PURPOSE: Survival analysis of gastric cancer patients requires knowledge about factors that affect survival time. This paper attempted to analyze the survival of patients with incomplete registered data by using imputation methods. MATERIALS AND METHODS: Three missing data imputation methods, including regression, expectation maximization algorithm, and multiple imputation (MI) using Monte Carlo Markov Chain methods, were applied to the data of cancer patients referred to the cancer institute at Imam Khomeini Hospital in Tehran in 2003 to 2008. The data included demographic variables, survival times, and censored variable of 471 patients with gastric cancer. After using imputation methods to account for missing covariate data, the data were analyzed using a Cox regression model and the results were compared. RESULTS: The mean patient survival time after diagnosis was 49.1+/-4.4 months. In the complete case analysis, which used information from 100 of the 471 patients, very wide and uninformative confidence intervals were obtained for the chemotherapy and surgery hazard ratios (HRs). However, after imputation, the maximum confidence interval widths for the chemotherapy and surgery HRs were 8.470 and 0.806, respectively. The minimum width corresponded with MI. Furthermore, the minimum Bayesian and Akaike information criteria values correlated with MI (-821.236 and -827.866, respectively). CONCLUSIONS: Missing value imputation increased the estimate precision and accuracy. In addition, MI yielded better results when compared with the expectation maximization algorithm and regression simple imputation methods.
Diagnosis ; Drug Therapy ; Humans ; Markov Chains ; Proportional Hazards Models ; Stomach Neoplasms* ; Survival Analysis*

BACKGROUND: Musculoskeletal disorders (MSDs) are a common problem among carpet weavers. This study was undertaken to introduce affecting personal and occupational factors in developing the number of MSDs among carpet weavers. METHODS: A cross-sectional study was performed among 862 weavers in seven towns with regard to workhouse location in urban or rural regions. Data were collected by using questionnaires that contain personal, workplace, and information tools and the modified Nordic MSDs questionnaire. Statistical analysis was performed by applying Poisson and negative binomial mixed models using a full Bayesian hierarchical approach. The deviance information criterion was used for comparison between models and model selection. RESULTS: The majority of weavers (72%) were female and carpet weaving was the main job of 85.2% of workers. The negative binomial mixed model with lowest deviance information criterion was selected as the best model. The criteria showed the convergence of chains. Based on 95% Bayesian credible interval, the main job and weaving type variables statistically affected the number of MSDs, but variables age, sex, weaving comb, work experience, and carpet weaving looms were not significant. CONCLUSION: According to the results of this study, it can be concluded that occupational factors are associated with the number of MSDs developing among carpet weavers. Thus, using standard tools and decreasing hours of work per day can reduce frequency of MSDs among carpet weavers.
Animals ; Bayes Theorem ; Comb and Wattles ; Cross-Sectional Studies ; Female ; Floors and Floorcoverings* ; Humans ; Iran* ; Occupational Diseases ; Risk Factors*

OBJECTIVE: The aim of this study was to investigate three-dimensional molar displacement after distalization via miniscrews and a horizontal modification of the trans-palatal-arch (TPA). METHODS: The subjects in this clinical trial were 26 Class II patients. After the preparation of a complete set of diagnostic records, miniscrews were inserted between the maxillary 2nd premolar and 1st molar on the palatal side. Elastic modules connected to the TPA exerting an average force of 150-200 g/side parallel to the occlusal plane were applied. Cone-beam computed tomography was utilized to evaluate the position of the miniscrews relative to the adjacent teeth and maxillary sinus, and the direction of force relative to molar furcation. The distances from the central point of the incisive papilla to the mesiopalatal cusps of the 1st maxillary molars and the distances between the mesiopalatal cusps of the left and right molars were measured to evaluate displacement of the maxillary molars on the horizontal plane. Interocclusal space was used to evaluate vertical changes. RESULTS: Mean maxillary 1st molar distalization was 2.3 +/- 1.1 mm, at a rate of 0.4 +/- 0.2 mm/month, and rotation was not significant. Intermolar width increased by 2.9 +/- 1.8 mm. Molars were intruded relative to the neighboring teeth, from 0.1 to 0.8 mm. CONCLUSIONS: Distalization of molars was possible without extrusion, using the appliance investigated. The intrusive component of force reduced the rate of distal movement.
Bicuspid ; Cone-Beam Computed Tomography ; Dental Models ; Dental Occlusion ; Humans ; Maxillary Sinus ; Molar* ; Orthodontic Anchorage Procedures ; Palate ; Tooth ; Tooth Movement

