Identifying predictors of progression to AIDS and mortality post-HIV infection using parametric multistate model

OBJECTIVES: The human immunodeficiency virus (HIV) has already remained as a major public health problem all over the world. This study aimed to identify the prognostic factors influencing the disease progression in patients with human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS) in Iran, using parametric multi-state models to take into account the intermediate event in the analysis.   METHOD(S): The data of the present retrospective cohort study was extracted in Tehran from April 2004 to March 2014. The number of 2473 HIV-infected patients in Behavioral Diseases Counseling Centers was enrolled. The outcomes of interest were the transition times from HIV diagnosis to AIDS and AIDS to death. The effect of several prognostic factors on both transitions was investigated. RESULTS: Parametric models indicated that AIDS progression was significantly associated with an increase in age (P = 0.017), low education (P = 0.026), and a decreased CD4 cell count (P = 0.001). Furthermore, the AIDS-related death was significantly associated with male sex (P = 0.010), tuberculosis coinfection (P = 0.001), antiretroviral therapy (P = 0.001) and a decreased CD4 cell count (P = 0.035). CONCLUSION: The results of this study indicated that CD4 cell count was one of the most important prognostic factors that affected and accelerated both HIV→AIDS and AIDS→DEATH transitions and antiretroviral treatment was found to be an effective measure in decelerating survival of patients with AIDS to death state. The usual Cox Model is not able to identify some of these prognostic factors.&nbsp

Application of random survival forest for competing risks in prediction of cumulative incidence function for progression to AIDS

Objective: 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. Method(s): 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. Result: 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. Conclusion: Considering nonlinear effects and interaction between variables, confection with tuberculosis was the most important variable in prediction of cumulative incidence probability of AIDS progression

A Support Vector Regression Approach for Three–Level Longitudinal Data

Background: Longitudinal data structure is frequently observed in health science. This introduces correlation to the data that needs to be handled in modelling process. Recently, machine learning approaches have been introduced in the context of longitudinal data for prediction of the response variable purpose. In this paper a mixed-effects least squares support vector regression model is presented for three-level longitudinal data. In the proposed model, multiple random-effect terms are used for considering the existing correlation structures in longitudinal data. The proposed model is flexible in modelling (non-)linear and complex relationships between predictors and response, while it takes into account the hierarchical structure of data and is computationally efficient.  Methods Both random intercept and random trend models with a special correlation structure of errors are illustrated. A real data example on human Brucellosis rate is analysed and two simulation studies are performed to illustrate the proposed model. The fitting and generalisation performance of the proposed model are investigated and compared with the ordinary least squares support vector regression and linear mixed-effects models.  Results: Based on the human Brucellosis rate example and two simulation studies, the proposed models had the best performance in generalisation. Also, the fitting performances of the proposed models were better than that of the classic models.  Conclusion: Our study revealed that in the presence of nonlinear relationship between covariates and outcome, the proposed MLS-SVR model has the best fitting and generalisation performance and can capture correlation of the data

Predictors of mortality among hemodialysis patients in Hamadan Province using random survival forests

Background: Hemodialysis patients are at the high risk for morbidity and mortality. The aim of this study was to find out the predictors of mortality and survival in hemodialysis patients in Hamadan province. Methods: The numbers of 785 patients during the entire 11 years were enrolled into this historical cohort study. Data were gathered by a checklist of hospital records. The survival time was the time between start of Hemodialysis treatment to patients death as the end point. Random survival forests (RSF) method was used to identify main predictors of survival among the patients. Results: The median survival time was 613 days. The number of 376 death was occurred. The three most important predictors of survival were hemoglobin, CRP and albumin. RSF method predicted survival better than the conventional Cox-proportional hazards model (out-of-bag C-index of 0.808 for RSF vs. 0.727 for Cox model). Conclusions: We found that higher levels of CRP, low serum albumin and low serum hemoglobin were the top three most important predictors of survival for HD patients

Modeling the trajectory of CD4 cell count and its effect on the risk of AIDS progression and TB infection among HIV-infected patients using a joint model of competing risks and longitudinal ordinal data

