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
Abstract
BACKGROUND

The widespread coronavirus disease 2019 (COVID-19) has led to high morbidity and mortality. Therefore, early risk identification of critically ill patients remains crucial.

AIM

To develop predictive rules at the time of admission to identify COVID-19 patients who might require intensive care unit (ICU) care.

METHODS

This retrospective study included a total of 361 patients with confirmed COVID-19 by reverse transcription-polymerase chain reaction between January 19, 2020, and March 14, 2020 in Shenzhen Third People’s Hospital. Multivariate logistic regression was applied to develop the predictive model. The performance of the predictive model was externally validated and evaluated based on a dataset involving 126 patients from the Wuhan Asia General Hospital between December 2019 and March 2020, by area under the receiver operating curve (AUROC), goodness-of-fit and the performance matrix including the sensitivity, specificity, and precision. A nomogram was also used to visualize the model.

RESULTS

Among the patients in the derivation and validation datasets, 38 and 9 participants (10.5% and 2.54%, respectively) developed severe COVID-19, respectively. In univariate analysis, 21 parameters such as age, sex (male), smoker, body mass index (BMI), time from onset to admission (> 5 d), asthenia, dry cough, expectoration, shortness of breath, asthenia, and Rox index < 18 (pulse oxygen saturation, SpO2)/(FiO2 × respiratory rate, RR) showed positive correlations with severe COVID-19. In multivariate logistic regression analysis, only six parameters including BMI [odds ratio (OR) 3.939; 95% confidence interval (CI): 1.409-11.015; P = 0.009], time from onset to admission (≥ 5 d) (OR 7.107; 95%CI: 1.449-34.849; P = 0.016), fever (OR 6.794; 95%CI: 1.401-32.951; P = 0.017), Charlson index (OR 2.917; 95%CI: 1.279-6.654; P = 0.011), PaO2/FiO2 ratio (OR 17.570; 95%CI: 1.117-276.383; P = 0.041), and neutrophil/lymphocyte ratio (OR 3.574; 95%CI: 1.048-12.191; P = 0.042) were found to be independent predictors of COVID-19. These factors were found to be significant risk factors for severe patients confirmed with COVID-19. The AUROC was 0.941 (95%CI: 0.901-0.981) and 0.936 (95%CI: 0.886-0.987) in both datasets. The calibration properties were good.

CONCLUSION

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.

Keywords: COVID-19; Communicable diseases; Clinical decision rules; Prognosis; Nomograms

Core Tip: This study established a risk-prediction model to estimate the prognosis of patients with coronavirus disease 2019 (COVID-19) for clinicians to more objectively calculate the severity of an individual patient and optimize the subsequent treatment. The model with the aforementioned six predictors could be used in clinical practice to identify high-risk dialysis patients for more investigations and interventions. This study provided a simple form of a probability prediction model to identify patients with severe COVID-19.