predicting model

 The covid-19 disease is one of the types of viral diseases that are asymptomatic in about 81% of cases or have mild to moderate symptoms, and cause severe symptoms in about 14%. Identifying the cases that have a higher chance of contracting the severe form of the disease at the first level of providing services in order to provide appropriate and timely care is of great importance, so this study aims to investigate the role of background diseases and demographic characteristics of people in the occurrence of severe form of covid using Logistic regression prediction model has been done

 In a historical cohort study, all patients with definite covid (positive PCR test) who have electronic health records in comprehensive health service centers of Mashhad were included in the study. Variables related to demographic characteristics and history of background diseases as predictor variables and severity of covid disease as a two-status variable as a dependent variable were analyzed by logistic regression. The final model was validated by evaluating the diagnosis index and calibration index

 Out of 30,364 patients with covid who were covered by Mashhad University of Medical Sciences from the beginning of the pandemic until the end of January 1400, 1,664 were hospitalized and 269 died 64 people were hospitalized in the intensive care unit. Among the demographic variables, age above 60 (OR: 2.86(, BMI (OR: 1.35) ), and among the background disease history of people, chronic kidney disease (OR:8.23), respiratory disease (OR: 5.51.), cardiovascular disease (OR: 5.29), diabetes (OR: 2.03) and hypertension (OR: 0.91) remained in the severe form of covid prediction model. The final model was suitable for predicting the severe form of the disease (hospitalization or death) with the area under the curve (AUC), 0.75.

 The results of this study can effectively help health workers in identifying people who have a higher chance of suffering from a severe form of the disease.