Background: This study was conducted to better understand the influence of prognostic factors and the trend of CD4 cell count on the risk of progression to acquired immunodeficiency syndrome (AIDS) and tuberculosis (TB) infection among patients with human immunodeficiency virus (HIV) in a developing country.  Methods: The information of 1530 HIV-infected patients admitted in Behavioral Diseases Counseling Centers, Tehran, Iran, (2004-2014) was analyzed in this study. A joint model of ordinal longitudinal outcome and competing events is used to model longitudinal measurements of CD4 cell count and the risk of TB-infection and AIDS-progression among HIV patients, simultaneously.  Results: The results revealed that the trend of CD4 cell count had a significant association with the risk of TB-infection and AIDS-progression (p<0.001). Higher ages (p<0.001), the history of being in prison (p=0.013), receiving antiretroviral therapy (ART) (p<0.001) and isoniazid preventive therapy (IPT) (p<0.001) were associated with the positive trend of CD4 cell count. Higher ages were also associated with higher risks of TB (p<0.001) and AIDS-progression (p<0.001). Furthermore, ART (p=.0009) and IPT (p<0.001) were associated with a lower risk of TB-infection. In addition, ART (p<0.001) was associated with a lower risk of AIDS-progression. Moreover, individuals being imprisoned (p=0.001) and abusing alcohol (p=0.012) were more likely to have TB-co-infection.  Conclusions: The used joint model provided a flexible framework for simultaneous studying of the effects of covariates on the level of CD4 cell count and the risk of progression to TB and AIDS. This model also assessed the effect of CD4 trajectory on the hazards of competing events.&nbsp

Predicting preeclampsia and related risk factors using data mining approaches: A cross-sectional study

Background: Preeclampsia is a type of pregnancy hypertension disorder that has adverse effects on both the mother and the fetus. Despite recent advances in the etiology of preeclampsia, no adequate clinical screening tests have been identified to diagnose the disorder. Objective: We aimed to provide a model based on data mining approaches that can be used as a screening tool to identify patients with this syndrome and also to identify the risk factors associated with it. Materials and Methods: The data used to perform this cross-sectional study were extracted from the clinical records of 726 mothers with preeclampsia and 726 mothers without preeclampsia who were referred to Fatemieh Hospital in Hamadan City during April 2005–March 2015. In this study, six data mining methods were adopted, including logistic regression, k-nearest neighborhood, C5.0 decision tree, discriminant analysis, random forest, and support vector machine, and their performance was compared using the criteria of accuracy, sensitivity, and specificity. Results: Underlying condition, age, pregnancy season and the number of pregnancies were the most important risk factors for diagnosing preeclampsia. The accuracy of the models were as follows: logistic regression (0.713), k-nearest neighborhood (0.742), C5.0 decision tree (0.788), discriminant analysis (0.687), random forest (0.758) and support vector machine (0.791). Conclusion: Among the data mining methods employed in this study, support vector machine was the most accurate in predicting preeclampsia. Therefore, this model can be considered as a screening tool to diagnose this disorder. Key words: Preeclampsia, Random forest, C5.0 decision tree, Support vector machine, Logistic regression

Usefulness of the SF-36 Health Survey questionnaire in screening for health-related quality of life among parents of children with cancer: Latent profile analysis

Background: Poor health-related quality of life (HRQOL) of parents of children with cancer as their main caregivers can adversely affect child’s HRQOL. Short Form-36 Health Survey (SF-36) is a widely used instrument to measure HRQOL. However, there are no clearly defined cut-off points for screening for parents with poor HRQOL. This study aimed to find appropriate cut-off points for the SF-36 questionnaire in a sample of parents of children with cancer using latent profile analysis to add another possibility to use it. Methods: In this cross-sectional study, 110 couples (110 mothers and 110 fathers) of children diagnosed with cancer selected by simple random sampling method from the patients' files were included. The study was conducted at two settings, pediatric hematology ward and pediatric hematology clinic of a university hospital in 2016-2017. Latent Profile analysis method was used to determine appropriate cut-off points for the SF-36 questionnaire. Data was analyzed by Mplus and R3.3.0 software. Results:  Based on the results, scores ≤44, 45-63 and ≥64 for mental health, and scores ≤43, 44-59 and ≥60 for physical health classes indicate weak, medium, and good, respectively. These cut-off points showed acceptable accuracy in classification of individuals. For the total quality of life, correct classification rates were 88%, 65% and 53% for each class respectively. For mental health (physical health), they were 79 (63), 50 (62) and 52 (63) for each class respectively.    Conclusion: The cut-off points for the classes identified here can be useful in screening parents of children with cancer in clinical setting to provide clinical interventions to protect vulnerable parents from negative outcomes

