Establishment of a Clinical Nomogram Model to Predict the Progression of COVID-19 to Severe Disease

Changli Tu; Guojie Wang; Yayuan Geng; Na Guo; Ning Cui; Jing Liu

doi:10.2147/TCRM.S308961

Establishment of a Clinical Nomogram Model to Predict the Progression of COVID-19 to Severe Disease

Ther Clin Risk Manag. 2021 Jun 1:17:553-561. doi: 10.2147/TCRM.S308961. eCollection 2021.

Authors

Changli Tu^#¹, Guojie Wang^#², Yayuan Geng³, Na Guo³, Ning Cui⁴, Jing Liu¹

Affiliations

¹ Department of Pulmonary and Critical Care Medicine (PCCM), The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, People's Republic of China.
² Department of Radiology, The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, People's Republic of China.
³ Huiying Medical Technology Co., Ltd, Room A206, B2, Dongsheng Science and Technology Park, Beijing, People's Republic of China.
⁴ Medical Imaging Center, Shiyan Taihe Hospital, Shiyan, People's Republic of China.

^# Contributed equally.

Abstract

Background: Coronavirus disease 2019 (COVID-19) is a worldwide public health pandemic with a high mortality rate, among severe cases. The disease is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. It is important to ensure early detection of the virus to curb disease progression to severe COVID-19. This study aims to establish a clinical-nomogram model to predict the progression to severe COVID-19 in a timely and efficient manner.

Methods: This retrospective study included 202 patients with COVID-19 who were admitted to the Fifth Affiliated Hospital of Sun Yat-sen University and Shiyan Taihe Hospital from January 17 to April 30, 2020. The patients were randomly assigned to the training dataset (n = 163, with 43 progressing to severe COVID-19) or the validation dataset (n = 39, with 10 progressing to severe COVID-19) at a ratio of 8:2. The optimal subset algorithm was applied to filter for the clinical factors most relevant to the disease progression. Based on these factors, the logistic regression model was fit to distinguish severe (including severe and critical cases) from non-severe (including mild and moderate cases) COVID-19. Sensitivity, specificity, and area under the curve (AUC) were calculated using the R software package to evaluate prediction performance. A clinical nomogram was established and performance assessed using the discrimination curve.

Results: Risk factors, including demographic data, symptoms, laboratory and image findings, were recorded for the 202 patients. Eight of the 53 variables that were entered into the selection process were selected via the best subset algorithm to establish the predictive model; they included gender, age, BMI, CRP, D-dimer, TP, ALB, and involved-lobe. AUC, sensitivity, and specificity were 0.91, 0.84 and 0.86 for the training dataset, and 0.87, 0.66, and 0.80 for the validation dataset.

Conclusion: We established an efficient and reliable clinical nomogram model which showed that gender, age, and initial indexes including BMI, CRP, D-dimer, involved-lobe, TP, and ALB could predict the risk of progression to severe COVID-19.

Keywords: COVID-19; clinical nomogram model; logistic regression; progression risk.

Grants and funding

This work was supported by “The program of Zhuhai Municipal Special Fund for Emergency Science and Technology Research in 2020 (ZH22036302200031PWC)”.