Designing a Predictive Model for Colorectal Neoplasia Diagnosis Based on Clinical and Laboratory Findings in Colonoscopy Candidate Patients.

BACKGROUND: Health authorities have expanded two strategies to diminish CRC-related influence: CR screening and improve diagnostic process in symptomatic patients. The aim of the current study is to design a predictive model to identify the most important risk factors that can efficiently predict patients who have high risk of colorectal neoplasia. METHOD: A cross-sectional study was constructed to include all patients who had positive test for FIT or had one or more risk factors for colorectal cancer based on the guidelines of detecting high-risk groups for colorectal cancer in Iran. Multivariable binary logistic regression model was constructed for prediction of colorectal neoplasia. We used sensitivity, specificity, positive and negative predictive value, and positive and negative likelihood ratio to check the accuracy. The Hosmer-Lemeshow test, chi-square test, and p value were used to determine the precision of model. RESULT: Following an AIC stepwise selection model, only nine potential variables, namely gender, watery diarrhea, IBD, abdominal pain, melena, body mass index, depression drug, anti-inflammatory drug, and age, were found to be a predictor of colorectal neoplasia. The best cut-point probability in the final model was 0.27 and results of sensitivity and specificity, based on maximizing these two criteria, were 66% and 62%, respectively. CONCLUSION: Overall, our model prediction was comparable with other risk prediction models for colorectal cancer. It had a modest discriminatory power to distinguish an individual's neoplasia colorectal risk.

Machine learning algorithms to predict seizure due to acute tramadol poisoning.

INTRODUCTION: This study was designed to develop and evaluate machine learning algorithms for predicting seizure due to acute tramadol poisoning, identifying high-risk patients and facilitating appropriate clinical decision-making. METHODS: Several characteristics of acute tramadol poisoning cases were collected in the Emergency Department (ED) (2013-2019). After selecting important variables in random forest method, prediction models were developed using the Support Vector Machine (SVM), Naïve Bayes (NB), Artificial Neural Network (ANN) and K-Nearest Neighbor (K-NN) algorithms. Area Under the Curve (AUC) and other diagnostic criteria were used to assess performance of models. RESULTS: In 909 patients, 544 (59.8%) experienced seizures. The important predictors of seizure were sex, pulse rate, arterial blood oxygen pressure, blood bicarbonate level and pH. SVM (AUC = 0.68), NB (AUC = 0.71) and ANN (AUC = 0.70) models outperformed k-NN model (AUC = 0.58). NB model had a higher sensitivity and negative predictive value and k-NN model had higher specificity and positive predictive values than other models. CONCLUSION: A perfect prediction model may help improve clinicians' decision-making and clinical care at EDs in hospitals and medical settings. SVM, ANN and NB models had no significant differences in the performance and accuracy; however, validated logistic regression (LR) was the superior model for predicting seizure due to acute tramadol poisoning.

Quality of life in phenylketonuria (PKU) patients residing in Iehran, Islamic republic of Iran.

Background: The current study tried to evaluate the quality of life (QOL) of phenylketonuria (PKU) patients residing in Tehran, Iran and it also tried to determine the average quality of life of patients. Various aspects of QOL have been analyzed depending on gender, age, and educational levels of the subjects. Methods: The sample of the study consisted of late-diagnosed PKU patients who were referred to Mofid Children's Medical Center as well as to Ali-Asghar Hospitals in order to receive metabolic diets on a one year period starting from spring 2013 to spring 2014. Due to the limited study population, subjects were selected via census, therefore 82 patients were enrolled. The research material consisted of the Persian edition of World Health Organization Quality of Life questionnaire (WHOQOL-BREF), designed to examine physical, mental, social and environmental health. The data was gathered on two levels-descriptive and inferential- by using the SPSS software, V.20. Results: Results indicated that the low quality of life in the late-diagnosed patients suffering from PKU, with mental, physical, social, and environmental aspects, was below the average. Still, even if it was not gender dependent, QOL was greatly influenced by the educational level of the patients. Moreover, it was discovered that the mental health of the patients above 40 years old was significantly lower than the other age groups. Conclusions: According to the findings of this study, it was recommended that special attention should be given to the improvement of the social and mental health of PKU patients.