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Evaluating new CD4 enumeration technologies for resource-constrained countries

Nature Reviews Microbiology volume 6, pages S29–S38 (2008)Cite this article

HIV-1 is responsible for significant morbidity and mortality and globally, an estimated 33.2 million individuals are currently infected with the virus1. The burden of disease is borne primarily by the developing world, particularly sub-Saharan Africa where 76% of AIDS-related deaths occur. Programmes that are designed to improve access to antiretroviral (ARV) drugs appear to have resulted in a decline in AIDS-related deaths globally, and improved access to treatment has been paralleled by improved access to rapid diagnostic assays for the identification of HIV infection2. As a result, clear guidelines on how to validate such rapid diagnostic tests have been published3. However, the development of the laboratory infrastructure that would be necessary to support HIV treatment initiatives has lagged behind, and thus access to monitoring assays such as CD4 enumeration and viral load analysis is still not widespread4,5,6,7.

This article focuses on one aspect of HIV monitoring: determining the absolute and percentage CD4 count in adults and the percentage CD4 count in infants. It has been unequivocally established that CD4 counts (both percentage and absolute) are crucial laboratory markers for assessing the degree of immunodeficiency and the risk of disease progression in the setting of HIV-1 infection8,9. Furthermore, the CD4 count is important for determining when to initiate ARV therapy or prophylaxis for opportunistic infections, monitoring the performance of ARVs and providing information on the immunological failure of treatment. In developed countries, ARV therapy is generally initiated when the CD4 count drops below 350 cells per μl10, and in resource-constrained countries therapy is initiated when the CD4 count is anywhere between 200 and 350 cells per μl. Guidelines advocate CD4 testing every 3 to 6 months following the initiation of treatment10,11,12 and also highlight the need for accurate, reproducible CD4 counts.

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References

  1. UNAIDS/WHO. AIDS epidemic update [online], (UNAIDS/WHO, Geneva, Switzerland, 2007).

  2. Plate, D. K. Evaluation and implementation of rapid HIV tests: the experience in 11 African countries. AIDS Res. Hum. Retroviruses 23, 1491–1498 (2007).

    Article  PubMed  Google Scholar 

  3. Peeling, R. W, Smith, P. G. & Bossuyt, P. M. A guide for diagnostic evaluations. Nature Rev. Microbiol. 4, S2–6 (2006).

    Article  Google Scholar 

  4. Petti, C. A., Polage, C. R., Quinn, T. C., Ronald, A. R. & Sande, M. A. Laboratory medicine in Africa: a barrier to effective health care. Clin. Infect. Dis. 42, 377–382 (2006).

    Article  PubMed  Google Scholar 

  5. Glencross, D. K., Stevens, G., Scott, L. E., Mendelow, B. V. & Stevens, W. The challenge of laboratory monitoring of HIV. S. Afr. Med. J. 92, 248 (2002).

    CAS  PubMed  Google Scholar 

  6. Glencross, D. K., Mendelow., B. V. & Stevens, W. S. Laboratory monitoring of HIV/AIDS in a resource-poor setting. S. Afr. Med. J. 93, 262–263 (2003).

    CAS  PubMed  Google Scholar 

  7. Stevens, W. S. et al. Affordable HIV diagnosis and monitoring for scaling up ARV treatment programs. S. Afr. J. HIV Med. 20, 38–41 (2005).

    Google Scholar 

  8. Mellors, J. W. et al. Plasma viral load and CD4+ lymphocytes as prognostic markers of HIV-1 infection. Ann. Intern. Med. 126, 946–954 (1997).

    Article  CAS  PubMed  Google Scholar 

  9. Fahey, J. L. et al. The prognostic value of cellular and serologic markers in infection with human immunodeficiency virus type 1. N. Engl. J. Med. 322, 166–172 (1990).

    Article  CAS  PubMed  Google Scholar 

  10. Panel on Antiretroviral Guidelines for Adults and Adolescents, Department of Health and Human Services. Guidelines for the use of antiretroviral agents in HIV-1-infected adults and adolescents [online], (Department of Health and Human Services, Washington, USA, 2008).

