Copyright
©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
Validated tool for early prediction of intensive care unit admission in COVID-19 patients
Hao-Fan Huang, Yong Liu, Jin-Xiu Li, Hui Dong, Shan Gao, Zheng-Yang Huang, Shou-Zhi Fu, Lu-Yu Yang, Hui-Zhi Lu, Liao-You Xia, Song Cao, Yi Gao, Xia-Xia Yu
Hao-Fan Huang, Shan Gao, Zheng-Yang Huang, Yi Gao, Xia-Xia Yu, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen 518060, Guangdong Province, China
Yong Liu, Expert Panel of Shenzhen 2019-nCoV Pneumonia, Shenzhen Hospital, Southern Medical University, Shenzhen 518000, Guangdong Province, China
Jin-Xiu Li, Department of Critical Care Medicine, Shenzhen Third People’s Hospital, Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen 518112, Guangdong Province, China
Hui Dong, Shou-Zhi Fu, Lu-Yu Yang, Hui-Zhi Lu, Liao-You Xia, Song Cao, Department of ICU/Emergency, Wuhan Third Hospital, Wuhan University, Wuhan 430000, Hubei Province, China
Author contributions: Yu XX and Gao Y conceived and coordinated the study, designed, performed and analyzed the experiments and wrote the paper; Dong H, Fu SZ, Liu Y, Lu HZ, Xia LY and Cao S carried out the data collection and preprocess of the raw data; Huang HF, Gao S and Huang ZY performed the data analysis; Liu Y and Li JX revised the paper; All authors reviewed the results and approved the final version of the manuscript.
Supported by Shenzhen Municipal Government’s "Peacock Plan", No. KQTD2016053112051497.
Institutional review board statement: The study protocol was approved by the Ethics Committees of the Third People’s Hospital of Shenzhen and Wuhan Third Hospital.
Informed consent statement: Informed consent was waived by the committee because of the retrospective nature of the study.
Conflict-of-interest statement: There are no conflicts of interest to report.
Data sharing statement: No additional data are available.
STROBE statement: The authors have read the STROBE Statement—checklist of items, and the manuscript was prepared and revised according to the STROBE Statement—checklist of items.
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: Xia-Xia Yu, PhD, Assistant Professor, School of Biomedical Engineering, Health Science Center, Shenzhen University, No. 3688 Nanhai Avenue, Shenzhen 518060, Guangdong Province, China. xiaxiayu@szu.edu.cn
Received: April 13, 2021
Peer-review started: April 13, 2021
First decision: May 11, 2021
Revised: May 12, 2021
Accepted: August 16, 2021
Article in press: August 16, 2021
Published online: October 6, 2021
ARTICLE HIGHLIGHTS
Research background
The novel coronavirus disease 2019 (COVID-19) pandemic is a global threat caused by the severe acute respiratory syndrome coronavirus-2.
Research motivation
The development of a prognostic model is crucial to address the problem of micro-allocation of scarce healthcare resources in the face of a pandemic.
Research objectives
To develop and validate a risk stratification tool for the early prediction of intensive care unit admission among COVID-19 patients at hospital admission.
Research methods
We selected 13 of 65 baseline laboratory results and developed a risk prediction model with the random forest algorithm. A nomogram for the logistic regression model was built based on six selected variables.
Research results
The accuracy, sensitivity and specificity for the random forest model were 91%, 88% and 93%, respectively, higher than those for the logistic regression model. The area under the receiver operating characteristic curve of our model was much better than those of two other published methods (0.90 vs 0.82 and 0.75).
Research conclusions
Our model can identify intensive care unit admission risk in COVID-19 patients at admission, who can then receive prompt care, thus improving medical resource allocation.
Research perspectives
In the future, research should include repeated measures data to identify whether temporal changes in clinical indicators are better able to predict disease prognosis in COVID-19.
