Observational Study
Copyright ©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Clin Cases. Oct 6, 2021; 9(28): 8388-8403
Published online Oct 6, 2021. doi: 10.12998/wjcc.v9.i28.8388
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

The novel coronavirus disease 2019 (COVID-19) pandemic is a global threat caused by the severe acute respiratory syndrome coronavirus-2.


To develop and validate a risk stratification tool for the early prediction of intensive care unit (ICU) admission among COVID-19 patients at hospital admission.


The training cohort included COVID-19 patients admitted to the Wuhan Third Hospital. We selected 13 of 65 baseline laboratory results to assess ICU admission risk, which were used to develop a risk prediction model with the random forest (RF) algorithm. A nomogram for the logistic regression model was built based on six selected variables. The predicted models were carefully calibrated, and the predictive performance was evaluated and compared with two previously published models.


There were 681 and 296 patients in the training and validation cohorts, respectively. The patients in the training cohort were older than those in the validation cohort (median age: 63.0 vs 49.0 years, P < 0.001), and the percentages of male gender were similar (49.6% vs 49.3%, P = 0.958). The top predictors selected in the RF model were neutrophil-to-lymphocyte ratio, age, lactate dehydrogenase, C-reactive protein, creatinine, D-dimer, albumin, procalcitonin, glucose, platelet, total bilirubin, lactate and creatine kinase. The accuracy, sensitivity and specificity for the RF 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). Model A underestimated risk of ICU admission in patients with a predicted risk less than 30%, whereas the RF risk score demonstrated excellent ability to categorize patients into different risk strata. Our predictive model provided a larger standardized net benefit across the major high-risk range compared with model A.


Our model can identify ICU admission risk in COVID-19 patients at admission, who can then receive prompt care, thus improving medical resource allocation.

Keywords: COVID-19, Intensive care units, Machine learning, Prognostic predictive model, Risk stratification

Core Tip: This study established a risk stratification tool for the early prediction of intensive care unit admission among coronavirus disease 2019 patients at hospital admission to enable such patients to receive immediate appropriate care, thus improving medical resource allocation. The model with 13 indicators selected from 65 laboratory results collected at hospital admission could be used to assess the risk of intensive care unit admission. This study provided a simple probability prediction model to identify intensive care unit admission risk in coronavirus disease 2019 patients at admission.