OBJECTIVES: Tuberculosis (TB) is a global public health problem that causes morbidity and mortality in millions of people per year. The purpose of this study was to examine the relationship of potential risk factors with TB mortality in Iran. METHODS: This cross-sectional study was performed on 9,151 patients with TB from March 2017 to March 2018 in Iran. Data were gathered from all 429 counties of Iran by the Ministry of Health and Medical Education and Statistical Center of Iran. In this study, a generalized estimating equation-based zero-inflated negative binomial model was used to determine the effect of related factors on TB mortality at the community level. For data analysis, R version 3.4.2 was used with the relevant packages. RESULTS: The risk of mortality from TB was found to increase with the unemployment rate (β^=0.02), illiteracy (β^=0.04), household density per residential unit (β^=1.29), distance between the center of the county and the provincial capital (β^=0.03), and urbanization (β^=0.81). The following other risk factors for TB mortality were identified: diabetes (β^=0.02), human immunodeficiency virus infection (β^=0.04), infection with TB in the most recent 2 years (β^=0.07), injection drug use (β^=0.07), long-term corticosteroid use (β^=0.09), malignant diseases (β^=0.09), chronic kidney disease (β^=0.32), gastrectomy (β^=0.50), chronic malnutrition (β^=0.38), and a body mass index more than 10% under the ideal weight (β^=0.01). However, silicosis had no effect. CONCLUSIONS: The results of this study provide useful information on risk factors for mortality from TB.
Body Mass Index ; Cross-Sectional Studies ; Education, Medical ; Family Characteristics ; Gastrectomy ; HIV ; Humans ; Iran ; Literacy ; Malnutrition ; Models, Statistical ; Mortality ; Public Health ; Renal Insufficiency, Chronic ; Risk Factors ; Silicosis ; Statistics as Topic ; Tuberculosis ; Unemployment ; Urbanization

OBJECTIVES: Diabetes is increasing in worldwide prevalence, toward epidemic levels. Diabetic neuropathy, one of the most common complications of diabetes mellitus, is a serious condition that can lead to amputation. This study used a multicategory support vector machine (MSVM) to predict diabetic peripheral neuropathy severity classified into four categories using patients' demographic characteristics and clinical features. METHODS: In this study, the data were collected at the Diabetes Center of Hamadan in Iran. Patients were enrolled by the convenience sampling method. Six hundred patients were recruited. After obtaining informed consent, a questionnaire collecting general information and a neuropathy disability score (NDS) questionnaire were administered. The NDS was used to classify the severity of the disease. We used MSVM with both one-against-all and one-against-one methods and three kernel functions, radial basis function (RBF), linear, and polynomial, to predict the class of disease with an unbalanced dataset. The synthetic minority class oversampling technique algorithm was used to improve model performance. To compare the performance of the models, the mean of accuracy was used. RESULTS: For predicting diabetic neuropathy, a classifier built from a balanced dataset and the RBF kernel function with a one-against-one strategy predicted the class to which a patient belonged with about 76% accuracy. CONCLUSIONS: The results of this study indicate that, in terms of overall classification accuracy, the MSVM model based on a balanced dataset can be useful for predicting the severity of diabetic neuropathy, and it should be further investigated for the prediction of other diseases.
Amputation ; Classification ; Cross-Sectional Studies* ; Dataset ; Diabetes Complications ; Diabetic Neuropathies ; Humans ; Informed Consent ; Iran ; Logistic Models ; Methods ; Peripheral Nervous System Diseases* ; Prevalence ; Support Vector Machine*

OBJECTIVES: Tuberculosis (TB) is a global public health problem that causes morbidity and mortality in millions of people per year. The purpose of this study was to examine the relationship of potential risk factors with TB mortality in Iran.METHODS: This cross-sectional study was performed on 9,151 patients with TB from March 2017 to March 2018 in Iran. Data were gathered from all 429 counties of Iran by the Ministry of Health and Medical Education and Statistical Center of Iran. In this study, a generalized estimating equation-based zero-inflated negative binomial model was used to determine the effect of related factors on TB mortality at the community level. For data analysis, R version 3.4.2 was used with the relevant packages.RESULTS: The risk of mortality from TB was found to increase with the unemployment rate (β^=0.02), illiteracy (β^=0.04), household density per residential unit (β^=1.29), distance between the center of the county and the provincial capital (β^=0.03), and urbanization (β^=0.81). The following other risk factors for TB mortality were identified: diabetes (β^=0.02), human immunodeficiency virus infection (β^=0.04), infection with TB in the most recent 2 years (β^=0.07), injection drug use (β^=0.07), long-term corticosteroid use (β^=0.09), malignant diseases (β^=0.09), chronic kidney disease (β^=0.32), gastrectomy (β^=0.50), chronic malnutrition (β^=0.38), and a body mass index more than 10% under the ideal weight (β^=0.01). However, silicosis had no effect.CONCLUSIONS: The results of this study provide useful information on risk factors for mortality from TB.
Body Mass Index ; Cross-Sectional Studies ; Education, Medical ; Family Characteristics ; Gastrectomy ; HIV ; Humans ; Iran ; Literacy ; Malnutrition ; Models, Statistical ; Mortality ; Public Health ; Renal Insufficiency, Chronic ; Risk Factors ; Silicosis ; Statistics as Topic ; Tuberculosis ; Unemployment ; Urbanization

