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Objectives@#Low birth weight (LBW) is a global concern associated with fetal and neonatal mortality as well as adverse consequences such as intellectual disability, impaired cognitive development, and chronic diseases in adulthood. Numerous factors contribute to LBW and vary based on the region. The main objectives of this study were to compare four machine learning classifiers in the prediction of LBW and to determine the most important factors related to this phenomenon in Hamadan, Iran. @*Methods@#We carried out a retrospective cross-sectional study on a dataset collected from Fatemieh Hospital in 2017 that included 741 mother-newborn pairs and 13 potential factors. Decision tree, random forest, artificial neural network, support vector machine, and logistic regression (LR) methods were used to predict LBW, with five evaluation criteria utilized to compare performance. @*Results@#Our findings revealed a 7% prevalence of LBW. The average accuracy of all models was 87% or higher. The LR method provided a sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and accuracy of 74%, 89%, 7.04%, 29%, and 88%, respectively. Using LR, gestational age, number of abortions, gravida, consanguinity, maternal age at delivery, and neonatal sex were determined to be the six most important variables associated with LBW. @*Conclusions@#Our findings underscore the importance of facilitating timely diagnosis of causes of abortion, providing genetic counseling to consanguineous couples, and strengthening care before and during pregnancy (particularly for young mothers) to reduce LBW.
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Purpose@#This study investigated the position of the hyoid bone and its relationship with airway dimensions in different skeletal malocclusion classes using cone-beam computed tomography (CBCT). @*Materials and Methods@#CBCT scans of 180 participants were categorized based on the A point-nasion-B point angle into class I, class Ⅱ, and class Ⅲ malocclusions. Eight linear and 2 angular hyoid parameters (H-C3, H-EB, H-PNS, H-Me, H-X, H-Y, H-[C3-Me], C3-Me, H-S-Ba, and H-N-S) were measured. A 3-dimensional airway model was designed to measure the minimum cross-sectional area, volume, and total and upper airway length. The mean cross-sectional area, morphology, and location of the airway were also evaluated. Data were analyzed using analysis of variance and the Pearson correlation test, with p values <0.05 indicating statistical significance. @*Results@#The mean airway volume differed significantly among the malocclusion classes (p<0.05). The smallest and largest volumes were noted in class Ⅱ (2107.8±844.7 mm3) and class Ⅲ (2826.6±2505.3 mm3), respectively. The means of most hyoid parameters (C3-Me, C3-H, H-Eb, H-Me, H-S-Ba, H-N-S, and H-PNS) differed significantly among the malocclusion classes. In all classes, H-Eb was correlated with the minimum cross-sectional area and airway morphology, and H-PNS was correlated with total airway length. A significant correlation was also noted between H-Y and total airway length in class Ⅱ and Ⅲ malocclusions and between H-Y and upper airway length in class I malocclusions. @*Conclusion@#The position of the hyoid bone was associated with airway dimensions and should be considered during orthognathic surgery due to the risk of airway obstruction.
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ObjectivesLongitudinal data are prevalent in clinical research; due to their correlated nature, special analysis must be used for this type of data. Creatinine is an important marker in predicting end-stage renal disease, and it is recorded longitudinally. This study compared the prediction performance of linear regression (LR), linear mixed-effects model (LMM), least-squares support vector regression (LS-SVR), and mixed-effects least-squares support vector regression (MLS-SVR) methods to predict serum creatinine as a longitudinal outcome.MethodsWe used a longitudinal dataset of hemodialysis patients in Hamadan city between 2013 and 2016. To evaluate the performance of the methods in serum creatinine prediction, the data was divided into two sets of training and testing samples. Then LR, LMM, LS-SVR, and MLS-SVR were fitted. The prediction performance was assessed and compared in terms of mean squared error (MSE), mean absolute error (MAE), mean absolute prediction error (MAPE), and determination coefficient (R2). Variable importance was calculated using the best model to select the most important predictors.ResultsThe MLS-SVR outperformed the other methods in terms of the least prediction error; MSE = 1.280, MAE = 0.833, and MAPE = 0.129 for the training set and MSE = 3.275, MAE = 1.319, and MAPE = 0.159 for the testing set. Also, the MLS-SVR had the highest R2, 0.805 and 0.654 for both the training and testing samples, respectively. Blood urea nitrogen was the most important factor in the prediction of creatinine.ConclusionsThe MLS-SVR achieved the best serum creatinine prediction performance in comparison to LR, LMM, and LS-SVR.
