The effect of longitudinal body weight and CD4 cell progression for the survival of HIV/AIDS patients

Abstract

It is about half a century since the HIV epidemic has been a menace to this world. Since then, several risk factors have been investigated for the prevalence of the disease, and the survival of Human Immunodeficiency Virus/Acquired Immune Deficiency Syndrome (HIV/AIDS) patients. The main purpose of the current study was to examine the current patient status in contrast with baseline facts and investigate the separate and joint effects of body weight and CD4 cell count progression for the survival of HIV/AIDS patients. A retrospective cohort study was conducted among HIV/AIDS patients, who were under Antiretroviral Therapy (ART) follow-up during 11 September 2013—5 September 2016 at Mekelle General Hospital, Ethiopia. A total of 216 HIV/AIDS patients were selected by using a systematic random sampling technique. Based on the complexity of the data and the desired objectives of the study, the authors have considered linear mixed-effects model (LMM) for continuous responses body weight and CD4 count, a Cox proportional hazard model for the survival outcome (time to death) and Joint model of longitudinal and survival outcome. The mean age, hemoglobin level, and body weight of HIV/AIDS patients at the start of ART were 34.8 years, 13.6 g/100 ml, and 49.2 kg, respectively. The average number of baseline CD4 cells count was 311.04 cells per mm3 with a standard deviation of 161 cells per mm3 of blood implying that patients were at a higher risk of getting HIV/AIDS-related illness. Out of 216 HIV/AIDS patients, 134 (62%) were female and 130 (60%) lived in an urban area. Similarly, among the sampled HIV/AIDS patients 23 (10.6%) were with HIV/TB co-infected. The present study has concerned on the comparison of separate and joint modeling. The results clearly demonstrate that the joint modeling of longitudinally CD4 count and weight measurements with survival outcomes fit the current dataset better than those obtained from the separate model, of course the authors realize in some specific cases both separate and joint analysis were consistent. However, the joint models were simpler as compared to the separate models as their effective member of parameters was smaller. In the analysis of joint modeling of longitudinal $$\sqrt {CD4 cell } $$ and log (body-weight) progression with survival time to death of HIV/AIDS patients, considered various sub-models and various significant factors were identified. In the event process the sub-model, Baseline CD4, fair, and good adherence, HIV/Tuberculosis (TB), and sex were significant factors of risk to short survival Time-to-Death on HIV/AIDS patients. In the first longitudinal process sub-model, Baseline CD4, Ambulatory functional status, HIV/TB (yes), Time*Ambulatory functional status, Time*Working functional status, and Time*Baseline CD4 were the significant factors of $$\sqrt {CD4 cell } $$ count progression. Moreover, In the second longitudinal process sub-model, visit time of follow-up, age, sex (male), baseline weight, Time*Ambulatory, and Time*Working functional status were the significant factors of log 10 (bodyweight) progression. In the present study, appropriate models were chosen and important significant factors also identified. Hence, the authors strongly suggest that special intervention, clinical practice, and health policy revision should be made on the risk factors that potentially determine the survival of HIV/AIDS patients.

Keywords

• art
• cd4 cell
• hiv/aids
• longitudinal data analysis
• statistical joint modeling