Emergency Medical Service Providers’ Perception of Health- Threatening Stressors in Emergency Missions: A qualitative Study

BACKGROUND: Unknown and unpredictable situations cause emergency medical service (EMS) providers to experience various stressful factors. These factors are affected by sociocultural conditions and expectations of the casualty and affect EMS providers' performance and health at the incident scene. The present study was conducted to explore EMS providers' perception of stressful and health-threatening factors in emergency missions.METHOD: This qualitative conventional content analysis was conducted in 2020. The participants included 16 EMS providers working at the Emergency Medical Services Department in Hamadan Province, Iran. The participants were selected using purposive sampling and underwent semi-structured interviews until data saturation. Data were analyzed using the Graneheim and Lundman method.RESULTS: Analysis of the interview data yielded six subcategories (i.e., incident scene hazards, violence-related injuries, physical injuries caused by patient care/handling, ambulance crash-related injuries, emotional impact of patients' suffering and ailments, and highly stressful missions), two main categories (i.e., physical injuries and psychological tensions), and a theme of occupational injuries.CONCLUSIONS: According to the results, in addition to having concerns about caring for patients and saving the injured, EMS providers also worry about potential threats to their own health. The present study identified and described some major stressors in emergency missions. Thus, for a better and more effective efficiency, the present study results can be used to reduce or modify stressors in EMS providers

Evaluation of Social Support for Parents of Children with Congenital Heart Disease Referred to Imam Khomeini Clinic at Hamadan City, 2021

Background and Objectives: Congenital heart disease is one of the most common congenital anomalies in children. The purpose of this study was the evaluation of social support in parents of children with congenital heart disease. Materials and Methods: This was a descriptive correlational study. The study population included 200 parents of children with congenital heart disease who referred to Imam Khomeini Clinic in Hamedan in 2020 via available sampling method and using the demographic information questionnaire, social support questionnaire Zimat. Data was analyzed using descriptive and inferential statistics. Results: The mean and standard deviation of the total score of social support 41.4 ± 5.61. The highest social support 15.71 ± 2.23 was related to the family dimension and the lowest social support 12.17 ± 2.39 was related to the dimension of friends. The results of this study showed that the average total score of social support in terms of all demographic variables of parents except maternal occupation has a statistically significant difference (P < 0.05). Conclusions: Social support is tool that improves the quality of life in parents of children with congenital heart disease, so it is recommended to develop a comprehensive care plan for these patients alongside, increasing their parent's social support

Prediction of low birth weight using Random Forest: A comparison with Logistic Regression

Low birth weight (neonate weighing less than 2500 g) is associated with several maternal and fetal factors, all interrelated with each other [1]. This study is aimed to survey maternal risk factors associated with low birth weight neonates using data mining (Random Forest) to account for interactions between them. We also intended to compare Random Forest with traditional Logistic regression. The dataset used in the present study consisted of 600 volunteer pregnant women.  This cross-sectional study was carried out in Milad hospital, Tehran, during 2005-2009. Ten potential risk factors that are commonly associated with low birth weight were selected by using Random Forest technique. Several criteria such as the area under ROC curve were considered in comparing Random Forest with Logistic Regression.According to both criteria, four top rank variables identified by Random Forest were pregnancy age, body mass index during the third three months of pregnancy, mother’s age and body mass index during the first three months of pregnancy, respectively. In addition, in terms of different criteria the Random Forest technique outperformed the Logistic regression (area under ROC curve: 93% ; Total Accuracy:95% ; Kappa Coefficient: 66%).The results of the present study showed that using Random Forest improved the prediction of low birth weight compared with Logistic Regression. This is because of the fact that the former accounts for all interactions between covariates. Therefore, this approach is a promising classifier for predicting low birth weight