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Objectives@#Determining the predictors of in-hospital death related to nosocomial infections is an essential part of efforts made in the overall health system to improve the delivery of health care to patients. Therefore, this study investigated the predictors of in-hospital death related to nosocomial infections. @*Methods@#This registry-based, longitudinal study analyzed data on 8,895 hospital-acquired infections (HAIs) in Hamadan Province, Iran from March 2017 to December 2019. The medical records of all patients who had been admitted to the hospitals were extracted from the Iranian Nosocomial Infections Surveillance Software. The effects of the type and site of infection, as well as age group, on in-hospital death were estimated using univariate and multivariable Cox regression models. @*Results@#In total, 4,232 (47.8%) patients with HAIs were males, and their mean age was 48.25±26.22 years. In both sexes, most nosocomial infections involved Gram-negative bacteria and the most common site of infection was the urinary tract. Older patients had a higher risk of in-hospital death (adjusted hazard ratio [aHR], 2.26; 95% confidence interval [CI], 1.38 to 3.69 for males; aHR, 2.44; 95% CI, 1.29 to 4.62 for females). In both sexes, compared with urinary tract infections, an increased risk of in-hospital death was found for ventilator-associated events (VAEs) (by 95% for males and 93% for females) and bloodstream infections (BSIs) (by 67% for males and 82% for females). @*Conclusion@#We found that VAEs, BSIs, and fungal infections were independently and strongly associated with increased mortality.
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OBJECTIVES@#This study investigated the prevalence of low back pain (LBP) and its risk factors among elementary-school students. @*METHODS@#In this cross-sectional study, 693 elementary students from Hamadan city, western Iran, were selected by multistage stratified cluster sampling. Data were collected through interviews using questionnaires. Posture and psychosocial elements were assessed using the observational Rapid Upper Limb Assessment (RULA) checklist and the standard Strengths and Difficulties Questionnaire, respectively. Penalized logistic regression with the group smoothly-clipped absolute deviation regularization method was used for variable selection and data analysis (α=0.05). The chi-square test was also used. @*RESULTS@#In total, 26.6% of the students (7-12 years old) reported LBP in the last month. Older age (odds ratio [OR], 3.08; 95% confidence interval [CI], 1.80 to 5.26), watching TV for more than 3 hours a day (OR, 2.62; 95% CI, 1.46 to 4.68), very short seat backrests (OR, 3.08; 95% CI, 1.61 to 5.90), excessively curved seat backrests (OR, 4.36; 95% CI, 2.08 to 9.13), very short desks (OR, 3.44; 95% CI, 1.61 to 7.35), a family history of LBP (OR, 2.49; 95% CI, 1.58 to 3.91), carrying a school bag on one shoulder (OR, 1.91; 95% CI, 1.03 to 3.54), and RULA scores of 3 (OR, 2.26; 95% CI, 1.13 to 4.50) or 4 (OR, 2.85; 95% CI, 1.37 to 5.91) were associated with LBP. @*CONCLUSIONS@#A high prevalence of LBP was found among elementary-school students. This study underscores the importance of recognizing vulnerable children and teenagers and developing interventional health promotion programs to prevent LBP based on an appropriate consideration of its contributory factors.
