Shahid Beheshti University of Medical Sciences and Health Services

About: Shahid Beheshti University of Medical Sciences and Health Services is a education organization based out in Tehran, Iran. It is known for research contribution in the topics: Population & Medicine. The organization has 19456 authors who have published 33659 publications receiving 365676 citations.

Appropriate waist circumference cut-off points among Iranian adults: the first report of the Iranian National Committee of Obesity.

Abstract: How well we can identify individuals at risk for mortality and morbidity using a simple measurement Obviously obesity is a growing threat to heath worldwide and anthropometric indicators are good means to screen and identify high-risk people It has been reported that compared to Body Mass Index (BMI), indices of central obesity are better discriminators for cardiovascular risk factors Hence, waist circumference (WC) cut-off points of �94 and

Prediction of the COVID-19 Pandemic for the Top 15 Affected Countries: Advanced Autoregressive Integrated Moving Average (ARIMA) Model.

Abstract: Background: The coronavirus disease (COVID-19) pandemic has affected more than 200 countries and has infected more than 2,800,000 people as of April 24, 2020. It was first identified in Wuhan City in China in December 2019. Objective: The aim of this study is to identify the top 15 countries with spatial mapping of the confirmed cases. A comparison was done between the identified top 15 countries for confirmed cases, deaths, and recoveries, and an advanced autoregressive integrated moving average (ARIMA) model was used for predicting the COVID-19 disease spread trajectories for the next 2 months. Methods: The comparison of recent cumulative and predicted cases was done for the top 15 countries with confirmed cases, deaths, and recoveries from COVID-19. The spatial map is useful to identify the intensity of COVID-19 infections in the top 15 countries and the continents. The recent reported data for confirmed cases, deaths, and recoveries for the last 3 months was represented and compared between the top 15 infected countries. The advanced ARIMA model was used for predicting future data based on time series data. The ARIMA model provides a weight to past values and error values to correct the model prediction, so it is better than other basic regression and exponential methods. The comparison of recent cumulative and predicted cases was done for the top 15 countries with confirmed cases, deaths, and recoveries from COVID-19. Results: The top 15 countries with a high number of confirmed cases were stratified to include the data in a mathematical model. The identified top 15 countries with cumulative cases, deaths, and recoveries from COVID-19 were compared. The United States, the United Kingdom, Turkey, China, and Russia saw a relatively fast spread of the disease. There was a fast recovery ratio in China, Switzerland, Germany, Iran, and Brazil, and a slow recovery ratio in the United States, the United Kingdom, the Netherlands, Russia, and Italy. There was a high death rate ratio in Italy and the United Kingdom and a lower death rate ratio in Russia, Turkey, China, and the United States. The ARIMA model was used to predict estimated confirmed cases, deaths, and recoveries for the top 15 countries from April 24 to July 7, 2020. Its value is represented with 95%, 80%, and 70% confidence interval values. The validation of the ARIMA model was done using the Akaike information criterion value; its values were about 20, 14, and 16 for cumulative confirmed cases, deaths, and recoveries of COVID-19, respectively, which represents acceptable results. Conclusions: The observed predicted values showed that the confirmed cases, deaths, and recoveries will double in all the observed countries except China, Switzerland, and Germany. It was also observed that the death and recovery rates were rose faster when compared to confirmed cases over the next 2 months. The associated mortality rate will be much higher in the United States, Spain, and Italy followed by France, Germany, and the United Kingdom. The forecast analysis of the COVID-19 dynamics showed a different angle for the whole world, and it looks scarier than imagined, but recovery numbers start looking promising by July 7, 2020.

Laboratory Parameters in Detection of COVID-19 Patients with Positive RT-PCR;a Diagnostic Accuracy Study

Abstract: Introduction: The role of laboratory parameters in screening of COVID-19 cases has not been definitely established This study aimed to evaluate the accuracy of laboratory parameters in predicting cases with positive RT-PCR for COVID-19 Methods: This diagnostic accuracy study was conducted on suspected COVID-19 patients, who presented to Behpooyan Clinic Medical center in Tehran (Iran) from 22 February to 14 March, 2020 Patients were divided into two groups based on the results of real time reverse transcriptase-polymerase chain reaction (RT-PCR) for COVID-19, and the accuracy of different laboratory parameters in predicting cases with positive RT-PCR was evaluated using area under the ROC curve (AUC) Results: Two hundred cases with the mean age of 41 3+/- 14 6 (range: 19-78) years were studied (0 53% male) The result of RT-PCR for COVID-19 was positive in 70 (35%) cases Patients with positive RT-PCR had significantly higher neutrophil (NEU) count (p = 0 0001), and C-reactive protein (CRP) (p = 0 04), lactate dehydrogenase (LDH) (p = 0 0001), aspartate aminotransferase (AST) (p = 0 001), alanine aminotransferase (ALT) (p = 0 0001), and Urea (p = 0 001) levels in serum In addition, patients with positive RT-PCR had lower white blood cell (WBC) count (p = 0 0001) and serum albumin level (p = 0 0001) compared to others ALT (AUC = 0 879), CRP (AUC = 0 870), NEU (AUC = 0 858), LDH (AUC = 0 835), and Urea (AUC = 0 835) had very good accuracy in predicting cases with positive RT-PCR for COVID-19, respectively Conclusion: Our findings suggest that level of LDH, CRP, ALT and NEU can be used to predict the result of COVID-19 test They can help in detection of COVID-19 patients