Clinical manifestations of COVID-19 versus other upper respiratory tract infections in pediatric patients.

OBJECTIVES: To explore the differences between COVID-19 and upper respiratory tract infections (URTI) in the pediatric population, emphasizing smell and taste disturbances. METHODS: A case-control study included 468 patients, 234 with COVID-19 (cases) and 234 with URTI (controls) at a tertiary hospital, Riyadh, Saudi Arabia, from 2020-2021. Patients with bacterial URTI, lower tract respiratory infections, and speech or developmental delays were excluded. Statistical analysis was carried out using Statistical Analysis System, 9.2 version. A p-value of ≤0.05 was considered significant. RESULTS: The male-to-female ratio was almost equal, with a mean age of 9.90±2.34. Multivariable logistic regression analysis showed that a change in taste significantly increases the probability of COVID-19 by 21.98 times. On the other hand, sore throat (81.5%), dyspnea (63.5%), nasal obstruction (72.7%), and otalgia significantly (74.8%) decrease the likelihood of COVID-19. CONCLUSION: Taste disturbances increase the probability of COVID-19 infections, whereas sore throat, dyspnea, nasal obstruction, and otalgia increase the likelihood of other URTIs. The described differences might aid physicians in their differential diagnosis and treatment during the pandemic.

A retrospective cohort study on the use of machine learning to predict stone-free status following percutaneous nephrolithotomy: An experience from Saudi Arabia.

Background: Machine learning techniques have been used extensively in the field of clinical medicine, especially when used for the construction of prediction models. The aim of the study was to use machine learning to predict the stone-free status after percutaneous nephrolithotomy (PCNL). Materials and methods: This is a retrospective cohort study of 137 patients. Data from adult patients who underwent PCNL at our institute were used for the purpose of this study. Three supervised machine learning algorithms were employed: Logistic Regression, XGBoost Regressor, and Random Forests. A set of variables comprising independent attributes including age, gender, body mass index (BMI), chronic kidney disease (CKD), hypertension (HTN), diabetes mellitus, gout, renal and stone factors (previous surgery, stone location, size, and staghorn status), and pre-operative surgical factors (infections, stent, hemoglobin, creatinine, and bacteriuria) were entered. Results: 137 patients were identified. The majority were males (65.4%; n = 89), aged 50 years and above (41.9%; n = 57). The stone-free status (SFS) rate was 86% (n = 118). An inverse relation was detected between SFS, and CKD and HTN. The accuracies were 71.4%, 74.5% and 75% using Logistic Regression, XGBoost, and Random Forest algorithms, respectively. Stone size, pre-operative hemoglobin, pre-operative creatinine, and stone type were the most important factors in predicting the SFS following PCNL. Conclusion: The Random Forest model showed the highest efficacy in predicting SFS. We developed an effective machine learning model to assist physicians and other healthcare professionals in selecting patients with renal stones who are most likely to have successful PCNL treatment based on their demographics and stone characteristics. Larger multicenter studies are needed to develop more powerful algorithms, such as deep learning and other AI subsets.

Knowledge, awareness, and vaccination compliance of hepatitis B among medical students in Riyadh's governmental universities.

BACKGROUND: Hepatitis B is a blood-borne infectious liver disease caused by the Hepatitis B Virus (HBV) and it is best prevented by immunization. Due to occupational exposure, medical students have an increased risk of contracting HBV. Therefore, it is essential for all medical students to have good knowledge about HBV and to complete their HBV vaccinations. AIMS: The aim of this study was to assess and compare HBV knowledge, awareness, and vaccination compliance among pre-clinical medical students in four universities. SETTINGS AND DESIGN: A cross-sectional study was conducted in September 2018 at the College of Medicine of four governmental universities: King Saud Bin Abdulaziz University for Health Sciences, King Saud University, Princess Noura university, and Imam Mohammed bin Saud Islamic University, in Riyadh, Saudi Arabia. METHODS AND MATERIALS: Two-hundred-sixty-three pre-clinical medical students completed a questionnaire with sections about demographics, HBV awareness, knowledge, and vaccination compliance. STATISTICAL ANALYSIS USED: The data was transferred to Excel and SPSS version 22 was used for statistical analysis. A significance level of P < 0.05 was considered statistically significant. RESULTS: The overall knowledge about HBV and vaccination compliance were poor. KSU students had the highest vaccination compliance (n = 52, 54.2%) and KSAU-HS the lowest (n = 19, 23,8%). The most-cited reasons for noncompliance were "forgetting about the vaccine" and "busy schedule". CONCLUSION: Overall, most of the participants had poor HBV knowledge and vaccine compliance. Therefore, we recommend the implementation of pre-clinical vaccine checking and the addition of an infectious disease awareness and prevention program.