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Objectives@#Determining the predictors of in-hospital death related to nosocomial infections is an essential part of efforts made in the overall health system to improve the delivery of health care to patients. Therefore, this study investigated the predictors of in-hospital death related to nosocomial infections. @*Methods@#This registry-based, longitudinal study analyzed data on 8,895 hospital-acquired infections (HAIs) in Hamadan Province, Iran from March 2017 to December 2019. The medical records of all patients who had been admitted to the hospitals were extracted from the Iranian Nosocomial Infections Surveillance Software. The effects of the type and site of infection, as well as age group, on in-hospital death were estimated using univariate and multivariable Cox regression models. @*Results@#In total, 4,232 (47.8%) patients with HAIs were males, and their mean age was 48.25±26.22 years. In both sexes, most nosocomial infections involved Gram-negative bacteria and the most common site of infection was the urinary tract. Older patients had a higher risk of in-hospital death (adjusted hazard ratio [aHR], 2.26; 95% confidence interval [CI], 1.38 to 3.69 for males; aHR, 2.44; 95% CI, 1.29 to 4.62 for females). In both sexes, compared with urinary tract infections, an increased risk of in-hospital death was found for ventilator-associated events (VAEs) (by 95% for males and 93% for females) and bloodstream infections (BSIs) (by 67% for males and 82% for females). @*Conclusion@#We found that VAEs, BSIs, and fungal infections were independently and strongly associated with increased mortality.
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OBJECTIVES@#This study investigated the prevalence of low back pain (LBP) and its risk factors among elementary-school students. @*METHODS@#In this cross-sectional study, 693 elementary students from Hamadan city, western Iran, were selected by multistage stratified cluster sampling. Data were collected through interviews using questionnaires. Posture and psychosocial elements were assessed using the observational Rapid Upper Limb Assessment (RULA) checklist and the standard Strengths and Difficulties Questionnaire, respectively. Penalized logistic regression with the group smoothly-clipped absolute deviation regularization method was used for variable selection and data analysis (α=0.05). The chi-square test was also used. @*RESULTS@#In total, 26.6% of the students (7-12 years old) reported LBP in the last month. Older age (odds ratio [OR], 3.08; 95% confidence interval [CI], 1.80 to 5.26), watching TV for more than 3 hours a day (OR, 2.62; 95% CI, 1.46 to 4.68), very short seat backrests (OR, 3.08; 95% CI, 1.61 to 5.90), excessively curved seat backrests (OR, 4.36; 95% CI, 2.08 to 9.13), very short desks (OR, 3.44; 95% CI, 1.61 to 7.35), a family history of LBP (OR, 2.49; 95% CI, 1.58 to 3.91), carrying a school bag on one shoulder (OR, 1.91; 95% CI, 1.03 to 3.54), and RULA scores of 3 (OR, 2.26; 95% CI, 1.13 to 4.50) or 4 (OR, 2.85; 95% CI, 1.37 to 5.91) were associated with LBP. @*CONCLUSIONS@#A high prevalence of LBP was found among elementary-school students. This study underscores the importance of recognizing vulnerable children and teenagers and developing interventional health promotion programs to prevent LBP based on an appropriate consideration of its contributory factors.
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OBJECTIVES: Kidney transplantation is the best renal replacement therapy for patients with end-stage renal disease. Several studies have attempted to identify predisposing factors of graft rejection; however, the results have been inconsistent. We aimed to identify prognostic factors associated with kidney transplant rejection using the artificial neural network (ANN) approach and to compare the results with those obtained by logistic regression (LR). METHODS: The study used information regarding 378 patients who had undergone kidney transplantation from a retrospective study conducted in Hamadan, Western Iran, from 1994 to 2011. ANN was used to identify potential important risk factors for chronic nonreversible graft rejection. RESULTS: Recipients' age, creatinine level, cold ischemic time, and hemoglobin level at discharge were identified as the most important prognostic factors by ANN. The ANN model showed higher total accuracy (0.75 vs. 0.55 for LR), and the area under the ROC curve (0.88 vs. 0.75 for LR) was better than that obtained with LR. CONCLUSIONS: The results of this study indicate that the ANN model outperformed LR in the prediction of kidney transplantation failure. Therefore, this approach is a promising classifier for predicting graft failure to improve patients' survival and quality of life, and it should be further investigated for the prediction of other clinical outcomes.