  11. WHO. Patient monitoring guidelines for HIV care and antiretroviral therapy (ART) [online], (WHO, Geneva, Switzerland, 2004).

  12. Gazzard, B. British HIV Association (BHIVA) guidelines for the treatment of HIV-infected adults with antiretroviral therapy. HIV Med. 7, 487–503 (2006).

    Article  CAS  PubMed  Google Scholar 

  13. Glencross, D. K., Scott, L. E., Jani, I. V., Barnett, D. & Janossy, G. CD45-assisted PanLeucogating for accurate, cost-effective dual-platform CD4+ T-cell enumeration. Cytometry 50, 69–77 (2002).

    Article  PubMed  Google Scholar 

  14. Mandy, F. F., Bergeron, M. & Minkus, T. Evolution of leukocyte immunophenotyping as influenced by the HIV/AIDS pandemic: a short history of the development of gating strategies for CD4+ T-cell enumeration. Cytometry 30, 157–165 (1997).

    Article  CAS  PubMed  Google Scholar 

  15. Stewart, C. C. & Steinkamp, J. A. Quantitation of cell concentration using the flow cytometer. Cytometry 2, 238–243 (1982).

    Article  CAS  PubMed  Google Scholar 

  16. Valet, G. A new method for fast blood cell counting and partial differentiation by flow cytometry. Blut 49, 83–90 (1984).

    Article  CAS  PubMed  Google Scholar 

  17. Connelly, M. C. et al. Standardization of absolute CD4+ lymphocyte counts across laboratories: an evaluation of the Ortho CytoronAbsolute flow cytometry system on normal donors. Cytometry 22, 200–210 (1995).

    Article  CAS  PubMed  Google Scholar 

  18. Mercolino, T. J. et al. Immunologic differentiation of absolute lymphocyte count with an integrated flow cytometric system: a new concept for absolute T cell subset determinations. Cytometry 22, 48–59 (1995).

    Article  CAS  PubMed  Google Scholar 

  19. Reimann, K. A. et al. Multisite comparison of CD4 and CD8 T-lymphocyte counting by single- versus multiple-platform methodologies: evaluation of Beckman Coulter flow-count fluorospheres and the tetraONE system.The NIAID DAIDS New Technologies Evaluation Group. Clin. Diagn. Lab. Immunol. 7, 344–351 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Schnizlein-Bick, C. T., Spritzler, J., Wilkening, C. L., Nicholson, J. K. & O'Gorman, M. R. Evaluation of TruCount absolute-count tubes for determining CD4 and CD8 cell numbers in human immunodeficiency virus-positive adults. Site Investigators and The NIAID DAIDS New Technologies Evaluation Group. Clin. Diagn. Lab. Immunol. 7, 336–343 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Janossy, G., Jani, I. & Gohde, W. Affordable CD4(+) T-cell counts on 'single-platform' flow cytometers I. Primary CD4 gating. Br. J. Haematol. 111, 1198–1208 (2000).

    Article  CAS  PubMed  Google Scholar 

  22. Glencross, D. K. et al. Large-scale affordable PanLeucogated CD4+ testing with proactive internal and external quality assessment: in support of the South African national comprehensive care, treatment and management programme for HIV and AIDS. Cytometry B Clin. Cytom. 74B (Suppl. 1), S40–S51 (2008).

    Article  Google Scholar 

  23. Storie, I. et al. Flow rate calibration II: a clinical evaluation study using PanLeucoGating as a single-platform protocol. Cytometry 55, 8–13 (2003).

    Article  PubMed  Google Scholar 

  24. Barnett, D., Granger, V., Whitby, L., Storie, I. & Reilly, J. T. Absolute CD4+ T-lymphocyte and CD34+ stem cell counts by single-platform flow cytometry: the way forward. Br. J. Haematol. 106, 1059–1062 (1999).

