Assessment of the Relationship between Hyperglycemia during the First 24 Hours Post-surgery and the Type of Calorie Intake in the Neonatal Intensive Care Unit

Background: The present study aimed to determine the mean blood glucose during the first 24 h post-surgery and its relation with the source of calorie intake.Methods: The data of the current observational retrospective study was collected from hospital medical records. A total of 45 neonates suffering from atresia in different parts of the gastrointestinal tract, who were candidates for open abdominal surgery from September to October 2016 were selected. Blood glucose within 24 h after the surgery were taken four times using a glucometer. Moreover, the mean blood glucose during this period was calculated. Independent Student's t-test, chi-square test, and logistic regression model were performed to assess the association of post-operative blood glucose with calorie and macronutrient intakes.Results: In one third of neonates, the mean blood glucose of the samples during the first day after the surgery was ≥180 mg/dl and the rest of them had mean blood glucose of 40-179 mg/dl. There was a significant relationship between blood glucose BG≥180 mg/dl and calorie (P=0.001), macronutrient (carbohydrate (PConclusion: The present study revealed that there was a significant relationship between mean blood glucose during the first 24 h after the surgery and intake of macronutrients (carbohydrate and fat)

Application of machine learning in predicting non-alcoholic fatty liver disease using anthropometric and body composition indices

Abstract Non-alcoholic fatty liver disease (NAFLD) is the most common chronic liver disease, which can progress from simple steatosis to advanced cirrhosis and hepatocellular carcinoma. Clinical diagnosis of NAFLD is crucial in the early stages of the disease. The main aim of this study was to apply machine learning (ML) methods to identify significant classifiers of NAFLD using body composition and anthropometric variables. A cross-sectional study was carried out among 513 individuals aged 13 years old or above in Iran. Anthropometric and body composition measurements were performed manually using body composition analyzer InBody 270. Hepatic steatosis and fibrosis were determined using a Fibroscan. ML methods including k-Nearest Neighbor (kNN), Support Vector Machine (SVM), Radial Basis Function (RBF) SVM, Gaussian Process (GP), Random Forest (RF), Neural Network (NN), Adaboost and Naïve Bayes were examined for model performance and to identify anthropometric and body composition predictors of fatty liver disease. RF generated the most accurate model for fatty liver (presence of any stage), steatosis stages and fibrosis stages with 82%, 52% and 57% accuracy, respectively. Abdomen circumference, waist circumference, chest circumference, trunk fat and body mass index were among the most important variables contributing to fatty liver disease. ML-based prediction of NAFLD using anthropometric and body composition data can assist clinicians in decision making. ML-based systems provide opportunities for NAFLD screening and early diagnosis, especially in population-level and remote areas