Predictive value of the preliminary findings in the severity of COVID-19 disease and the effect on therapeutic approaches.

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.