Preadmission Use Of Antidiabetic Medications And Mortality Among Patients With Covid-19 Having Type 2 Diabetes: A Meta-analysis

Introduction: Diabetes is an independent predictor of poor outcomes in patients with COVID-19. We compared the effects of the preadmission use of antidiabetic medications on the in-hospital mortality of patients with COVID-19 having type 2 diabetes. Methods: A systematic search was performed until November 30, 2021. We used a random-effects meta-analysis to calculate the pooled OR (95% CI). Results: We included 61 studies (3,061,584 individuals). We found some medications protective against COVID-related death, including metformin, GLP-1RA and SGLT-2i. DPP-4i and insulin users were more likely to die during hospitalization. SU, TZD, and AGI were mortality neutral. Metformin use was associated with better outcome in a dose-response manner. Conclusions: Metformin, GLP-1RA, and SGLT-2i were associated with lower mortality rate in patients with COVID-19 having type 2 diabetes. DPP-4i and insulin were linked to increased mortality. SU, TZD and AGI were mortality neutral.Presentation: No date and time listed

Deep learning application for real-time prediction of COVID-19 outbreak with susceptible-infected-recovered-deceased model

Due to the complex nature of a pandemic such as COVID-19, forecasting how it would behave is difficult, but it is indeed of utmost necessity. Furthermore, adapting predictive models to different data sets obtained from different countries and areas is necessary, as it can provide a wider view of the global pandemic situation and more information on how models can be improved. Therefore, we combine here the long-short-term memory (LSTM) model and the traditional susceptible-infected-recovered-deceased (SIRD) model for the COVID-19 prediction task in Ho Chi Minh City, Vietnam. In particular, LSTM shows its strength in processing and making accurate numerical predictions on a large set of historical input. Following the SIRD model, the whole population is divided into 4 states (S), (I), (R), and (D), and the changes from one state to another are governed by a parameter set. By assessing the numerical output and the corresponding parameter set, we could reveal more insights about the root causes of the changes. The predictive model updates every 10 days to produce an output that is closest to reality. In general, such a combination delivers transparent, accurate, and up-to-date predictions for human experts, which is important for research on COVID-19. © 2022 Institute of Advanced Engineering and Science. All rights reserved.