-poor settings where low-cost antiretroviral medications are now being offered, there has been a disorganized scramble to come up with alternative ways to monitor therapy. One approach has been to simplify or modify existing technology to squeeze out greater efficiency. Researchers have been experimenting with generic versions of expensive reagents and test materials and using them in smaller quantities to attain significant savings. A major maker of laboratory-based cell counting equipment now offers a rugged, battery powered unit that can be taken into the field to perform CD4 tests. Less successful have been attempts to substitute the easily obtained total lymphocyte count (TLC) for the far more specific CD4 count. These adaptations, while helpful, still require significant investments in equipment, training and maintenance. What is needed, and what remains on the horizon, are low-cost ($2-$4) technologies that leapfrog past existing systems to offer simple, rapid, robust point-of-care diagnostics, similar to what is available for diabetes or for detecting HIV antibodies.

At the International AIDS Conference in Bangkok, a generous handful of posters and presentations found promise in some of these approaches and put several nails in the coffin for total lymphocyte count as a CD4 surrogate.

CD4 Counts!

The standard method for enumerating CD4 T-cells uses a flow cytometer, a machine in which the cells of interest in a sample of blood are tagged with fluorescent monoclonal antibodies and passed in a single-cell column in front of laser light. When the light illuminates a cell, the light is scattered in a pattern that can be read by a photosensor to indicate the cell's size. Simultaneously, when the laser light strikes an antibody, it glows brightly and the cell is counted by a sensor attached to a microscope. Several different antibodies are typically used to identify different types of white blood cells. A computer calculates the number of CD4 T-cells by analyzing the size of the cell and which of the antibodies it has been tagged with. The overall process is called fluorescence-activated cell sorting (FACS). CD4 counts are expensive because FACS machines are expensive, the antibodies are expensive and trained persons are necessary to perform the tests and maintain the equipment.

A group of researchers loosely organized as the Afford CD4 Group, led by Professor George Jannosy of London, have sought to simplify cell sorting protocols and reduce the price per test by using generic, "home-brew" monoclonal antibodies instead of commercial reagents.

Researchers in Thailand performed a quality comparison of state-of-the-art two- and three-color cell sorting (using multiple monoclonal antibodies) with a simplified protocol called panleucogating that can use just one monoclonal antibody. However, this method only produces the relative percentage of CD4 T-cells in the blood; determining the familiar absolute CD4 count requires additional steps. They also compared panleucogating using generic reagents with the same technique using commercial reagents. Results from each of the methods correlated well with the others for determining CD4 percentage in 142 HIV-positive samples and 26 HIV-negative samples. The authors estimate that panleucogating with generic reagents could reduce the cost per test from $11.50 to $2.30 each. While these saving are significant, this method still relies on an initial investment in equipment that can run from $20,000 and up with expensive yearly maintenance contracts. (Pattanapanyasat, B3087)

Researchers in Barbados compared a panleucogating protocol to a sophisticated four-color method of cell counting using commercial monoclonal reagents. The coefficient of correlation was a respectable 0.97 over the entire range of counts (25 to 989 cells/mm³). The authors conclude that panleucogating is "an accurate method for enumerating CD4 T-cells and has major cost implications for the sustainability of the National HIV containment program in Barbados." (Sippy, B3108)

Flow Show

With its cute and compact off-road vehicle on display, the Cyflow booth was a popular spot on the commercial exhibition floor of the International AIDS Conference. Partec Inc., a manufacturer of lab-based cell counting equipment, makes a portable flow cytometry system called Cyflow that is designed for use in resource-limited areas. The machine is less expensive and more robust than conventional FACS systems, uses less expensive reagents, and is able to produce an absolute CD4 count without additional instrumentation. The trade show exhibit demonstrated a complete, mobile CD4 counting lab, ready to roll into the bush. Cyflow is able to produce an absolute CD4 T-cell count using two monoclonal reagents. Several posters at Bangkok evaluated the quality of the Cyflow system.

