Showing 5 results for Antiretroviral Therapy
Yalda A, Seyyed Alinaghi Sa, Hajiabdolbaghi M,
Volume 66, Issue 7 (10-2008)
Abstract
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There are limited published investigations about
adherence to antiretroviral and its determinants. Many determinants influence on
adherence to therapy. The effects of some determinants on adherence are
controversial. More studies are needed to be fulfilled about adherence and its
determinants to compile strategies. Key to the success of antiretroviral
therapies is the ability and willingness of HIV-positive individuals to adhere to antiretroviral
regimens. There are
different definitions for full adherence. In the most studies, adherence is defined as taking ≥95% of prescribed medication.
Adherence rate needs to be >95% to prevent virologic failure and for complete supper-ssion.
The consequences of poor adherence include not only diminished benefits for the
patient, but also the public health threat of the emergence of
multidrug-resistant viruses, as these resistant strains can then be transmitted
from a patient to their contacts. Evaluating adherence has proven to be
difficult and
there is no gold standard for evaluating adherence to medication. Adherence is assessed
in various ways. The most studies evaluate adherence to treatment by
using patient's self report and the pill count
method but these are methods known to overestimate adherence. Some determinants
are associated with adherence include: age, gender, addiction specially
injection drug users, alcohol consumption, depression, social support, level of
education, work situation, adverse antiretroviral effects, pregnancy, type of
antiretroviral drug regimen, number of pills and daily doses received, severe traumas, social and psychological factors,
and relationship between clinician and patient.0
Sara Jambarsang , Alireza Akbarzadeh Baghban , Seyed Saeed Hashemi Nazari, Farid Zayeri , Ali Nikfarjam ,
Volume 73, Issue 9 (12-2015)
Abstract
Background: After primary infection, the number of CD4 T-cells decreases with disease progress. The patient’s immunological status could inform by The CD4 T-cell counts over the time. The main purpose of this study is to assess the trend of CD4 cell count in HIV+ patient that received Antiretroviral Therapy (ART) by using a multistate Markov model to estimate transition intensities and transition probabilities among various states.
Methods: A total of 122 HIV+ patients were included in this cohort study who are undergoing Antiretroviral Therapy treatment in the Iran AIDS center in Imam Khomeini Hospital in Tehran that inter during March 1995 to January 2005 and then fallow up to October 2014. All adults with at least two follow-up visits in addition to their pre-ART treatment were considered to be eligible for inclusion in the study. Continuous-time Markov processes are used to describe the evolution of a disease over different states. The mean sojourn time for each state was estimated by multi state Markov model.
Results: Sample included 22 (18%) female with a mean age of 43.32 (standard deviation 8.33) years and 100 (82%) male with a mean age of 45.28 (standard deviation 8.34) year. Age was divided in to two categories, 40 years old and lower than that 66 (54.1) patents and persons older than 40 years old 56 (45.9) patents. A total of 122 patients were included. 29 patients died during follow-up. One year transition probability for staying in state 1 of CD4 cell count was 51%. This probability for six year was 33%. The mean sojourn time for sate 4 was 21 month. The hazard ratio of transition from state 3 to state 4 was 4.4 in men related to women.
Conclusion: The use of antiretroviral therapy in the treatment of HIV infected persons reduce viral replication and increase in CD4 T lymphocyte count, and delay the progression of disease. This paper is shown the progression of this trend.
Mahsa Nazari, Farid Zayeri , Seyed Saeed Hashemi Nazari , Sara Jambarsang, Ali Nikfarjam , Alireza Akbarzadeh Baghban ,
Volume 77, Issue 2 (5-2019)
Abstract
Background: The Multi state Markov models have extensively application with categorization of laboratory marker of CD4 cells for evaluation of HIV disease progression. These models with different states result in different effects of covariates and prediction of HIV disease trend. The main purpose of this study was comparison of four and five states models with the three- state in order to select the model with better prediction ability of occurrence of HIV and finally death in HIV positive people.
Methods: A total of 305 HIV positive people were included in this cohort study in the Iran AIDS center in Imam Khomeini Hospital in Tehran that entered during March 1995 to January 2005 and then fallowed up to October 2014. The three continuous- time Markov models of three-, four- and five- state models were fitted to data to describe the evolution of a HIV disease Trend over different states.
For comparison of models, two criteria of modification of Akaike’s criterion (DRAIC) and likelihood cross-validation criterion (DRLCV) along with their 95% tracking interval was used. For fitting of these models and estimation of transition matrix and the hazard ratio of gender and treatment independent variables, the msm package of R project for statistical computing, version R 3.2.4 (www.r-project.org) was used.
