Retrospective Study
Copyright ©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Clin Cases. May 6, 2021; 9(13): 2994-3007
Published online May 6, 2021. doi: 10.12998/wjcc.v9.i13.2994
Clinical diagnosis of severe COVID-19: A derivation and validation of a prediction rule
Ming Tang, Xia-Xia Yu, Jia Huang, Jun-Ling Gao, Fu-Lan Cen, Qi Xiao, Shou-Zhi Fu, Yang Yang, Bo Xiong, Yong-Jun Pan, Ying-Xia Liu, Yong-Wen Feng, Jin-Xiu Li, Yong Liu
Ming Tang, Jia Huang, Fu-Lan Cen, Qi Xiao, Ying-Xia Liu, Department of Critical Care Medicine, Shenzhen Third People’s Hospital, The Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen 518114, Guangdong Province, China
Xia-Xia Yu, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518060, Guangdong Province, China
Jun-Ling Gao, Buddhism and Science Research Laboratory, Centre of Buddhist Studies, The University of Hong Kong, Hong Kong 999077, China
Shou-Zhi Fu, Department of Critical Care Medicine, Wuhan Third Hospital, Wuhan 433304, Hubei Province, China
Yang Yang, Shenzhen Key Laboratory of Pathogen and Immunity, National Clinical Research Center for Infectious Disease, State Key Discipline of Infectious Disease, Shenzhen Third People's Hospital, The Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen 518114, Guangdong Province, China
Bo Xiong, Department of Pediatrics, Wuhan Asia General Hospital, Wuhan 430022, Hubei Province, China
Yong-Jun Pan, Department of Critical Care Medicine, Southern University of Science and Technology Hospital, Shenzhen 518055, Guangdong Province, China
Yong-Wen Feng, Department of Critical Care Medicine, The Second People's Hospital of Shenzhen, Shenzhen 518035, Guangdong Province, China
Jin-Xiu Li, Department of Critical Care Medicine, The Second Xiangya Hospital, Central South University, Changsha 410011, Hunan Province, China
Yong Liu, Shenzhen Hospital, Southern Medical University, Shenzhen 518000, Guangdong Province, China
Author contributions: Huang J, Cen FL, Xiao Q, Fu SZ, Yang Y, Xiong B and Pan YJ contributed data curation; Tang M, Liu Y and Yu XX contributed formal analysis; Liu YX, Feng YW and Li JX contributed investigation; Tang M, Liu Y and Yu XX contributed methodology; Liu Y, Feng YW and Li JX contributed project administration; Cen FL, Liu YX and Feng YW contributed resources; Yu XX contributed software; Li JX contributed supervision; Gao JL contributed validation; Tang M wrote the original draft; all authors have read and approved the final manuscript.
Institutional review board statement: This study was approved by the Institutional Review Board of Shenzhen Third People’s Hospital (study number: RC2020-102).
Informed consent statement: All study participants, or their legal guardian, provided informed written consent prior to study enrollment.
Conflict-of-interest statement: The authors declare that they have no competing interests.
Data sharing statement: No additional data are available.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Yong Liu, MD, Chief Doctor, Shenzhen Hospital, Southern Medical University, No. 1333 Xinhu Road, Baoan District, Shenzhen 518000, Guangdong Province, China. liuyongjoy@outlook.com
Received: November 28, 2020
Peer-review started: November 28, 2020
First decision: December 24, 2020
Revised: December 30, 2020
Accepted: February 24, 2021
Article in press: February 24, 2021
Published online: May 6, 2021
Processing time: 140 Days and 3.4 Hours
ARTICLE HIGHLIGHTS
Research background

The outbreak of coronavirus disease 2019 (COVID-19) led to high mortality and the intensive care unit (ICU) is needed to reduce the number of deaths. This causes a shortage of medical resources, especially in ICU settings.

Research motivation

The goal of this study was to develop and externally validate prediction rules to risk stratify patients by severity at an early stage.

Research objectives

The study aimed to risk stratify patients by severity of the illness at an early stage to better microallocate limited medical resources.

Research methods

This multicenter retrospective cohort study included 487 adult patients with confirmed COVID-19 between January 19, 2020, and March 14, 2020, in Shenzhen Third People’s Hospital and the Wuhan Asia General Hospital. Independent variables included sociodemographic factors, clinical symptoms, comorbidity, travel and contact history, and laboratory tests. The outcome variables were whether patients were defined as severe or critical. Logistic regression was applied to identify the independent factors that were associated with critical COVID-19 patients. Stepwise multivariate regression was used to select adjusted independent predictors (P < 0.05 was considered significant). The 10-fold cross-validations were included to internally validate the performance of the newly proposed predictive model. The data were randomly split into 10 approximately equal sizes, in which 9 were used to develop the model and one was used for internal validation. Furthermore, the newly developed predictive model was externally validated by the data collected from the Wuhan Asia General Hospital. In addition to area under the receiver operating curve, the predictive performance of the proposed model was measured by its sensitivity, specificity, and precision. The goodness-of-fit test was applied by comparing the observed and predicted events of COVID-19 using the risk group deciles by the Hosmer-Lemeshow χ2 test.

Research results

The model with the aforementioned six predictors could be used in clinical practice to identify high-risk dialysis patients. A simple form of a probability prediction model was put forward to identify patients with severe COVID-19. The proposed predictive model, including the aforementioned 6 factors, assists in predicting the prognosis with high accuracy.

Research conclusions

The proposed risk-prediction model for COVID-19 in this study showed significant association with the presence of obesity, delayed admission, fever, existing comorbidities, hypoxemia, and higher neutrophil/lymphocyte ratio. The model accurately predicted the severity of patients with COVID-19. This could practically help clinicians perform timely interventions and control measures to prevent overcrowding or delayed diagnosis of severe COVID-19 in hospital settings.

Research perspectives

The proposed predictive model had great potential in severity prediction of COVID-19 in the ICU. It assisted the ICU clinicians in making timely decisions for the target population.