Objectives: Heart failure (HF) is a common disease with a high hospital readmission rate. This study considered class imbalance and missing data, which are two common issues in medical data. The current study’s main goal was to compare the performance of six machine learning (ML) methods for predicting hospital readmission in HF patients. Methods: In this retrospective cohort study, information of 1,856 HF patients was analyzed. These patients were hospitalized in Farshchian Heart Center in Hamadan Province in Western Iran, from October 2015 to July 2019. The support vector machine (SVM), least-square SVM (LS-SVM), bagging, random forest (RF), AdaBoost, and naïve Bayes (NB) methods were used to predict hospital readmission. These methods’ performance was evaluated using sensitivity, specificity, positive predictive value, negative predictive value, and accuracy. Two imputation methods were also used to deal with missing data. Results: Of the 1,856 HF patients, 29.9% had at least one hospital readmission. Among the ML methods, LS-SVM performed the worst, with accuracy in the range of 0.57–0.60, while RF performed the best, with the highest accuracy (range, 0.90–0.91). Other ML methods showed relatively good performance, with accuracy exceeding 0.84 in the test datasets. Furthermore, the performance of the SVM and LS-SVM methods in terms of accuracy was higher with the multiple imputation method than with the median imputation method. Conclusions This study showed that RF performed better, in terms of accuracy, than other methods for predicting hospital readmission in HF patients.

OBJECTIVES: Tuberculosis (TB) is a global public health problem that causes morbidity and mortality in millions of people per year. The purpose of this study was to examine the relationship of potential risk factors with TB mortality in Iran. METHODS: This cross-sectional study was performed on 9,151 patients with TB from March 2017 to March 2018 in Iran. Data were gathered from all 429 counties of Iran by the Ministry of Health and Medical Education and Statistical Center of Iran. In this study, a generalized estimating equation-based zero-inflated negative binomial model was used to determine the effect of related factors on TB mortality at the community level. For data analysis, R version 3.4.2 was used with the relevant packages. RESULTS: The risk of mortality from TB was found to increase with the unemployment rate (β^=0.02), illiteracy (β^=0.04), household density per residential unit (β^=1.29), distance between the center of the county and the provincial capital (β^=0.03), and urbanization (β^=0.81). The following other risk factors for TB mortality were identified: diabetes (β^=0.02), human immunodeficiency virus infection (β^=0.04), infection with TB in the most recent 2 years (β^=0.07), injection drug use (β^=0.07), long-term corticosteroid use (β^=0.09), malignant diseases (β^=0.09), chronic kidney disease (β^=0.32), gastrectomy (β^=0.50), chronic malnutrition (β^=0.38), and a body mass index more than 10% under the ideal weight (β^=0.01). However, silicosis had no effect. CONCLUSIONS The results of this study provide useful information on risk factors for mortality from TB.

OBJECTIVES: Assessment of dental pain severity is very challenging in dentistry. Previous studies have suggested that elevated salivary alpha amylase may contribute to increased physical stresses. There is a close association between salivary alpha amylase and plasma norepinephrine under stressful physical conditions. The aim of this study was to evaluate the relationship between pain severity and salivary alpha amylase levels in patients with symptomatic irreversible pulpitis. MATERIALS AND METHODS: Thirty-six patients (20 females and 16 males) with severe tooth pain due to symptomatic irreversible pulpitis were selected. The visual analogue scale (VAS) score was used to assess the pain severity in each patient. Unstimulated whole saliva was collected, and the level of alpha amylase activity was assessed by the spectrophotometric method. Statistical analysis was performed using SPSS 13. RESULTS: The level of alpha amylase was significantly increased in the saliva in association with pain severity assessed by VAS. The salivary alpha amylase was also elevated with increased age and in males. CONCLUSIONS: There was a significant correlation between the VAS pain scale and salivary alpha amylase level, which indicates this biomarker may be a good index for the objective assessment of pain intensity.
alpha-Amylases ; Dentistry ; Female ; Humans ; Norepinephrine ; Plasma ; Pulpitis ; Saliva ; Tooth