Results: The results showed that the four- state model has more prediction ability than five-state model for evaluation of HIV disease Trend. In the four-state model, the progression hazard ratio to death for people who received highly active antiretroviral therapy (HAART) was 0.64 lower than who didn’t get this therapy. Moreover, the progression hazard ratio for men was 2.33 fold in comparison to women. The disease progression hazard ratio to death was 4.9 fold for men in comparison to women.
Conclusion: The (DRAIC) and (DRLCV) criterions showed that the four-state model has more predictive ability of the progression trend of HIV disease in comparison of five-state model.
Ahmad Tavakoli , Maryam Esghaei , Angila Ataei-Pirkooh , Mohsen Moghoofei , Hadi Ghaffari , Farah Bokharaei-Salim ,
Volume 77, Issue 5 (8-2019)
Abstract
Currently, there are about 37 million people worldwide living with human immunodeficiency virus (HIV) /AIDS, with an estimated two million new cases per year globally. According to estimates from the World Health Organization (WHO), only 75% of the population with HIV know their status. Initially, HIV infection was associated with significantly increased rates of mortality and morbidity. However, the rapid advances in treatment and the advent of different classes of antiretroviral drugs over time have led to change the face of HIV/AIDS from a deadly infection to chronic and manageable disease. There is strong evidence that HIV-infected patients undergoing antiretroviral therapy have longer lives and are less likely to transmit infection to their sexual partners. Since the introduction of zidovudine in 1987 as the first antiretroviral drug, significant strides have been made in antiretroviral therapy. The introduction of potent antiretroviral drugs for the treatment of HIV infection has been one of the significant events in the evolution of modern medicine. Antiretroviral therapy refers to the use of drugs in the treatment of HIV. Generally, these drugs are categorized based on the steps of the HIV life cycle suppressed by them. There are six main classes of antiretroviral agents including nucleoside/ nucleotide reverse transcriptase inhibitors, non-nucleoside reverse transcriptase inhibitors, protease inhibitors, fusion inhibitors, co-receptor inhibitors, and integrase inhibitors. Combination antiretroviral therapy should be considered for HIV patients to achieve the highest viral suppression rate, and to reduce the risk of resistance development and morbidity and mortality associated with AIDS. Achieving and maintaining HIV viral load suppression among treated patients has remarkably increased over the last years due to the development of potent and well-tolerated agents which can be co-formulated as a once-daily single-tablet or fixed-dose combination for simplification. However, there are some limitations preventing patients to benefit from this treatment. The main goals of HIV therapy in the future are to overcome the limitations of current treatment, including side effects. This review will provide an overview of advances in the current antiretroviral drugs by focusing on their pharmacokinetics, mechanism of action, dosing recommendations, and adverse events for each drug class.
Saedeh Ebrahimi, Saeed Kalantari , Soheil Rahmani Fard , Mitra Kohandel, Zahra Amiri, Yousef Alimohamadi , Sara Minaeian,
Volume 80, Issue 2 (5-2022)
Abstract
Background: Despite the considerable advances in acquired immunodeficiency syndrome (AIDS) treatment and management, finding the cure for this disease has been hindered by emerging challenges such as virus resistance and treatment failures. The purpose of this study is to compare the cytokine profiles of patients with successful treatment and patients with unsuccessful treatment to gain a better understanding of treatment failure mechanisms.
Methods: Sixty-nine human immunodeficiency virus (HIV) positive patients who were referred to the west health center of Tehran between September 2018 and March 2021 were included in this study. Blood CD4+ cell count and viral load was measured using the flow cytometry and quantitative real-time polymerase chain reaction (RT-qPCR) methods respectively. Based on the viral load test results patients were divided into successful treatment (viral load<200 copies/ml, n=36) and unsuccessful treatment (viral load>200 copies/ml, n=33) groups. Subsequently, tumor necrosis factor-α (TNF-α) and interleukin-10 (IL-10) serum levels were measured using the enzyme-linked immunosorbent assay (ELISA) method.
Results: Analysis of data revealed that there was no difference in demographic data, medical history and clinical laboratory test results between the study groups. Elisa test results showed that serum TNF-α levels were significantly higher in the unsuccessful treatment group compared to the successful treatment group (10.43±10.17 vs 5.37±5.25, P=0.01) but no differences were observed in IL-10 levels between the study groups. Furthermore, age and sex-adjusted linear regression models showed that non-nucleoside reverse-transcriptase inhibitors (NNRTI)-based treatment regimen is positively associated with serum IL-10 levels in patients with unsuccessful treatment (B coefficient 10.88 (95% CI: 1.32-20.45), P=0.03). Moreover, based on the results of the linear regression models, no relationship between HIV viral load and serum IL-10 and TNF-α level was observed.
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Conclusion: Results of this study showcased the importance of TNF-α in disease progression and treatment failure. Further future studies regarding this relationship can provide vital information in AIDS treatment research.
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