Machine learning-based models to predict the conversion of normal blood pressure to hypertension within 5-year follow-up.

BACKGROUND: Factors contributing to the development of hypertension exhibit significant variations across countries and regions. Our objective was to predict individuals at risk of developing hypertension within a 5-year period in a rural Middle Eastern area. METHODS: This longitudinal study utilized data from the Fasa Adults Cohort Study (FACS). The study initially included 10,118 participants aged 35-70 years in rural districts of Fasa, Iran, with a follow-up of 3,000 participants after 5 years using random sampling. A total of 160 variables were included in the machine learning (ML) models, and feature scaling and one-hot encoding were employed for data processing. Ten supervised ML algorithms were utilized, namely logistic regression (LR), support vector machine (SVM), random forest (RF), Gaussian naive Bayes (GNB), linear discriminant analysis (LDA), k-nearest neighbors (KNN), gradient boosting machine (GBM), extreme gradient boosting (XGB), cat boost (CAT), and light gradient boosting machine (LGBM). Hyperparameter tuning was performed using various combinations of hyperparameters to identify the optimal model. Synthetic Minority Over-sampling Technology (SMOTE) was used to balance the training data, and feature selection was conducted using SHapley Additive exPlanations (SHAP). RESULTS: Out of 2,288 participants who met the criteria, 251 individuals (10.9%) were diagnosed with new hypertension. The LGBM model (determined to be the optimal model) with the top 30 features achieved an AUC of 0.67, an f1-score of 0.23, and an AUC-PR of 0.26. The top three predictors of hypertension were baseline systolic blood pressure (SBP), gender, and waist-to-hip ratio (WHR), with AUCs of 0.66, 0.58, and 0.63, respectively. Hematuria in urine tests and family history of hypertension ranked fourth and fifth. CONCLUSION: ML models have the potential to be valuable decision-making tools in evaluating the need for early lifestyle modification or medical intervention in individuals at risk of developing hypertension.

The role of vitamin D receptor and IL-6 in COVID-19.

BACKGROUND: Vitamin D (Vit.D) has an important role in protecting COVID-19 patients. This study investigated the changes in vitamin D receptor (VDR) expression and interleukin 6 levels in patients with COVID-19. MATERIALS AND METHODS: 120 hospitalized patients and 120 healthy people participated in this study, both group adjusted by sex and age. Vit.D was measured with HPLC, the expression of VDR gene was done with Real-time PCR, and IL-6 was measured with ELISA assay. RESULTS: Our findings showed no significant difference in the case of Vit.D (25-OH-D3) between the two studied groups, interestingly the expression of VDR was statistically lower in the patients with COVID-19, p-value = 0.003. VDR expression was lower in the patient with diabetes, hypertension and cardiovascular disease, significantly, p-value = 0.002. The level of IL-6 was statistically higher in the COVID-19 group, p-value = 0.003. CONCLUSION: Alongside the important role of 25-OH-D3 in COVID-19 patients, the quality and quantity of the VDR expression and its role in the level of IL-6 are the promising risk factors in the future. Further studies are needed to determine the factors increasing the expression level of VDR, especially in the patients with diabetes, hypertension and cardiovascular disease.

Prognosis prediction in traumatic brain injury patients using machine learning algorithms.

Predicting treatment outcomes in traumatic brain injury (TBI) patients is challenging worldwide. The present study aimed to achieve the most accurate machine learning (ML) algorithms to predict the outcomes of TBI treatment by evaluating demographic features, laboratory data, imaging indices, and clinical features. We used data from 3347 patients admitted to a tertiary trauma centre in Iran from 2016 to 2021. After the exclusion of incomplete data, 1653 patients remained. We used ML algorithms such as random forest (RF) and decision tree (DT) with ten-fold cross-validation to develop the best prediction model. Our findings reveal that among different variables included in this study, the motor component of the Glasgow coma scale, the condition of pupils, and the condition of cisterns were the most reliable features for predicting in-hospital mortality, while the patients' age takes the place of cisterns condition when considering the long-term survival of TBI patients. Also, we found that the RF algorithm is the best model to predict the short-term mortality of TBI patients. However, the generalized linear model (GLM) algorithm showed the best performance (with an accuracy rate of 82.03 ± 2.34) in predicting the long-term survival of patients. Our results showed that using appropriate markers and with further development, ML has the potential to predict TBI patients' survival in the short- and long-term.