    Article  CAS  PubMed  Google Scholar 

  25. Gratama, J. W., Kraan, J., Keeney, M., Granger, V. & Barnett, D. Reduction of variation in T-cell subset enumeration among 55 laboratories using single-platform, three or four-color flow cytometry based on CD45 and SSC-based gating of lymphocytes. Cytometry 50, 92–101 (2002).

    Article  PubMed  Google Scholar 

  26. CLSI. Evaluation of precision performance of quantitative measurement methods. Approved guideline — second edition. NCCLS document EP5-A2 [online], (CLSI, Wayne, Pennysylvania, USA, 2004).

  27. Mandy, F. F., Nicholson, J. K. & McDougal, J. S. Guidelines for performing single-platform absolute CD4+ T-cell determinations with CD45 gating for persons infected with human immunodeficiency virus. Morbid. Mortal. Wkly Rep. 52, 1–13 (2003).

    Google Scholar 

  28. Barnett, D. et al. Guidelines for the enumeration of CD4+ T lymphocytes in immunosuppressed individuals. Clin. Lab. Haematol. 19, 231–241 (1997).

    Article  Google Scholar 

  29. Mandy, F., Nicholson, J., Autran, B. & Janossy, G. T-cell subset counting and the fight against AIDS: reflections over a 20-year struggle. Cytometry 50, 39–45 (2002).

    Article  PubMed  Google Scholar 

  30. Whitby, L. et al. Quality control of CD4+ T-lymphocyte enumeration: results from the last 9 years of the United Kingdom National External Quality Assessment Scheme for Immune Monitoring (1993–2001). Cytometry 50, 102–110 (2002).

    Article  PubMed  Google Scholar 

  31. FDA. Food and Drugs Chapter 1 — Food and Drug Administration Department of Health and Human Services Subchapter H — Medical Devices Part 820 Quality System Regulation [online], (FDA, Rockville, Maryland, USA, 2007).

  32. Stevens, W. Good clinical laboratory practice (GCLP): the need for a hybrid of good laboratory practice and good clinical practice guidelines/standards for medical testing laboratories conducting clinical trials in developing countries. Qual. Assur. 10, 83–89 (2003).

    Article  CAS  PubMed  Google Scholar 

  33. Banoo, S. et al. Evaluation of diagnostic tests for infectious diseases: general principles. Nature Rev. Microbiol. 4, S20–32 (2006).

    Article  Google Scholar 

  34. Balakrishnan, P. et al. An inexpensive, simple, and manual method of CD4 T-cell quantitation in HIV-infected individuals for use in developing countries. J. Acquir. Immune Defic. Syndr. 36, 1006–1010 (2004).

    Article  PubMed  Google Scholar 

  35. Carella, A. V., Moss, M. W., Provost, V. & Quinn, T. C. A manual bead assay for the determination of absolute CD4+ and CD8+ lymphocyte counts in human immunodeficiency virus-infected individuals. Clin. Diagn. Lab. Immunol. 2, 623–625 (1995).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Diagbouga, S. et al. Successful implementation of a low-cost method for enumerating CD4+ T lymphocytes in resource-limited settings: the ANRS 12-26 study. AIDS 17, 2201–2208 (2003).

    Article  PubMed  Google Scholar 

  37. Didier, J. M. et al. Comparative assessment of five alternative methods for CD4+ T-lymphocyte enumeration for implementation in developing countries. J. Acquir. Immune Defic. Syndr. 26, 193–195 (2001).

    Article  CAS  PubMed  Google Scholar 

  38. Imade, G. E. et al. Comparison of a new, affordable flow cytometric method and the manual magnetic bead technique for CD4 T-lymphocyte counting in a northern nigerian setting. Clin. Diagn. Lab. Immunol. 12, 224–227 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Karcher, H., Bohning, D., Downing, R., Mashate, S. & Harms, G. Comparison of two alternative methods for CD4+ T-cell determination (Coulter manual CD4 count and CyFlow) against standard dual platform flow cytometry in Uganda. Cytometry B Clin. Cytom. 70B, 163–169 (2006).

    Article  Google Scholar 

  40. Landay, A. et al. A rapid manual method for CD4+ T-cell quantitation for use in developing countries. AIDS 7, 1565–1568 (1993).

