CD4 Count Pattern and Demographic Distribution in HIV Positive Patients in Northern Part of India

Research Article

J Fam Med. 2019; 6(6): 1183.

CD4 Count Pattern and Demographic Distribution in HIV Positive Patients in Northern Part of India

Kumar M1*, Kumar R2, Mahdi AA3 and Dhole TN1

1Department of Microbiology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, Uttar Pradesh 226014, India

2Department of Life Sciences, Chhatrapati Shahu Ji Maharaj University, Kanpur, Uttar Pradesh 208024, India

3Department of Biochemistry, King George's Medical University, Lucknow, Uttar Pradesh 226003, India

*Corresponding author: Manoj Kumar, Department of Microbiology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow, Uttar Pradesh 226014, India

Received: April 16, 2019; Accepted: July 16, 2019; Published: July 23, 2019

Abstract

The aim of this study is to determine the percentage of HIV - positive people requiring treatment at the time of enrollment using the count of CD4 as a tool.

One hundred cases (37 males and 63 females) of different kinds of HIV/ AIDS were included in this study. Written consent was obtained from each participant for their information to be stored in the clinic database and used for research purposes. Blood samples were collected from these volunteers in the sterilized vials and processed for CD4 count. Data were analyzed with statistical package (SPSS 16.0) software and correlation coefficients and correlation matrix were determined.

One hundred cases (37 males and 63 females) of different kinds of HIV/ AIDS were included in this study with a mean age ranged from 33.45 ± 13.052. Half of the patients below 29 years of age and only 4% are above 59 years of age. Overall, the mean CD4 count was of 467.23 ± 291.34cells/mm3. The mean CD4 count of males was 436.43 ± 283.50 cells/mm3 and that of females 485.32 ± 269.59 cells/mm3. There was no significant gender difference in CD4+ve patients by CD4 cell count.

Females account for more than half of registered patients in HIV clinic and have a comparatively higher CD4 count than males. Nearly 3/4 of HIV positives require antiretroviral therapy at the time of registration.

Keywords: CD4 Count; Demographic; HIV; Blood; Gender; India

Introduction

India has the world's third biggest HIV epidemic. The prevalence of HIV among adults (15-49 years old) was estimated at 0.2% in 2017. This figure is small compared to most other middle - income countries, but this is equivalent to 2.1 million people living with HIV due to India's huge population (1.3 billion people) [1-2]. Overall, India’s HIV epidemic is slowing down. Between 2010 and 2017 new infections declined by 27% and AIDS-related deaths more than halved, falling by 56%. However, in 2017, new infections increased to 88,000 from 80,000 and AIDS-related deaths increased to 69,000 from 62,000 [3].

CD4 count measures the degree of immunosuppression in HIVpositive patients. There is an inverse relationship between CD4 count and degree of immunosuppression. CD4 count is used in monitoring disease progression, deciding when to commence therapy, staging the disease, determining treatment failure, and defining the risk for mother-to-child transmission. Laboratory markers used in monitoring management in HIV-positive patients are HIV-RNA assay (Viral load) and CD4 count. The former is the gold standard, its use is, however, limited because of its cost and technology. Furthermore, there is a mismatch between an undetectable viral load (‹50 copies/mL) and the absence of immune reconstitution, which can be confusing to both the treatment provider and patient.

Several studies have shown that CD4 count is the strongest predictor of disease progression and survival [4]. The cost of CD4 count is cheaper than viral load, it is increasingly becoming more affordable to patients in resource-poor countries [5-6]. All HIVpositive patients in resource rich and an increasing number of patients in resource-poor countries have baseline CD4 count on enrollment [7].

The U.S. Centre for Disease Control (CDC) and the prevention [8] staging system used the CD4 count as a tool to stage HIV into categories A, B, and C based on whether the CD4 count is >500 cells/ mm3, between 200–499 cells/mm3 and ‹200 cells/mm3, respectively. It defines AIDS as all HIV-positive patients with CD4 count ‹200 cells/ mm3 or CD4% < 14%. On the contrary, in order to accommodate resource - constrained settings where CD4 count testing may not be available, the WHO staging is based on clinical findings and does not require CD4 count.

CD4 count is an important tool to determine HIV - positive patients ' treatment failure. The 2010 World Health Organization (WHO) [9] revised guideline defined immunological failure as a fall of CD4 count to baseline level or below, or 50% fall from on-treatment peak value or persistent CD4 count below 100 cells/mm3. There must, however, be absence of concomitant infection to cause transient CD4 count decrease. A patient presenting with immunological or clinical failure (new or recurrent stage 4 disease) with viral load copies >5000 copies/mL is deemed to have treatment failure and switched to second-line regimens [9].

The introduction of HAART as a treatment method in HIV - positive patients resulted in a dramatic reduction in morbidity and mortality associated with AIDS and a significant improvement in the number of patients with CD4 [10]. Hence, HAART - experienced patients must be excluded from the study in order to determine the true picture of CD4 count pattern in HIV positive. The data can be used to determine the percentage of patients infected with HIV who need antiretroviral therapy (ART) when registering. This will help clinicians and policy makers determine the point to start treatment and the percentage of infected patients requiring treatment at registration. Therefore, this study aimed to determine the percentage of HIV - positive people requiring treatment at enrollment using the count of CD4 as a tool.

Materials and Methods

One hundred cases of different kinds of HIV/AIDS, age ranging from 18 to 60 years were selected from Sanjay Gandhi Post Graduate Institute of Medical Sciences (SGPGIMS), Lucknow, India. Written consent was obtained from each participant for their information to be stored in the clinic database and used for research purposes.

Lucknow is the capital city of Uttar Pradesh's Indian state, and is also the administrative headquarters of the district and division of the same name. It is India's 11th - populated city and 12th - populated urban agglomeration. SGPGIMS is a state law medical institute based in Lucknow, Uttar Pradesh. SGPGIMS provides tertiary medical care, teaching, training and research super - specialties.

Using vacutainer / needle, tenniquoit, and a swab spirit, the CD4 count blood samples were taken at 9am from the antecubital vein of the patient. The swab spirit was used to clean the area in which blood was taken from the antecubital vein with the tenniquoit tied just above the antecubital area and 10ml of blood was taken as previously described [11]. 20μl of the entire blood sample was mixed with 20μl CD4 easy count antibody in a level tube, then incubated for 15min at room temperature in the dark. Using cyflow SL green, 800μl of CD4 easy count no lyse buffer was diluted. It was evaluated after reading and the count noted per ml. To find the CD4 count in μl, the report was created using a report template (software, cyflow SL Green, Partec, Germany) to arrive at CD4/μl.

Data were analyzed with statistical package (SPSS 16.0) software and correlation coefficients and correlation matrix were determined. Results were presented with frequencies and percentages in simple tables, while differences were considered statistically significant when the P value obtained was less than 0.05.

Results

One hundred cases (37 males and 63 females) of different kinds of HIV/AIDS were included in this study with a mean age ranged from 33.45 ± 13.052. Half of the patients below 29 years of age and only 4% are above 59 years of age. Frequency distribution of age intervals according to CD4 positivity are summarized in Table 1. Statistically, this percentage difference among groups was not significant (p › 0.05).