The outbreak of the coronavirus disease 2019 (COVID-19) has globally strained medical resources and caused significant mortality. This study is aimed to develop and validate a prediction model based on clinical features to estimate the risk of patients with COVID-19 at admission progressing to critical patients.
Patients admitted to hospital between January 16, 2020 and March 10, 2020 were retrospectively enrolled, and they will be observed for at least 14 days after admission to determine whether they develop into severe pneumonia. According to the clinical symptoms, all patients are divided into 4 groups: mild, normal, severe, and critical.
A total of 390 patients with COVID-19 pneumonia were identified, including 212 severe patients and 178 non-severe patients. The Least Absolute Shrinkage and Selection Operator (LASSO) regression reduces the variables in the model to 6, which are age, number of comorbidities, CT severity score, lymphocyte count, aspartate aminotransferase and albumin. The Area Under Curve(AUC) of the model in the training set is 0.898, and the specificity and sensitivity were 89.7% and 75.5%.
The prediction model, nomogram might be useful to access the onset of severe and critical illness among COVID-19 patients at admission, which is instructive for clinical diagnosis. This article is protected by copyright. All rights reserved.

This article is protected by copyright. All rights reserved.