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https://www.selleckchem.com/products/exarafenib.html In this retrospective multicenter case series study, the predictive value of initial findings of confirm COVID-19 cases in determining outcome of the disease was assessed. Patients were divided into two groups based on the outcome low risk (hospitalization in the infectious disease ward and discharge) and high risk (hospitalization in ICU or death). A total of 164 patients with positive PCR-RT were enrolled in this study. About 36 patients (22%) were in the high-risk group and 128 (78%) were in the low-risk group. Results of statistical analysis revealed a significant relationship between age, fatigue, history of cerebrovascular disease, organ failure, white blood cells (WBC), neutrophil-to-lymphocyte ratio (NLR), and derived neutrophil-to-lymphocyte ratio (dNLR) with increased risk of disease. The artificial neural network (ANN) could predict the high-risk group with an accuracy of 87.2%. Preliminary findings of COVID-19 patients can be used in predicting their outcome and ANN can determine the outcome of patients with appropriate accuracy (87.2%). Most treatment in Covid-19 are supportive and depend on the severity of the disease and its complications. The first step in treatment is to determine the severity of the disease. This study can improve the treatment of patients by predicting the severity of the disease using the initial finding of patients and improve the management of disease with differentiating high-risk from low-risk groups. There are limited data on the impact of coronavirus disease 2019 (COVID-19) on hospitalized patients with autoimmune and chronic inflammatory disease (AICID) compared with patients who do not have AICID. We sought to evaluate whether patients with AICID who have confirmed COVID-19 presenting to the hospital are at higher risk of adverse outcomes compared with those patients without AICID who are infected with COVID-19 and whether immunosuppressive medications impact this risk.

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