Identifying risk factors for progression to AIDS and mortality post-HIV infection using illness-death multistate model

Abstract

Background: There has remained a need to better understanding of prognostic factors that affect the survival or risk in patients with human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS), particularly in developing countries. The aim of the present study aimed to identify the prognostic factors influencing AIDS progression in HIV positive patients in Hamadan province of Iran, using random survival forest in the presence of competing risks (death from causes not related to AIDS). This method considers all interactions between variables and their nonlinear effects. Methods: A data set of 585 HIV-infected patients extracted from 1997 to 2011 was utilized. The effect of several prognostic factors on cumulative incidence function (probability) of AIDS progression and death were investigated. Results: The used model indicated that using antiretroviral therapy tuberculosis co-infection are two top most important variables in predicting cumulative incidence function for AIDS progression in the presence of competing risks, respectively. The patients with tuberculosis had much higher predicted cumulative incidence probability. Predicted cumulative incidence probability of AIDS progression was also higher for mother to child mode of HIV transmission. Moreover, transmission type and gender were two top most important variables for the competing event. Men and those patients with IDUS transmission mode had higher predicted risk compared to others. Conclusions: Considering nonlinear effects and interaction between variables, confection with tuberculosis was the most important variable in prediction of cumulative incidence probability of AIDS progression.

Modelling of viral load dynamics and CD4 cell count progression in an antiretroviral naive cohort: using a joint linear mixed and multistate Markov model

“…There was a total of 8760 follow-up visits recorded from 219 HIV infected black women with a median age of 25 years (Interquartile range, IQR, [22][23][24][25][26][27][28][29][30]. Of these patients, 9.2% of them were co-infected with TB.…”

“…Here T = [0, τ] for τ < ∞. This Markov process has an initial probability, denoted by P(S(0) = m), m ∈ E, which evolves over time and with a history (H E ), which contains the state previously visited, durations and times of transitions [30,31]. The multi-state process is defined through transition probabilities between two states m and j relative to the given process history, as:…”

Modelling immune deterioration, immune recovery and state-specific duration of HIV-infected women with viral load adjustment: using parametric multistate model

“…There was a total of 8760 follow-up visits recorded from 219 HIV infected women. Of these patients, 9.2% of them were co-infected with TB and all were black females, with a median age of 25 years (Interquartile range, IQR, [22][23][24][25][26][27][28][29][30]. Over half (69.9%) reported having completed grades 11/12 of schooling.…”

“…Let a Markov chain process {S(t), t ∈ T}, T = [0, τ] for τ < ∞, that has finite space, denoted by E = {1, 2, 3, 4}, be a representation of the transition process, where for each patient, a multi-state process is observed. This Markov chain process has an initial probability, denoted by P(S(0) = m), m ∈ E, which evolves over time and with a history (H E ), which contains the states previously visited, durations and times of transitions [25,26]. The transition probability of the individual being in state j at time t, given that the individual was in state m at time z, is defined by…”

Modelling HIV disease process and progression in seroconversion among South Africa women: using transition-specific parametric multi-state model

“…This is an important point that has not been considered in HIV/AIDS cohort studies particularly in Sub-Saharan Africa. A multi-state transition-specific parametric model allows rich approaching into complex disease processes and progression pathways, where the patients may experience some intermediate endpoints, and in addition, the model permits the analysis to examine the possible covariate effects on each specific transition [10][11][12]. Multi-state models are very useful for describing event-history data, giving a deeper understanding of disease process and progression and how other patient's demographic and clinical characteristics affect the entire disease progression pathway.…”