    Article  CAS  PubMed  Google Scholar 

  41. Lopez, A. et al. Enumeration of CD4(+) T-cells in the peripheral blood of HIV-infected patients: an interlaboratory study of the FACSCount system. Cytometry 38, 231–237 (1999).

    Article  CAS  PubMed  Google Scholar 

  42. Strauss, K. et al. Performance evaluation of the FACSCount System: a dedicated system for clinical cellular analysis. Cytometry 26, 52–59 (1996).

    Article  CAS  PubMed  Google Scholar 

  43. Balakrishnan, P. et al. A reliable and inexpensive EasyCD4 assay for monitoring HIV-infected individuals in resource-limited settings. J. Acquir. Immune Defic. Syndr. 43, 23–26 (2006).

    Article  PubMed  Google Scholar 

  44. Pattanapanyasat, K. et al. Evaluation of a single-platform microcapillary flow cytometer for enumeration of absolute CD4+ T-lymphocyte counts in HIV-1 infected Thai patients. Cytometry B Clin. Cytom. 72B, 387–396 (2007).

    Article  Google Scholar 

  45. Fryland, M. et al. The Partec CyFlow Counter could provide an option for CD4+ T-cell monitoring in the context of scaling-up antiretroviral treatment at the district level in Malawi. Trans. Roy. Soc. Trop. Med. Hyg. 100, 980–985 (2006).

    Article  CAS  PubMed  Google Scholar 

  46. Zijenah, L. S. et al. Affordable flow cytometry for enumeration of absolute CD4+ T-lymphocytes to identify subtype C HIV-1 infected adults requiring antiretroviral therapy (ART) and monitoring response to ART in a resource-limited setting. J. Transl. Med. 4, 33 (2006).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Dieye, T. N. et al. Evaluation of an affordable instrument for absolute CD4 counting in resource-poor settings against two reference clinical flow cytometers. Antiviral Ther. 8 (Suppl. 1) Abstract 1126 (2003).

    Google Scholar 

  48. Scott, L. E., Hansen, P., Glencross, D. K., & Stevens, W. PointCARE CD4 testing: the new kid on the block, Abstract 742, session 126. 12th Conference on Retroviruses and Opportunistic infections (Boston, USA, 2005).

  49. Glencross, D. K., Aggett, H. M., Stevens, W. S. & Mandy, F. African regional external quality assessment for CD4 T-cell enumeration: development, outcomes, and performance of laboratories. Cytometry B Clin. Cytom. 74B (Suppl. 1), S69–79 (2008).

    Article  Google Scholar 

  50. Bergeron, M., Houle, G., Azevedo, L., Grangerrio, A. & Mandy, F. F. The impact of single platform absolute counting technology with a linage specific marker and an external quality assessment program. Abstract B12532. AIDS 2006 — XVI International AIDS Conference (Bangkok, Thailand, 2006).

  51. Pattanapanyasat, K., Sukapirom, K., Kowawisatsut, L. & Thepthai, C. New BD FACSCount CD4 reagent system for simultaneous enumeration of percent and absolute CD4 T-lymphocytes in HIV-1-infected pediatric patients. Cytometry B Clin. Cytom. 74B (Suppl. 1) S98–106 (2008).

    Article  Google Scholar 

  52. CLSI. User verification of performance for precision and trueness. Approved guideline—second edition. CLSI document EP15-A2 (CLSI, Wayne, Pennysylvania, USA, 2005).

  53. International Organization for Standardization. Statistics — Vocabulary and symbols — Part 1: General statistical terms and terms used in probability (ISO-3534-1). (International Organization for Standardization, Geneva, Switzerland, 1993)

  54. Denny, T. N. et al. NIAID-DAIDS Immunology Quality Assessment Program. A North American multilaboratory study of CD4 counts using flow cytometric panLeukogating (PLG): a NIAID-DAIDS Immunology Quality Assessment Program Study. Cytometry B Clin. Cytom. 74B (Suppl. 1) S52–64 (2008).

