Comparison of Hormone Therapy with Expectant Management in the Clinical Management of Functional Ovarian Cysts: A Randomized Clinical Trial

AbstractHormonal Therapy for functional cysts is widely used in clinical practice, but the efficacy of this treatment has not been determined.The aim of this study was to compare the effect of oral contraceptive with expectant management in treatment of functional ovarian cysts, for this purpose we selected 50 women who came to our clinic as   out patients with functional ovarian cysts(between 50 to 95 mm), that those had been identified by vaginal ultrasound, all the patients had stable vital sign,     mild to moderate abdominal discomfort. the patients were divided into two equal groups and followed for 8 weeks. The cases were all18_48 years old and subjects with PCO, Dermoid cysts and Endometrioma were excluded for this study. At last 9 patients need surgery because of increasing their symptoms and emergency condition.Group A (n=20) observed without hormonal  treatment, only some analgesic if needed and Group B were Received oral contraceptive(n=21). we concluded that expectant management is as effective as oral contraceptive for resolution of functional ovarian cysts .it means the rate of disappearance of functional ovarian cyst was not affected by OCP user. However, studies with a larger number of case are needed to increase the power of results and to obtain a firm conclusion

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

Comparison of pharyngeal airway volume in different skeletal facial patterns using cone beam computed tomography

This study aimed to compare the pharyngeal airway volume in class I,II and III skeletal malocclusion patients using cone beam computed tomography (CBCT). This retrospective, cross sectional study was conducted on lateral cephalograms of 71 patients derived from their CBCT scans. Using the ANB angle, the patients were divided into class I,II and III malocclusion. Two observers used Dolphin 3D software to calculate the pharyngeal airway volume, airway area, minimum axial area, minimum area location, airway length and morphology. Data were analyzed using one-way ANOVA, Kruskal-Wallis test, Tukey?s test, Spearman?s correlation coefficient and multiple regression analysis. The three skeletal classes were significantly different in airway volume, minimum axial area, mean airway area and airway morphology (P<0.05). Significant differences were found in airway volume and mean airway area between class II and III patients (P<0.05). The minimum axial area and airway morphology in class III patients were greater than those in class I and II patients (P<0.05). Every one unit increase in the ANB angle decreased the airway volume by 0.261 units. The effect of ANB angle on airway volume was statistically significant and it was shown that one unit increase in the angle decreased the airway volume by 453.509 units. A significant correlation exists between the skeletal facial pattern and upper airway dimensions. In our study, the total airway volume and the mean airway area of class III patients were larger than those in class II patients

Factors affecting long-survival of patients with esophageal cancer using non-mixture cure fraction model

Objective: Esophageal cancer (EC) is one of the gastrointestinal malignancies with a very high morbidity and mortality rate due to poor prognosis. This study aims to assess the effects of risk factors on survival and cure fraction of patients with EC in a population of Iranian patients using a non-mixture cure fraction model. Methods: This retrospective cohort study was conducted on 127 patients with EC who were diagnosed during 2009-2010 and were followed up for 5 years in East-Azarbaijan, Iran. Stepwise selection and non-mixture cure fraction model were used to find the risk factors of EC survival patients. Results: The mean (±standard deviation) diagnosis age of the EC was 66.92(±11.95). One, three and five-year survival probabilities were 0.44 (95% confidence interval (CI): 0.36-0.54), 0.2 (95% CI: 0.14-0.28) and 0.13 (95% CI: 0.08-0.2) respectively. Female sex (Estimate=-0.99; 95% confidence interval (CI): -1.41,-0.58; p-value<0.001), low level socioeconomic status (Estimate=0.39; 95%CI: 0.12,0.66; p-value=0.043), the group who did not do esophagectomy surgery (Estimate=0.58; 95%CI: 0.17,0.99; p-value=0.005) and unmarried group (Estimate=0.58; 95%CI: 0.11-1.05; p-value=0.015) were found as the significant predictor of survival and cure fraction of the EC patients. Population cure rate was 0.11 (95%CI: 0.07-0.19) and Cure fraction was estimated 5.11 percent. Conclusion: This study found gender, socioeconomic status, Esophagectomy surgery and marital status as the potential risk factors for survival and cure fraction of Iranian EC patients. Moreover, non- mixture cure fraction provides more accurate and more reliable insight into long-term advantages of EC therapy compared to standard classic survival analysis alternatives

