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

• Published in: BMC medical imaging 2020-10, Vol.20 (1), p.111-12, Article 111
• Main Authors: Yu, Yixing, Wang, Ximing, Li, Min, Gu, Lan, Xie, Zongyu, Gu, Wenhao, Xu, Feng, Bao, Yaxing, Liu, Rongrong, Hu, Su, Hu, Mengjie, Hu, Chunhong
• Format: Article
• Language: English
• Subjects: Adolescent; Adult; Aged; Aged, 80 and over; Area Under Curve; Calibration; Cardiovascular disease; CAT scans; Child; Computed tomography; Coronaviridae; Coronavirus Infections - diagnostic imaging; Coronaviruses; COVID-19; Early Diagnosis; Fatalities; Humans; Lung - diagnostic imaging; Lungs; Middle Aged; Nomogram; Nomograms; Pandemics; Performance prediction; Perfusion; Pneumonia; Pneumonia, Viral - diagnostic imaging; Random Allocation; Retrospective Studies; Risk analysis; Risk factors; Sensitivity; Sensitivity and Specificity; Severity of Illness Index; Tomography; Tomography, X-Ray Computed; Training; Viral diseases; X-ray computed; Young Adult
• ISSN: 1471-2342; 1471-2342
• DOI: 10.1186/s12880-020-00513-z

To develop and validate a nomogram for early identification of severe coronavirus disease 2019 (COVID-19) based on initial clinical and CT characteristics. 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. In the training cohort, the severity score of lung in the severe group (7, interquartile range [IQR]:5-9) was significantly higher than that of the mild group (4, IQR,2-5) (P