Nomogram to identify severe coronavirus disease 2019 (COVID-19) based on initial clinical and CT characteristics: a multi-center study.

ABSTRACT:

Background

To develop and validate a nomogram for early identification of severe coronavirus disease 2019 (COVID-19) based on initial clinical and CT characteristics.

Methods

The initial clinical and CT imaging data of 217 patients with COVID-19 were analyzed retrospectively from January to March 2020. Two hundred seventeen patients with 146 mild cases and 71 severe cases were randomly divided into training and validation cohorts. Independent risk factors were selected to construct the nomogram for predicting severe COVID-19. Nomogram performance in terms of discrimination and calibration ability was evaluated using the area under the curve (AUC), calibration curve, decision curve, clinical impact curve and risk chart.

Results

In the training cohort, the severity scor

SUBMITTER: Yu Y 

PROVIDER: S-EPMC7530870 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature