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Evaluation of the Risk Prediction Tools for Patients with Coronavirus Disease 2019 in Wuhan, China: A Single-Centered, Retrospective, Observational Study

32 Pages Posted: 8 Apr 2020

See all articles by Fang-Yan Liu

Fang-Yan Liu

Capital Medical University - Department of Emergency

Xue-Lian Sun

Capital Medical University - Department of Emergency

Yong Zhang

Huazhong University of Science and Technology - Department of Hepatobiliary Surgery

Lin Ge

Huazhong University of Science and Technology - Department of Hepatobiliary Surgery

Jing Wang

Capital Medical University - Department of Emergency

Xiao Liang

Capital Medical University - Department of Emergency

Jun-Fen Li

Capital Medical University - Department of Emergency

Chang-Liang Wang

Capital Medical University - Department of Emergency

Zheng-Tao Xing

Capital Medical University - Department of Emergency

Jagadish K. Chhetri

Capital Medical University - Department of Neurology

Peng Sun

Huazhong University of Science and Technology - Department of Emergency Medicine

Piu Chan

Capital Medical University - Department of Neurology

More...

Abstract

Background: WHO recently characterized Coronavirus disease 2019 (COVID-19) as a pandemic. We aimed to evaluate and compare the efficacy of a National Early Warning Score (NEWS), Rapid Emergency Medicine Score (REMS), quickSOFA (qSOFA) and CRB65 on predicting bad outcome in patients with COVID-19.

Methods: In this single-centered, retrospective, observational study, we enrolled 114 adult patients with COVID-19 who were admitted to the west campus of Wuhan Union hospital between Jan 30, 2020 and Feb 24, 2020. The primary endpoint was in-hospital death. Secondary composite endpoint was the admission to intensive care unit (ICU), or death.

Findings: Total number of patients was 114 (47% male) and mean age was 58·49(SD, 14·32). With follow-up of 30 days, 12(11%) were death, and additional five (4%) were admitted to the ICU from general ward. For predicting in-hospital death: AUROC analysis demonstrated the predictability of NEWS = 0·946, REMS =0·871, CRB65=0·794 and qSOFA=0·799. For predicting admission to ICU or in-hospital death: AUROC analysis demonstrated the predictability of NEWS=0·889, REMS=0·810, qSOFA=0·734, CRB65=0·647. Among the parameters of NEWS, the oxygen saturations score was found to be the most significant predictor of in-hospital death. The AUROC for oxygen saturations score for predicting death was 0·874 and 0·848 for predicting admission to ICU or in-hospital death.

Interpretation: The efficiency of NEWS on predicting poor outcomes such as admission to ICU or death was found to be superior to REMS, qSOFA and CRB65 in patients with COVID-19. Peripheral oxygen saturation may independently predict admission to ICU or death in these patients.

Funding Statement: None.

Declaration of Interests: None reported.

Ethics Approval Statement: The study was approved by Union Hospital Ethics Committee.

Keywords: Coronavirus; COVID-19; prognosis; pandemic; National Early Warning Score; peripheral oxygen saturation

Suggested Citation

Liu, Fang-Yan and Sun, Xue-Lian and Zhang, Yong and Ge, Lin and Wang, Jing and Liang, Xiao and Li, Jun-Fen and Wang, Chang-Liang and Xing, Zheng-Tao and Chhetri, Jagadish K. and Sun, Peng and Chan, Piu, Evaluation of the Risk Prediction Tools for Patients with Coronavirus Disease 2019 in Wuhan, China: A Single-Centered, Retrospective, Observational Study (3/25/2020). Available at SSRN: https://ssrn.com/abstract=3562483 or http://dx.doi.org/10.2139/ssrn.3562483

Fang-Yan Liu

Capital Medical University - Department of Emergency

China

Xue-Lian Sun

Capital Medical University - Department of Emergency

China

Yong Zhang

Huazhong University of Science and Technology - Department of Hepatobiliary Surgery

Wuhan
China

Lin Ge

Huazhong University of Science and Technology - Department of Hepatobiliary Surgery

Wuhan
China

Jing Wang

Capital Medical University - Department of Emergency

China

Xiao Liang

Capital Medical University - Department of Emergency

China

Jun-Fen Li

Capital Medical University - Department of Emergency

China

Chang-Liang Wang

Capital Medical University - Department of Emergency

China

Zheng-Tao Xing

Capital Medical University - Department of Emergency

China

Jagadish K. Chhetri

Capital Medical University - Department of Neurology

Fengtai
China

Peng Sun

Huazhong University of Science and Technology - Department of Emergency Medicine ( email )

No.1277 Jiefang Avenue
Wuhan, 430022
China

Piu Chan (Contact Author)

Capital Medical University - Department of Neurology ( email )

Fengtai
China

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