Researchers in Cambodia evaluated the precision of the Cyflow system by performing multiple repeated counts of the same samples. They also performed comparisons of results produced by Cyflow with those produce by a lab-based FACS. The coefficient of variance for the precision of the machine varied between 2.8% and 4.9%. The correlation with FACS was very high, with the Cyflow tending to undercount by about 20 cells on a mean CD4 count of 289. The system detected CD4 counts below 200 cells/mm³ with sensitivity of 100% and a positive predictive value of 97%. (Teav, B3089)

Researchers in Thailand compared Cyflow with two different FACS systems (3-color FACScan and 2-color FACScount) and reported high correlation between the results, with Cyflow producing mean counts 41.5 and 18.0 cells/mm³ lower than the two FACS systems in the range of 100-300 CD4 cells/mm³. The authors note that Cyflow offers advantages in cost and sample preparation, "but it requires technical expertise." (Pattanpanyasat, B3176)

In Malawi, investigators compared Cyflow to FACS on 311 blood samples. The mean difference in CD4 counts by Cyflow was -8.68 cells compared to FACS, with a correlation coefficient of 0.92. The authors note that "local district laboratory staff found the Partec machine easy to manipulate and robust under routine field conditions." (Fryland, B1149)

Researchers in Rwanda compared results from Cyflow (4-parameter, direct volumetric counting) with FACS (bead-based, FACScount) on samples from 73 HIV-positive pregnant women. Mean CD4 counts were 346 cells/mm³ with FACS and 377 cells/mm³ with Cyflow. (Jervais, B3172)

One limitation of performing CD4 counts in resource-limited settings is that there may be a significant time lag between when samples are collected and when they finally reach a testing facility. Researchers in Cambodia compared CD4 counts obtained by Cyflow on samples collected the same day and on EDTA-preserved samples kept at room temperature (<30C) for 4 days. In 27 samples tested, the correlation between fresh blood and aged blood was high, with aged blood samples tending to come in about 5 cells lower on a mean CD4 count of 241. (Teav, B3110)

No Flow Slow Go

Dynabeads is a low-cost, low-throughput, method of counting CD4 cells that does not rely on expensive flow cytometry equipment. CD4 cells are tagged with the Dynabeads and counted under a conventional light microscope. Researchers in Japan compared a simplified Dynabeads protocol to FACS in samples from 242 patients. The correlation coefficient between the methods was 0.91, sensitivity of 79% and specificity of 94% for samples below 200 cells/mm³. Sensitivity improved to 97% with samples above 350 cells/mm³. The authors estimate the cost of Dynabeads and other disposable materials to be less than $3.00 per test. While this price is attractive and equipment needs are not forbidding, this technique relies on operator expertise and its precision is subject to operator fatigue. Not a satisfying solution. (Bi, B2038)

Total Lymphocyte Washout

The practical value of using total lymphocyte count (TLC) as a surrogate for absolute CD4 cell counts in adults came in for hard knocks from nearly every study that evaluated it. One focus of inquiry is to find what "cut-off" value of TLC corresponds to the all-important CD4 cell count of 200, which signals the advent of AIDS and an urgent need for OI prophylaxis and ART therapy. Currently, the World Health Organization says that TLC of 1200 cells/mm³ should be the key number, although these reports call that into question.

CD4 counts were correlated with TLC for 747 participants in HIVNAT clinical trials in Thailand to determine the value of TLC for monitoring response to ART. Samples were collected at baseline and at weeks 12, 24 and 48 after beginning ART. Of 3578 paired samples, 29% had CD4 <200 cells/mm³. At baseline, TLC <1200 cells/mm³ had a sensitivity of 40% for predicting CD4 <200 cells/mm³ (specificity = 94%). Sensitivity was reduced to only 20% at week 48. "Thus, TLC is clearly not a good surrogate marker for monitoring HAART." (Ruxrungtham, B3154)

Researchers in Bahia, Brazil evaluated paired CD4 and TLC for 498 patients during May to December 2003. A TLC cutoff of 1000 cells/mm³ predicted CD4 count <200 cells/mm³ with sensitivity of 44% (specificity = 98.5%) and had a positive predictive value of 70.2%. Raising the cutoff to TLC <1500 cells/mm³ improved sensitivity to 76.9%, although the positive predictive value at this level was only 38.7%. The authors conclude that TLC estimates of CD4 counts for monitoring HAART are inaccurate. (Angelo, B3164)

Researchers in Jakarta, Indonesia evaluated 1062 paired CD4 and TLC results obtained between January 2002 and September 2003. Of 355 samples with TLC <1200 cells/mm³, 81% had CD4 counts <200 cells/mm³, while 20% of samples with TLC >1200 cells/mm³ had CD4 counts <200 cells/mm³. The authors conclude that if TLC alone were used to determine when to start ART, "then 39% of HIV infected Indonesians would be misclassified." (Donegan, B3177)

