https://doi.org/10.4081/mm.2025.13809
Evaluation of CD4 count and viral load as predictors of clinical progression and treatment failure in newly diagnosed Human Immunodeficiency Virus-positive patients - a cross sectional study
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Published: 2 March 2026
Background and Aims: Human Immunodeficiency Virus (HIV) is a major global public health issue. India contributes third highest burden. CD4 T cells are prime target for HIV virus, so as the HIV infection progresses, the number of these cells declines. HIV-1 RNA Viral load used as a marker to progression of the disease. The aim of this study was to determine and correlate the role of CD4 count and Viral load as predictors of clinical progression and treatment failure in newly diagnosed HIV positive patients.
Materials and Methods: a cross-sectional study was conducted from July 2022 to February 2023. Sample collection (for both CD4 count and Viral load), packaging, storage, transportation and processing are done according to NACO guidelines.
Results: In the present study comparison of baseline, third and sixth month CD4 count, viral load and TND (Target Not Detected) were carried out after three months of HAART treatment where clinical progression and treatment failure were predicted earlier. Even after six months of HAART treatment 21 patients showed ≤350 cells/mm3 CD4 count and 23 patients showed ≥1,000 copies/ml viral load may develop opportunistic infections due to treatment failure. Whereas, patients with viral load ≥150 copies/ml reduced to 39 (14.6%) after six months due to enhanced adherence to treatment and Opportunistic Infection (OI) management.
Conclusions: Estimation of CD4 count and viral load at third month from the time of ART initiation which is different from routine sixth month testing will increase the chance of predicting clinical progression and treatment failure earlier to control OIs and non-adherence to treatment.
Downloads
1. Beach MC, Keruly J, Moore RD. Is the quality of the patient-provider relationship associated with better adherence and health outcomes for patients with HIV? Journal of General Internal Medicine 2006;21:661-5.
2. Bhatti AB, Usman M, Kandi V. Current scenario of HIV/AIDS, treatment options, and major challenges with compliance to antiretroviral therapy. Cureus 2016;8:e515.
3. Broyles LN, Luo R, Boeras D, Vojnov L. The risk of sexual transmission of HIV in individuals with low-level HIV viraemia: a systematic review. The Lancet 2023;402:464-71.
4. Chaisson RE, Gallant JE, Keruly JC, Moore RD. Impact of opportunistic disease on survival in patients with HIV infection. AIDS 1998;12:29-33.
5. Deeks SG, Barbour JD, Martin JN, et al. Sustained CD4+ T cell response after virologic failure of protease inhibitor-based regimens in patients with HIV infection. J Infect Dis 2000;181:946-53.
6. Fauci AS, Kasper DL, Braunwald E, et al. Human Immunodeficiency virus (HIV) disease: AIDS and related disorders. In: Harrison’s Principles of Internal Medicine. 17th edition. Mc Graw Hill; Columbus, USA; 2008.
7. Gale HB, Rodriguez MD, Hoffman HJ, et al. Progress realized: trends in HIV-1 viral load and CD4 cell count in a tertiary-care center from 1999 through 2011. PLoS One 2013;8:e56845.
8. Gebremariam MK, Bjune GA, Frich JC. Barriers and facilitators of adherence to TB treatment in patients on concomitant TB and HIV treatment: a qualitative study. BMC Public Health 2010;10:1-9.
9. Gezie LD. Predictors of CD4 count over time among HIV patients initiated ART in Felege Hiwot Referral Hospital, northwest Ethiopia: multilevel analysis. BMC Research Notes 2016;9:1-9.
10. Hall GS. Bailey & Scott’s Diagnostic Microbiology. 15th edition. Elsevier; Amsterdam, The Netherlands; 2021.
11. Ickovics JR, Meade CS. Adherence to HAART among patients with HIV: breakthroughs and barriers. AIDS Care 2002;14:309-18.
12. Ledergerber B, Egger M, Opravil M, et al. Clinical progression and virologic failure on highly active antiretroviral therapy in HIV-1 patients: a prospective cohort study. Lancet 1999;353:863-8.
13. Mellors JW, Munoz A, Giorgi JV, et al. Plasma viral load and CD4+ lymphocytes as prognostic markers of HIV-1 infection. Annals of Internal Medicine 1997;126:946-54.
14. Miller V, Staszewski S, Nisius G, et al. Risk of new AIDS diseases in people on triple therapy. Lancet 1999;353:463.
15. Mocroft A, Katlama C, Johnson AM, et al. AIDS across Europe, 1994-98: the EuroSIDA study. Lancet 2000;356:291-6.
16. Monforte AD, Adorni F, Meroni L, et al. Predictive role of the three-month CD4 cell count in the long-term clinical outcome of the first HAART regimen. Biomedicine & Pharmacotherapy 2001;55:16-22.
17. NACO Manual Guidelines for Collection, Handling and Transport of Specimens for CD4 Testing, March 2007. Available from: https://www.naco.gov.in/sites/default/files/CD-4.pdf
18. National Guidelines For HIV-1 Viral Load Testing February 2018 – NACO. Available from: https://naco.gov.in/sites/default/files/NationalGuidelinesForHIV-1ViralLoadLaboratoryTestingApril2018%20%282%29.pdf
19. National Operational Guidelines for Viral Load Testing March 2018 – NACO. Available from: https://naco.gov.in/sites/default/files/National%20Operational%20Guidelines%20for%20Viral%20Load%20Testing%20Mar%2718.pdf
20. Notermans DW, Goudsmit J, Danner SA, et al. Rate of HIV-1 decline following antiretroviral therapy is related to viral load at baseline and drug regimen. AIDS 1998;12:1483-90.
21. Procop GW, Church DL, Hall GS, Janda WM. Koneman's Color Atlas and Textbook of Diagnostic Microbiology. Available from: https://books.google.it/books?id=4gWwsEiMwu8C&printsec=frontcover&redir_esc=y#v=onepage&q&f=false
22. Rangarajan S, Colby DJ, Bui DD, et al. Factors associated with HIV viral load suppression on antiretroviral therapy in Vietnam. Journal of Virus Eradication 2016;2:94-101.
23. Raval Payal N, Purav G Patel. A retrospective study comprising analysis of CD4 Cell count among HIV infected patients in tertiary care hospital in western India. Indian J Microbiol Res 2017;4:431-3.
24. Shoko C, Chikobvu D. A superiority of viral load over CD4 cell count when predicting mortality in HIV patients on therapy. BMC Infectious Diseases 2019;19:1-0.
25. Staszewski S, Miller V, Sabin CA, et al. Determinants of sustainable CD4 lymphocyte count increases in response to antiretroviral therapy. AIDS 1999;13:951-6.
26. Testori V, Adorni F, Castelnuovo B, et al. CD4 cell counts at the third month of HAART may predict clinical failure. AIDS 1999;13:1669-76.
27. Wilson DP, Law MG, Grulich AE, et al. Relation between HIV viral load and infectiousness: a model-based analysis. The Lancet 2008;372:314-20.
28. World Health Organization (WHO). HIV/AIDS Factsheet. Available from: https://www.who.int/news-room/fact-sheets/detail/hiv-aids
29. World Health Organization (WHO). HIV & AIDS in Asia-Pacific region. Available from: https://www.aidsdatahub.org/sites/default/files/resource/regional-review-hiv-aids-asia-pacific-region.pdf
30. Yehia BR, Fleishman JA, Metlay JP, et al. Sustained viral suppression in HIV-infected patients receiving antiretroviral therapy. JAMA 2012;308:339-42.
How to Cite
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.