COVID-19 Research Paper Volume 14, Issue 2 pp 544—556
Severe versus common COVID-19: an early warning nomogram model
- 1 Biliary Tract Surgery Department IV, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai 200438, PR China
- 2 Infectious Disease Department IV, Hubei Maternal and Child Health Hospital, Wuhan 430074, PR China
- 3 Department of Integrated Traditional and Western Medicine, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai 200438, PR China
- 4 Biliary Tract Surgery Department II, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai 200438, PR China
- 5 Infectious Disease Department II, Huoshenshan Hospital, Wuhan 430113, PR China
- 6 Department of Anesthesiology, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai 200438, PR China
- 7 Department of Biotherapy, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai 200438, PR China
Received: March 22, 2021 Accepted: December 25, 2021 Published: January 17, 2022https://doi.org/10.18632/aging.203832
How to Cite
Copyright: © 2022 Chang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
The wide spread of coronavirus disease 2019 is currently the most rigorous health threat, and the clinical outcomes of severe patients are extremely poor. In this study, we establish an early warning nomogram model related to severe versus common COVID-19. A total of 1059 COVID-19 patients were analyzed in the primary cohort and divided into common and severe according to the guidelines on the Diagnosis and Treatment of COVID-19 by the National Health Commission of China (7th version). The clinical data were collected for logistic regression analysis to assess the risk factors for severe versus common type. Furthermore, 123 COVID-19 patients were reviewed as the validation cohort to assess the performance of this model. Multivariate logistic analysis revealed that age, dyspnea, lymphocyte count, C-reactive protein and interleukin-6 were independent factors for prewarning the severe type occurrence. Then, the early warning nomogram model including these risk factors for inferring the severe disease occurrence out of common type of COVID-19 was constructed. The C-index of this nomogram in the primary cohort was 0.863, 95% confidence interval (CI) (0.836–0.889). Meanwhile, in the validation cohort, the C-index of this nomogram was 0.889, 95% CI (0.828–0.950). In both the primary cohort and validation cohorts, the calibration curve showed good agreement between prediction and actual probability. The early warning model shows that data at the very beginning including age, dyspnea, lymphocyte count, CRP, and IL-6 may prewarn the severe disease occurrence to some extent, which could help clinicians early and timely treatment.