Researchers in Malvinas Argentinas, Argentina evaluated paired TLC and CD4 counts from 66 patients. While TLC <1500 cells/mm³ significantly predicted CD4 <200 cells/mm³ (p=0.01), sensitivity was 65% and specificity was 69%. With a cutoff of TLC <1200 cells/mm³, sensitivity was 50% (specificity = 89%) and with a cutoff of <1000 cells/mm³, sensitivity was 45% (specificity = 93.4%). The authors find that the sensitivity and specificity of TLC to predict CD4 cell counts <200 cells/mm³ is low, although lower cutoffs improve specificity. (Hojman, B7219)

Based on paired FACS and TLC data from 2419 patients, researchers in Pune, India derived an equation (CD4 = 0.24*TLC-5.97) to calculate CD4 counts using TLC values. They found that TLC was significantly correlated with CD4 (r=0.43; p<0.001). Using a TLC cutoff of <1500 cells/mm³ predicted CD4 counts <350 cells/mm³ with sensitivity of 72% and specificity of 78%. The positive predictive value was 79% and negative predictive value was 91%. However, for CD4 counts <200 cells/mm³, the equation was only 49% sensitive. (Thakar, B3105)

In Sagamu, Nigeria, investigators collected blood samples from 64 patients during a one year period ending in September 2003. CD4 counts were evaluated by FACS and by Dynakit, and TLC was calculated. While FACS and Dynakit were significantly correlated (r=0.831; p=0.001), TLC did not correlate with either method (FACS: r=0.061; p=.573). The authors conclude that TLC "is not a reliable substitute for CD4 countÉin this resource-limited setting." (Osho, B3096)

The one report to speak favorably of TLC also found fault with the CD4 count when it came to spotting opportunistic infections (OI).

Researchers at a major London, England hospital retroactively identified all patients with an AIDS-defining opportunistic infection (n=1097) to see if CD4 counts or TLC within the three months prior to the illness was more predictive of having an OI when compared to patients who did not develop OIs. TLC was significantly correlated with CD4 count (r=0.70; p<0.001) and the optimal cut-off for TLC was 1500 cells/mm³. While patients with TLC between 1000 and 1500 were 40% more likely to have an OI than those above 1500 (sensitivity = 68.6%; specificity = 75.6%), when using the CD4 count cutoff of 200 cells/mm³, those with CD4 between 150 and 200 were only 34% more likely to have an OI (sensitivity = 73.8%; specificity = 75.6%). The authors conclude that while CD4 <200 cells/mm³ is taken as the "gold standard for therapeutic intervention, this has relatively low specificity/sensitivity and results suggest that TLC is only moderately less reliable." (Jones, B3120)

Symptomatic Erratic

Another report found value in TLC only when it was paired with careful clinical evaluation. Researchers in Kampala, Uganda evaluated paired TLC and CD4 results along with clinical features in 202 patients enrolled between June 2002 and November 2003. The correlation between TLC and CD4 was significant (r=0.72; p<0.0001). TLC <1200 cells/mm³ predicted CD4 count <200 with a positive predictive value of 100% (negative predictive value = 32%), although this cutoff identified only 63 of 137 patients with WHO stage 2 or 3 and CD4 counts <200 cells/mm³. The authors conclude that despite a good correlation between the methods, "it requires a combination of TLC and clinical features in an algorithm to identify patients with CD4 cell counts less than 200 cells/mm³ in Uganda." (Semitala, B4531)

There has been some interest in quantifying the success of making treatment decisions based on symptoms and clinical features, such as weight gain after starting ART. Yet the limits to this are apparent.

Researchers from the Centers for Disease Control (CDC) in Atlanta, Georgia investigated if weight gain could serve as a surrogate marker for response to ART by reviewing medical records from 709 patients with weight measurements at the time of and subsequent to beginning ART. Overall, the cumulative probability of having at least a 10% weight gain at 12 months was 0.15. The probability was increased in those starting ART with <200 CD4 cells/mm³ (0.31) and for those starting ART with BMI <20 (0.33). Although weight gain of 10% or more occurred in about one third of patients with low CD4 counts or low BMI, weight gain was not correlated with viral load reduction. The authors conclude that "weight gain following HAART initiation does not necessarily mean that there is virologic improvement." (Teshale, B3101)

