Geographic distribution and time trends of water-pipe use among Iranian youth and teenage students : A meta-analysis and systematic review

Water-pipe tobacco smoking is harmful to health, yet its rate of prevalence remains uncertain. Recent evidence has shown that the prevalence of water-pipe smoking among students is higher than in the general population. In this study, a systematic review of related literature on water-pipe use was conducted, and for this purpose, 76 articles were examined in the study. In this vein, geographic distribution and time trends of water-pipe consumption in Iran were considered. The results of this study showed that lifetime, last-year, and last-month prevalence of water-pipe smoking use among Iranian students were 28.78 (25.07–32.49), 20.84 (16.01–25.66), and 16.36 (11.86–20.85), respectively. The results also showed a wide variation by the region and sex in Iran. This study has shown the importance of addressing public prevention and alerting programs in schools and universities.acceptedVersionPeer reviewe

The relationship between the intake of fruits, vegetables, and dairy products with hypertension: findings from the STEPS study

Abstract Background The current research aimed to evaluate the relationship between fruit, vegetable (FV), and dairy consumption with the odds of developing hypertension based on nationwide Stepwise approach to surveillance (STEPS) data in Iran. Methods This cross-sectional study was accomplished by the research center of non-communicable diseases (NCDs) in Tehran. In total, 29,378 individuals’ data were analyzed. Participants were classified into normal, elevated BP, stage I, and stage II hypertension according to systolic blood pressure (SBP) and diastolic blood pressure (DBP) examinations. Based on the STEPS questionnaire, the consumption of FVs and dairy products was evaluated. Multinomial logistic regression was applied to assess the relationship between the consumption of FVs and dairy products with hypertension. Results The findings revealed that only fruit consumption (≥ 2 servings/day) was negatively related to stage I hypertension (odds ratio (OR) = 0.81; 95% confidence interval (CI): 0.69–0.95) in two servings per day and OR = 0.81; 95% CI: 0.68–0.96 in > two servings per day) in the adjusted model. There was no significant relationship between consuming vegetables and dairy products with elevated BP and hypertension. Conclusion Our study showed that increasing fruit consumption was related to reducing hypertension odds. Regarding the consumption of dairy products and vegetables, no significant relationship was found with the odds of hypertension. More studies, especially cohorts, are needed to evaluate the impacts of FV and dairy products on the risk of hypertension

Prediction prolonged mechanical ventilation in trauma patients of the intensive care unit according to initial medical factors: a machine learning approach

Abstract The goal of this study was to develop a predictive machine learning model to predict the risk of prolonged mechanical ventilation (PMV) in patients admitted to the intensive care unit (ICU), with a focus on laboratory and Arterial Blood Gas (ABG) data. This retrospective cohort study included ICU patients admitted to Rajaei Hospital in Shiraz between 2016 and March 20, 2022. All adult patients requiring mechanical ventilation and seeking ICU admission had their data analyzed. Six models were created in this study using five machine learning models (PMV more than 3, 5, 7, 10, 14, and 23 days). Patients’ demographic characteristics, Apache II, laboratory information, ABG, and comorbidity were predictors. This study used Logistic regression (LR), artificial neural networks (ANN), support vector machines (SVM), random forest (RF), and C.5 decision tree (C.5 DT) to predict PMV. The study enrolled 1138 eligible patients, excluding brain-dead patients and those without mechanical ventilation or a tracheostomy. The model PMV > 14 days showed the best performance (Accuracy: 83.63–98.54). The essential ABG variables in our two optimal models (artificial neural network and decision tree) in the PMV > 14 models include FiO2, paCO2, and paO2. This study provides evidence that machine learning methods outperform traditional methods and offer a perspective for achieving a consensus definition of PMV. It also introduces ABG and laboratory information as the two most important variables for predicting PMV. Therefore, there is significant value in deploying such models in clinical practice and making them accessible to clinicians to support their decision-making