parvane h baghaei shiva

There are variables whose influence on the risk of tuberculosis (TB) recurrence change over time. Therefore, this study aimed to assess the time-dependent effects of these variables on the hazard of TB recurrence.

In this historical cohort study, data were collected from 4,564 TB patients who were referred to the TB research center of Dr. Masih Daneshvari Hospital, Tehran, from 2005 to 2015, in order to evaluate factors affecting the hazard of TB recurrence in terms of time dependency or time constancy. Data were analyzed in STATA 14 software using a statistical test based on Schoenfeld residuals, the time-dependent effects method, and the time-varying effects model (considering time function as f (t) = t).

The results showed that only the impact of the variables of drug adverse effects and passive smoker were inconstant over time and had time-dependent effects, and they also influenced the hazard of TB recurrence. Also, the effect of the two mentioned variables on the hazard of TB recurrence displayed a decreasing and increasing trend with time, respectively.

Using the time-varying effects model in the study of the hazard of TB recurrence allows evaluating the time-dependent effects of the studied variables and also can differentiate them from the time-independent variables.

Diagnosis and treatment of patients with multidrug-resistant tuberculosis (MDR-TB) are very important. Hence, it is necessary to predict and diagnose these patients based on individual, demographic and clinical characteristics before starting treatment. This study aimed to predict MDR-TB in TB patients using the perceptron artificial neural networks (ANNs) model.

This retrospective cohort study was conducted on 1,050 TB patients who have been treated in Masih Daneshvari Hospital, Tehran, Iran from 2005 to 2015. Data on personal and demographic information, as well as medical data such as drug therapy, final outcome of treatment, and the diagnosis of MDR-TB, were collected from the patients' medical records.

The results of this study indicated that the predictive power of MDR-TB for both training and testing groups was 85% and 80%, respectively. Also, the variables of marital status, education, drug use, being imprisoned, extrapulmonary TB, history of comorbidities, AIDS, patients' age, and family size were identified as very effective factors. However, variables of residence, smoking history, contact with a TB person, pulmonary TB, drug side effects, nationality, and diabetes were found as effective factors in predicting the development of MDR-TB.

Application of the perceptron ANNs model in the study of MDR-TB is able to create new horizons in the diagnosis of these patients due to high predictive accuracy.

Today, due to progressing technology and improving the standard of living of humans, the study of diseases has become more complex. This complexity has led to using new methods, such as the model of artificial neural networks (ANNs), to study many chronic diseases, especially tuberculosis (TB). The present study aimed to investigate the mechanism of disease relapse events by applying a multilayer perceptron artificial neural network (MLP-ANN) model among TB patients.

This retrospective cohort study examined information of 4,564 TB patients treated in Masih Daneshvari Hospital, Tehran, Iran, from 2005 to 2015. TB disease relapse was considered as a study event, and the relapse mechanism was investigated using an MLP-ANN model consisting of three layers.

Based on an MLP-ANN model comprising three layers, the power to accurately predict disease relapse in TB patients was 96%. Also, variables of family size, adverse effects of exposure to cigarette smoke, patient age, and education as very effective factors, and marital status, history of drug use, imprisonment, pulmonary TB, diabetes, and AIDS as effective factors were identified in predicting the mechanism of TB disease relapse.

Using an ANN model in the study of TB relapse due to its flexibility and high predictive accuracy can clarify any ambiguous aspects of this disease.

The success of treatment strategies to control the disease relapse requires determining factors affecting the incident short-time and long-time of disease relapse. Therefore, this study was aimed to identify the factors affecting of short-and long-time of occurrence of disease relapse in patients with tuberculosis (TB) using a parametric mixture cure model.

In this historical cohort study; the data was collected from 4564 patients with TB who referred to the Tuberculosis and Lung Diseases Research Center of Dr. Masih Daneshvari Hospital from 2005 to 2015. In order to evaluate the factors affecting of short-and long-time of occurrence of disease relapse, a parametric mixture cure model was used.

In this study, the estimation of the annual incidence of TB relapse showed that the probability of recurrence in the first year is 1% and in the third and tenth years after treatment is 3% and 5%, respectively. In addition, the results of this study showed that the variables of residence, exposure to cigarette smoke, adverse effects of drug use, incarceration, and pulmonary and extra- pulmonary tuberculosis were the factors affecting the short-time recurrence of TB. The variables of drug use, pulmonary and extra- pulmonary tuberculosis, and also incarceration affected the long-term recurrence of this disease.

Cure models by separating factors affecting the short-time occurrence from the long-time occurrence of disease relapse can provide more accurate information to researchers to control and reduce TB relapse.

 The development of treatment methods and increasing the survival of patients with tuberculosis (TB) has led to the complication of relationships between independent and dependent variables associated with this disease. Therefore, it is important to use new methods to model the TB process that can accurately estimate the current situation. This study aimed to model the survival of patients with tuberculosis based on the model of perceptron artificial multilayer neural network (MLP-ANN).

In this retrospective cohort study, the data was collected from 2366 TB patients who were treated in Dr. Masih Daneshvari Hospital in Tehran from 2005 to 2015. To model the predictive power of survival in TB patients, an MLP-ANN model consisting of three layers was applied.

The results of this study showed that based on the MLP-ANN model, the correct predictive power of survival in TB patients is 88.4%. In this study, the variables of patients' age and family size as very effective variables also variables of patients’ gender, marital status, education, adverse drug effects, exposure to cigarette smoke, imprisonment, pulmonary tuberculosis, and AIDS as effective variables in predicting the survival of patients were diagnosed.

In the model of artificial neural networks, no restrictions are considered for the data structure and the type of relationship between variables. Therefore, these models with their flexibility and high accuracy can be one of the best methods for modeling health data.