Longitudinal Joint Modelling of Binary and Continuous Outcomes: A Comparison of Bridge and Normal Distributions

Background: Longitudinal joint models consider the variation caused by repeated measurements over time as well as the association among the response variables. In the case of combining binary and continuous response variables using generalized linear mixed models, integrating over a normally distributed random intercept in the binary logistic regression sub-model does not yield to a closed form. In this paper, we assessed the impact of assuming a Bridge distribution for the random intercept in the binary logistic regression submodel and compared the results to that of normal distribution. &nbsp;Method: The response variables are combined through correlated random intercepts. The random intercept in the continuous outcome submodel follows a normal distribution. The random intercept in the binary outcome submodel follows a normal or Bridge distribution. The estimations were carried out using a likelihood-based approach in direct and conditional joint modeling approaches. To illustrate the performance of the models, a simulation study was conducted Results: Based on the simulation results and regardless of the joint modeling approach, the models with a Bridge distribution for the random intercept of the binary outcome resulted in a slightly more accurate estimations and better performance. Conclusion: In addition to the fact that assuming a bridge distribution for the random intercept in binary logistic regression yields to the same interpretation of parameter estimates in marginal and conditional forms, our study revealed that even if the random intercept of binary logistic regression is normally distributed, assuming a Bridge distribution in the model will result in more accurate results.&nbsp

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

Determining the lymphadenopathy characteristics of the mediastinum in lung CT scan of children with tuberculosis

AbstractObjective/Background: Most tuberculosis cases in children are primary infection, with difficult and imprecise diagnosis mainly based on the existence of mediastinal lymphadenopathy. Here, we investigated the characteristics of mediastinal lymphadenopathy in lung computed tomography (CT) scans of children with tuberculosis. Methods: This cross-sectional study was performed on 75 children with tuberculosis referred to Masih Daneshvari Hospital in Tehran, Iran, from 2009 to 2013. Their medical records were investigated, and CT-scan characteristics were extracted by a radiologist. Results: Mean±standard deviation age of cases was 11.2±4.6years. CT-scan results indicated 94.7% of cases had lymphadenopathy, with lower paratracheal, upper paratracheal, hilar, and subcarinal forms observed in 81.7%, 69.1%, 53.5%, and 47.9% of cases as the most involved stations in lymph nodes, respectively. In 74.6% of patients with mediastinal lymphadenopathy, perilymph node fat inflammation (matting) was observed, with 52.11% exhibiting conglomeration. Bronchial pressure was observed in 4.23% of children with tuberculosis, and bilateral-, right-, and left-parenchymal involvement was observed in 42.7%, 25.3%, and 8% of these cases, respectively. Left- and right-pleural effusion and calcification was reported in 6.7%, 12%, and 5.6% of patients, respectively. Additionally, nearly 80% of patients exhibited mediastinal lymphadenopathy and lung-parenchyma involvement simultaneously. Lung-parenchyma involvement was significantly correlated with subcarinal (p<.001), hilar (p<.001), subaortic (p=.030), lower paratracheal (p=.037), and axillary (p=.006) stations. Conclusion: Situation of mediastinal lymphadenopathy and its synchronicity with lung-parenchyma involvement can help in differential diagnosis of pulmonary tuberculosis from other lung diseases

Identification of Risk Factors Associated with Tuberculosis in Southwest Iran: A Machine Learning Method

