Evaluation of Dynabeads and Cytospheres Compared With Flow Cytometry to Enumerate CD41 T Cells in HIV-Infected Ugandans on Antiretroviral Therapy
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Lippincott Williams & Wilkins.
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Correspondence to: Lisa A. Spacek, MD, PhD, Assistant Professor, Division
of Infectious Diseases, Johns Hopkins Medical Institutions, 1830 East
Monument Street, Room 421, Baltimore, MD 21287 (e-mail: lspacek@
jhmi.edu).
Background: Laboratory-based monitoring of antiretroviral therapy is essential but adds a significant cost to HIV care. The World Health Organization 2006 guidelines support the use of CD4 lymphocyte count (CD4) to define treatment failure in resource-limited settings. Methods: We compared CD4 obtained on replicate samples from 497 HIV-positive Ugandans (before and during ART) followed for 18 months by 2 manual bead–based assays, Dynabeads (Dynal Biotech), and Cytospheres (Beckman Coulter) with those generated by flow cytometry at the Infectious Diseases Institute in Kampala, Uganda. Results: We tested 1671 samples (123 before ART) with Dynabeads and 1444 samples (91 before ART) with Cytospheres. Mean CD4 was 231 cells/mm3 (SD, 139) and 239 cells/mm3 (SD, 140) by Dynabeads and flow cytometry, respectively. Mean CD4 was 186 cells/mm3 (SD, 101) and 242 cells/mm3 (SD, 136) by Cytospheres and flow cytometry, respectively. The mean difference in CD4 count by flow cytometry versus Dynabeads were 8.8 cells/mm3 (SD, 76.0) and versus Cytospheres were 56.8 cells/mm3 (SD, 85.8). The limits of agreement were 2140.9 to 158.4 cells/mm3 for Dynabeads and 2112.2 to 225.8 cells/mm3 for Cytospheres. Linear regression analysis showed higher correlation between flow cytometry and Dynabeads (r = 0.85, r2 = 0.73, slope = 0.85, intercept = 28) compared with the correlation between flow cytometry and Cytospheres (r = 0.78, r2 = 0.60, slope = 0.58, intercept = 45). Area under the receiver operating characteristics curve to predict CD4 ,200 cells/mm3 was 0.928 for Dynabeads and 0.886 for Cytospheres. Conclusion: Although Dynabeads and Cytospheres both underestimated CD4 lymphocyte count compared with flow cytometry, in resource-limited settings with low daily throughput, manual bead– based assays may provide a less expensive alternative to flow cytometry.
Background: Laboratory-based monitoring of antiretroviral therapy is essential but adds a significant cost to HIV care. The World Health Organization 2006 guidelines support the use of CD4 lymphocyte count (CD4) to define treatment failure in resource-limited settings. Methods: We compared CD4 obtained on replicate samples from 497 HIV-positive Ugandans (before and during ART) followed for 18 months by 2 manual bead–based assays, Dynabeads (Dynal Biotech), and Cytospheres (Beckman Coulter) with those generated by flow cytometry at the Infectious Diseases Institute in Kampala, Uganda. Results: We tested 1671 samples (123 before ART) with Dynabeads and 1444 samples (91 before ART) with Cytospheres. Mean CD4 was 231 cells/mm3 (SD, 139) and 239 cells/mm3 (SD, 140) by Dynabeads and flow cytometry, respectively. Mean CD4 was 186 cells/mm3 (SD, 101) and 242 cells/mm3 (SD, 136) by Cytospheres and flow cytometry, respectively. The mean difference in CD4 count by flow cytometry versus Dynabeads were 8.8 cells/mm3 (SD, 76.0) and versus Cytospheres were 56.8 cells/mm3 (SD, 85.8). The limits of agreement were 2140.9 to 158.4 cells/mm3 for Dynabeads and 2112.2 to 225.8 cells/mm3 for Cytospheres. Linear regression analysis showed higher correlation between flow cytometry and Dynabeads (r = 0.85, r2 = 0.73, slope = 0.85, intercept = 28) compared with the correlation between flow cytometry and Cytospheres (r = 0.78, r2 = 0.60, slope = 0.58, intercept = 45). Area under the receiver operating characteristics curve to predict CD4 ,200 cells/mm3 was 0.928 for Dynabeads and 0.886 for Cytospheres. Conclusion: Although Dynabeads and Cytospheres both underestimated CD4 lymphocyte count compared with flow cytometry, in resource-limited settings with low daily throughput, manual bead– based assays may provide a less expensive alternative to flow cytometry.
Keywords
CD4 lymphocyte counts, HIV-1 viral load, Monitoring and evaluation, Antiretroviral therapy, Resource-limited setting, Uganda