Researchers in Uganda operating an ART program evaluated methods of screening for eligibility for therapy in 907 patients. Patient history, review of records, and physical exam to determine CDC Class C and B symptoms as well as other signs of HIV disease were performed. Following screening, CD4 counts were obtained and compared to clinical findings. Of 376 patients with CD4 counts ³, 39% met the clinical criteria for starting ART and 91% of those with CD4 counts >200 did not meet the criteria. The sensitivity of the clinical criteria was 39%, specificity was 91% and the positive and negative predictive values were 76% and 68%, respectively. The authors conclude that CD4 testing would be important to detect the two thirds of patients who qualify for starting ART. (Solberg, B2035) There seems to be no good way around having an absolute CD4 count in hand to make decisions about when to start therapy and to monitor response to therapy once it has begun. Some recent innovations seem to provide acceptable results with significant cost savings. Yet they fall short of an ideal solution to the need for low-cost, point-of-care monitoring. To get the most out of the expected scale up of treatment in the coming years, a breakthrough in diagnostic technology must be made a priority.

References (all The XV International AIDS Conference, 2004)

1. Angelo ALD, et al. Evaluating the absolute lymphocyte count as a substitute for CD4 count in the follow up of AIDS patients under HAART. Abstract MoPeB3164.

2. Bi X, et al. Modifications of dynabeads method for enumerating CD4+ T cell count in resource-limited situations. Abstract TuPpB2038.

3. Donegan E, et al. Preliminary report on absolute lymphocyte counts as an indicator for anti-retroviral treatment in Indonesia. Abstract MoPeB3177.

4. Fryland M, et al. The PARTEC CyFlow counter for CD4+ T-cell counting produces high quality results and is robust when evaluated under routine field conditions in Malawi. Abstract TuOrB1149.

5. Hojman MA, et al. Total lymphocyte count and Initiation of highly active antiretroviral therapy in resource-limited settings. Abstract ThPeB7219.

6. Jones R, et al. A Cohort Study To Review The Efficacy of Total Lymphocyte Count (TLC) as a Predictor of AIDS defining Opportunistic Infection (ADOI) In HIV Infected Patients. Abstract MoPeB3120.

7. Osho OB, et al. Absolute lymphocyte count as a substitute for CD4 count in a limited resource area; Sagamu, Ogun state, Nigeria. Abstract MoPeB3096.

8. Pattanapanyasat K, et al. Generic reagents and the PanLeucogating protocol: Is this the way towards affordable CD4 enumeration in Thailand? Abstract MoPeB3087.

9. Pattanapanyasat K, et al. Evaluation of a new single-platform volumetric flow cytometer for enumeration of absolute CD4 T-lymphocyte counts in HIV-1 infected Thai patients. Abstract MoPeB3176.

10. Ruxrungtham K, et al. Total Lymphocyte Count (TLC) is not a good surrogate marker for Monitoring Antiretroviral Therapy (ART) in HIV-1 infected Thai patients: HIVNAT cohort analysis. Abstract MoPeB3154.

11. Semitala FC, et al. Total lymphocyte count of 1200 is not a sensitive predictor of CD4 lymphocyte count among patients with HIV disease in Kampala, Uganda. Abstract TuPeB4531.

12. Servais J, et al. Comparison of two flow cytometry methods for the determination of CD4 counts in Rwanda: RWA/021 TRAC/NRL project, Lux Development. Abstract MoPeB3172.

13. Sippy N, et al. Comparison of the Panleucogating technique with four-colour heterogenous gating for CD4+ T cell enumeration in HIV infected individuals in Barbados. Abstract MoPeB3108.

14. Solberg P, et al. Comparison of clinical criteria and CD4 cell counts to determine eligibility for antiretroviral therapy in Uganda. Abstract TuPpB2035.

15. Teav S, et al. Alternative CD4 counting using Cyflow in Cambodia: precision and comparison with Facscount. Abstract MoPeB3089.

16. Teav S, et al. CD4 lymphocyte counts with Cyflow in Cambodia: stability of sample results over time. Abstract MoPeB3110.

17. Teshale EH, et al. Can weight gain be used as a marker for viral load response in HIV-infected patients following initiation of highly active antiretroviral therapy (HAART)? Abstract MoPeB3101.

18. Thakar MR, et al. Absolute lymphocyte count is a useful marker of HIV-1 disease progression in HIV-1 imfected Individuals in Pune, India. Abstract MoPeB3105.