    Article  Google Scholar 

  55. Storie, I. et al. Perfect count: a novel approach for the single platform enumeration of absolute CD4+ T-lymphocytes. Cytometry B Clin. Cytom. 57, 47–52 (2004).

    Article  PubMed  Google Scholar 

  56. CLSI. Preliminary evaluation of quantitative clinical laboratory measurement procedures. Approved guideline — third edition. CLSI document EP10-A3 (CLSI, Wayne, Pennysylvania, USA, 2006).

  57. Sokal, R. R & Braumann, C. A. Significance tests for coefficients of variation and variability profiles. Syst. Zool. 29, 50–66 (1980).

    Article  Google Scholar 

  58. Haldane, J. B. S. The measurement of variation. Evolution 9, 484 (1955).

    Article  Google Scholar 

  59. Mandy, F., Bergeron, M., Houle, G., Bradley, J. & Fahey, J. Impact of the international program for Quality Assessment and Standardization for Immunological Measures Relevant to HIV/AIDS: QASI. Cytometry 50, 111–116 (2002).

    Article  PubMed  Google Scholar 

  60. Pattanapanyasat, K. et al. Thailand program for quality assessment and standardization for flow cytometric determination of CD4 T-lymphocyte counts for persons with HIV/AIDS. Abstract MoPeB3179. 15th International Conference on AIDS (Bangkok, Thailand, 2004).

  61. Levey, S. & Jennings, E. R. The use of control charts in the clinical laboratory. Am. J. Clin. Pathol. 20, 1059–1066 (1950).

    Article  CAS  PubMed  Google Scholar 

  62. Brando, B. et al. Cytofluorometric methods for assessing absolute numbers of cell subsets in blood. European Working Group on Clinical Cell Analysis. Cytometry 42, 327–346 (2000).

    Article  CAS  PubMed  Google Scholar 

  63. Bergeron, M. et al. Stability of currently used cytometers facilitates the identification of pipetting errors and their volumetric operation: “time” can tell all. Cytometry B Clin. Cytom. 52, 37–39 (2003).

    Article  CAS  PubMed  Google Scholar 

  64. Scott, L. E. & Glencross, D. K. Monitoring reproducibility of single analysis, single platform CD4 cell counts in a high volume, low resource laboratory setting using sequential bead count rates. Cytometry B Clin. Cytom. 67, 31–32 (2005).

    Article  PubMed  Google Scholar 

  65. Coetzee, L. M., Scott, L. E., Lawrie, D., Glencross, D. K. & Stevens, W. Evaluation of pipetting methodology to assess precision and accuracy of single platform PLG CD4 enumeration. Abstract P32. 2nd South African AIDS Meeting (Durban, South Africa, 2005).

  66. Bland, J. M. & Altman, D. G. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1, 307–310 (1986).

    Article  CAS  PubMed  Google Scholar 

  67. Scott, L. E., Galpin, J. S. & Glencross, D. K. Multiple method comparison: statistical model using percentage similarity. Cytometry B Clin. Cytom. 54B, 46–53 (2003).

    Article  Google Scholar 

  68. Pollock, M. A. et al. Method comparison — a different approach. Ann. Clin. Biochem. 29, 556–560 (1992).

    Article  CAS  PubMed  Google Scholar 

  69. Bossuyt, P. M. et al. Towards complete and accurate reporting of studies of diagnostic accuracy: the STARD initiative. Standards for Reporting of Diagnostic Accuracy. Clin. Chem. 49, 1–6 (2003).

    Article  CAS  PubMed  Google Scholar 

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Author notes

  1. The findings and conclusions of this report are those of the authors and do not necssarily represent the official position of the Centers for Disease Control or Prevention or the Agency for Toxic Substances Disease Registry.

