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Showing 3 results for Jambarsang
The effects of temperature and birth weight on the transition rate of hypothermia in hospitalized neonates using Markov models
Akbarzadeh Baghban A, Jambarsang S, Pezeshk H, Nayeri F,
Volume 70, Issue 5 (5 2012)
Abstract
Background: Hypothermia is an important determinant of survival in newborns, especially among low-birth-weight ones. Prolonged hypothermia leads to edema, generalized hemorrhage, jaundice and ultimately death. This study was undertaken to examine the factors affecting transition from hypothermic state in neonates.
Methods:  The study consisted of 439 neonates hospitalized in NICU of Valiasr in Tehran, Iran in 2005. The neonates' rectal temperature was measured immediately after birth and every 30 minutes afterwards, until neonates passed hypothermia stages. In order to estimate the rate of transition from neonatal hypothermic state, we used multi-state Markov models with two covariates, birth weight and environmental temperature. We also used R package to fit the model.
Results:  Estimated transition rates from severe hypothermia and mild hypothermia were 0.1192 and 0.0549 per minute, respectively. Weight had a significant effect on transition from hypothermia to normal condition (95% CI: 0.1364-0.4165, P<0.001). Environmental temperature significantly affected the transition from hypothermia to normal stage (95% CI: 0.0439-0.4963, P<0.001).
Conclusion:  The results of this study showed that neonates with normal weight and neonates in an environmental temperature greater than 28 °C had a higher transition rate from hypothermia stages. Since birth weight at the time of delivery is not under the control of medical staff, keeping the environmental temperature in an optimum level could help neonates to pass through the hypothermiastages faster.


Modeling trend of the immune system in HIV positive people treated with antiretroviral drugs, using Markov model
Sara Jambarsang , Alireza Akbarzadeh Baghban , Seyed Saeed Hashemi Nazari, Farid Zayeri , Ali Nikfarjam ,
Volume 73, Issue 9 (December 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.


Comparative study of predictive ability of AIDS incidence in HIV positive people using Markov model according to two criteria, WHO and CDC in CD4 cell categorization
Mahsa Nazari, Farid Zayeri , Seyed Saeed Hashemi Nazari , Sara Jambarsang, Ali Nikfarjam , Alireza Akbarzadeh Baghban ,
Volume 77, Issue 2 (May 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.

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