BackgroundTuberculosis is a principal public health issue. Reducing and controlling tuberculosis did not result in the expected success despite implementing effective preventive and therapeutic programs, one of the reasons for which is the delay in definitive diagnosis. Therefore, creating a diagnostic aid system for tuberculosis screening can help in the early diagnosis of this disease. This research aims to use machine learning techniques to identify economic, social, and environmental factors affecting tuberculosis.MethodsThis case-control study included 80 individuals with TB and 172 participants as controls. During January-October 2021, information was collected from thirty-six health centers in Ahvaz, southwest Iran. Five different machine learning approaches were used to identify factors associated with TB, including BMI, sex, age , marital status, education, employment status, size of the family, monthly income, cigarette smoking, hookah smoking, history of chronic illness, history of imprisonment, history of hospital admission, first-class family, second-class family, third-class family, friend, co-worker, neighbor, market, store, hospital, health center, workplace, restaurant, park, mosque, Basij base, Hairdressers and school. The data was analyzed using the statistical programming R software version 4.1.1.ResultsAccording to the calculated evaluation criteria, the accuracy level of 5 SVM, RF, LSSVM, KNN, and NB models is 0.99, 0.72, 0.97,0.99, and 0.95, respectively, and except for RF, the other models had the highest accuracy. Among the 39 investigated variables, 16 factors including First-class family (20.83%), friend (17.01%), health center (41.67%), hospital (24.74%), store (18.49%), market (14.32%), workplace (9.46%), history of hospital admission (51.82%), BMI (43.75%), sex (40.36%), age (22.83%), educational status (60.59%), employment status (43.58%), monthly income (63.80%), addiction (44.10%), history of imprisonment (38.19%) were of the highest importance on tuberculosis.ConclusionThe obtained results demonstrated that machine-learning techniques are effective in identifying economic, social, and environmental factors associated with tuberculosis. Identifying these different factors plays a significant role in preventing and performing appropriate and timely interventions to control this disease

Effect of training after discharge on re-admission and re-hospitalization of patients with heart failure (randomized single-blind clinical trial)

    Discharge is the process of transferring a patient from hospital which involves a transfer of responsibility from inpatient service providers or hospitalist to the patient and primary care physicians. Inappropriate follow up after discharge will increase the risk of re-admission and re-hospitalization which leads to the poor performance of the health system. The aim of this study was to determine the effect of physician's caring after discharge on re-admission and referral to doctors.This study was conducted as a clinical trial on patients with early intervention for educational instruction. The clinical trial was conducted at a later stage on 120 patients with heart failure who were hospitalized in Taleghani Hospital, Tehran. For a period of five months after discharge, using block randomization, the subjects were divided into two groups, including intervention and control groups. At the time of discharge, the patients in the intervention group received instructions and were trained by physicians, while no intervention was applied for the subjects in the control group. In addition to demographic questions, the patients were asked about two main outcomes, i.e. "re-admission" and "referral to doctors".  To collect the required data, the subjects in both groups were contacted via telephone calls (nine times) every week in the first month after discharge and two times per week in the following two months. Generalized linear mixed effects model method was used for evaluating the effect of physicians caring after discharge on re-admission and re-hospitalization.The results of this study showed that with the passage of time (weekly) after discharge, there was a significant increase in the rate of re-admission in the control group, while there was no significant increase in re-hospitalization. There was no statistical evidence showing a significant difference between the rates of re-admission along with the time in the treatment intervals. In other words, the patients in the control group experienced a significant increase in the odds ratio of re-admission over the time. 

Forecasting Schizophrenia Incidence Frequencies Using Time Series Approach

Introduction: Understanding the prevalence of schizophrenia has important implications for both health service planning and risk factor epidemiology. The aims of this study are to systematically identify and collate studies describing the prevalence of schizophrenia, to summarize the findings of these studies, and to explore selected factors that may influence prevalence estimates.Methods: This historical cohort study was done on schizophrenia patients in Farshchian psychiatric hospital from April 2008 to April 2016. To analyze the data, the Holt-Winters Exponential Smoothing (HWES) method was applied. All the analyses were done by R.3.2.3. Software using the packages “forecast” and “tseries”. The statistical significant level was assumed as 0.05.Results: Our investigation show that a constant frequency of Schizophrenia incidence happens every month from August 2008 to February 2015 while a considerable increase occurs in March 2015. The high frequency of Schizophrenia incidence remains constant to the end of 2015 and a decrease is shown in 2016. Also, data demonstrate the development of Schizophrenia in the next 24 months with 95% confidence interval.Conclusion: Our study showed that a significant increase happens in the frequency of Schizophrenia from 2016. Although the development is not constant and the same for all months, the amount of increase is considerably high comparing to before 2016.