  2. Copyright © WHO, on behalf of TDR (WHO/TDR) 2008.

Authors and Affiliations

  1. Deborah K. Glencross and Lesley E. Scott are at the Department of Molecular Medicine and Haematology, Wendy Stevens, University of the Witwatersrand and National Health Laboratory Services, 7 York Rd Parktown, Johannesburg, South Africa.,

    Wendy Stevens, Deborah K. Glencross & Lesley E. Scott

  2. Rebecca Gelman is at the Department of Biostatistics, Harvard School of Public Health and Dana Farber Cancer Institute, Boston, Massachusetts 02115, USA.,

    Rebecca Gelman

  3. Suzanne M. Crowe is at the Macfarlane Burnet Institute for Medical Research and Public Health and Department of Medicine, Monash University and The Alfred Hospital, Melbourne, Victoria, Australia.,

    Suzanne M. Crowe

  4. Thomas Spira is at the Global AIDS Programme, NCHHSTP, CCID, Centers for Disease Control and Prevention, Atlanta, Georgia 30333, USA.,

    Thomas Spira

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Corresponding author

Correspondence to Wendy Stevens.

Supplementary information

Table

CD4 enumeration technologies (PDF 389 kb)

Glossary

Accuracy

A general term used to describe how similar the results obtained for individual specimens with a new CD4 assay are to the results obtained with a reference CD4 assay. Measures of accuracy include bias and misclassification probabilities.

Bias

A measure of how much the CD4 count that is obtained using a new assay differs from the CD4 count that is obtained using a reference assay when the same specimen is analysed using both assays. (Bias need not be constant over the clinically important range of CD4 counts, and need not be the same for different laboratories using the same reference and new technologies.)

Clinically important range

The clinically important range (CIR) of CD4 counts includes most of the CD4 counts (obtained using the reference technology) from HIV-positive patients in a particular locality (clinic, hospital, country or region) for whom a CD4 assay is carried out to determine whether to initiate therapy (antiretroviral therapy or prophylaxis for opportunistic infections). The CIR should include the relevant CD4 count cut-offs as well as a reasonable margin around these cut-offs.

Coefficient of variation

The coefficient of variation (CV) is usually defined as the ratio of the standard deviation to the mean, which is usually estimated as the standard deviation divided by the average. This is often multiplied by 100 and expressed as a percentage (CV%).

Mean

The average value of a random variable, X. The most common estimate of the mean is the average of all the observations.

Median

The value m (of a continuous random variable, X) such that the probability that X is less than m is one half.

Misclassification probabilities

The upward and/or downward misclassification probability can be calculated for a particular cut-off, C. Among specimens with a CD4 count (obtained using the reference technology) which is less than C, the upward misclassification probability is defined as the percentage of specimens for which the CD4 count (obtained using the new technology) is greater than or equal to C (this is analogous to definition of 1 minus the sensitivity in a diagnostic test with only two values: the patient has the disease and the patient does not have the disease). Among specimens with a CD4 count (obtained using the reference technology) which is greater than or equal to C, the downward misclassification probability is defined as the percentage of specimens for which the CD4 count (obtained using the new technology) is less than C (this is analogous to definition of 1 minus the specificity in a diagnostic test with only two values: the patient has the disease and the patient does not have the disease).

Percentage (p) range

The difference between two percentiles that include p% of the range of a random variable; this need not be the middle p%. For example, the difference between the 100th percentile (maximum) and the 10th percentile is a 90 percentile range, as is the difference between the 95th percentile and the 5th percentile.

Precision

Precision (also known as reproducibility) is a general term for how close the results on replicate specimens are (using a single assay technology). Several sorts of precision can be relevant, including within-technician, between-technician, within-laboratory and between-laboratory precision. Measures of precision include the standard deviation, coefficient of variation, interquartile range and percentage (p) range.

Standard deviation

The standard deviation of a distribution (σ for a true value and s for an estimated value) is the positive square root of the variance. This should not be confused with the standard deviation of the mean (sometimes called the standard error); the standard error is the standard deviation divided by the square root of the number of observations.

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Stevens, W., Gelman, R., Glencross, D. et al. Evaluating new CD4 enumeration technologies for resource-constrained countries. Nat Rev Microbiol 6 (Suppl 11), S29–S38 (2008). https://doi.org/10.1038/nrmicro2000

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